diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b --- /dev/null +++ b/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..6987b57f15d164cfb1609214824b6bf7030c50e6 --- /dev/null +++ b/LICENSE @@ -0,0 +1,447 @@ +Arc Tagger - Copyright (c) 2026 Whissle Inc. 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+872804b77cdd8b215b27b3c1ace7e20fa2849fe0c1a8ebcec607d8d48a6adde4 arctag/_core/arc_audio/ontology.py +fedc9597f01197dee22bf28f9e9b5a4d53a7c80a33261c71bf140145e3bb8ddb arctag/_core/arc_audio/seqlayer/__init__.py +df44f2e78db745111a679a84fc22cb7d8dd56918af585e2709729f41eee087f3 arctag/_core/arc_audio/seqlayer/layer.py +e9fe230e1d1e877a098c6f49e608d32642976dc49a9d6c1dade0bf8883605c4d arctag/_core/arc_audio/seqlayer/scan.py +892f06ace1617c01f4ffdac73995ae870f762e4de68391a93d7379211706606c arctag/_core/arc_audio/seqlayer/ssd.py +2faa4fa63660650e5a513096aa4f3bb1b11d4f79bdbad03554b7abd0a447b606 arctag/_core/arc_audio/wordch.py +2af662448ae49a01c5b59ef7a25695c5266a4e2755c0144c527db59dc4e5102f arctag/_core/arc_text.py +9dc7f7aa6dc85aba2fbf961516d22dcfdbd02271202435b9a4d9a24ff480b70d arctag/_core/engine.py +0cd87a5208d6cacf380ae5f8299139280b88245055e03ecfa73bb8ea53725e59 arctag/_core/fast_arc.py +bb7ec0d8d70201b23e582fa88118f10969f6bb905e8115a405959a6e07cacc58 arctag/_core/kb.py +d2847c0108d3be14344e92046062bdc41ab9ffea7374a7400044886dc409a6b9 arctag/_core/kb_strfeat.py +eee600365bf666e31e0091cbe36c02d1cc9cc86c86b1f62acde1a826afcfb24c arctag/_core/losses.py +03591292f42f0f034cc10fcc7190ba020db2baaab35fa92cb8d2ee9a298cd367 arctag/_core/speaker_link.py +5998a8c6c069c6dc41731159eb4d1be7298ba6a8a90846378e7650750c365d0c arctag/_core/stream_arc.py +714370c392852f1b55e380bfa74218217574ac779b9112f4c2aefbb696e624c9 arctag/_core/tagtable.py +1c94629e51663239ff48ee67ad98e04f112d4ae16640541eedf9c3383d9e1238 arctag/_core/v2.py +8956f7eb7761bdd79055fea959df778db1e1396650f97847c41f4c501bb96cba arctag/asr.py +8d74c99704f96993e8250a729603e8265b8c4e781db1d92781edd5235d9de395 arctag/driver.py +d7a970463ce98ea33e5c3cb8d0eb782e8431b116ace61918c355f0c75a94b2e9 arctag/live.py +f4252a6c4c8a5ca4611662f5a56f104243e0d305bc680bcaa3837da97e4a1305 config.json +138e674cbfcdfa5fc633363cb5d05e4d177bd7c0b4bb8f1d9822f39d9ed2cd03 engine_settings.json +9b4c7a7792bbb6cfdda21ee519a390655b6a4fc9832c7de3db876bac002a3a49 example.py +d738a97a12beba8752435937d8f72c967c484ab8a659bf891881d528545b0632 model.safetensors +a08984acc688fe0f4b015f4c0c5697674631eee07b769af1f0c3a46c647d7750 onnx/SHA256SUMS +9f464b8142c0557ce66f653f137acfdcf90908c692ccb761b176e68524226247 onnx/arctag.onnx +0c265f76f842e24b13da8fcff30f4dc683c3676e60d2aec9ff1bf5a1e1e0ed9c onnx/arctag_int8.onnx +ca2d012fe35bfad1863f49c1cd7fa7a41ff628e4d0e47c497e0f6dea15a89314 onnx/arctag_meta.json +2ae4623fc283d9b25b83e9efb9bf376f24c8974a01a2bbbe6cf8b0b29ac6fc35 onnx/tokenizer.json +4cae0a09bc0767a64902bc6e7f0178886183a52120f65392b33ffa50088f142e ontology/ext_v7_aff.json +8517490cb23605464ae554142c16b3671409f1dae7ad3164d85c08e4ff55806b ontology/ext_v7_aff_E.npy +05cc12d2bdafa7ef463797ca2997ecda2dd914eaf4718a86959f45f0a8840f96 ontology/head_E_r8_bge_small.npy +6e96abcb6d28d3083b9fa02e3104cfc04a1b0b0a942c97e1d5b7dbb8f2b6bb86 ontology/tags_v1_r8.json +8eaaf4ff2304a3da023f8f5ceac25bc9bf525d9faf4820696035a6303ccf0435 ontology/text_tags.json +1b1cfb1fa299e1a8751d3e39262e163813a8202883038a002f5603ce744bfc92 requirements.txt +eac774542b53d8f4b1ffd3c2d94157b49a6fa258e5f4e356b83620b74990fac8 stream_deepgram.py +1eec9eeae9256330142f43cbb006b6ed37695a82fce85a9f1401ae912ae71e7e stream_speechmatics.py +f50933b9ce357dbe9a3867bf20e5bbf8072b11841028e4f70faa77a6132be4df tests/fixtures/deepgram_live.jsonl +bc864bfa452e9c04b3453b7aebb4643d1c50e30fb4f2343ca9ea3d992caae952 tests/fixtures/speechmatics_rt.jsonl +7f9411bd79038fd236a80d1d86c196098e98b3b18d6c571ef9ea6a8179f63a5d tests/test_adapters.py +639e33d1a2a7eabd0d36276f7b8f48964317ac32899564de85d34e3c07f169e3 thresholds/display.json +2ae4623fc283d9b25b83e9efb9bf376f24c8974a01a2bbbe6cf8b0b29ac6fc35 tok/bpe8k/tokenizer.json +6dc720dd382f90cc514b18bbda9b1c8e083b0cba606bf3ab5129d98bc9389d3b tok/bpe8k/tokenizer_config.json diff --git a/NOTICE b/NOTICE new file mode 100644 index 0000000000000000000000000000000000000000..9d840b13b3a232fd12ca577621bc81ac9ca1b648 --- /dev/null +++ b/NOTICE @@ -0,0 +1,14 @@ +Arc Tagger +Copyright (c) 2026 Whissle Inc. + +Arc Tagger (code, model weights, tag ontology and documentation) is licensed under CC BY-NC 4.0 plus the Whissle terms; see LICENSE. + +Every file in this repository is a work of Whissle Inc. and is covered by LICENSE, including: + + arctag/ Python package + arctag/_core/ streaming engine + arctag/_core/arc_audio/ model.py, wordch.py, ontology.py, seqlayer/ (from Whissle Arc arc_audio 1.4.2, Whissle Inc.; + docstrings, comments and self-tests removed) + onnx/ ONNX exports and metadata + +No third-party code is included. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c576f56c35a10e87bff7c496816eb6c9f0480392 --- /dev/null +++ b/README.md @@ -0,0 +1,347 @@ +--- +language: +- en +license: cc-by-nc-4.0 +library_name: pytorch +pipeline_tag: token-classification +tags: +- streaming +- asr +- ner +- pii +- redaction +- intent +- dialogue-act +--- + +# Arc Tagger (10M), v0.4.0 + +Arc Tagger is a **text-only streaming tagger** for live speech transcripts. It reads the word stream of any ASR (Deepgram, +Speechmatics, Whisper, NeMo, your own ASR, or plain text). As each word arrives, it emits: + +* entity and number spans; +* PII spans for redaction; +* profanity and toxicity; +* discourse acts, intents and safety flags, including prompt-injection / jailbreak attempts; +* speaker roles; +* events, such as speaker change, turn yield, sports, legal and meeting events; +* content state; +* affect: emotion, arousal, valence and intensity (experimental, text only). + +The model has about 10 M parameters and runs on CPU. Interim ASR hypotheses can be revised: only the revised words are +re-processed. + +## Usage terms + +* **Inference use only.** This repository provides the model and the inference code, nothing else. +* **Non-commercial.** The weights and code are licensed under CC BY-NC 4.0 plus the Whissle terms (see [LICENSE](LICENSE)). +* **Commercial licensing:** contact WhissleAI (ksingla@whissle.ai). + +## Tag families + +284 tags across 10 read-outs (365 `tag@readout` columns). `ontology/` holds the full definitions, and `config.json["columns"]` +lists every column. + +| family | tags (examples) | read-out | +|---|---|---| +| Entities | `entity.mention.{person,organization,team,place,event_name,legal_ref,term,...}` (10) | word spans | +| Numbers | `entity.number.{date,time_of_day,duration,count,money,percent,measure,score,clock,ordinal,id_pii,...}` (16), `entity.numeral` | word spans | +| PII / redaction | `safety.pii.*` (11): `{private_name,phone_number,email,street_address,card_account_number,gov_id,date_of_birth,url_handle,public_contact}` spans; `safety.pii.{request,leak}` per utterance | spans and utterance | +| Profanity / toxicity | `safety.profanity.{mild,strong}`, `safety.toxicity.*` (6) | spans and utterance | +| Safety | `safety.jailbreak.attempt` (experimental), `safety.scam.*` (6), `safety.crisis.self_harm`, `safety.threat.escalation`, `safety.child.*` (2), `safety.solicitation.dating_romantic` | utterance | +| Discourse act | `discourse.act.*` (7: statement, question, command_request, ...), `discourse.answer`, `discourse.backchannel`, `discourse.assistant.*` (2) | utterance | +| Intent | 26 cross-domain `intent.*` (request_information, schedule_arrange, complain_criticize, ...), 60 assistant `discourse.intent.*` (alarm_set, weather_query, ...) | utterance | +| Speaker role | `speaker.role.*` (19, including contact-centre agent and customer) | per speaker (needs speaker ids on the words) | +| Events | `speaker.change`, `discourse.turn_yield`, `discourse.interruption`, `event.sports.*` (18), `event.legal.*` (3), `event.meeting.{decision,action_item}` | per word | +| Content state | `content.mode` (8), `content.subject` (12), `content.game_state` (6), `content.legal_phase` (9), `content.meeting_phase` (6), `content.sports_focus` (7) | per word | +| Affect (experimental) | `affect.emotion.*` (5), `affect.arousal.{low,medium,high}`, `affect.valence.{neg,neu,pos,mixed}`, `affect.intensity.{1,2,3}` | utterance | + +## Quickstart + +```bash +pip install huggingface_hub +huggingface-cli download WhissleAI/arc-tagger --local-dir arc-tagger +cd arc-tagger +pip install -r requirements.txt # CPU torch is enough: pip install torch --index-url https://download.pytorch.org/whl/cpu +python example.py +``` + +### Offline + +```python +import sys; sys.path.insert(0, "arc-tagger") # the arctag package ships in the repo +from arctag import ArcTagger + +tg = ArcTagger.from_pretrained("arc-tagger") # a local dir, or the repo id "WhissleAI/arc-tagger" (the default) +out = tg.tag("Hi, this is Maria from Northwind Bank. Can I get your phone number?", domain="talk") +print(out["redacted_text"]) # Hi, this is [NAME] from Northwind Bank. ... +print(out["spans"]) # entity / number / PII / profanity spans with probabilities +print(out["utterances"]) # per-utterance intent, act, safety, role, state, affect +``` + +### Streaming + +Push words as the ASR emits them. An interim hypothesis is replaced with `revise_from=k` (it replaces `words[k:]`). + +```python +st = tg.stream(domain="assistant") +st.push_words(["set", "an", "alarm", "for"], final=False) # interim +up = st.push_words(["set", "an", "alarm", "for", "seven", "thirty."], revise_from=0, final=True) +up = st.endpoint() # end of utterance: utterance read-outs arrive in up["utterances"] +res = st.finalize() # the full result (same schema as tag()) +``` + +## Live ASR scripts (low latency) + +Two scripts connect a live ASR to the tagger. Each takes a wav file or the microphone, and prints the live tags and redacted text +as JSON lines. The API key is read **only** from an environment variable. + +```bash +pip install websockets # plus sounddevice for --mic + +export DEEPGRAM_API_KEY=... +python stream_deepgram.py --wav call.wav --domain talk +python stream_deepgram.py --mic --domain assistant --seconds 30 + +export SPEECHMATICS_API_KEY=... +python stream_speechmatics.py --wav call.wav --domain talk +python stream_speechmatics.py --mic --domain meeting --url wss://eu2.rt.speechmatics.com/v2 +``` + +How provider messages map to the stream: + +| provider message | tagger call | +|---|---| +| Deepgram `Results`, `is_final=false` | `push_words(words, final=False, revise_from=n_final)` | +| Deepgram `Results`, `is_final=true` | `push_words(words, final=True, revise_from=n_final)` | +| Deepgram `speech_final=true` or `UtteranceEnd` | `endpoint()` | +| Speechmatics `AddPartialTranscript` | `push_words(words, final=False, revise_from=n_final)` | +| Speechmatics `AddTranscript` | `push_words(words, final=True, revise_from=n_final)` | +| Speechmatics `EndOfUtterance` | `endpoint()` | + +* `n_final` is the number of words the provider has already finalised, so interim words never rewrite final ones. +* An end of utterance that arrives while interim words are pending is applied right after those words become final. +* Speaker ids from diarization (Deepgram `diarize=true`, Speechmatics `diarization: "speaker"`) are passed on as `spk`. + They enable speaker roles. +* Useful options: + * `--no-interim`: print only final updates. + * `--result out.json`: write the full result at the end. + * `--tier T1`: delayed, more stable labels. +* Each line is `{"type": "update", "source": "interim" | "final" | "endpoint", "spans", "events", "utterances", "running", "redacted_text"}`. + The last line is `{"type": "done", ...}`. + +**Status.** +* Deepgram: the adapter is tested offline against recorded-format message sequences. +* Speechmatics: tested against the documented message format; not yet tested live. + +The fixtures and tests are in `tests/` (`python tests/test_adapters.py`). The adapters themselves (`arctag/asr.py`) have no +network code, so you can reuse them with any client. + +## Input + +`tag(x, domain="auto", tier="T0")` + +`x` can be any of: +* a `str`; +* a list of words; +* a list of word dicts `{"w": str, "t0": s, "t1": s, "conf": float, "spk": str|int, "final": bool}`. Every field except `w` is optional. + +Offline, utterances end after words ending in `. ? !`. In streaming, call `endpoint()` at the ASR's end of utterance. + +`domain` is one of `auto | talk | meeting | court | assistant | sports | news`. +* It selects the per-domain behaviour and thresholds. +* `talk` covers contact centre, podcasts and chat. +* In `sports` and `court`, the specific league or court type is detected from the words. +* `auto` is weaker. **Set the domain when you know it.** + +Streaming: `push_words(words, final=True, revise_from=None, endpoint=False)`, `endpoint()`, `finalize()`. + +## Output (JSON) + +```jsonc +{ + "meta": {"model": "arc-tagger", "version": "0.4.0", "domain": "talk", "fine_domain": "talk", "tier": "T0", + "thresholds": "display + default"}, + "words": ["Hi,", "this", "is", "Maria", ...], + "spans": [ // word indices [start, end) + {"tag": "safety.pii.private_name", "family": "pii", "start": 3, "end": 4, "text": "Maria", "p": 0.97, "thr": 0.42, + "alt": {"tag": "...", "p": 0.01}, // runner-up type + "ver": 0.99, // span verifier probability + "src": "head"} + ], + "entities": [...], "pii": [...], "profanity_toxicity": [...], // spans split by family + "redacted_text": "Hi, this is [NAME] from ...", // PII, profanity and toxicity spans masked (public_contact is not) + "utterances": [ + {"start": 0, "end": 8, "text": "...", + "intent": ["intent.request_information"], "act": ["discourse.act.question"], "safety": ["safety.pii.request"], + "role": [], "role_note": "speaker role needs diarization (no speaker id on words)", + "item": [], "state": {"content.mode": ["content.mode.call_in"]}, + "emotion": [{"tag": "affect.emotion.neutral", "p": 0.93, "thr": 0.5, "on": true}, // top tag of each affect group: + {"tag": "affect.arousal.low", ...}, {"tag": "affect.valence.neu", ...}, {"tag": "affect.intensity.1", ...}], + "scores": {"intent": [{"tag": "...", "p": 0.0, "thr": 0.0, "on": false}], "act": [...], ...}} // top-k with probabilities + ], + "events": [{"tag": "speaker.change", "at": 15, "word": "Yeah,", "p": 0.94, "thr": 0.3, "tier": "T0", "read": "ev_rec"}] +} +``` + +* The label lists (`intent`, `act`, `safety`, ...) hold the tags at or above their threshold. +* `scores` keeps the top-k with probabilities. +* `emotion` holds the most likely tag of each affect group (emotion, arousal, valence, intensity) with its probability. + +Each streaming update (from `push_words` or `endpoint`) contains: +* `n_words` and `changed_from`; +* `spans`: the re-decoded window (replace your spans that end after its start); +* `events`: new events only; +* `utterances`: the utterances that just ended; +* `running`: the live intent, act, safety and state of the open utterance. + +## Latency tiers + +| tier | lookahead | use | +|---|---|---| +| T0 | 0 words | live display and zero-lag redaction (default) | +| T1 | up to 3 words | revisions a few words later | +| T2 | to the utterance end | final utterance labels | + +All three tiers are read from the same pass, so they add no compute. Utterance-level labels (intent, act, safety, role, affect) +are read at the endpoint. + +**Measured CPU latency** (1 thread, one word per call, on a heavily shared server CPU; expect lower on an idle core): + +| | p50 | p95 | +|---|---|---| +| model step per word | 16.62 ms | 31.66 ms | +| full `push_words()` call per word (span decoding and read-outs included) | 24.3 ms | 46.4 ms | + +## Evaluation + +The internal numbers are on the Whissle internal held-out test set (multi-domain: contact centre, assistant, meetings, news, +talk, sports and courts; ASR word streams). Its labels are internal, so these numbers are not comparable to public +leaderboards. Public benchmark results follow below. + +### Whissle internal held-out test set, offline pass (609 sessions, 249,866 words) + +| metric | value | +|---|---| +| primary score (spans, AP, events, exclusive groups, PII leak) | 0.7694 | +| span exact F1 (families, mean over tiers T0 to T2) | 0.7090 | +| macro AP over tags (mean over tiers) | 0.7562 | +| macro AP over tags, T0 / T1 / T2 | 0.7444 / 0.7632 / 0.7611 | +| macro AUC over tags, T0 / T1 / T2 | 0.9764 / 0.9768 / 0.9769 | +| event F1 (±2 words, T0, events with at least 20 positives) | 0.8961 | +| exclusive-group macro F1 (T0) | 0.6356 | +| PII words shown unmasked at first display (T0) | 1.23 % (non-PII words masked: 0.889 %) | + +| family | span exact F1, T0 | span exact F1, T2 | AP, T0 | +|---|---|---|---| +| PII (`safety.pii`) | 0.7314 | 0.7666 | 0.8750 | +| Profanity (`safety.profanity`) | 0.8536 | 0.8586 | 0.9717 | +| Numbers (`entity.number`) | 0.6865 | 0.7318 | 0.7618 | +| Entities (`entity.mention`) | 0.5093 | 0.5099 | 0.5773 | +| Speaker change (event) | F1 0.9688 | | 0.9953 | +| Turn yield (event) | | | 0.7864 | +| Speaker role (`speaker.role`) | | | 0.8210 | +| Assistant intent (`discourse.intent`) | top-1 0.6395, top-3 0.8172 | | 0.6770 | +| Solicitation (`safety.solicitation`) | | | 0.9475 | +| Content mode (`content.mode`) | | | 0.9929 | + +Span F1 is for the span-head decode, which the package uses for the PII, profanity, number and entity families. + +### Whissle internal held-out test set, streaming first display (35 sessions, 36,662 words) + +The sessions are replayed through the streaming engine as a live-ASR message sequence (interim words, revisions, finals, +endpoints). Each label is scored as first shown to the user (T0), with the default thresholds. + +| read-out (streaming, T0 first display) | precision | recall | F1 | positives | +|---|---|---|---|---| +| spans (word level, all span families) | 0.758 | 0.702 | 0.729 | 3,516 | +| intents (utterance) | 0.681 | 0.579 | 0.626 | 368 | +| turn events (speaker change, turn yield, ...) | 0.770 | 0.377 | 0.506 | 1,668 | +| speaker roles | 0.625 | 0.714 | 0.667 | 21 | +| utterance safety flags | 0.818 | 1.000 | 0.900 | 9 | +| discourse acts (utterance) | 0.346 | 0.450 | 0.391 | 40 | + +| span family (streaming, span exact F1) | T0 | T1 | T2 | +|---|---|---|---| +| Numbers (`entity.number`) | 0.5899 | 0.6827 | 0.6667 | +| Entities (`entity.mention`) | 0.4151 | 0.4344 | 0.4689 | + +The speaker-role, act and safety rows have few positives (21, 40 and 9). + +### Public safety benchmarks + +Whole-utterance mode, CPU, scored on the public test splits. Arc Tagger's utterance score is a logistic-regression +combination of its safety-tag probabilities, fitted on a separate development split of the same benchmark (OpenAI +Moderation: the other half of the 1,680 prompts; ToxicChat: a development subset disjoint from the test split). The other +models were run locally on the same rows. 95 % bootstrap confidence intervals in brackets. + +| benchmark | metric | Arc Tagger 0.4.0 | Detoxify (original) | Detoxify (unbiased) | s-nlp RoBERTa toxicity | +|---|---|---|---|---|---| +| OpenAI Moderation (test half: 840 prompts, 261 flagged) | AUPRC | 0.726 [0.669, 0.779] | 0.783 | 0.730 | 0.790 | +| ToxicChat 0124 test (5,083 prompts, 362 toxic) | AUPRC | 0.518 [0.470, 0.574] | 0.402 | 0.395 | 0.496 | + +| benchmark | metric | Arc Tagger 0.4.0 | +|---|---|---| +| SemEval-2021 Task 5 Toxic Spans (full test, 2,000 texts) | character F1 | 0.531 [0.513, 0.549] | + +SemEval toxic spans are read from the toxicity and profanity span scores, with one score threshold chosen on the SemEval +trial split. + +## ONNX / C runtime + +`onnx/` holds the same model exported for the T0 read-outs: `arctag.onnx` (fp32) and `arctag_int8.onnx` (int8), with +`arctag_meta.json` (read-out columns, thresholds, domain profiles), `tokenizer.json` and `SHA256SUMS`. A dependency-light C +runtime that loads these files is at [github.com/WhissleAI/arc-tagger-c](https://github.com/WhissleAI/arc-tagger-c). + +| | fp32 | int8 | +|---|---|---| +| file size | 34.0 MB | 11.5 MB | +| resident memory (RSS) | 115 MB | 76 MB | +| span F1 of int8 against fp32 output | | 0.962 | +| span F1 on the Whissle internal held-out test set (1,286 reference spans) | 0.546 | 0.543 | + +## Files + +| file | what | +|---|---| +| `model.safetensors` | weights (fp32, 45 MB) | +| `config.json` | read-out columns, ontology id, default thresholds and the span families decoded by the span head | +| `tok/bpe8k/` | tokenizer | +| `ontology/` | tag ontology, text-tag table, the ontology extension (contact-centre roles, jailbreak, affect valence and intensity), tag-definition embeddings | +| `thresholds/display.json` | display thresholds per tag, read-out and domain (tags without an entry use the default thresholds in `config.json`) | +| `engine_settings.json` | the engine's display floors and domain mapping (for reference) | +| `arctag/` | Python package: `ArcTagger`, `driver.py` (session to JSON), `asr.py` (live ASR adapters), `live.py` (audio input), `_core/` (streaming engine) | +| `stream_deepgram.py`, `stream_speechmatics.py` | live ASR scripts | +| `tests/` | adapter tests and provider message fixtures | +| `example.py` | quickstart | +| `LICENSE`, `NOTICE` | licence (CC BY-NC 4.0 + Whissle terms) and copyright notice | +| `onnx/` | fp32 and int8 ONNX exports, their metadata, tokenizer and checksums (see ONNX / C runtime) | +| `MANIFEST.sha256` | checksums | + +## Limitations + +* **English only.** Non-English input gives meaningless output. +* Text only, with no audio cues. The model cannot detect speaker identity, emotion in the voice or vocal events. + **Speaker roles need speaker ids** (`spk`) on the words, for example from ASR diarization. +* The internal test set above has no toxicity spans; the toxicity numbers on this card are the public benchmarks above. + On OpenAI Moderation the tagger is behind the dedicated toxicity classifiers. Validate toxicity detection on your own + data before relying on it. +* Entity mention spans (`entity.mention`) are the weakest span family (exact F1 about 0.51 on the test set). +* Spans shown at first display (T0, often on interim ASR words) are less accurate than the same spans a few words later (T1) + or at the utterance end (T2). Use `tier="T1"` where a delay of up to 3 words is acceptable. +* Utterance-level safety, jailbreak and scam flags can fire on short or out-of-domain utterances, and on utterances that + follow a flagged one. For anything consequential, use `scores` (p) and your own policy. +* Affect (emotion, arousal, valence, intensity) comes from the words only and is experimental; this card reports no affect + evaluation. +* The `auto` domain is weaker than setting the domain. Some sports events fire loosely outside sports. +* Redaction masks the decoded spans. It is not a compliance guarantee: review it for your use case. +* The default thresholds favour precision. Tune them for your traffic if you can. +* The Python package runs fp32 on CPU; the ONNX exports cover the T0 read-outs only. + +## Licence + +The weights and the code, including all inference code: CC BY-NC 4.0 plus the Whissle terms in [LICENSE](LICENSE). +* Non-commercial use only. +* Commercial use needs a separate written licence from Whissle Inc.: ksingla@whissle.ai. + +This covers every file in the repository, including `arctag/_core/` and `onnx/` (see [NOTICE](NOTICE)). diff --git a/arctag/__init__.py b/arctag/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..15827d9e2cec0118cc73975d2156c38926a966af --- /dev/null +++ b/arctag/__init__.py @@ -0,0 +1,130 @@ +"""Arc Tagger: text-only streaming tagger (about 10 M parameters, CPU) for ASR word streams. + + from arctag import ArcTagger + tg = ArcTagger.from_pretrained() # WhissleAI/arc-tagger from the Hub, or a local dir + out = tg.tag("Hi, this is Dana from Acme. Can you move my appointment to Friday?") + + st = tg.stream(domain="talk") # streaming: words in as the ASR emits them + st.push_words(["hi", "this", "is"], final=False) # interim + st.push_words(["hi,", "this", "is", "Dana."], revise_from=0, final=True) + st.endpoint() + res = st.finalize() + +CPU only. The model code is the streaming engine in arctag/_core. +""" +import importlib.util, json, os, sys + +__version__ = "0.4.0" +_HERE = os.path.dirname(os.path.abspath(__file__)) +_CORE = os.path.join(_HERE, "_core") +_EN = None + + +def _engine(home): + """import the vendored engine once (module-level path constants resolve against `home`)""" + global _EN + if _EN is None: + os.environ.setdefault("CUDA_VISIBLE_DEVICES", "") + os.environ.setdefault("ARCTAG_HOME", home) + os.environ["ARCTAG_ROLE"] = "offline" + if _CORE not in sys.path: + sys.path.insert(0, _CORE) + spec = importlib.util.spec_from_file_location("arctag_engine", os.path.join(_CORE, "engine.py")) + _EN = importlib.util.module_from_spec(spec); sys.modules["arctag_engine"] = _EN + spec.loader.exec_module(_EN) + return _EN + + +def _driver(): + from . import driver + return driver + + +class ArcTagger: + def __init__(self, home, EN, loaded, cfg): + self.home, self.EN, self.loaded, self.config = home, EN, loaded, cfg + + @classmethod + def from_pretrained(cls, path="WhissleAI/arc-tagger", revision=None, thresholds="refit"): + """path: release dir (model.safetensors, config.json, tok/, ontology/, thresholds/) or a Hugging Face repo id. + thresholds: "refit" (default: thresholds/display.json, plus thresholds/events.json and span_head.json when present) + or "none" (the default thresholds in config.json only; engine display floors still apply).""" + if not os.path.isdir(path): + from huggingface_hub import snapshot_download + path = snapshot_download(path, revision=revision, allow_patterns=["*.json", "*.safetensors", "*.npy", "*.py", "*.txt", "*.md", + "LICENSE*", "tok/*", "ontology/*", "thresholds/*", "arctag/*"]) + home = os.path.abspath(path) + EN = _engine(home) + AT = sys.modules["arc_text"]; TT = sys.modules["tagtable"] + AT.BPE8K = os.path.join(home, "tok", "bpe8k", "tokenizer.json") + cfg = json.load(open(os.path.join(home, "config.json"))) + ext = os.path.join(home, cfg.get("onto_ext") or "ontology/ext_v7_aff.json") + T = TT.TextTable(r8=os.path.join(home, "ontology", "tags_v1_r8.json"), + text_tags=os.path.join(home, "ontology", "text_tags.json"), ext=ext) + T.load_E(os.path.join(home, "ontology", "head_E_r8_bge_small.npy")) + if T.sha != cfg.get("ontology_sha"): + raise RuntimeError(f"ontology sha {T.sha} != config {cfg.get('ontology_sha')}") + loaded = _load_model(EN, home, cfg, T, thresholds) + return cls(home, EN, loaded, cfg) + + def stream(self, domain="auto", tier="T0"): + """a streaming session (driver.Stream): push_words(words, final=True, revise_from=None, endpoint=False), endpoint(), + finalize() -> result dict""" + return _driver().Stream(self.EN, self.loaded, domain=domain, tier=tier, meta=self._meta()) + + def tag(self, x, domain="auto", tier="T0"): + """x: text (str), a list of words (str) or word dicts {"w", "t0", "t1", "conf", "spk", "final"}. Utterances end after + words ending in . ? ! Returns the structured result (see README "Output").""" + return _driver().tag(self.EN, self.loaded, x, domain=domain, tier=tier, meta=self._meta()) + + def _meta(self): + return {"model": "arc-tagger", "version": __version__} + + +def _read_thr(display, events): + """thresholds/display.json {"thresholds": {tag@readout: {domain|_all: thr}}} and thresholds/events.json {"tier", "hide", + "thresholds"}""" + pref, pstep, ev, evstep = {}, None, {}, None + if os.path.exists(display): + pref, pstep = json.load(open(display)).get("thresholds") or {}, "display" + if os.path.exists(events): + ev, evstep = json.load(open(events)), "events" + return pref, pstep, ev, evstep + + +def _load_model(EN, home, cfg, T, thresholds="refit"): + """load model.safetensors and the threshold tables of a release dir""" + from safetensors.torch import load_file + AT = sys.modules["arc_text"]; SA = sys.modules["stream_arc"] + step = None + m = AT.ArcText(T, scratch=cfg.get("scratch") or AT.SCRATCH_10M) + sd = load_file(os.path.join(home, "model.safetensors")) + SA.add_modules_for(m, sd) + missing, unexpected = m.load_state_dict(sd, strict=False) + if unexpected: + raise RuntimeError(f"weights without a module: {unexpected[:6]}") + bad = [k for k in missing if not k.startswith(("head.E",))] + if bad: + import warnings + warnings.warn(f"{len(bad)} missing keys (e.g. {bad[:3]})") + m.eval() + own = cfg.get("thresholds") or {} + pref, pstep, ev, evstep = {}, None, {}, None + if thresholds == "refit": + d = os.path.join(home, "thresholds") + pref, pstep, ev, evstep = _read_thr(os.path.join(d, "display.json"), os.path.join(d, "events.json")) + sh = os.path.join(d, "span_head.json") # span-head thresholds per tier + if os.path.exists(sh): + ts = {t: dict(v) for t, v in (cfg.get("thresholds_span") or {}).items()} + for t, v in (json.load(open(sh)).get("thresholds_span") or {}).items(): + ts.setdefault(t, {}).update(v) + cfg = dict(cfg, thresholds_span=ts) + elif thresholds != "none": + raise ValueError("thresholds must be 'refit' or 'none'") + thr = EN.Thresholds(own, {}, None, pref=pref, pref_step=pstep) + thr.set_events(ev, evstep) + L = EN.Loaded(step, home, m, T, thr, cfg) + fams = cfg.get("span_head_families") # span families decoded from the span head (others: typed span decode) + if fams is not None and L.head_tags: + L.head_use, L.head_why = set(fams) & set(L.head_tags), "config" + return L diff --git a/arctag/_core/__init__.py b/arctag/_core/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..861d2c8dfcae95741eeb9f3fd7d60cce47b38a64 --- /dev/null +++ b/arctag/_core/__init__.py @@ -0,0 +1,2 @@ +# Arc Tagger inference engine. Copyright (c) 2026 Whissle Inc. CC BY-NC 4.0 + Whissle terms, see LICENSE. + diff --git a/arctag/_core/arc_audio/__init__.py b/arctag/_core/arc_audio/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2b44bdc320adfcb36a2a0d4bfbd387b5f9ddb83b --- /dev/null +++ b/arctag/_core/arc_audio/__init__.py @@ -0,0 +1,2 @@ +# Arc Tagger inference engine. Copyright (c) 2026 Whissle Inc. CC BY-NC 4.0 + Whissle terms, see LICENSE. +pass diff --git a/arctag/_core/arc_audio/model.py b/arctag/_core/arc_audio/model.py new file mode 100644 index 0000000000000000000000000000000000000000..8132d3c8c48349c0cfffa38c4ab33e35f8cad72a --- /dev/null +++ b/arctag/_core/arc_audio/model.py @@ -0,0 +1,331 @@ +# Arc Tagger inference engine. Copyright (c) 2026 Whissle Inc. CC BY-NC 4.0 + Whissle terms, see LICENSE. +import math +import torch, torch.nn as nn, torch.nn.functional as F +from .seqlayer import SeqLayer +from .seqlayer.layer import rotate_pairs +from .seqlayer.ssd import seq_layer_scan_state, _f +from .ontology import QUERIES, POOLED, FINE_DOMAINS +NEG = -10000.0 +S_MAX = 64 + +class Trunk(nn.Module): + + def __init__(self, d_in=640, d_model=512, n_layers=5, d_state=64, head_dim=64, dropout=0.2, in_drop=0.2, chunk=128, n_dom=len(FINE_DOMAINS), delays=(0, 1, 2, 3, 5, 8), adapters=64, long_mem=True): + super().__init__() + self.cfg = dict(d_in=d_in, d_model=d_model, n_layers=n_layers, d_state=d_state, head_dim=head_dim, dropout=dropout, in_drop=in_drop, chunk=chunk, n_dom=n_dom, delays=list(delays), adapters=adapters, long_mem=long_mem) + self.chunk = chunk + self.in_norm = nn.LayerNorm(d_in) + self.inp = nn.Linear(d_in, d_model) + self.dom_emb = nn.Embedding(n_dom, d_model) + self.film = nn.ModuleList([nn.Linear(d_model, 2 * d_model) for _ in range(n_layers)]) + self.delays = list(delays) + self.delay_emb = nn.Embedding(len(delays), d_model) + self.delay_film = nn.Embedding(len(delays), d_model) + lut = torch.full((max(delays) + 1,), -1, dtype=torch.long) + for i, d in enumerate(delays): + lut[d] = i + self.register_buffer('delay_lut', lut, persistent=False) + self.adapt_down = nn.Parameter(torch.zeros(n_dom, d_model, adapters)) + self.adapt_up = nn.Parameter(torch.zeros(n_dom, adapters, d_model)) + self.layers = nn.ModuleList([SeqLayer(d_model, d_state=d_state, rank=1, head_dim=head_dim) for _ in range(n_layers)]) + self.mlps = nn.ModuleList([nn.Sequential(nn.RMSNorm(d_model), nn.Linear(d_model, 2 * d_model), nn.GELU(), nn.Dropout(dropout), nn.Linear(2 * d_model, d_model)) for _ in range(n_layers)]) + self.out_norm = nn.RMSNorm(d_model) + + def _cond(self, x, dom, delay): + di = self.delay_lut[delay.clamp(0, len(self.delay_lut) - 1)] + if (di < 0).any(): + raise ValueError(f'delay {delay.tolist()} frames not in the supported set {self.delays}') + h = self.inp(self.in_norm(x)) + e = self.dom_emb(dom) + self.delay_film(di) + return (h + (self.dom_emb(dom) + self.delay_emb(di))[:, None], e) + + def _adapt(self, h, dom): + Dn, Up = (self.adapt_down[dom].to(h.dtype), self.adapt_up[dom].to(h.dtype)) + return h + torch.einsum('btr,brd->btd', F.gelu(torch.einsum('btd,bdr->btr', h, Dn)), Up) + + def forward(self, x, dom, delay, state=None): + h, e = self._cond(x, dom, delay) + new = [] + for i, (lay, mlp) in enumerate(zip(self.layers, self.mlps)): + gb = self.film[i](e)[:, None] + h = h * (1 + gb[..., :h.shape[-1]]) + gb[..., h.shape[-1]:] + st = None if state is None else tuple((_f(t) for t in state[i][:3])) + s, ns = seq_layer_scan_state(lay, _f(h), st, self.chunk) + h = h + s + h = h + mlp(h) + new.append(ns) + return (self.out_norm(self._adapt(h, dom)), new) + + def step(self, x, dom, delay, state=None): + h_all, e = self._cond(x, dom, delay) + B, T, D = h_all.shape + st = [None] * len(self.layers) if state is None else list(state) + gbs = [self.film[i](e) for i in range(len(self.layers))] + outs = [] + for t in range(T): + h = h_all[:, t] + for i, (lay, mlp) in enumerate(zip(self.layers, self.mlps)): + gb = gbs[i] + h = h * (1 + gb[..., :D]) + gb[..., D:] + y, st[i] = layer_step(lay, _f(h), st[i]) + h = h + y + h = h + mlp(h) + outs.append(h) + h = torch.stack(outs, 1) + return (self.out_norm(self._adapt(h, dom)), st) + +def layer_step(lay, u, state): + Bsz = u.shape[0] + nu = lay.norm(u.unsqueeze(1)) + alpha, beta, gamma, Bm, Cm, x, theta = lay._coeffs(nu) + H, N, P = (lay.H, lay.N, lay.P) + bx = Bm[:, 0] @ x[:, 0].transpose(-1, -2) + if state is None: + h = gamma[:, 0, :, None, None] * bx + else: + h0, Bl, xl = (_f(t) for t in state[:3]) + prev_bx = _f(state[3]) if len(state) > 3 else Bl @ xl.transpose(-1, -2) + pre = alpha[:, 0, :, None, None] * h0 + beta[:, 0, :, None, None] * prev_bx + h = rotate_pairs(pre.transpose(-1, -2), theta[:, 0].unsqueeze(-2).expand(-1, -1, P, -1)).transpose(-1, -2) + h = h + gamma[:, 0, :, None, None] * bx + y = (Cm[:, 0].transpose(-1, -2) @ h).reshape(Bsz, -1) + return (lay.gate_and_proj(y, nu[:, 0]), (h, Bm[:, 0], x[:, 0], bx)) + +def pool_speaker(z, spk, carry): + B, T, D = z.shape + sp = torch.where((spk >= 0) & (spk < S_MAX), spk, torch.full_like(spk, -1)) + key = (torch.arange(B, device=z.device)[:, None] * (S_MAX + 1) + (sp + 1)) * T + torch.arange(T, device=z.device)[None] + order = torch.argsort(key.reshape(-1), stable=True) + zs = z.reshape(B * T, D)[order].double() + gs = key.reshape(-1)[order] // T + cs = torch.cumsum(zs, 0) + start = torch.ones_like(gs, dtype=torch.bool) + start[1:] = gs[1:] != gs[:-1] + pos = torch.arange(B * T, device=z.device) + first = torch.cummax(torch.where(start, pos, torch.zeros_like(pos)), 0).values + base = torch.where(first[:, None] > 0, cs[(first - 1).clamp(min=0)], torch.zeros_like(cs)) + num = torch.empty_like(cs) + num[order] = cs - base + cnt = torch.empty(B * T, dtype=torch.float64, device=z.device) + cnt[order] = (pos - first + 1).double() + num = num.reshape(B, T, D) + cnt = cnt.reshape(B, T) + cs_, cn_ = carry + sidx = sp.clamp(min=0) + num = num + torch.gather(cs_, 1, sidx[..., None].expand(B, T, D)) + cnt = cnt + torch.gather(cn_, 1, sidx) + out = (num / cnt.clamp(min=1)[..., None]).float() * (sp >= 0)[..., None].float() + oh = F.one_hot(sidx, S_MAX).double() * (sp >= 0)[..., None].double() + new = (cs_ + torch.einsum('bts,btd->bsd', oh, z.double()), cn_ + oh.sum(1)) + return (out, new) + +def pool_segment(z, starts, carry): + B, T, D = z.shape + st = torch.zeros(B, T, dtype=torch.bool, device=z.device) if starts is None else starts + cs = torch.cumsum(z.double(), 1) + cs0 = F.pad(cs, (0, 0, 1, 0))[:, :-1] + t = torch.arange(T, device=z.device)[None].expand(B, T) + first = torch.cummax(torch.where(st, t, torch.full_like(t, -1)), 1).values + base = torch.gather(cs0, 1, first.clamp(min=0)[..., None].expand(B, T, D)) + n = (t - first.clamp(min=0) + 1).double() + open_ = first < 0 + num = torch.where(open_[..., None], cs + carry[0][:, None], cs - base) + n = torch.where(open_, (t + 1).double() + carry[1][:, None], n) + out = (num / n.clamp(min=1)[..., None]).float() + last = first[:, -1] + new_s = torch.where((last < 0)[:, None], carry[0] + cs[:, -1], cs[:, -1] - torch.gather(cs0, 1, last.clamp(min=0)[:, None, None].expand(B, 1, D))[:, 0]) + new_n = torch.where(last < 0, carry[1] + T, (T - last).double()) + return (out, (new_s, new_n)) + +def zero_carry(B, D, device): + z = lambda *s: torch.zeros(*s, dtype=torch.float64, device=device) + return dict(utt=(z(B, D), z(B)), speaker=(z(B, S_MAX, D), z(B, S_MAX)), item=(z(B, D), z(B))) + +class TagHead(nn.Module): + + def __init__(self, table, n_text=384, d_model=512, d_k=512): + super().__init__() + self.table = table + self.register_buffer('E', torch.zeros(table.n, n_text)) + self.register_buffer('valid', torch.as_tensor(table.valid), persistent=False) + self.P = nn.Linear(n_text, d_k, bias=False) + self.r = nn.Parameter(torch.zeros(table.n, d_k)) + self.b = nn.Parameter(torch.zeros(table.n)) + self.bq = nn.ParameterDict({q: nn.Parameter(torch.zeros(table.n)) for q in QUERIES}) + self.Q = nn.ModuleDict({q: nn.Linear(d_model, d_k) for q in QUERIES}) + self.pool_in = nn.ModuleDict({q: nn.Sequential(nn.Linear(d_model, d_model), nn.GELU(), nn.Linear(d_model, d_model)) for q in POOLED}) + for q in QUERIES: + self.register_buffer(f'cols_{q}', torch.tensor(table.cols[q], dtype=torch.long), persistent=False) + + def cols(self, q): + return getattr(self, f'cols_{q}') + + def keys(self): + return self.P(self.E) + self.r + + def pooled(self, h, spk, ustart, carry, h_utt=None): + zin, new = ({}, {}) + for q in POOLED: + hq = h_utt if q == 'utt' and h_utt is not None else h + z = hq + self.pool_in[q](hq) + if q == 'speaker': + zin[q], new[q] = pool_speaker(z.float(), spk, carry[q]) + elif q == 'utt': + zin[q], new[q] = pool_segment(z.float(), ustart, carry[q]) + else: + zin[q], new[q] = pool_segment(z.float(), None, carry[q]) + return (zin, new) + + def logits_q(self, q, zq, dom, K=None): + K = self.keys() if K is None else K + c = self.cols(q) + z = self.Q[q](zq.float()) @ K[c].T + (self.b + self.bq[q])[c] + return z.masked_fill(~self.valid[dom][:, c][:, None, :], NEG) +NAME_MAX_BYTES = 40 +C_KINDS = ['person', 'team', 'organization', 'place', 'work_product', 'event_name', 'legal_ref', 'game_entity', 'game_map', 'other'] +MENTION_KINDS = {'entity.mention.person': [0, 9], 'entity.mention.team': [1, 2], 'entity.mention.organization': [2, 1, 9], 'entity.mention.place': [3, 9], 'entity.mention.work_product': [4, 9], 'entity.mention.event_name': [5, 9], 'entity.mention.legal_ref': [6, 9], 'entity.mention.game_entity': [7, 9], 'entity.mention.game_map': [8, 9]} +K_SLOTS = 4 +CLOCK = 3 +SPAN_G_MULT = 20.0 + +def encode_names(names, max_len=NAME_MAX_BYTES, keep='head'): + import numpy as np + ids = np.zeros((len(names), max_len), np.int64) + for i, n in enumerate(names): + b = ' '.join(str(n).lower().split()).encode('utf-8') + b = b[:max_len] if keep == 'head' else b[-max_len:] + ids[i, :len(b)] = np.frombuffer(b, np.uint8).astype(np.int64) + 1 + return ids + +class NameEncoder(nn.Module): + + def __init__(self, d=128, char_dim=48): + super().__init__() + self.d = d + self.emb = nn.Embedding(257, char_dim, padding_idx=0) + self.conv_in = nn.Conv1d(char_dim, d, 3, padding=1) + self.convs = nn.ModuleList([nn.Conv1d(d, d, 3, padding=dl, dilation=dl) for dl in (1, 2, 4)]) + self.kind = nn.Embedding(len(C_KINDS), d) + self.out = nn.Sequential(nn.Linear(2 * d, d), nn.GELU(), nn.Linear(d, d)) + + def forward(self, ids, kinds): + sh = ids.shape[:-1] + ids = ids.reshape(-1, ids.shape[-1]) + m = (ids > 0).float()[:, None] + x = self.emb(ids).float().transpose(1, 2) + h = F.gelu(self.conv_in(x)) * m + for cv in self.convs: + h = h + F.gelu(cv(h)) * m + mean = h.sum(-1) / m.sum(-1).clamp(min=1.0) + mx = h.masked_fill(m == 0, -10000.0).max(-1).values * (m.sum(-1) > 0).float() + e = self.out(torch.cat([mean, mx], -1)) + self.kind(kinds.reshape(-1)) + return F.normalize(e, dim=-1).reshape(*sh, self.d) + +class EntityHeads(nn.Module): + + def __init__(self, d_model=512, d_key=512, d=128, n_units=64, span=True, span_new=True, sim=False): + super().__init__() + self.names = NameEncoder(d) + self.query = nn.Sequential(nn.Linear(d_model, d), nn.GELU(), nn.Linear(d, d)) + self.key_in = nn.Linear(d_key, d_model, bias=False) + self.special = nn.Parameter(torch.zeros(2, d)) + self.special_b = nn.Parameter(torch.zeros(2)) + self.log_scale = nn.Parameter(torch.tensor([math.log(10.0)])) + self.nwt = nn.Linear(d_model, K_SLOTS * 43) + self.unit = nn.Linear(d_model, n_units + 1) + self.clock = nn.Linear(d_model, CLOCK) + self.approx = nn.Linear(d_model, 2) + self.n_units = n_units + self.span_q = nn.Linear(d, d) if span else None + if span: + self.span_g = nn.Parameter(torch.zeros(1)) + if span and span_new: + self.span_new = nn.Parameter(torch.zeros(1)) + self.sim_w = None + if sim: + self.sim_w = nn.Parameter(torch.zeros(3, 6)) + self.sim_new = nn.Parameter(torch.zeros(3, 3)) + if span: + self.span_g_src = nn.Parameter(torch.zeros(3)) + + def ptr_logits(self, h, cls_key, E, cmask, Es=None, Sf=None, src=0): + qr = self.query(h.float() + self.key_in(cls_key.float())) + use_span = Es is not None and self.span_q is not None + if use_span: + qr = qr + self.span_q(Es.float()) + q = F.normalize(qr, dim=-1) + sc = self.log_scale.clamp(max=math.log(50.0)).exp() + s = torch.einsum('btd,brd->btr', q, E.float()) * sc + if use_span: + g_ = self.span_g if getattr(self, 'span_g_src', None) is None else self.span_g + self.span_g_src[int(src)] + s = s + SPAN_G_MULT * g_ * sc * torch.einsum('btd,brd->btr', Es.float(), E.float()) + use_sim = Sf is not None and self.sim_w is not None + if use_sim: + Sf = Sf.float() + s = s + SPAN_G_MULT * sc * torch.einsum('btrf,f->btr', Sf, self.sim_w[int(src)]) + s = s.masked_fill(~cmask, NEG) + sp = torch.einsum('btd,kd->btk', q, F.normalize(self.special, dim=-1)) * sc + self.special_b + if use_sim: + hs = (Sf.abs().sum((-1, -2)) > 0).float() + mb = Sf[..., 4].masked_fill(~cmask, 0.0).max(-1).values + msh = Sf[..., 5].masked_fill(~cmask, 0.0).max(-1).values + sn = self.sim_new[int(src)] + tn = sn[0] + sn[1] * (1.0 - mb) + sn[2] * (1.0 - msh) + sp = torch.cat([sp[..., :1] + (SPAN_G_MULT * sc * hs * tn)[..., None], sp[..., 1:]], -1) + if use_span and getattr(self, 'span_new', None) is not None: + ms = torch.einsum('btd,brd->btr', Es.float(), E.float()).masked_fill(~cmask, -1.0).max(-1).values.clamp(min=-1.0) + has = (Es.float().abs().sum(-1) > 0).float() + sp = torch.cat([sp[..., :1] + SPAN_G_MULT * self.span_new * sc * has[..., None] * (1.0 - ms[..., None]), sp[..., 1:]], -1) + return torch.cat([sp, s], -1) + + def span_embed(self, text): + ids = torch.as_tensor(encode_names([text], keep='tail'), device=self.special.device) + return self.names(ids, torch.zeros(1, dtype=torch.long, device=ids.device))[0] + + def value_logits(self, h): + return dict(nwt=self.nwt(h).float().reshape(*h.shape[:-1], K_SLOTS, -1), unit=self.unit(h).float(), clock=self.clock(h).float(), approx=self.approx(h).float()) + +class BioHead(nn.Module): + EV = ('cough', 'throat_clear', 'breath', 'sneeze') + + def __init__(self, d_model=512): + super().__init__() + self.ev = nn.Linear(d_model, len(self.EV)) + +class DiarHeads(nn.Module): + + def __init__(self, d_model=512, d_in=640, k=8, d=128, hidden=256, n_extra=0): + super().__init__() + self.k = k + n_extra + self.slot = nn.Linear(d_model, 1 + k) + self.h_proj = nn.Linear(d_model, hidden) + self.x_norm = nn.LayerNorm(d_in) + self.x_proj = nn.Linear(d_in, hidden) + self.out = nn.Sequential(nn.GELU(), nn.Linear(hidden, hidden), nn.GELU(), nn.Linear(hidden, d)) + self.slot_ext = nn.Linear(d_model, n_extra) if n_extra else None + + def slot_logits(self, h): + z = self.slot(h) + return torch.cat([z, self.slot_ext(h)], -1) if self.slot_ext is not None else z + + def embed(self, h, x): + return F.normalize(self.out(self.h_proj(h.float()) + self.x_proj(self.x_norm(x.float()))), dim=-1) + +class ArcNet(nn.Module): + + def __init__(self, table, cfg): + super().__init__() + self.trunk = Trunk(**cfg['trunk']) + d = self.trunk.cfg['d_model'] + self.head = TagHead(table, n_text=int(cfg.get('n_text', 384)), d_model=d) + self.ent = EntityHeads(d_model=d, d_key=d, n_units=int(cfg.get('n_units', 64)), span=bool(cfg.get('ptr_span_text')), span_new=bool(cfg.get('ptr_span_new')), sim=bool(cfg.get('ptr_sim_feat'))) + self.diar = DiarHeads(d_model=d, d_in=self.trunk.cfg['d_in'], n_extra=max(0, int(cfg.get('diar_slots', 8)) - 8)) + self.bio = BioHead(d) if cfg.get('bio_head') else None + self.word = None + if cfg.get('word_ch'): + from .wordch import WordChannel + self.word = WordChannel(d_model=d, d_k=d, n_in=len(table.cols['span_in']), k_slots=K_SLOTS, n_nwt=int(cfg.get('n_nwt', 43))) + self.cfg = cfg + + def trunk_run(self, x, dom, delay, state=None, recurrent=False): + return (self.trunk.step if recurrent else self.trunk)(x, dom, delay, state) diff --git a/arctag/_core/arc_audio/ontology.py b/arctag/_core/arc_audio/ontology.py new file mode 100644 index 0000000000000000000000000000000000000000..ab9bf09889cf037748f5a12b43237bde0599d057 --- /dev/null +++ b/arctag/_core/arc_audio/ontology.py @@ -0,0 +1,73 @@ +# Arc Tagger inference engine. Copyright (c) 2026 Whissle Inc. CC BY-NC 4.0 + Whissle terms, see LICENSE. +import hashlib, json +import numpy as np +FINE_DOMAINS = ['nba', 'nhl', 'nfl', 'cricket', 'soccer', 'esports', 'news', 'talk', 'stream_chat', 'meeting', 'court_appellate', 'court_trial', 'assistant', 'assistant_noisy', 'short_clips', 'unknown'] +QUERIES = ('frame', 'span_in', 'span_end', 'utt', 'speaker', 'ev_at', 'ev_imm', 'ev_rec', 'item') +POOLED = ('utt', 'speaker', 'item') +SPORTS = ('nba', 'nhl', 'nfl', 'cricket', 'soccer') + +def readouts(t): + u = t['unit'] + if u in ('F', 'G'): + return ('frame',) + if u == 'W': + return ('span_in', 'span_end') + if u in ('U', 'T'): + return ('utt',) + if u == 'S': + return ('speaker',) + if u == 'D': + return ('item',) + if u == 'E': + return ('ev_rec',) if t['id'].endswith('.recent') else ('ev_imm',) if t['id'].endswith('.imminent') else ('ev_at',) + raise ValueError(f"unknown unit {u} for {t['id']}") + +def domain_index(domain): + d = (domain or 'unknown').lower() + if d not in FINE_DOMAINS: + raise ValueError(f'unknown domain {domain!r}; use one of {FINE_DOMAINS}') + return FINE_DOMAINS.index(d) + +class Table: + + def __init__(self, path): + raw = open(path, 'rb').read() + self.sha = hashlib.sha256(raw).hexdigest() + j = json.loads(raw) + self.version = j['version'] + self.j = j + tags = sorted(j['tags'], key=lambda t: t['index']) + self.n = max((t['index'] for t in tags)) + 1 + self.tag = [None] * self.n + for t in tags: + self.tag[t['index']] = t + self.ids = [t['id'] if t else f'_unused{i}' for i, t in enumerate(self.tag)] + self.idx = {t: i for i, t in enumerate(self.ids)} + rg = {g['id']: g for g in j['groups']} + self.groups, self.members = ([], {}) + for t in tags: + if t.get('exclusivity') not in (None, 'independent'): + if t['group'] not in self.members: + self.groups.append(t['group']) + self.members[t['group']] = [] + self.members[t['group']].append(t['index']) + self.group_of = {i: g for g, m in self.members.items() for i in m} + self.gate = {g: rg.get(g, {}).get('gate') for g in self.groups} + self.group_info = rg + self.active = [i for i, t in enumerate(self.tag) if t and t.get('status') not in ('reserved', 'derived')] + dg = j['domains'] + V = np.zeros((len(FINE_DOMAINS), self.n), bool) + for i in self.active: + keys = self.tag[i]['domains'] + for d, fd in enumerate(FINE_DOMAINS): + V[d, i] = any((k == '*' or k == fd or fd in dg.get(k, []) for k in keys)) + V[FINE_DOMAINS.index('unknown')] |= V.any(0) + self.valid = V + self.ro = {i: readouts(self.tag[i]) for i in self.active} + self.cols = {q: [i for i in self.active if q in self.ro[i]] for q in QUERIES} + self.facet = {i: self.tag[i]['facet'] for i in self.active} + self.status = {i: self.tag[i]['status'] for i in self.active} + + def describe(self, tag_id): + t = self.tag[self.idx[tag_id]] + return dict(id=t['id'], facet=t['facet'], unit=t['unit'], group=t.get('group'), description=t.get('description'), paraphrases=t.get('paraphrases') or [], status=t.get('status')) diff --git a/arctag/_core/arc_audio/seqlayer/__init__.py b/arctag/_core/arc_audio/seqlayer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8a081ccd8912d7721dbcb401531d7600c4b1b2ed --- /dev/null +++ b/arctag/_core/arc_audio/seqlayer/__init__.py @@ -0,0 +1,3 @@ +# Arc Tagger inference engine. Copyright (c) 2026 Whissle Inc. CC BY-NC 4.0 + Whissle terms, see LICENSE. +from .layer import SeqLayer +from .ssd import seq_layer_scan, _f diff --git a/arctag/_core/arc_audio/seqlayer/layer.py b/arctag/_core/arc_audio/seqlayer/layer.py new file mode 100644 index 0000000000000000000000000000000000000000..fc046d516116341b2fd8067f62864e92c2805e4d --- /dev/null +++ b/arctag/_core/arc_audio/seqlayer/layer.py @@ -0,0 +1,146 @@ +# Arc Tagger inference engine. Copyright (c) 2026 Whissle Inc. CC BY-NC 4.0 + Whissle terms, see LICENSE. +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + +def rotate_pairs(h: torch.Tensor, ang: torch.Tensor) -> torch.Tensor: + a, b = (h[..., 0::2], h[..., 1::2]) + c, s = (torch.cos(ang), torch.sin(ang)) + out = torch.empty_like(h) + out[..., 0::2] = c * a - s * b + out[..., 1::2] = s * a + c * b + return out + +def ssm_scan(alpha, beta, gamma, theta, S, Cm, P, state=None, prev_S=None): + B, T, H, N, _ = S.shape + R = Cm.shape[-1] + h = torch.zeros(B, H, N, P, device=S.device, dtype=S.dtype) if state is None else state + ys = [] + for t in range(T): + prev = S[:, t - 1] if t > 0 else prev_S + pre = alpha[:, t, :, None, None] * h + if prev is not None: + pre = pre + beta[:, t, :, None, None] * prev + h = rotate_pairs(pre.transpose(-1, -2), theta[:, t].unsqueeze(-2).expand(-1, -1, P, -1)).transpose(-1, -2) + h = h + gamma[:, t, :, None, None] * S[:, t] + ys.append((Cm[:, t].transpose(-1, -2) @ h).reshape(B, H * R * P)) + return (torch.stack(ys, 1), h, S[:, -1]) + +class SeqLayer(nn.Module): + + def __init__(self, d_model: int, d_state: int=64, rank: int=1, head_dim: int=32, dt_min: float=0.001, dt_max: float=0.5): + super().__init__() + assert d_state % 2 == 0, 'd_state must be even: the state rotates in pairs' + assert d_model % head_dim == 0, 'd_model must divide into heads of head_dim' + self.d_model, self.N, self.R, self.P = (d_model, d_state, rank, head_dim) + self.H = d_model // head_dim + self.in_proj = nn.Linear(d_model, d_model * rank) + self.B_proj = nn.Linear(d_model, self.H * d_state * rank) + self.C_proj = nn.Linear(d_model, self.H * d_state * rank) + self.x_proj = nn.Linear(d_model, d_model * rank) + self.dt_proj = nn.Linear(d_model, self.H) + self.theta_proj = nn.Linear(d_model, self.H * (d_state // 2)) + self.lam_proj = nn.Linear(d_model, self.H) + nn.init.zeros_(self.lam_proj.weight) + nn.init.zeros_(self.lam_proj.bias) + self.out_proj = nn.Linear(d_model * rank, d_model) + self.norm = nn.RMSNorm(d_model) if hasattr(nn, 'RMSNorm') else nn.LayerNorm(d_model) + rms = nn.RMSNorm if hasattr(nn, 'RMSNorm') else nn.LayerNorm + self.B_norm = rms(d_state) + self.C_norm = rms(d_state) + self.B_bias = nn.Parameter(torch.ones(self.H, d_state, rank)) + self.C_bias = nn.Parameter(torch.ones(self.H, d_state, rank)) + self.A_log = nn.Parameter(torch.log(torch.rand(self.H) * 0.9 + 0.05)) + dt = torch.exp(torch.rand(self.H) * (math.log(dt_max) - math.log(dt_min)) + math.log(dt_min)) + self.dt_proj.bias.data = dt + torch.log(-torch.expm1(-dt)) + + def _coeffs(self, u: torch.Tensor): + Bsz, T, _ = u.shape + H, N, R, P = (self.H, self.N, self.R, self.P) + dt = F.softplus(self.dt_proj(u)) + A = -torch.exp(self.A_log) + alpha = torch.exp(dt * A) + lam = torch.sigmoid(self.lam_proj(u)) + gamma = lam * dt + beta = (1.0 - lam) * dt * alpha + Bm = self.B_proj(u).view(Bsz, T, H, R, N) + Bm = self.B_norm(Bm).permute(0, 1, 2, 4, 3) + self.B_bias + Cm = self.C_proj(u).view(Bsz, T, H, R, N) + Cm = self.C_norm(Cm).permute(0, 1, 2, 4, 3) + self.C_bias + x = self.x_proj(u).view(Bsz, T, H, P, R) + theta = self.theta_proj(u).view(Bsz, T, H, N // 2) * dt.unsqueeze(-1) + return (alpha, beta, gamma, Bm, Cm, x, theta) + + def gate_and_proj(self, y_flat: torch.Tensor, nu: torch.Tensor): + return self.out_proj(y_flat * F.silu(self.in_proj(nu))) + + def forward(self, u: torch.Tensor, state=None, return_state=False): + nu = self.norm(u) + alpha, beta, gamma, Bm, Cm, x, theta = self._coeffs(nu) + S = Bm @ x.transpose(-1, -2) + y_flat, h, _ = ssm_scan(alpha, beta, gamma, theta, S, Cm, self.P, state=state) + out = self.gate_and_proj(y_flat, nu) + return (out, h) if return_state else out + + def step(self, u_t: torch.Tensor, state, prev_bx=None): + Bsz = u_t.shape[0] + nu = self.norm(u_t.unsqueeze(1)) + alpha, beta, gamma, Bm, Cm, x, theta = self._coeffs(nu) + H, N, P = (self.H, self.N, self.P) + h = torch.zeros(Bsz, H, N, P, device=u_t.device, dtype=u_t.dtype) if state is None else state + pre = alpha[:, 0, :, None, None] * h + if prev_bx is not None: + pre = pre + beta[:, 0, :, None, None] * prev_bx + h = rotate_pairs(pre.transpose(-1, -2), theta[:, 0].unsqueeze(-2).expand(-1, -1, P, -1)).transpose(-1, -2) + bx = Bm[:, 0] @ x[:, 0].transpose(-1, -2) + h = h + gamma[:, 0, :, None, None] * bx + y = (Cm[:, 0].transpose(-1, -2) @ h).reshape(Bsz, -1) + return (self.gate_and_proj(y, nu[:, 0]), h, bx) + +def selftest(verbose: bool=True) -> dict: + torch.manual_seed(0) + torch.set_default_dtype(torch.float64) + net = SeqLayer(d_model=32, d_state=16, rank=2, head_dim=8).eval() + B, T = (2, 20) + u = torch.randn(B, T, 32) + out = {} + with torch.no_grad(): + y = net(u) + drifts = [] + for mag in (1.0, 100.0, 10000.0): + u2 = torch.cat([u, torch.randn(B, 6, 32) * mag], 1) + drifts.append(float((net(u2)[:, :T] - y).abs().max())) + out['causality'] = max(drifts) + out['causality_scaling'] = max(drifts) / max(min(drifts), 1e-300) + st = bx = None + steps = [] + for t in range(T): + o, st, bx = net.step(u[:, t], st, bx) + steps.append(o) + out['streaming'] = float((torch.stack(steps, 1) - y).abs().max()) + h = torch.randn(B, 4, 16, 8) + ang = torch.randn(B, 4, 1, 8) + hr = rotate_pairs(h.transpose(-1, -2), ang).transpose(-1, -2) + out['orthogonal'] = float((hr.norm(dim=-2) - h.norm(dim=-2)).abs().max()) + up = u.clone() + up[:, 3] += 1.0 + yp = net(up) + out['twotap_y4'] = float((yp[:, 4] - y[:, 4]).abs().max()) + out['twotap_y2'] = float((yp[:, 2] - y[:, 2]).abs().max()) + a, b, g, Bm, Cm, x, th = net._coeffs(net.norm(u)) + rms = (Bm - net.B_bias).pow(2).mean(-2).sqrt() + out['bcnorm_rms'] = float((rms - 1.0).abs().max()) + out['params'] = sum((p.numel() for p in net.parameters())) + torch.set_default_dtype(torch.float32) + if verbose: + ok = lambda c: 'OK' if c else 'FAIL' + print('layer selftest (float64)') + print(f" 1 causality (future->past) {out['causality']:.3e} {ok(out['causality'] < 1e-12)}") + print(f" drift x1 vs x10000 ratio {out['causality_scaling']:.3f} {ok(out['causality_scaling'] < 1.5)} (a leak would scale)") + print(f" 2 step() == forward() {out['streaming']:.3e} {ok(out['streaming'] < 1e-10)}") + print(f" 3 rotation orthogonal {out['orthogonal']:.3e} {ok(out['orthogonal'] < 1e-10)}") + print(f" 4 trapezoidal two-tap y4 {out['twotap_y4']:.3e} y2 {out['twotap_y2']:.3e} {ok(out['twotap_y4'] > 1e-06 and out['twotap_y2'] == 0.0)}") + print(f" 5 BCNorm rows unit RMS {out['bcnorm_rms']:.3e} {ok(out['bcnorm_rms'] < 1e-10)}") + print(f" {out['params']:,} params") + return out diff --git a/arctag/_core/arc_audio/seqlayer/scan.py b/arctag/_core/arc_audio/seqlayer/scan.py new file mode 100644 index 0000000000000000000000000000000000000000..40da6bfb1cb09013f9cd6e5187b869e4c1f7ed92 --- /dev/null +++ b/arctag/_core/arc_audio/seqlayer/scan.py @@ -0,0 +1,87 @@ +# Arc Tagger inference engine. Copyright (c) 2026 Whissle Inc. CC BY-NC 4.0 + Whissle terms, see LICENSE. +import math +import torch + +def cumulative_angles(theta: torch.Tensor) -> torch.Tensor: + return torch.cumsum(theta, dim=1) + +def rotate_by(v: torch.Tensor, ang: torch.Tensor) -> torch.Tensor: + a, b = (v[..., 0::2, :], v[..., 1::2, :]) + c, s = (torch.cos(ang).unsqueeze(-1), torch.sin(ang).unsqueeze(-1)) + even, odd = (c * a - s * b, s * a + c * b) + return torch.stack((even, odd), dim=-2).flatten(-3, -2) + +def chunked_scan(alpha: torch.Tensor, w: torch.Tensor, chunk: int=64, state=None): + B, T, H = alpha.shape + N, P = (w.shape[-2], w.shape[-1]) + pieces = [] + g = torch.zeros(B, H, N, P, device=w.device, dtype=w.dtype) if state is None else state + la = torch.log(alpha.clamp_min(1e-30)) + for s in range(0, T, chunk): + e = min(s + chunk, T) + L = e - s + cl = torch.cumsum(la[:, s:e], dim=1) + d = torch.exp(cl) + rel = cl.unsqueeze(2) - cl.unsqueeze(1) + mask = torch.tril(torch.ones(L, L, device=w.device, dtype=torch.bool)) + M = torch.where(mask.view(1, L, L, 1), torch.exp(rel), torch.zeros((), device=w.device, dtype=w.dtype)) + wf = w[:, s:e].reshape(B, L, H, N * P).permute(0, 2, 1, 3) + Mp = M.permute(0, 3, 1, 2) + acc = (Mp @ wf).permute(0, 2, 1, 3).reshape(B, L, H, N, P) + piece = acc + d.unsqueeze(-1).unsqueeze(-1) * g.unsqueeze(1) + pieces.append(piece) + g = piece[:, -1] + return (torch.cat(pieces, dim=1), g) + +def scan_forward(layer, u: torch.Tensor, chunk: int=64): + Bsz, T, _ = u.shape + H, N, R, P = (layer.H, layer.N, layer.R, layer.P) + nu = layer.norm(u) + alpha, beta, gamma, Bm, Cm, x, theta = layer._coeffs(nu) + S = Bm @ x.transpose(-1, -2) + Sprev = torch.cat([torch.zeros_like(S[:, :1]), S[:, :-1]], dim=1) + Phi = cumulative_angles(theta) + Phi_prev = torch.cat([torch.zeros_like(Phi[:, :1]), Phi[:, :-1]], dim=1) + w = gamma[..., None, None] * rotate_by(S, -Phi) + beta[..., None, None] * rotate_by(Sprev, -Phi_prev) + g, _ = chunked_scan(alpha, w, chunk) + h = rotate_by(g, Phi) + y = (Cm.transpose(-1, -2) @ h).reshape(Bsz, T, H * R * P) + return layer.gate_and_proj(y, nu) + +def verify(chunks=(8, 16, 64), verbose: bool=True) -> dict: + import sys + from pathlib import Path + sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) + from seqlayer.layer import SeqLayer + torch.manual_seed(0) + torch.set_default_dtype(torch.float64) + out = {} + lay = SeqLayer(d_model=32, d_state=16, rank=2, head_dim=8).eval() + u = torch.randn(3, 40, 32) + with torch.no_grad(): + ref = lay(u) + for c in chunks: + got = scan_forward(lay, u, chunk=c) + out[f'chunk{c}'] = float((got - ref).abs().max()) + u2 = torch.randn(2, 37, 32) + r2 = lay(u2) + out['ragged'] = float((scan_forward(lay, u2, chunk=16) - r2).abs().max()) + u3 = torch.cat([u, torch.randn(3, 9, 32) * 50], 1) + out['causality'] = float((scan_forward(lay, u3, chunk=16)[:, :40] - scan_forward(lay, u, chunk=16)).abs().max()) + ug = torch.randn(3, 24, 32, requires_grad=True) + lay.zero_grad() + lay(ug).pow(2).sum().backward() + gu_rec = ug.grad.clone() + gp_rec = {n: p.grad.clone() for n, p in lay.named_parameters() if p.grad is not None} + ug.grad = None + lay.zero_grad() + scan_forward(lay, ug, chunk=8).pow(2).sum().backward() + out['grad_input'] = float((gu_rec - ug.grad).abs().max()) + out['grad_params'] = max((float((gp_rec[n] - p.grad).abs().max()) for n, p in lay.named_parameters() if p.grad is not None)) + torch.set_default_dtype(torch.float32) + if verbose: + ok = lambda c: 'OK ' if c else 'FAIL' + print('chunked scan vs recurrence (float64)') + for k, v in out.items(): + print(f' {k:<12}{v:.3e} {ok(v < 1e-09)}') + return out diff --git a/arctag/_core/arc_audio/seqlayer/ssd.py b/arctag/_core/arc_audio/seqlayer/ssd.py new file mode 100644 index 0000000000000000000000000000000000000000..46b68dc88a635904292c92c1717fc0517ae71409 --- /dev/null +++ b/arctag/_core/arc_audio/seqlayer/ssd.py @@ -0,0 +1,98 @@ +# Arc Tagger inference engine. Copyright (c) 2026 Whissle Inc. CC BY-NC 4.0 + Whissle terms, see LICENSE. +import torch +from .scan import rotate_by +from .layer import SeqLayer + +def _f(t): + return t.float() if t is not None and t.dtype in (torch.float16, torch.bfloat16) else t + +def _shift(x): + return torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + +def ssd_safe(alpha, Beff, xeff, Ctil, chunk: int=64): + B, T, H, N, K = Beff.shape + P = xeff.shape[-2] + la = torch.log(alpha.clamp_min(1e-30)) + g = torch.zeros(B, H, N, P, dtype=Beff.dtype, device=Beff.device) + ys = [] + for s0 in range(0, T, chunk): + e = min(s0 + chunk, T) + L = e - s0 + Bc, xc, Cc = (Beff[:, s0:e], xeff[:, s0:e], Ctil[:, s0:e]) + cl = torch.cumsum(la[:, s0:e], dim=1) + d = torch.exp(cl) + rel = cl.unsqueeze(2) - cl.unsqueeze(1) + tril = torch.tril(torch.ones(L, L, dtype=torch.bool, device=Beff.device)).view(1, L, L, 1) + Ldec = torch.exp(rel.masked_fill(~tril, float('-inf'))) + G = torch.einsum('blhnr,bmhnk->bhlmrk', Cc, Bc) + y_intra = torch.einsum('blmh,bhlmrk,bmhpk->blhrp', Ldec, G, xc) + y_inter = torch.einsum('blh,blhnr,bhnp->blhrp', d, Cc, g) + ys.append(y_intra + y_inter) + coef = torch.exp(cl[:, -1:, :] - cl) + g = d[:, -1].unsqueeze(-1).unsqueeze(-1) * g + torch.einsum('blh,blhnk,blhpk->bhnp', coef, Bc, xc) + return torch.cat(ys, dim=1) + +def _ssd_from_coeffs(alpha, beta, gamma, Bc, xc, Cm, theta, chunk): + Phi = torch.cumsum(theta, dim=1) + Phi_prev = _shift(Phi) + Btil = rotate_by(Bc, -Phi) + Ctil = rotate_by(Cm, -Phi) + Btil_prev = rotate_by(_shift(Bc), -Phi_prev) + Beff = torch.cat([gamma[..., None, None] * Btil, beta[..., None, None] * Btil_prev], dim=-1) + xeff = torch.cat([xc, _shift(xc)], dim=-1) + return ssd_safe(alpha, Beff, xeff, Ctil, chunk) + +def seq_layer_scan(layer: SeqLayer, u: torch.Tensor, chunk: int=64): + nu = layer.norm(u) + alpha, beta, gamma, Bm, Cm, x, theta = layer._coeffs(nu) + y = _ssd_from_coeffs(alpha, beta, gamma, Bm, x, Cm, theta, chunk) + Bsz, T = u.shape[:2] + return layer.gate_and_proj(y.reshape(Bsz, T, -1), nu) + +def ssd_safe_state(alpha, Beff, xeff, Ctil, g0=None, chunk: int=64): + B, T, H, N, K = Beff.shape + P = xeff.shape[-2] + la = torch.log(alpha.clamp_min(1e-30)) + g = torch.zeros(B, H, N, P, dtype=Beff.dtype, device=Beff.device) if g0 is None else g0.to(Beff.dtype) + ys = [] + for s0 in range(0, T, chunk): + e = min(s0 + chunk, T) + L = e - s0 + Bc, xc, Cc = (Beff[:, s0:e], xeff[:, s0:e], Ctil[:, s0:e]) + cl = torch.cumsum(la[:, s0:e], dim=1) + d = torch.exp(cl) + rel = cl.unsqueeze(2) - cl.unsqueeze(1) + tril = torch.tril(torch.ones(L, L, dtype=torch.bool, device=Beff.device)).view(1, L, L, 1) + Ldec = torch.exp(rel.masked_fill(~tril, float('-inf'))) + G = torch.einsum('blhnr,bmhnk->bhlmrk', Cc, Bc) + y_intra = torch.einsum('blmh,bhlmrk,bmhpk->blhrp', Ldec, G, xc) + y_inter = torch.einsum('blh,blhnr,bhnp->blhrp', d, Cc, g) + ys.append(y_intra + y_inter) + coef = torch.exp(cl[:, -1:, :] - cl) + g = d[:, -1].unsqueeze(-1).unsqueeze(-1) * g + torch.einsum('blh,blhnk,blhpk->bhnp', coef, Bc, xc) + return (torch.cat(ys, dim=1), g) + +def seq_layer_scan_state(layer: SeqLayer, u: torch.Tensor, state=None, chunk: int=64): + nu = layer.norm(u) + alpha, beta, gamma, Bm, Cm, x, theta = layer._coeffs(nu) + Phi = torch.cumsum(theta, dim=1) + Phi_prev = _shift(Phi) + if state is None: + h0 = None + Bp = torch.zeros_like(Bm[:, :1]) + xp = torch.zeros_like(x[:, :1]) + else: + h0, Bl, xl = state + Bp = Bl[:, None].to(Bm.dtype) + xp = xl[:, None].to(x.dtype) + Bsh = torch.cat([Bp, Bm[:, :-1]], 1) + xsh = torch.cat([xp, x[:, :-1]], 1) + Btil = rotate_by(Bm, -Phi) + Ctil = rotate_by(Cm, -Phi) + Btil_prev = rotate_by(Bsh, -Phi_prev) + Beff = torch.cat([gamma[..., None, None] * Btil, beta[..., None, None] * Btil_prev], dim=-1) + xeff = torch.cat([x, xsh], dim=-1) + y, g = ssd_safe_state(alpha, Beff, xeff, Ctil, h0, chunk) + hT = rotate_by(g.unsqueeze(1), Phi[:, -1:])[:, 0] + Bsz, T = u.shape[:2] + return (layer.gate_and_proj(y.reshape(Bsz, T, -1), nu), (hT, Bm[:, -1], x[:, -1])) diff --git a/arctag/_core/arc_audio/wordch.py b/arctag/_core/arc_audio/wordch.py new file mode 100644 index 0000000000000000000000000000000000000000..71d80fbe02df63dc4c81f6b8463d3d958f93a8c6 --- /dev/null +++ b/arctag/_core/arc_audio/wordch.py @@ -0,0 +1,339 @@ +# Arc Tagger inference engine. Copyright (c) 2026 Whissle Inc. CC BY-NC 4.0 + Whissle terms, see LICENSE. +import math, re +import numpy as np +import torch, torch.nn as nn, torch.nn.functional as F +P_MAX = 8 +K_CTX = 8 +BACK = 6 +ENC_WIN = 32 +N_ENC = 2 +RF = (ENC_WIN - 1) * N_ENC +OFF_CLIP = 32 +SRC_ROW = dict(npz=0, ms=1, corrupt=2, none=3) +_PUNCT = re.compile('^[^\\w]+|[^\\w]+$') + +def norm_word(w): + return _PUNCT.sub('', ' '.join(str(w).lower().split())) + +def own_ends(f0, f1): + f0 = np.asarray(f0, np.int64) + f1 = np.asarray(f1, np.int64) + oe = f1.copy() + nxt = np.r_[f0[1:], np.iinfo(np.int64).max] + m = (f0 < nxt) & (nxt <= f1) + oe[m] = nxt[m] - 1 + return np.maximum(oe, f0) + +def utt_ends(f0, oe, gap=4): + W = len(f0) + if not W: + return np.zeros(0, np.int64) + brk = np.r_[f0[1:] - oe[:-1] > gap, True] + ue = np.where(brk, oe, np.iinfo(np.int64).max) + return np.minimum.accumulate(ue[::-1])[::-1] + +def rule_arrival(f0, oe, d, mode='final', Lf=15, L=8): + if not len(f0): + return np.zeros(0, np.int64) + a = oe + L if mode == 'partial' else utt_ends(f0, oe) + Lf + a = np.maximum(a, oe + d) + return np.maximum.accumulate(a) - d + +class Pieces: + + def __init__(self, tok_path): + from tokenizers import Tokenizer + self.t = Tokenizer.from_file(tok_path) + self.c = {} + + def __call__(self, w): + if w not in self.c: + ids = self.t.encode(w, add_special_tokens=False).ids if w else [] + if len(ids) > P_MAX: + ids = ids[:P_MAX - 1] + ids[-1:] + if len(self.c) > 200000: + self.c.clear() + self.c[w] = ids + return self.c[w] + +def word_rows(texts, f0, oe, src_row, pieces, prev_oe=None): + W = len(texts) + tok = np.zeros((W, P_MAX), np.int64) + npc = np.zeros(W, np.int64) + flags = np.zeros((W, 5), np.int64) + for i, w in enumerate(texts): + p = pieces(w) + npc[i] = len(p) + tok[i, :len(p)] = p + hd = any((ch.isdigit() for ch in w)) + flags[i, 0] = int(hd) + flags[i, 1] = int(hd and w.isdigit()) + flags[:, 2] = np.clip(npc, 0, P_MAX) + if W: + flags[:, 3] = np.clip(oe - f0 + 1, 1, 20) + flags[:, 4] = np.clip(np.r_[20 if prev_oe is None else f0[0] - prev_oe - 1, f0[1:] - oe[:-1] - 1], 0, 20) + return (tok, npc, flags) + +def frame_window(f0, avail, t0, t1, K=K_CTX, back=BACK): + t = np.arange(t0, t1) + n = np.searchsorted(avail, t, side='right') + p = np.searchsorted(f0, t, side='right') + q = np.minimum(p, n) + idx = q[:, None] - back + np.arange(K)[None] + ok = (idx >= 0) & (idx < n[:, None]) + return np.where(ok, idx, -1) + +def _rope(x, pos): + dh = x.shape[-1] + inv = 1.0 / 10000 ** (torch.arange(0, dh, 2, device=x.device, dtype=torch.float32) / dh) + ang = pos.float()[..., None] * inv + while ang.dim() < x.dim(): + ang = ang.unsqueeze(1) + c, s_ = (torch.cos(ang), torch.sin(ang)) + x1, x2 = (x[..., 0::2], x[..., 1::2]) + return torch.stack([x1 * c - x2 * s_, x1 * s_ + x2 * c], -1).flatten(-2) + +class EncLayer(nn.Module): + + def __init__(self, d=256, h=4, ff=512, win=ENC_WIN, dropout=0.1): + super().__init__() + self.h, self.win = (h, win) + self.n1 = nn.LayerNorm(d) + self.qkv = nn.Linear(d, 3 * d) + self.o = nn.Linear(d, d) + self.n2 = nn.LayerNorm(d) + self.ff = nn.Sequential(nn.Linear(d, ff), nn.GELU(), nn.Dropout(dropout), nn.Linear(ff, d)) + self.drop = nn.Dropout(dropout) + + def forward(self, x, valid, pos0=0): + Bn, W, D = x.shape + h, w = (self.h, self.win) + dh = D // h + q, k, v = self.qkv(self.n1(x)).float().view(Bn, W, 3, h, dh).unbind(2) + pos = (torch.arange(W, device=x.device) + pos0)[None].expand(Bn, W) + q = _rope(q.transpose(1, 2), pos) + k = _rope(k.transpose(1, 2), pos) + v = v.transpose(1, 2) + nb = (W + w - 1) // w + Wp = nb * w + pad = Wp - W + if pad: + q, k, v = (F.pad(t_, (0, 0, 0, pad)) for t_ in (q, k, v)) + vm = F.pad(valid, (0, pad)) if pad else valid + kp = F.pad(k, (0, 0, w, 0)).view(Bn, h, nb + 1, w, dh) + vp = F.pad(v, (0, 0, w, 0)).view(Bn, h, nb + 1, w, dh) + kb = torch.cat([kp[:, :, :-1], kp[:, :, 1:]], 3) + vb = torch.cat([vp[:, :, :-1], vp[:, :, 1:]], 3) + qb = q.view(Bn, h, nb, w, dh) + qi = torch.arange(nb, device=x.device)[:, None] * w + torch.arange(w, device=x.device)[None] + ki = torch.arange(nb, device=x.device)[:, None] * w - w + torch.arange(2 * w, device=x.device)[None] + mp = F.pad(vm, (w, 0)).view(Bn, nb + 1, w) + km = torch.cat([mp[:, :-1], mp[:, 1:]], 2) + m = (ki[:, None, :] <= qi[:, :, None]) & (ki[:, None, :] > qi[:, :, None] - w) & (ki[:, None, :] >= 0) + m = m[None] & km[:, :, None, :] + m = m | (ki[:, None, :] == qi[:, :, None])[None] + a = F.scaled_dot_product_attention(qb.permute(0, 2, 1, 3, 4).reshape(Bn * nb, h, w, dh), kb.permute(0, 2, 1, 3, 4).reshape(Bn * nb, h, 2 * w, dh), vb.permute(0, 2, 1, 3, 4).reshape(Bn * nb, h, 2 * w, dh), attn_mask=m.reshape(Bn * nb, 1, w, 2 * w)) + a = a.view(Bn, nb, h, w, dh).permute(0, 2, 1, 3, 4).reshape(Bn, h, Wp, dh)[:, :, :W].transpose(1, 2).reshape(Bn, W, D) + x = x + self.drop(self.o(a.to(x.dtype))) + return x + self.drop(self.ff(self.n2(x))) + +class WordChannel(nn.Module): + + def __init__(self, d_model=512, d_k=512, n_in=41, k_slots=4, n_nwt=43, d_w=256, d_c=128, heads_c=4, n_tok=32768, d_e=320): + super().__init__() + self.register_buffer('tok_E', torch.zeros(n_tok, d_e, dtype=torch.float16)) + self.ppos = nn.Parameter(torch.zeros(P_MAX, d_e)) + self.W1 = nn.Linear(d_e, d_w) + self.W2 = nn.Linear(d_e, d_w, bias=False) + self.ln = nn.LayerNorm(d_w) + self.fl = nn.ModuleList([nn.Embedding(n, d_w) for n in (2, 2, P_MAX + 1, 21, 21)]) + self.src = nn.Embedding(len(SRC_ROW) + 1, d_w) + self.Wa = nn.Linear(2 * d_model, d_w) + self.hav = nn.Embedding(2, d_w) + self.enc = nn.ModuleList([EncLayer(d_w) for _ in range(N_ENC)]) + self.enc_norm = nn.LayerNorm(d_w) + self.hc = heads_c + self.fq = nn.Linear(d_model, d_c) + self.fk = nn.Linear(d_w, d_c) + self.fv = nn.Linear(d_w, d_c) + self.R0 = nn.Embedding(2 * OFF_CLIP + 1, d_c) + self.R1 = nn.Embedding(2 * OFF_CLIP + 1, d_c) + self.fo = nn.Linear(d_c, d_model) + self.war_r = nn.Linear(d_w, d_w) + self.war_k = nn.Embedding(3, d_w) + self.war_ln = nn.LayerNorm(d_w) + self.QB = nn.Linear(d_w, d_k) + self.QI = nn.Linear(d_w, d_k) + self.bB = nn.Parameter(torch.zeros(n_in)) + self.bI = nn.Parameter(torch.zeros(n_in)) + self.nwt = nn.Linear(d_w, k_slots * n_nwt) + self.k_slots, self.n_nwt = (k_slots, n_nwt) + + def embed_in(self, tok, npc, flags, src, hm, he, have): + Et = self.tok_E[tok].float() + pm = (torch.arange(P_MAX, device=tok.device)[None, None] < npc[..., None]).float()[..., None] + mean = ((Et + self.ppos) * pm).sum(2) / npc.clamp(min=1)[..., None].float() + last = torch.gather(Et, 2, (npc - 1).clamp(min=0)[..., None, None].expand(-1, -1, 1, Et.shape[-1]))[:, :, 0] + e = self.ln(self.W1(mean) + self.W2(last)) + for i, m in enumerate(self.fl): + e = e + m(flags[..., i].clamp(0, m.num_embeddings - 1)) + e = e + self.src(src.clamp(0, self.src.num_embeddings - 1)) + return e + self.Wa(torch.cat([hm, he], -1)) + self.hav(have.long()) + + def run_enc(self, x, valid, pos0=0): + for lay in self.enc: + x = lay(x, valid, pos0) + return self.enc_norm(x) * valid[..., None].float() + + def frame_ctx(self, h, u, f0, oe, wctx, t0=0): + Bn, T, Kc = wctx.shape + ok = wctx >= 0 + idx = wctx.clamp(min=0) + dc = self.fk.out_features + hh = self.hc + dh = dc // hh + flat = idx.reshape(Bn, T * Kc) + gk = torch.gather(self.fk(u), 1, flat[..., None].expand(-1, -1, dc)).view(Bn, T, Kc, dc) + gv = torch.gather(self.fv(u), 1, flat[..., None].expand(-1, -1, dc)).view(Bn, T, Kc, dc) + t = torch.arange(t0, t0 + T, device=h.device)[None, :, None] + o0 = (t - torch.gather(f0, 1, flat).view(Bn, T, Kc)).clamp(-OFF_CLIP, OFF_CLIP) + OFF_CLIP + o1 = (t - torch.gather(oe, 1, flat).view(Bn, T, Kc)).clamp(-OFF_CLIP, OFF_CLIP) + OFF_CLIP + gk = gk + self.R0(o0) + self.R1(o1) + q = self.fq(h).view(Bn, T, 1, hh, dh) + sc = (q * gk.view(Bn, T, Kc, hh, dh)).sum(-1).float() / math.sqrt(dh) + sc = sc.masked_fill(~ok[..., None], float('-inf')) + any_ = ok.any(-1) + att = torch.softmax(sc, 2).nan_to_num(0.0) + ctx = (att[..., None] * gv.view(Bn, T, Kc, hh, dh).float()).sum(2).reshape(Bn, T, dc) + return self.fo(ctx.to(h.dtype)) * any_[..., None].to(h.dtype) + + def war_logits(self, u, valid, K_in, k): + Bn, W, D = u.shape + if k: + us = F.pad(u[:, k:], (0, 0, 0, k)) + ok = F.pad(valid[:, k:], (0, k)) & valid + else: + us = torch.zeros_like(u) + ok = valid + v = self.war_ln(u + self.war_r(us) + self.war_k.weight[k]) + return (self.QB(v).float() @ K_in.T + self.bB, self.QI(v).float() @ K_in.T + self.bI, ok, v) + + @torch.no_grad() + def war_probs(self, u, valid, K_in, k=1): + zB, zI, ok, v = self.war_logits(u, valid, K_in, k) + if k: + zB0, zI0, _, _ = self.war_logits(u, valid, K_in, k - 1) if k > 1 else self.war_logits(u, valid, K_in, 0) + zB = torch.where(ok[..., None], zB, zB0) + zI = torch.where(ok[..., None], zI, zI0) + p = torch.softmax(torch.stack([torch.zeros_like(zB), zB, zI], -1), -1) + return (p[..., 1], p[..., 2], v) + + def nwt_logits(self, v): + return self.nwt(v).float().reshape(*v.shape[:-1], self.k_slots, self.n_nwt) + +class WordFeed: + + def __init__(self, wch, pieces, dev, d): + self.w, self.P, self.dev, self.d = (wch, pieces, dev, int(d)) + self.keys = [] + self.txt, self.f0, self.oe, self.f1, self.src, self.av = ([], [], [], [], [], []) + self.X = None + self.U = None + self.n_x = 0 + self.ver = None + + def __len__(self): + return len(self.keys) + + def sync(self, finals, f_now, arrival=None, src_of=None): + ws = [w for w in finals if norm_word(w['w'])] + keys = [(w['start'], w['end'], w['w']) for w in ws] + n0 = 0 + m = min(len(keys), len(self.keys)) + while n0 < m and keys[n0] == self.keys[n0]: + n0 += 1 + if n0 == len(keys) == len(self.keys): + return False + for L in (self.keys, self.txt, self.f0, self.oe, self.f1, self.src, self.av): + del L[n0:] + self.n_x = min(self.n_x, n0) + if self.X is not None: + self.X = self.X[:self.n_x] + if self.U is not None: + self.U = self.U[:min(len(self.U), n0)] + for j in range(n0, len(ws)): + w = ws[j] + self.keys.append(keys[j]) + self.txt.append(norm_word(w['w'])) + self.f0.append(int(w['f0'])) + self.f1.append(int(w['f1'])) + self.src.append(src_of(w) if src_of else SRC_ROW['ms']) + self.av.append(None) + f0 = np.asarray(self.f0, np.int64) + oe = own_ends(f0, np.asarray(self.f1, np.int64)) + old = self.oe[:min(n0, self.n_x)] + self.oe = [int(x) for x in oe] + self.oe[:len(old)] = old + if arrival is not None: + self.av = [int(x) for x in arrival] + else: + for j in range(n0, len(self.keys)): + a = max(int(f_now), self.oe[j]) + if j: + a = max(a, self.av[j - 1]) + self.av[j] = a + return True + + @torch.no_grad() + def encode_upto(self, f_done, h_at, f_start=None, settle=4): + n_new = 0 + while self.n_x + n_new < len(self.keys) and self.oe[self.n_x + n_new] < f_done: + k = self.n_x + n_new + if k + 1 >= len(self.keys) and f_done <= self.f1[k] + settle: + break + n_new += 1 + if not n_new: + return + if f_start is not None: + for k in range(self.n_x, len(self.av)): + lo_ = f_start if k < self.n_x + n_new else self.av[k - 1] + self.av[k] = max(self.av[k], lo_, self.av[k - 1] if k else lo_) + J = list(range(self.n_x, self.n_x + n_new)) + f0 = np.asarray([self.f0[j] for j in J]) + oe = np.asarray([self.oe[j] for j in J]) + tok, npc, flags = word_rows([self.txt[j] for j in J], f0, oe, None, self.P, prev_oe=self.oe[J[0] - 1] if J[0] > 0 else None) + hm, he = ([], []) + for a, b in zip(f0, oe): + H = h_at(list(range(int(a), int(b) + 1))).float() + hm.append(H.mean(0)) + he.append(H[-1]) + T = lambda a, dt=torch.long: torch.as_tensor(np.asarray(a), device=self.dev, dtype=dt)[None] + x = self.w.embed_in(T(tok), T(npc), T(flags), T([self.src[j] for j in J]), torch.stack(hm)[None], torch.stack(he)[None], torch.ones(1, n_new, dtype=torch.bool, device=self.dev))[0] + self.X = x if self.X is None or not len(self.X) else torch.cat([self.X, x], 0) + self.n_x += n_new + n_u = 0 if self.U is None else len(self.U) + lo = max(0, n_u - RF) + xs = self.X[lo:self.n_x][None] + u = self.w.run_enc(xs, torch.ones(xs.shape[:2], dtype=torch.bool, device=self.dev), pos0=lo)[0][n_u - lo:] + self.U = u if self.U is None or not len(self.U) else torch.cat([self.U, u], 0) + + @torch.no_grad() + def fuse(self, h, f_start): + T = h.shape[0] + n_u = 0 if self.U is None else len(self.U) + if not n_u: + return h + f0 = np.asarray(self.f0[:n_u], np.int64) + av = np.asarray(self.av[:n_u], np.int64) + idx = frame_window(f0, av, f_start, f_start + T) + if (idx < 0).all(): + return h + lo = int(idx[idx >= 0].min()) + hi = int(idx.max()) + 1 + loc = np.where(idx >= 0, idx - lo, -1) + dv = self.dev + u = self.U[lo:hi][None] + F0 = torch.as_tensor(f0[lo:hi], device=dv)[None] + OE = torch.as_tensor(np.asarray(self.oe[lo:hi], np.int64), device=dv)[None] + c = self.w.frame_ctx(h[None], u, F0, OE, torch.as_tensor(loc, device=dv)[None], t0=f_start)[0] + return h + c.to(h.dtype) diff --git a/arctag/_core/arc_text.py b/arctag/_core/arc_text.py new file mode 100644 index 0000000000000000000000000000000000000000..81e58492a43720bbd8ec3d9c6d2dbc4e551ee440 --- /dev/null +++ b/arctag/_core/arc_text.py @@ -0,0 +1,374 @@ +# Arc Tagger inference engine. Copyright (c) 2026 Whissle Inc. CC BY-NC 4.0 + Whissle terms, see LICENSE. +import math, os, sys +import numpy as np +import torch, torch.nn as nn, torch.nn.functional as F +ARC_PKG = os.path.dirname(os.path.abspath(__file__)) +if ARC_PKG not in sys.path: + sys.path.insert(0, ARC_PKG) +from arc_audio.model import Trunk, TagHead, EntityHeads, zero_carry, encode_names, NEG +from arc_audio.ontology import QUERIES as ARC_Q, POOLED +from arc_audio.wordch import WordChannel, P_MAX, SRC_ROW +HERE = os.path.dirname(os.path.abspath(__file__)) +sys.path.insert(0, HERE) +from tagtable import FINE_DOMAINS, ASR_SOURCES +TOK_PATH = os.path.join(ARC_PKG, 'moonshine-streaming-tiny', 'tokenizer.json') +SCRATCH_10M = dict(d=256, d_w=256, d_e=256, vocab=8192, n_layers=6, adapters=16, d_state=64, head_dim=64, dropout=0.1) +WORD_Q = ('frame', 'span_beg', 'span_in', 'span_end', 'ev_at', 'ev_imm', 'ev_rec') +FEATS = {'gap': 10, 'dur': 8, 'fin': 3, 'conf': 7, 'chg': 3, 'asr': 8} +D_MAX = 8 +TIER_W = {'T0': 1.0, 'T1': 0.5, 'T2': 0.5} +ARC_CFG = dict(d_in=640, d_model=512, n_layers=5, d_state=64, head_dim=64, dropout=0.2, in_drop=0.2, chunk=128, n_dom=16, delays=[0, 1, 2, 3, 5, 8], adapters=64, long_mem=True) +BPE8K = os.path.join(os.environ.get('ARCTAG_HOME', os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))), 'tok', 'bpe8k', 'tokenizer.json') + +class ArcPieces: + + def __init__(self, path=TOK_PATH): + from tokenizers import Tokenizer + self.path = path + self.t = Tokenizer.from_file(path) + self.vocab_size = self.t.get_vocab_size() + self.space = 'bpe8k' in path or '/tok/' in path + self.cls_token_id = 0 + self.pad_token_id = 0 + self.unk_token_id = 0 + self.mask_token_id = 0 + + def word(self, w): + return ' ' + w.strip() if self.space else w.strip() + + def __call__(self, texts, add_special_tokens=False): + enc = self.t.encode_batch([self.word(t) for t in texts], add_special_tokens=False) + return {'input_ids': [e.ids for e in enc]} + + def encode_word(self, w): + return self.t.encode(self.word(w), add_special_tokens=False).ids + +class TableShim: + + def __init__(self, T): + self.n = T.n + self.valid = T.valid + self.cols = {q: list(T.cols[q]) for q in ARC_Q} + +def frames(t): + return int(round(t * 10)) if t is not None else None + +class ArcText(nn.Module): + + def __init__(self, table, n_in_war=41, n_units=83, d_in_new=256, tok_param=True, scratch=None): + super().__init__() + self.T = table + self.scratch = scratch + tc = dict(ARC_CFG) + tc['d_in'] = d_in_new + if scratch: + tc.update(d_in=scratch['d_w'], d_model=scratch['d'], n_layers=scratch['n_layers'], adapters=scratch['adapters'], d_state=scratch.get('d_state', 64), head_dim=scratch.get('head_dim', 64), delays=[0], dropout=scratch.get('dropout', 0.1)) + self.trunk = Trunk(**tc) + d = tc['d_model'] + self.d = d + nn.init.ones_(self.trunk.in_norm.weight) + nn.init.zeros_(self.trunk.in_norm.bias) + self.head = TagHead(TableShim(table), n_text=table.E.shape[1], d_model=d, d_k=d) + with torch.no_grad(): + self.head.E.copy_(torch.as_tensor(table.E)) + if scratch: + self.ent = EntityHeads(d_model=d, d_key=d, n_units=n_units, span=False, span_new=False, sim=False) + for k in ('nwt', 'unit', 'clock', 'approx'): + delattr(self.ent, k) + self.word = WordChannel(d_model=d, d_k=d, n_in=n_in_war, k_slots=4, n_nwt=43, d_w=scratch['d_w'], n_tok=scratch['vocab'], d_e=scratch['d_e'], d_c=64) + for k in ('fq', 'fk', 'fv', 'fo', 'R0', 'R1', 'QI', 'bI', 'bB', 'nwt', 'Wa', 'hav'): + delattr(self.word, k) + del self.word._buffers['tok_E'] + self.word.tok_E = nn.Parameter(torch.randn(scratch['vocab'], scratch['d_e']) * 0.02) + else: + self.ent = EntityHeads(d_model=d, d_key=d, n_units=n_units, span=True, span_new=True, sim=True) + self.word = WordChannel(d_model=d, d_k=d, n_in=n_in_war, k_slots=4, n_nwt=43) + if tok_param: + del self.word._buffers['tok_E'] + self.word.tok_E = nn.Parameter(torch.zeros(32768, 320)) + self.feat = nn.ModuleDict({k: nn.Embedding(n, self.word.ln.normalized_shape[0]) for k, n in FEATS.items()}) + for m in self.feat.values(): + nn.init.zeros_(m.weight) + self.delay_q = nn.Embedding(D_MAX + 1, d) + nn.init.zeros_(self.delay_q.weight) + self.bB_all = nn.Parameter(torch.zeros(table.n)) + self.register_buffer('cols_span_beg', torch.tensor(table.cols['span_beg'], dtype=torch.long), persistent=False) + self.ver = nn.Linear(2 * d, 1) + self.lm = nn.Linear(d, self.word.tok_E.shape[1]) + + def word_inputs(self, b): + ids, tok_word, feats = (b['ids'], b['tok_word'], b['feats']) + W = b['wvalid'].shape[1] + B, S = ids.shape + dev = ids.device + valid = tok_word >= 0 + tw = tok_word.clamp(min=0) + pos = torch.arange(S, device=dev)[None].expand(B, S) + first = torch.full((B, W), S, dtype=torch.long, device=dev).scatter_reduce(1, tw, torch.where(valid, pos, torch.full_like(pos, S)), 'amin') + rank = pos - torch.gather(first, 1, tw) + npc_all = torch.zeros(B, W, dtype=torch.long, device=dev).scatter_add(1, tw, valid.long()) + nw = torch.gather(npc_all, 1, tw) + keep = valid & ((rank < P_MAX - 1) | (rank == nw - 1)) + slot = torch.where(rank < P_MAX - 1, rank, torch.full_like(rank, P_MAX - 1)) + pcs = torch.zeros(B, W, P_MAX, dtype=torch.long, device=dev) + bi = torch.arange(B, device=dev)[:, None].expand(B, S) + pcs[bi[keep], tw[keep], slot[keep]] = ids[keep] + npc = npc_all.clamp(max=P_MAX) + sc = lambda src: torch.zeros(B, W, dtype=torch.long, device=dev).scatter(1, torch.where(valid, tw, torch.zeros_like(tw)), torch.where(valid, src, torch.zeros_like(src))) + fw = {k: sc(feats[k]) for k in self.feat if k in feats} + spk = sc(feats['spk']) if 'spk' in feats else torch.zeros(B, W, dtype=torch.long, device=dev) + return (pcs, npc, fw, spk) + + def embed(self, pcs, npc, fw, wfl, wsrc): + fl = wfl.clone() + fl[..., 2] = npc.clamp(0, P_MAX) + if self.scratch: + w = self.word + Et = w.tok_E[pcs].float() + pm = (torch.arange(P_MAX, device=pcs.device)[None, None] < npc[..., None]).float()[..., None] + mean = ((Et + w.ppos) * pm).sum(2) / npc.clamp(min=1)[..., None].float() + last = torch.gather(Et, 2, (npc - 1).clamp(min=0)[..., None, None].expand(-1, -1, 1, Et.shape[-1]))[:, :, 0] + e = w.ln(w.W1(mean) + w.W2(last)) + for i, m in enumerate(w.fl): + e = e + m(fl[..., i].clamp(0, m.num_embeddings - 1)) + e = e + w.src(wsrc.clamp(0, w.src.num_embeddings - 1)) + else: + z = torch.zeros(*pcs.shape[:2], self.d, device=pcs.device) + e = self.word.embed_in(pcs, npc, fl, wsrc, z, z, torch.zeros(pcs.shape[:2], dtype=torch.bool, device=pcs.device)) + for k, m in self.feat.items(): + if k in fw: + e = e + m(fw[k].clamp(0, m.num_embeddings - 1)) + return e + + def keys(self): + return self.head.keys() + + def span_beg_logits(self, u, valid, k_i, dom, K): + c = self.cols_span_beg + Kb = K[c] + w = self.word + W = u.shape[1] + out = None + for k in range(3): + us = F.pad(u[:, k:], (0, 0, 0, k)) if k else torch.zeros_like(u) + v = w.war_ln(u + w.war_r(us) + w.war_k.weight[k]) + zB = w.QB(v).float() @ Kb.T + self.bB_all[c] + out = zB if out is None else torch.where((k_i == k)[..., None], zB, out) + return out.masked_fill(~self.head.valid[dom][:, c][:, None, :], NEG) + + @staticmethod + def utt_last(uend, wvalid): + B, W = uend.shape + dev = uend.device + t = torch.arange(W, device=dev)[None].expand(B, W) + last_valid = (wvalid.long().sum(1) - 1).clamp(min=0)[:, None] + e = torch.where(uend | (t == last_valid), t, torch.full_like(t, W - 1)) + return torch.flip(torch.cummin(torch.flip(e, [1]), 1).values, [1]) + + def tier_delays(self, tier, uend, wvalid): + B, W = uend.shape + t = torch.arange(W, device=uend.device)[None].expand(B, W) + to_end = self.utt_last(uend, wvalid) - t + if tier == 'T0': + return torch.zeros_like(t) + return to_end.clamp(min=0, max=3 if tier == 'T1' else D_MAX) + + def delayed(self, h, d_i): + W = h.shape[1] + j = (torch.arange(W, device=h.device)[None] + d_i).clamp(max=W - 1) + out = torch.gather(h, 1, j[..., None].expand(-1, -1, h.shape[-1])) + self.delay_q(d_i.clamp(0, D_MAX)) + if hasattr(self, 'dattn'): + out = out + self.dattn(h, d_i) + elif hasattr(self, 'dmix'): + out = out + self.dmix(h) * (d_i > 0)[..., None].to(h.dtype) + return out + + def add_delay_mix(self): + self.dmix = nn.Linear(self.d, self.d, bias=False) + nn.init.zeros_(self.dmix.weight) + return self + + def add_v2(self, gattn_at=(1, 3), delay_attn=True, span_head=True, utt_att=True, gattn_bias=False): + import v2 as V2 + if gattn_at: + self.gat = nn.ModuleDict({str(i): V2.GlobalAttn(self.d, biased=gattn_bias) for i in gattn_at}) + if delay_attn: + self.dattn = V2.DelayAttn(self.d, D_MAX) + if hasattr(self, 'dmix'): + del self.dmix + if span_head: + self.span_head = V2.SpanHead(self.d, self.T) + if utt_att: + self.utt_att = nn.Linear(self.d, 1) + nn.init.zeros_(self.utt_att.weight) + nn.init.zeros_(self.utt_att.bias) + return self + + def add_memread(self, d_ext=0): + import v2 as V2 + self.memr = V2.MemRead(self.d, d_ext=d_ext, d_u=len(self.cols_span_beg)) + return self + + def utt_pool_att(self, h, ustart): + z = (h + self.head.pool_in['utt'](h)).float() + w = torch.exp(self.utt_att(z).clamp(-10, 10)) + cw = torch.cumsum(w, 1) + cz = torch.cumsum(w * z, 1) + idx = torch.arange(h.shape[1], device=h.device)[None].expand_as(ustart) + st = torch.cummax(torch.where(ustart, idx, torch.zeros_like(idx)), 1).values + prev = (st - 1).clamp(min=0) + has = (st > 0)[..., None] + cw0 = torch.where(has, torch.gather(cw, 1, prev[..., None]), torch.zeros_like(cw)) + cz0 = torch.where(has, torch.gather(cz, 1, prev[..., None].expand(-1, -1, z.shape[-1])), torch.zeros_like(cz)) + return (cz - cz0) / (cw - cw0).clamp(min=1e-09) + + def encode(self, b): + pcs, npc, fw, spk = self.word_inputs(b) + e = self.embed(pcs, npc, fw, b['wfl'], b['wsrc']) + u = self.word.run_enc(e, b['wvalid']) + dom = b['dom'] + if hasattr(self, 'gat'): + import v2 as V2 + h = V2.trunk_forward(self.trunk, self.gat, self.trunk_in(u), dom, torch.zeros_like(dom)) + return (pcs, spk, u, h) + h, _ = self.trunk(self.trunk_in(u), dom, torch.zeros_like(dom)) + return (pcs, spk, u, h) + + def trunk_in(self, u): + return u + + def forward(self, b, tiers=('T0',), lm=False): + pcs, spk, u, h = self.encode(b) + dom = b['dom'] + W = h.shape[1] + spk_i = torch.where(spk > 0, spk - 1, torch.full_like(spk, -1)) + st = b['ustart'].clone() + st[:, 0] = True + zin, _ = self.head.pooled(h, spk_i, st, zero_carry(h.shape[0], h.shape[-1], h.device)) + if hasattr(self, 'utt_att'): + zin['utt'] = self.utt_pool_att(h, st) + K = self.keys() + pooled = {q: self.head.logits_q(q, zin[q], dom, K) for q in POOLED} + out = {'h': h, 'u': u, 'tiers': {}} + mkv = None + for tier in tiers: + dl = self.tier_delays(tier, b['uend'], b['wvalid']) + hd = self.delayed(h, dl) + if hasattr(self, 'memr'): + if mkv is None: + mkv = self.memr.keys(h, b.get('cng')) + y_, a_ = self.memr(hd, h, kv=mkv) + hd = hd + y_.to(hd.dtype) + lg = {q: self.head.logits_q(q, hd, dom, K) for q in WORD_Q if q != 'span_beg'} + lg['span_beg'] = self.span_beg_logits(u, b['wvalid'], dl.clamp(max=2), dom, K) + if hasattr(self, 'memr'): + lg['span_beg'] = lg['span_beg'] + self.memr.to_u(a_).float() + lg.update(pooled) + out['tiers'][tier] = lg + if hasattr(self, 'kb_spot'): + out.setdefault('kb_spot', {})[tier] = self.kb_spot(hd).float() + if hasattr(self, 'span_head'): + out.setdefault('span_head', {})[tier] = self.span_head(hd) + if tier == 'T0' and hasattr(self, 'pii_imm'): + out['pii_imm'] = self.pii_imm(hd).float() + out['logits'] = out['tiers'][tiers[0]] + if lm: + out['lm_h'] = self.lm(h) + out['lm'] = None if getattr(self, 'lm_rows', False) else out['lm_h'] @ self.word.tok_E.float().t() + out['lm_target'] = torch.cat([pcs[:, 1:, 0], torch.zeros_like(pcs[:, :1, 0])], 1) + out['lm_valid'] = torch.cat([b['wvalid'][:, 1:], torch.zeros_like(b['wvalid'][:, :1])], 1) + return out + + def span_rep(self, hw, spans): + cs = F.pad(torch.cumsum(hw, 0), (0, 0, 1, 0)) + mean = (cs[spans[:, 1]] - cs[spans[:, 0]]) / (spans[:, 1] - spans[:, 0]).clamp(min=1)[:, None].to(hw.dtype) + return torch.cat([mean, hw[spans[:, 1] - 1]], -1) + + def add_kb_long(self): + import kb as KBM + D = self.d + dn = self.ent.names.d + self.kb_len = nn.Embedding(7, D) + self.kb_type = nn.Embedding(len(KBM.TYPES), dn) + self.kb_spot = nn.Linear(D, 6) + self.ver_e = nn.Sequential(nn.Linear(2 * D + 4 * dn, 128), nn.GELU(), nn.Linear(128, 1)) + for m in (self.kb_len, self.kb_type): + nn.init.zeros_(m.weight) + nn.init.zeros_(self.kb_spot.weight) + nn.init.constant_(self.kb_spot.bias, -4.0) + return self + + def add_kb_str(self, cng=True, vstr=True): + import kb_strfeat as KS + dn = self.ent.names.d + if cng: + self.kb_cng = nn.EmbeddingBag(KS.NB, dn, mode='mean') + nn.init.zeros_(self.kb_cng.weight) + if vstr: + self.ver_s = nn.Linear(KS.NF, self.ver_e[0].out_features, bias=False) + nn.init.zeros_(self.ver_s.weight) + return self + + def add_redact(self, n=8): + self.pii_imm = nn.Linear(self.d, n) + nn.init.zeros_(self.pii_imm.weight) + nn.init.constant_(self.pii_imm.bias, -4.0) + return self + + def add_spk_link(self, flux=True): + import speaker_link as SL + n_roles = len(role_cols(self.T)) + self.spk_link = SL.SpkLink(self.d, n_roles) + dw = self.word.ln.normalized_shape[0] + self.feat['spksrc'] = nn.Embedding(3, dw) + nn.init.zeros_(self.feat['spksrc'].weight) + if flux: + self.feat['turn'] = nn.Embedding(4, dw) + nn.init.zeros_(self.feat['turn'].weight) + return self + + def kb_entry_vecs(self, texts, types): + dev = self.bB_all.device + ids = torch.as_tensor(encode_names(texts), device=dev) + e = self.ent.names(ids, torch.zeros(len(texts), dtype=torch.long, device=dev)) + return F.normalize(e + self.kb_type(torch.as_tensor(types, device=dev)), dim=-1) + + def kb_query(self, h, bi, a, b, qtext=None): + cs = F.pad(torch.cumsum(h, 1), (0, 0, 1, 0)) + mean = (cs[bi, b] - cs[bi, a]) / (b - a).clamp(min=1)[:, None].to(h.dtype) + hend = h[bi, b - 1] + rep = torch.cat([mean, hend], -1) + q0 = self.ent.query(hend.float() + self.kb_len((b - a).clamp(1, 6))) + if qtext is not None and hasattr(self, 'kb_cng'): + import kb_strfeat as KS + ids, off = KS.cng_batch(qtext) + q0 = q0 + self.kb_cng(torch.as_tensor(ids, device=h.device), torch.as_tensor(off, device=h.device)).to(q0.dtype) + q = F.normalize(q0, dim=-1) + return (rep, q) + + def kb_scores(self, q, E): + sc = self.ent.log_scale.clamp(max=math.log(50.0)).exp() + s = torch.einsum('nd,nkd->nk', q, E) if E.dim() == 3 else q @ E.t() + null = q @ F.normalize(self.ent.special, dim=-1)[0] * sc + self.ent.special_b[0] + return (s * sc, null) + + def kb_ver_e(self, rep, q, e, sf=None): + x = torch.cat([rep.float(), q, e, q * e, (q - e).abs()], -1) + if sf is None or not hasattr(self, 'ver_s'): + return self.ver_e(x)[..., 0] + z = self.ver_e[0](x) + self.ver_s(sf.to(x.dtype)) + return self.ver_e[2](self.ver_e[1](z))[..., 0] + + def kb_embed(self, names, kinds=None): + ids = torch.as_tensor(encode_names(names), device=self.bB_all.device) + kk = torch.zeros(len(names), dtype=torch.long, device=ids.device) if kinds is None else kinds + return self.ent.names(ids, kk) + + def kb_logits(self, h_end, kbe): + n = h_end.shape[0] + cm = torch.ones(1, n, kbe.shape[0], dtype=torch.bool, device=h_end.device) + return self.ent.ptr_logits(h_end[None], torch.zeros(1, n, self.d, device=h_end.device), kbe[None], cm)[0] + +@torch.no_grad() +def role_cols(T): + return [k for k, i in enumerate(T.cols['speaker']) if T.ids[i].startswith('speaker.role.')] diff --git a/arctag/_core/engine.py b/arctag/_core/engine.py new file mode 100644 index 0000000000000000000000000000000000000000..78542584029cd2e984a27414ff90678b0db436b6 --- /dev/null +++ b/arctag/_core/engine.py @@ -0,0 +1,646 @@ +# Arc Tagger inference engine. Copyright (c) 2026 Whissle Inc. CC BY-NC 4.0 + Whissle terms, see LICENSE. +import glob, json, math, os, re, sys, threading, time +import numpy as np +os.environ.setdefault('CUDA_VISIBLE_DEVICES', '') +import torch +import torch.nn.functional as F +HERE = os.path.dirname(os.path.abspath(__file__)) +B = os.path.dirname(HERE) +CODE = HERE +sys.path.insert(0, CODE) +import arc_text as AT +import stream_arc as SA +import fast_arc as FA +from tagtable import TextTable, FINE_DOMAINS, domain_index +from losses import GroupIndex +from arc_text import POOLED +ARCH_SWITCH = os.environ.get('ARCTAG_ARCH_SWITCH') == '1' +SPAN_HEAD_MODE = os.environ.get('ARCTAG_SPAN_HEAD', 'auto') +SPAN_HEAD = SPAN_HEAD_MODE != '0' +HEAD_TOL = 0.05 +SPAN_THR_FILE = json.load(open(os.environ['ARCTAG_SPAN_THR'])) if os.environ.get('ARCTAG_SPAN_THR') else {} +UI_DOMAINS = {'auto': ['unknown'], 'talk': ['talk', 'stream_chat', 'news'], 'meeting': ['meeting'], 'court': ['court_appellate', 'court_trial'], 'assistant': ['assistant', 'assistant_noisy'], 'sports': ['nba', 'nhl', 'nfl', 'soccer', 'cricket'], 'news': ['news', 'talk']} +FINE_CUES = {'sports': {'nba': 'basketball nba rebound rebounds dunk layup three-pointer threes jumper assist assists paint free-throw buzzer lakers celtics warriors knicks bulls heat nuggets bucks suns mavericks clippers rockets spurs nets sixers raptors dribble backcourt fourth-quarter timeout', 'nhl': 'hockey nhl puck goalie power-play penalty-kill slapshot slap faceoff icing blue-line rink skate skates bruins rangers penguins maple leafs canadiens blackhawks oilers flames capitals lightning stick', 'nfl': 'nfl football touchdown quarterback yard yards field-goal end-zone interception sack sacks fumble punt kickoff linebacker receiver tight-end scrimmage patriots cowboys packers chiefs eagles steelers 49ers broncos down-and', 'soccer': 'soccer goalkeeper keeper offside midfield midfielder striker penalty-area corner free-kick pitch premier league champions-league fifa uefa header crossbar extra-time stoppage arsenal chelsea liverpool madrid barcelona united city tottenham', 'cricket': 'cricket wicket wickets bowler bowling batsman batter innings over overs boundary six sixes lbw stumps crease spinner pace yorker bouncer test odi t20 ipl umpire maiden run-out century fifty'}, 'court': {'court_appellate': 'chief-justice justice justices petitioner petitioners respondent respondents argument amicus certiorari circuit appellate appeal brief briefs precedent statute statutory may-it-please constitutional rebuttal counsel', 'court_trial': 'witness witnesses objection sustained overruled jury jurors exhibit cross-examination direct-examination redirect testimony testify testified sworn defendant plaintiff prosecution verdict sidebar approach bailiff deposition'}} +FINE_MIN, FINE_RATIO = (4, 2.0) +FINE_REDOMAIN = os.environ.get('ARCTAG_FINE_REDOMAIN', '1') != '0' + +def _fine_index(): + ix = {} + for ui, d in FINE_CUES.items(): + for fine, cues in d.items(): + for c in cues.split(): + ix.setdefault(ui, {})[tuple(c.replace('-', ' ').split())] = fine + return ix +FINE_IX = _fine_index() + +def fine_hint(ui, words, last=3000): + ix = FINE_IX.get(ui) + if not ix or not words: + return None + toks = [re.sub('[^a-z0-9]', '', w['w'].lower()) for w in words[-last:]] + cnt = {} + for i in range(len(toks)): + for L in (1, 2, 3): + f = ix.get(tuple(toks[i:i + L])) if i + L <= len(toks) else None + if f: + cnt[f] = cnt.get(f, 0) + 1 + if not cnt: + return None + r = sorted(cnt.items(), key=lambda x: -x[1]) + if r[0][1] >= FINE_MIN and (len(r) == 1 or r[0][1] >= FINE_RATIO * r[1][1]): + return r[0][0] + return None +FAMILY = [('pii', ('safety.pii.',)), ('profanity', ('safety.profanity.', 'safety.toxicity.')), ('number', ('entity.number.', 'entity.numeral')), ('entity', ('entity.mention.',))] +SKIP_PREFIX = ('affect.',) +STATE_GROUPS = ['content.mode', 'content.game_state', 'content.legal_phase', 'content.meeting_phase', 'content.subject'] +DECODE_FAM = [('number', ('entity.number.',)), ('entity', ('entity.mention.',)), ('pii', ('safety.pii.',)), ('profanity', ('safety.profanity.',)), ('toxicity', ('safety.toxicity.',))] +SPAN_EXCL = os.environ.get('ARCTAG_SPAN_EXCL', '1') != '0' +UMBRELLA = ('entity.numeral', 'safety.toxicity.toxic_any') + +def decode_family(tag): + if tag in UMBRELLA: + return None + for fam, pre in DECODE_FAM: + if tag.startswith(pre): + return fam + return None + +def family_of(tag): + for fam, pre in FAMILY: + if tag.startswith(pre): + return fam + return 'other' +DISPLAY_TIER = os.environ.get('ARCTAG_DISPLAY_TIER', 'T0') +AUTO_REDOMAIN = os.environ.get('ARCTAG_AUTO_REDOMAIN', '1') != '0' +SAFETY_DOMS = {'talk', 'stream_chat', 'short_clips', 'esports'} +CLAMP_SPAN = 0.3 +CLAMP_TOX = 0.5 +SAME_VERT = [{'nba', 'nhl', 'nfl', 'cricket', 'soccer'}, {'court_appellate', 'court_trial'}, {'assistant', 'assistant_noisy'}] +THR_FALLBACK = os.environ.get('ARCTAG_THR_FALLBACK', 'pooled') + +def lookup_thr(v, doms): + if not v: + return None + if THR_FALLBACK == 'pooled' and '_all' in v: + d0 = doms[0] if doms else 'unknown' + sib = next((g for g in SAME_VERT if d0 in g), {d0}) + for d in [d0] + [d for d in doms if d in sib and d != d0]: + if d in v: + return float(v[d]) + return float(v['_all']) + for d in list(doms) + ['unknown', '_all']: + if d in v: + return float(v[d]) + return float(np.median(list(v.values()))) + +def clamp_thr(col, doms, x): + tag, _, q = col.rpartition('@') + if q in ('span_in', 'span_beg'): + x = max(x, CLAMP_SPAN) + if tag.startswith('safety.toxicity.') and (not doms or doms[0] not in SAFETY_DOMS): + x = max(x, CLAMP_TOX) + return x + +class Thresholds: + + def __init__(self, own, older=None, older_step=None, default=0.5, pref=None, pref_step=None): + self.own = own or {} + self.older = older or {} + self.older_step = older_step + self.default = default + self.pref = pref or {} + self.pref_step = pref_step + self.ev = {} + self.ev_step = None + + def set_events(self, doc, step): + self.ev = doc or {} + self.ev_step = step + if self.ev.get('thresholds'): + self.pref = dict(self.pref, **self.ev['thresholds']) + + def ev_tier(self, tag, default='T0'): + return (self.ev.get('tier') or {}).get(tag, default) + + def ev_hidden(self, tag): + return tag in set(self.ev.get('hide') or ()) + + @staticmethod + def _look(th, col, doms): + return lookup_thr(th.get(col), doms) + + def tables(self): + return [t for t in (self.pref, self.own, self.older) if t] + + def __call__(self, col, ui_dom): + doms = list(ui_dom) if isinstance(ui_dom, (list, tuple)) else UI_DOMAINS.get(ui_dom, ['unknown']) + x = None + for th in self.tables(): + x = self._look(th, col, doms) + if x is not None: + break + return clamp_thr(col, doms, self.default if x is None else x) + +class Loaded: + + def __init__(self, step, path, model, table, thr, cfg): + self.step, self.path, self.model, self.table, self.thr, self.cfg = (step, path, model, table, thr, cfg) + self.GI = GroupIndex(table) + self.has_kb = hasattr(model, 'ent') and hasattr(model.ent, 'ptr_logits') + self.has_head = hasattr(model, 'span_head') + self.arch = tuple(sorted((k for k in ('span_head', 'gat', 'dattn', 'dmix', 'kb_spot', 'pii_imm', 'spk_link') if hasattr(model, k)))) + self.head_tags = {f: list(v[2]) for f, v in model.span_head.fm.items() if v[0]} if self.has_head else {} + self.head_use, self.head_why = self._head_families() + self.loaded_at = time.time() + + def _head_families(self): + if not self.head_tags or SPAN_HEAD_MODE == '0': + return (set(), 'off') + if SPAN_HEAD_MODE == '1': + return (set(self.head_tags), 'all') + f1 = SPAN_THR_FILE.get('dev_exact_f1', {}).get('T0') if SPAN_THR_FILE.get('dev_exact_f1') else None + if f1 is None: + try: + sp = json.load(open(os.path.join(self.path, 'eval.json')))['dev']['span_f1']['spans']['T0'] + f1 = {k: (v.get('all') or {}).get('exact_f1') for k, v in sp.items()} + except Exception: + f1 = None + if not f1: + return (set(self.head_tags), 'all (no dev F1)') + use = {f for f in self.head_tags if f1.get('head:' + f) is not None and (f1.get('fam:' + f) is None or f1['head:' + f] >= f1['fam:' + f] - HEAD_TOL)} + return (use, {f: (f1.get('head:' + f), f1.get('fam:' + f)) for f in self.head_tags}) + + def head_thr(self, tier, fam): + ts = (self.cfg or {}).get('thresholds_span') or SPAN_THR_FILE.get('thresholds_span') or {} + for t in (tier, 'T0'): + v = (ts.get(t) or {}).get('head:' + fam) + if v is not None: + return float(v) + return 0.5 + +class ArcSession: + SPAN_WIN = 96 + + def __init__(self, loaded, ui_domain='auto', asr='deepgram', kb=None, int8=False, floor=0.0): + self.ui_domain = ui_domain if ui_domain in UI_DOMAINS else 'auto' + self.fine = UI_DOMAINS[self.ui_domain][0] + self.asr = asr + self.int8 = int8 + self.floor = float(floor or 0.0) + self.kb_names = [k.strip() for k in kb or [] if k and k.strip()][:256] + self.words = [] + self.utt_bounds = [] + self.events = [] + self._ev_keys = set() + self.alerts = [] + self._al_keys = set() + self.utt_final = [] + self.lat = [] + self._spksrc = None + self._bind(loaded) + + def _bind(self, loaded): + self.L = loaded + T = loaded.table + self.e = FA.FastArc(loaded.model, T, domain=self.fine, asr=self.asr) + if self.int8: + FA.quantize_fast(self.e) + self._set_xfeat() + self.store = {'T0': {}, 'T1': {}, 'T2': {}} + self.pooled_now = None + self.cn = {q: [n.split('@')[0] for n in T.col_names(q)] for q in ('frame', 'span_beg', 'span_in', 'span_end', 'ev_at', 'ev_imm', 'ev_rec', 'utt', 'speaker', 'item')} + thr = loaded.thr + d = self.doms + self.th_raw = {q: np.array([thr(f'{t}@{q}', d) for t in names], np.float32) for q, names in self.cn.items()} + self.th = {q: np.maximum(v, self.floor) for q, v in self.th_raw.items()} + valid = T.valid[domain_index(self.fine)] + self.valid = {q: np.array([bool(valid[T.idx[t]]) for t in names]) for q, names in self.cn.items()} + bi = {t: i for i, t in enumerate(self.cn['span_beg'])} + self.beg_of_in = np.array([bi.get(t, -1) for t in self.cn['span_in']]) + norm = {T.ids[i] for i in T.norm_tags} + self.is_norm = np.array([t in norm for t in self.cn['span_in']]) + self.kbe = None + if self.kb_names and loaded.has_kb: + with torch.inference_mode(): + self.kbe = loaded.model.kb_embed(self.kb_names) + + def _set_xfeat(self): + if self._spksrc is not None and ('spksrc' in self.e.feat or 'turn' in self.e.feat): + self.e.xfeat = {'spksrc': self._spksrc, 'turn': 0} + + def set_kb(self, names): + self.kb_names = [k.strip() for k in names or [] if k and k.strip()][:256] + self.kbe = None + if self.kb_names and self.L.has_kb: + with torch.inference_mode(): + self.kbe = self.L.model.kb_embed(self.kb_names) + + def rebind(self, loaded): + ws = [dict(w) for w in self.words] + ub = list(self.utt_bounds) + self.utt_bounds = [] + self._bind(loaded) + self.words = ws + t = time.perf_counter() + lo = 0 + for k in ub + [len(ws)]: + if k > lo: + self._absorb(self.e.update(lo, ws[lo:k])) + if k in ub: + self._absorb(self.e.endpoint()) + self.utt_bounds.append(k) + lo = k + self.words = ws + return round(1000 * (time.perf_counter() - t), 1) + + @property + def doms(self): + return [self.fine] + [d for d in UI_DOMAINS[self.ui_domain] if d != self.fine] + + def set_domain(self, ui_domain, fine=None): + ui = ui_domain if ui_domain in UI_DOMAINS else 'auto' + fine = fine if fine in UI_DOMAINS[ui] else UI_DOMAINS[ui][0] + if ui == self.ui_domain and fine == self.fine: + return None + self.ui_domain = ui + self.fine = fine + return self.rebind(self.L) + + def _update(self, k, ws): + e = self.e + if k < e.n: + s0 = max([x for x in e.snap if x <= k], default=None) + if s0 is None or s0 < k: + d1 = dict(e.update(k, [])) + d2 = e.update(k, ws) + d1.update(d2) + return d1 + return e.update(k, ws) + + def apply(self, evs): + lo = None + utt_done = [] + for x in evs: + t = x['type'] + if t == 'words': + k = x['from'] + ws = x['words'] + j = 0 + while j < len(ws) and k + j < len(self.e.words) and (ws[j] == self.e.words[k + j]): + j += 1 + if j == len(ws) and k + j == len(self.e.words): + continue + k2 = k + j + if self._spksrc is None and 'spksrc' in self.e.feat: + ov = os.environ.get('ARCTAG_SPKSRC') + self._spksrc = int(ov) if ov else 2 if any((w.get('spk') is not None for w in ws)) else 0 + self._set_xfeat() + t0 = time.perf_counter() + due = self._update(k2, ws[j:]) + self.lat.append((1000 * (time.perf_counter() - t0), max(1, len(self.e.words) - min(k2, len(self.words))))) + self.words = list(self.e.words) + self._prune() + self._absorb(due) + self._running() + lo = k2 if lo is None else min(lo, k2) + elif t == 'finalize': + ws = self.e.words + up = min(x['upto'], len(ws)) + first = next((i for i in range(up) if not ws[i].get('final', True)), None) + if first is not None: + t0 = time.perf_counter() + due = self._update(first, [dict(w, final=True) for w in ws[first:up]] + ws[up:]) + self.lat.append((1000 * (time.perf_counter() - t0), max(1, len(ws) - first))) + self.words = list(self.e.words) + self._prune() + self._absorb(due) + self._running() + lo = first if lo is None else min(lo, first) + elif t == 'endpoint': + if not self.e.n or (self.utt_bounds and self.utt_bounds[-1] >= self.e.n): + continue + due = self.e.endpoint() + self._absorb(due) + s = self.utt_bounds[-1] if self.utt_bounds else 0 + self.utt_bounds.append(self.e.n) + ro = self.readouts(self.e.n - 1, final=True) + u = {'w0': s, 'w1': self.e.n, 'readouts': ro, 'text': ' '.join((w['w'] for w in self.words[s:self.e.n]))} + self.utt_final.append(u) + utt_done.append(u) + self._alerts_utt(u) + lo = s if lo is None else min(lo, s) + return (lo, utt_done) + + def _prune(self): + for tier, st in self.store.items(): + have = self.e.out[tier] + for i in [i for i in st if i not in have or i >= self.e.n]: + del st[i] + + def _absorb(self, due): + GI = self.L.GI + for key, lg in due.items(): + tier, i = key + if tier == 'pooled': + continue + P = {} + for q, z in lg.items(): + if q == 'span_head': + P[q] = {f: (zt.softmax(-1).numpy(), zb.softmax(-1).numpy()) for f, (zt, zb) in z.items()} + continue + P[q] = GI.probs(q, z[None])[0].numpy() if z.numel() else np.zeros(0, np.float32) + self.store[tier][i] = P + self._events(tier, i, P) + + def _running(self): + if self.e.n: + self.pooled_now = self.e.n - 1 + + def spans(self, tier, lo=None): + st = self.store[tier] + n = self.e.n + a0 = max(0, n - self.SPAN_WIN) + idx = [i for i in range(max(0, a0 - 16), n) if i in st] + if not idx: + return (a0, []) + ON = np.stack([st[i]['span_in'] for i in idx]) + PIN = ON + ON = (ON >= self.th['span_in']) & self.valid['span_in'] + bo = self.beg_of_in + hb = bo >= 0 + PB = np.stack([st[i]['span_beg'] for i in idx]) + BEG = np.zeros_like(ON) + BEG[:, hb] = PB[:, bo[hb]] >= self.th['span_beg'][bo[hb]] + us = np.array([bool(self.e.ustart[i]) if i < len(self.e.ustart) else False for i in idx]) + BEG[us] = True + head = self._head_cols() if self.L.has_head and SPAN_HEAD else {} + if head: + ON[:, [c for cs in head.values() for c in cs]] = False + res = [] + for c in np.nonzero(ON.any(0))[0]: + a = None + for r in range(len(idx) + 1): + on = r < len(idx) and ON[r, c] + gap = r < len(idx) and r > 0 and (idx[r] != idx[r - 1] + 1) + if a is not None and (not on or gap or BEG[r, c]): + w0, w1 = (idx[a], idx[r - 1] + 1) + if w1 > a0: + res.append((c, w0, w1, float(PIN[a:r, c].max()))) + a = None + if on and a is None: + a = r + alt = {} + if SPAN_EXCL: + res, alt = self._exclusive(res, idx, PIN) + hth = {} + if head: + hres, alt2, hth = self._head_spans(tier, idx, us, a0, head) + res = res + hres + alt.update(alt2) + out = [] + th_in = self.th['span_in'] + for c, a, b, p in res: + tag = self.cn['span_in'][c] + s = {'tag': tag, 'fam': family_of(tag), 'a': int(a), 'b': int(b), 'p': round(p, 3), 'thr': round(float(hth.get(c, th_in[c])), 3), 'text': ' '.join((w['w'] for w in self.words[a:b]))} + if c in hth: + s['src'] = 'head' + if (c, a, b) in alt: + s['alt'] = alt[c, a, b] + if self.is_norm[c]: + s.update(self._verify(a, b)) + out.append(s) + out.sort(key=lambda s: (s['a'], -s['b'])) + return (a0, out) + + def _head_cols(self): + hc = getattr(self, '_hc', None) + if hc is None or hc[0] is not self.L: + ci = {t: c for c, t in enumerate(self.cn['span_in'])} + m = {f: [ci[t] for t in tags if t in ci and self.valid['span_in'][ci[t]]] for f, tags in self.L.head_tags.items() if f in self.L.head_use} + hc = self._hc = (self.L, {f: cs for f, cs in m.items() if cs}) + return hc[1] + + def head_thr(self, tier, fam): + f_ = self.L.head_tags.get(fam) or [fam + '.x'] + return clamp_thr(f'{f_[0]}@span_in', self.doms, max(self.L.head_thr(tier, fam), self.floor)) + + def _head_spans(self, tier, idx, us, a0, head): + st = self.store[tier] + ci = {t: c for c, t in enumerate(self.cn['span_in'])} + res, alt, hth = ([], {}, {}) + brk = np.array([bool(us[r]) or (r > 0 and idx[r] != idx[r - 1] + 1) for r in range(len(idx))]) + for f, cols in head.items(): + rows = [r for r, i in enumerate(idx) if 'span_head' in st[i] and f in st[i]['span_head']] + if len(rows) != len(idx): + continue + Pt = np.stack([st[i]['span_head'][f][0] for i in idx]) + Pb = np.stack([st[i]['span_head'][f][1] for i in idx]) + tags = self.L.head_tags[f] + mem = [k for k, t in enumerate(tags) if t in ci and ci[t] in cols] + th = self.head_thr(tier, f) + pin = 1.0 - Pb[:, 0] + if tier == 'T0': + lab = np.where(pin < th, 0, np.where(brk | (Pb[:, 1] > Pb[:, 2]), 1, 2)) + for r in range(1, len(idx)): + if lab[r] == 2 and lab[r - 1] == 0: + lab[r] = 1 + if len(idx): + lab[0] = 1 if lab[0] else 0 + else: + lab = self._viterbi(Pb, th, brk) + lt = np.log(np.clip(Pt[:, mem], 1e-09, 1.0)) + r = 0 + W = len(idx) + while r < W: + if lab[r] == 0: + r += 1 + continue + e = r + 1 + while e < W and lab[e] == 2: + e += 1 + w0, w1 = (idx[r], idx[e - 1] + 1) + if w1 > a0: + sc = lt[r:e].sum(0) + o = np.argsort(-sc) + c = ci[tags[mem[int(o[0])]]] + res.append((c, w0, w1, float(pin[r:e].max()))) + hth[c] = th + if len(o) > 1: + pm = Pt[r:e][:, mem].mean(0) + alt[c, w0, w1] = {'tag': tags[mem[int(o[1])]], 'p': round(float(pm[o[1]]), 3)} + r = e + return (res, alt, hth) + + @staticmethod + def _viterbi(Pb, th, brk): + W = len(Pb) + if not W: + return np.zeros(0, int) + pin = np.clip(1.0 - Pb[:, 0], 1e-09, 1.0) + bias = math.log((1.0 - th) / max(th, 1e-06)) + cb = np.clip(Pb[:, 1:], 1e-09, 1.0) + cb = np.log(cb / cb.max(1, keepdims=True)) + L = np.empty((W, 3)) + L[:, 0] = np.log(np.clip(Pb[:, 0], 1e-09, 1.0)) + L[:, 1:] = (np.log(pin) + bias)[:, None] + cb + NEG = -1000000000.0 + dp = np.full((W, 3), NEG) + bp = np.zeros((W, 3), int) + dp[0, 0], dp[0, 1] = (L[0, 0], L[0, 1]) + for r in range(1, W): + p = dp[r - 1] + for s_ in (0, 1): + k = int(p.argmax()) + dp[r, s_] = p[k] + L[r, s_] + bp[r, s_] = k + if not brk[r]: + k = 1 if p[1] >= p[2] else 2 + dp[r, 2] = p[k] + L[r, 2] + bp[r, 2] = k + lab = np.zeros(W, int) + lab[-1] = int(dp[-1].argmax()) + for r in range(W - 1, 0, -1): + lab[r - 1] = bp[r, lab[r]] + return lab + + def _exclusive(self, res, idx, PIN): + fam_cols = getattr(self, '_fam_cols', None) + if fam_cols is None: + fam_cols = self._fam_cols = {} + for c, t in enumerate(self.cn['span_in']): + f = decode_family(t) + if f and self.valid['span_in'][c]: + fam_cols.setdefault(f, []).append(c) + row = {w: r for r, w in enumerate(idx)} + by = {} + keep = [] + for x in res: + f = decode_family(self.cn['span_in'][x[0]]) + (by.setdefault(f, []) if f else keep).append(x) + out = list(keep) + alt = {} + for f, xs in by.items(): + xs.sort(key=lambda x: (x[1], x[2])) + groups = [] + for x in xs: + if groups and x[1] < groups[-1][1]: + groups[-1][1] = max(groups[-1][1], x[2]) + groups[-1][2].append(x) + else: + groups.append([x[1], x[2], [x]]) + cols = fam_cols.get(f, []) + for a, b, mem in groups: + if len(mem) == 1 or not cols: + out.append(mem[0]) + continue + rr = [row[w] for w in range(a, b) if w in row] + M = PIN[rr][:, cols].mean(0) + o = np.argsort(-M) + c = cols[int(o[0])] + out.append((c, a, b, float(PIN[rr, c].max()))) + if len(o) > 1: + c2 = cols[int(o[1])] + alt[c, a, b] = {'tag': self.cn['span_in'][c2], 'p': round(float(M[o[1]]), 3)} + return (out, alt) + + @torch.inference_mode() + def _verify(self, a, b): + e = self.e + ws = e.words[a:b] + c = getattr(self, '_vcache', None) + if c is None or c[0] is not e or c[1] is not self.kbe: + c = self._vcache = (e, self.kbe, {}) + hit = c[2].get((a, b)) + if hit is not None and len(hit[0]) == len(ws) and all((x is y for x, y in zip(hit[0], ws))): + return dict(hit[1]) + out = self._verify_raw(a, b) + if len(c[2]) > 4000: + c[2].clear() + c[2][a, b] = (tuple(ws), out) + return dict(out) + + def _verify_raw(self, a, b): + m = self.L.model + H = self.e.Hbuf + sp = torch.tensor([[0, b - a]]) + rep = m.span_rep(H[a:b], sp) + out = {'ver': round(float(torch.sigmoid(m.ver(rep)[0, 0])), 3)} + if self.kbe is not None: + z = m.kb_logits(H[b - 1][None], self.kbe)[0].float() + p = torch.softmax(z, -1) + j = int(p[2:].argmax()) if p.numel() > 2 else -1 + out['kb'] = {'p_new': round(float(p[0]), 3), 'p_generic': round(float(p[1]), 3)} + if j >= 0: + out['kb'].update(entry=self.kb_names[j], p=round(float(p[2 + j]), 3), match=bool(p[2 + j] > max(p[0], p[1]))) + return out + + def _events(self, tier, i, P): + if tier == 'T2': + return + w = self.words[i] if i < len(self.words) else {} + for q in ('ev_at', 'ev_imm', 'ev_rec'): + p = P.get(q) + if p is None or not len(p): + continue + on = np.nonzero((p >= self.th[q]) & self.valid[q])[0] + for c in on: + tag = self.cn[q][c] + if any(((tier, tag, j) in self._ev_keys for j in range(i - 3, i + 1))): + continue + self._ev_keys.add((tier, tag, i)) + self.events.append({'tier': tier, 'tag': tag, 'q': q, 'i': int(i), 'p': round(float(p[c]), 3), 'thr': round(float(self.th[q][c]), 3), 't': w.get('t1'), 'wall': time.time(), 'word': w.get('w', '')}) + + def _alerts_utt(self, u): + for r in u['readouts'].get('safety', []): + k = (r['tag'], u['w0']) + if r['on'] and k not in self._al_keys: + self._al_keys.add(k) + self.alerts.append({'tag': r['tag'], 'p': r['p'], 'thr': r['thr'], 'w0': u['w0'], 'w1': u['w1'], 'text': u['text'][-160:], 'wall': time.time(), 'kind': 'utterance'}) + + def span_alerts(self, spans, tier): + for s in spans: + if s['fam'] in ('pii', 'profanity'): + k = (tier, s['tag'], s['a']) + if k not in self._al_keys: + self._al_keys.add(k) + self.alerts.append({'tag': s['tag'], 'p': s['p'], 'thr': s['thr'], 'w0': s['a'], 'w1': s['b'], 'text': s['text'], 'wall': time.time(), 'kind': f'span {tier}'}) + + def _top(self, q, P, prefix, k=3): + names = self.cn[q] + idx = [c for c, t in enumerate(names) if t.startswith(prefix) and self.valid[q][c]] + idx.sort(key=lambda c: -P[c]) + return [{'tag': names[c], 'p': round(float(P[c]), 3), 'thr': round(float(self.th[q][c]), 3), 'on': bool(P[c] >= self.th[q][c])} for c in idx[:k]] + + @torch.inference_mode() + def readouts(self, j=None, final=False, tier='T0'): + if j is None: + j = self.pooled_now + if j is None or j not in self.e.pooled: + return {} + GI = self.L.GI + pl = self.e.pooled_logits(j) + U = GI.probs('utt', pl['utt'][None])[0].numpy() + S = GI.probs('speaker', pl['speaker'][None])[0].numpy() + I = GI.probs('item', pl['item'][None])[0].numpy() + sp = self.e.spk[j] if j < len(self.e.spk) else 0 + ro = {'j': int(j), 'final': final, 'intent': self._top('utt', U, 'intent.', 3) + self._top('utt', U, 'discourse.intent.', 3), 'act': self._top('utt', U, 'discourse.act.', 3) + self._top('utt', U, 'discourse.answer', 1) + self._top('utt', U, 'discourse.backchannel', 1) + self._top('utt', U, 'discourse.assistant.', 1), 'role': self._top('speaker', S, 'speaker.role.', 3) if sp > 0 else [], 'role_note': None if sp > 0 else 'speaker role needs diarization (no speaker id on words)', 'safety': [r for r in self._top('utt', U, 'safety.', 20) if r['p'] >= 0.5 * r['thr']][:6], 'item': self._top('item', I, 'item.', 3)} + ro['intent'].sort(key=lambda r: -r['p']) + ro['intent'] = ro['intent'][:4] + fr = {} + jt = max([i for i in self.store[tier] if i <= j], default=None) + if jt is not None and 'frame' in self.store[tier][jt]: + Fp = self.store[tier][jt]['frame'] + for g in STATE_GROUPS: + top = self._top('frame', Fp, g + '.', 3) + if top: + fr[g] = top + ro['state'] = fr + return ro + + def stats(self): + L = self.lat[-500:] + if not L: + return {} + per_upd = np.array([x[0] for x in L]) + per_w = np.array([x[0] / x[1] for x in L]) + return {'upd_p50': round(float(np.percentile(per_upd, 50)), 2), 'upd_p95': round(float(np.percentile(per_upd, 95)), 2), 'word_p50': round(float(np.percentile(per_w, 50)), 2), 'word_p95': round(float(np.percentile(per_w, 95)), 2), 'n_updates': len(self.lat)} diff --git a/arctag/_core/fast_arc.py b/arctag/_core/fast_arc.py new file mode 100644 index 0000000000000000000000000000000000000000..703641915ed60a28f4a7c12e081a0996c565cf77 --- /dev/null +++ b/arctag/_core/fast_arc.py @@ -0,0 +1,551 @@ +# Arc Tagger inference engine. Copyright (c) 2026 Whissle Inc. CC BY-NC 4.0 + Whissle terms, see LICENSE. +import argparse, bisect, json, math, os, sys, time +import numpy as np, torch, torch.nn.functional as F +HERE = os.path.dirname(os.path.abspath(__file__)) +sys.path.insert(0, HERE) +import arc_text as AT +from arc_text import WORD_Q, POOLED, D_MAX, P_MAX, NEG +from arc_audio.seqlayer.layer import rotate_pairs +from tagtable import domain_index, asr_index +from losses import GroupIndex +import stream_arc as SA +KEEP = 64 +WQ = [q for q in WORD_Q if q != 'span_beg'] + +class FastArc: + + @torch.inference_mode() + def __init__(self, model, table, domain=None, asr=None, max_words=20000): + m = self.m = model.eval() + self.T = table + self.GI = GroupIndex(table) + self.asr_name = asr or 'unknown' + self.asr = asr_index(asr) + di = domain_index(domain) + self.dom = torch.tensor([di]) + tr = m.trunk + d = m.d + self.d = d + self.tok = AT.ArcPieces(AT.BPE8K if getattr(model, 'scratch', None) else AT.TOK_PATH) + self.cache = {} + f32 = lambda t: t.detach().float().contiguous() + w = m.word + self.tokE = w.tok_E + self.ppos = f32(w.ppos) + self.W1 = f32(w.W1.weight).t().contiguous() + self.b1 = f32(w.W1.bias) + self.W2 = f32(w.W2.weight).t().contiguous() + self.ln = (f32(w.ln.weight), f32(w.ln.bias), w.ln.eps) + self.fl = [f32(e.weight) for e in w.fl] + src = 1 if self.asr_name == 'arc_transcriber' else 0 + self.c_in = f32(w.src.weight[src]) + (f32(w.Wa.bias) + f32(w.hav.weight[0]) if hasattr(w, 'Wa') else 0) + self.feat = {k: f32(e.weight) for k, e in m.feat.items()} + self.enc = [] + for lay in w.enc: + self.enc.append(dict(n1=(f32(lay.n1.weight), f32(lay.n1.bias), lay.n1.eps), Wqkv=f32(lay.qkv.weight).t().contiguous(), bqkv=f32(lay.qkv.bias), Wo=f32(lay.o.weight).t().contiguous(), bo=f32(lay.o.bias), n2=(f32(lay.n2.weight), f32(lay.n2.bias), lay.n2.eps), F1=f32(lay.ff[0].weight).t().contiguous(), f1=f32(lay.ff[0].bias), F2=f32(lay.ff[3].weight).t().contiguous(), f2=f32(lay.ff[3].bias), h=lay.h, win=lay.win)) + self.enc_norm = (f32(w.enc_norm.weight), f32(w.enc_norm.bias), w.enc_norm.eps) + dw = w.ln.normalized_shape[0] + hE = self.enc[0]['h'] + dh = dw // hE + self.dh = dh + self.hE = hE + self.dw = dw + self.win = self.enc[0]['win'] + inv = 1.0 / 10000 ** (torch.arange(0, dh, 2, dtype=torch.float32) / dh) + self.rope_inv = inv + ang = torch.arange(max_words, dtype=torch.float32)[:, None] * inv + self.rope_c, self.rope_s = (torch.cos(ang)[:, None, :], torch.sin(ang)[:, None, :]) + di_ = tr.delay_lut[0] + e_ = f32(tr.dom_emb.weight[di]) + f32(tr.delay_film.weight[di_]) + self.in_norm = (f32(tr.in_norm.weight), f32(tr.in_norm.bias), tr.in_norm.eps) + self.Winp = f32(tr.inp.weight).t().contiguous() + self.binp = f32(tr.inp.bias) + f32(tr.dom_emb.weight[di]) + f32(tr.delay_emb.weight[di_]) + self.layers = [] + for i, (lay, mlp) in enumerate(zip(tr.layers, tr.mlps)): + gb = f32(tr.film[i](e_)) + g, bb = (1 + gb[:d], gb[d:]) + H, N, P = (lay.H, lay.N, lay.P) + Ws = [lay.in_proj, lay.x_proj, lay.B_proj, lay.C_proj, lay.theta_proj, lay.dt_proj, lay.lam_proj] + Wf = torch.cat([f32(l.weight) for l in Ws], 0).t().contiguous() + bf = torch.cat([f32(l.bias) for l in Ws]) + sizes = [l.out_features for l in Ws] + self.layers.append(dict(g=g, b=bb, nw=f32(lay.norm.weight), neps=lay.norm.eps, Wf=Wf, bf=bf, sizes=sizes, H=H, N=N, P=P, A=-torch.exp(f32(lay.A_log)), Bn=(f32(lay.B_norm.weight), lay.B_norm.eps), Cn=(f32(lay.C_norm.weight), lay.C_norm.eps), Bb=f32(lay.B_bias)[..., 0], Cb=f32(lay.C_bias)[..., 0], BCw=torch.stack([f32(lay.B_norm.weight), f32(lay.C_norm.weight)])[:, None, :], BCb=torch.stack([f32(lay.B_bias)[..., 0], f32(lay.C_bias)[..., 0]]), Wout=f32(lay.out_proj.weight).t().contiguous(), bout=f32(lay.out_proj.bias), mn=(f32(mlp[0].weight), mlp[0].eps), M1=f32(mlp[1].weight).t().contiguous(), m1=f32(mlp[1].bias), M2=f32(mlp[4].weight).t().contiguous(), m2=f32(mlp[4].bias))) + self.adD = f32(tr.adapt_down[di]) + self.adU = f32(tr.adapt_up[di]) + self.on = (f32(tr.out_norm.weight), tr.out_norm.eps) + import v2 as V2 + self.V2 = V2 + self.gat = {} + for k_, G in (getattr(m, 'gat', None) or {}).items(): + self.gat[int(k_)] = dict(nw=f32(G.norm.weight), neps=G.norm.eps, Wqkv=f32(G.qkv.weight).t().contiguous(), qn=(f32(G.qn.weight), G.qn.eps), kn=(f32(G.kn.weight), G.kn.eps), Wo=f32(G.o.weight).t().contiguous(), h=G.h, dh=G.dh, mem=G.mem, ch=G.chunk, slots=G.slots, K=torch.zeros(max_words, G.h, G.dh), Kr=torch.zeros(max_words, G.h, G.dh), V=torch.zeros(max_words, G.h, G.dh)) + if getattr(G, 'biased', False): + self.gat[int(k_)].update(biased=True, slope=f32(G.lslope).exp(), kb=f32(G.kb), kmW=f32(G.kmark.weight)[0], kmb=float(G.kmark.bias), gate=float(torch.tanh(G.gate.detach().float())), M=torch.zeros(max_words)) + self.gat[int(k_)]['Wo'] = self.gat[int(k_)]['Wo'] * self.gat[int(k_)]['gate'] + if self.gat: + g0 = next(iter(self.gat.values())) + self.grc, self.grs = V2.rope_tab(torch.arange(max_words), g0['dh']) + self.da = None + if hasattr(m, 'dattn'): + A = m.dattn + self.da = dict(Wq=f32(A.q.weight).t().contiguous(), bq=f32(A.q.bias), Wk=f32(A.k.weight).t().contiguous(), bk=f32(A.k.bias), Wv=f32(A.v.weight).t().contiguous(), bv=f32(A.v.bias), qd=f32(A.qd.weight), ko=f32(A.koff.weight), Wo=f32(A.o.weight).t().contiguous(), dk=A.dk) + self.sh = None + if hasattr(m, 'span_head'): + S_ = m.span_head + ws_, bs_, self.sh_sl = ([], [], {}) + o_ = 0 + for f in S_.fm: + for kind, mod in (('t', S_.typ[f.replace('.', '_')]), ('b', S_.bio[f.replace('.', '_')])): + ws_.append(f32(mod.weight).t()) + bs_.append(f32(mod.bias)) + self.sh_sl[f, kind] = (o_, o_ + mod.out_features) + o_ += mod.out_features + self.sh = (torch.cat(ws_, 1).contiguous(), torch.cat(bs_)) + self.sh_dq = f32(m.delay_q.weight) @ self.sh[0] + self.Zs = torch.zeros(max_words, o_) + self.ua = (f32(m.utt_att.weight)[0], float(m.utt_att.bias)) if hasattr(m, 'utt_att') else None + h_ = m.head + K = f32(m.keys()) + valid = h_.valid[di] + cols, Ms, bs, self.wslices = ([], [], [], {}) + o = 0 + for q in WQ: + c = h_.cols(q) + Kc = K[c] + Ms.append(f32(h_.Q[q].weight).t() @ Kc.t()) + bs.append(f32(h_.Q[q].bias) @ Kc.t() + f32(h_.b + h_.bq[q])[c]) + msk = ~valid[c] + bs[-1] = bs[-1].masked_fill(msk, NEG) + self.wslices[q] = (o, o + len(c)) + o += len(c) + self.Mw = torch.cat(Ms, 1).contiguous() + self.bw = torch.cat(bs) + self.dq = f32(m.delay_q.weight) @ self.Mw + self.Md = (f32(m.dmix.weight).t() @ self.Mw).contiguous() if hasattr(m, 'dmix') else None + self.dq[:, self.bw <= NEG / 2] = 0 + cb = m.cols_span_beg + Kb = K[cb] + self.war = dict(r=(f32(w.war_r.weight).t().contiguous(), f32(w.war_r.bias)), k=f32(w.war_k.weight), ln=(f32(w.war_ln.weight), f32(w.war_ln.bias), w.war_ln.eps), M=(f32(w.QB.weight).t() @ Kb.t()).contiguous(), b=(f32(w.QB.bias) @ Kb.t() + f32(m.bB_all)[cb]).masked_fill(~valid[cb], NEG)) + self.pool = {} + P1 = torch.cat([f32(h_.pool_in[q][0].weight) for q in POOLED], 0).t().contiguous() + p1 = torch.cat([f32(h_.pool_in[q][0].bias) for q in POOLED]) + self.P1, self.p1 = (P1, p1) + self.P2 = [(f32(h_.pool_in[q][2].weight).t().contiguous(), f32(h_.pool_in[q][2].bias)) for q in POOLED] + for q in POOLED: + c = h_.cols(q) + Kc = K[c] + self.pool[q] = ((f32(h_.Q[q].weight).t() @ Kc.t()).contiguous(), (f32(h_.Q[q].bias) @ Kc.t() + f32(h_.b + h_.bq[q])[c]).masked_fill(~valid[c], NEG)) + self.Hbuf = torch.zeros(max_words, d) + self.Ubuf = torch.zeros(max_words, dw) + self.Zw = torch.zeros(max_words, self.Mw.shape[1]) + self.Zd = torch.zeros(max_words, self.Mw.shape[1]) if self.Md is not None else None + self.reset() + + def reset(self): + self.mr = getattr(self.m, 'memr', None) + if self.mr is not None: + dk = self.mr.dk + mw = self.Hbuf.shape[0] + self.Kc = torch.zeros(mw, dk) + self.Vc = torch.zeros(mw, dk) + self.Bc = torch.zeros(mw) + self._mk = 0 + self._mrc = {} + self.dqv = self.m.delay_q.weight.detach().float() + self.mr_g = math.tanh(float(self.mr.gate)) + self.mr_gu = math.tanh(float(self.mr.gate_u)) + self.n = 0 + self.words = [] + self.state = None + self.ustart = [] + L = len(self.enc) + self.ring = [(torch.zeros(self.win, self.hE, self.dh), torch.zeros(self.win, self.hE, self.dh)) for _ in range(L)] + self.ring_ver = 0 + self.carry = dict(utt=(torch.zeros(self.d, dtype=torch.float64), 0.0), item=(torch.zeros(self.d, dtype=torch.float64), 0.0), speaker=(torch.zeros(10, self.d, dtype=torch.float64), torch.zeros(10, dtype=torch.float64))) + self.slots = {} + self.prev = None + self.next_ustart = True + self.spk = [] + self.snap = {} + self.out = {'T0': {}, 'T1': {}, 'T2': {}} + self.pooled = {} + self.utt0 = 0 + self.gsum = {li: {} for li in self.gat} + + @staticmethod + def _mm(x, W): + return W.mm(x) if isinstance(W, QLin) else x @ W + + @staticmethod + def _ln(x, p): + return F.layer_norm(x, (x.shape[-1],), p[0], p[1], p[2]) + + @staticmethod + def _rms(x, w, eps): + return F.rms_norm(x, (x.shape[-1],), w, eps) + + def _rope(self, x, pos): + c, s = (self.rope_c[pos], self.rope_s[pos]) + x1, x2 = (x[..., 0::2], x[..., 1::2]) + return torch.stack([x1 * c - x2 * s, x1 * s + x2 * c], -1).flatten(-2) + + def _embed(self, ws, fes, fls): + T = len(ws) + Pm = torch.zeros(T, P_MAX, dtype=torch.long) + npc = torch.zeros(T, dtype=torch.long) + for i, p in enumerate(ws): + Pm[i, :len(p)] = torch.tensor(p) + npc[i] = len(p) + Et = self.tokE[Pm].float() + pm = (torch.arange(P_MAX)[None] < npc[:, None]).float()[..., None] + mean = ((Et + self.ppos) * pm).sum(1) / npc.clamp(min=1)[:, None].float() + last = Et[torch.arange(T), (npc - 1).clamp(min=0)] + e = self._ln(mean @ self.W1 + self.b1 + last @ self.W2, self.ln) + self.c_in + fl = torch.tensor(fls) + fl[:, 2] = npc.clamp(0, P_MAX) + for i, tab in enumerate(self.fl): + e = e + tab[fl[:, i].clamp(0, tab.shape[0] - 1)] + for k, tab in self.feat.items(): + xf = getattr(self, 'xfeat', None) or {} + e = e + tab[torch.tensor([f[k] if k in f else xf.get(k, 0) for f in fes]).clamp(0, tab.shape[0] - 1)] + return e + + def _enc(self, x, j0): + T = x.shape[0] + hE, dh, win = (self.hE, self.dh, self.win) + pos = torch.arange(j0, j0 + T) + for li, L in enumerate(self.enc): + q, k, v = (self._mm(self._ln(x, L['n1']), L['Wqkv']) + L['bqkv']).view(T, 3, hE, dh).unbind(1) + q = self._rope(q, pos) + k = self._rope(k, pos) + Kr, Vr = self.ring[li] + npast = min(j0, win - 1) + pidx = torch.arange(j0 - npast, j0) % win + Kall = torch.cat([Kr[pidx], k], 0) + Vall = torch.cat([Vr[pidx], v], 0) + kp = torch.arange(j0 - npast, j0 + T) + msk = (kp[None] <= pos[:, None]) & (kp[None] > pos[:, None] - win) + a = F.scaled_dot_product_attention(q.transpose(0, 1)[None], Kall.transpose(0, 1)[None], Vall.transpose(0, 1)[None], attn_mask=msk[None, None]) + a = a[0].transpose(0, 1).reshape(T, hE * dh) + Kn, Vn = (Kr.clone(), Vr.clone()) + Kn[pos % win] = k + Vn[pos % win] = v + self.ring[li] = (Kn, Vn) + x = x + self._mm(a, L['Wo']) + L['bo'] + x = x + self._mm(F.gelu(self._mm(self._ln(x, L['n2']), L['F1']) + L['f1']), L['F2']) + L['f2'] + return self._ln(x, self.enc_norm) + + def _trunk(self, u): + T = u.shape[0] + h = self._mm(self._ln(u, self.in_norm), self.Winp) + self.binp + state = self.state + new = [] + for i, L in enumerate(self.layers): + h = h * L['g'] + L['b'] + nu = self._rms(h, L['nw'], L['neps']) + pr = self._mm(nu, L['Wf']) + L['bf'] + zi, xx, Bp, Cp, th, dtp, lmp = torch.split(pr, L['sizes'], -1) + H, N, P = (L['H'], L['N'], L['P']) + dt = F.softplus(dtp) + alpha = torch.exp(dt * L['A']) + lam = torch.sigmoid(lmp) + gamma = lam * dt + beta = (1 - lam) * dt * alpha + BC = F.rms_norm(torch.stack([Bp.view(T, H, N), Cp.view(T, H, N)], 1), (N,), None, L['Bn'][1]) * L['BCw'] + L['BCb'] + Bm, Cm = (BC[:, 0], BC[:, 1]) + xv = xx.view(T, H, P) + theta = th.view(T, H, N // 2) * dt[..., None] + bx = Bm[..., :, None] * xv[..., None, :] + st = None if state is None else state[i] + cth, sth = (torch.cos(theta)[..., None], torch.sin(theta)[..., None]) + ys = [] + for t in range(T): + if st is None: + hs = gamma[t, :, None, None] * bx[t] + else: + pre = alpha[t, :, None, None] * st[0] + beta[t, :, None, None] * st[1] + c_, s_ = (cth[t], sth[t]) + a_, b_ = (pre[:, 0::2], pre[:, 1::2]) + hs = torch.stack([c_ * a_ - s_ * b_, s_ * a_ + c_ * b_], 2).reshape(H, N, P) + gamma[t, :, None, None] * bx[t] + st = (hs, bx[t]) + ys.append((Cm[t][:, None, :] @ hs).reshape(-1)) + new.append(st) + y = torch.stack(ys) + h = h + self._mm(y * F.silu(zi), L['Wout']) + L['bout'] + h = h + self._mm(F.gelu(self._mm(self._rms(h, *L['mn']), L['M1']) + L['m1']), L['M2']) + L['m2'] + if i in self.gat: + h = h + self._gattn(i, h) + self.state = new + h = h + F.gelu(h @ self.adD) @ self.adU + return self._rms(h, *self.on) + + def _gattn(self, li, h, qb=256): + G = self.gat[li] + T = h.shape[0] + j0 = self.n_run + H, dh, mem, ch = (G['h'], G['dh'], G['mem'], G['ch']) + hn = self._rms(h, G['nw'], G['neps']) + q, k, v = self._mm(hn, G['Wqkv']).view(T, 3, H, dh).unbind(1) + bi = G.get('biased', False) + q = self._rms(q, *G['qn']) + k = self._rms(k, *G['kn']) + pos = torch.arange(j0, j0 + T) + c, s = (self.grc[pos], self.grs[pos]) + qr = self.V2.rope(q, c, s) + G['K'][pos] = k + G['Kr'][pos] = self.V2.rope(k, c, s) + G['V'][pos] = v + if bi: + G['M'][pos] = torch.sigmoid(hn @ G['kmW'] + G['kmb']) + cache = self.gsum[li] + for cc in [cc for cc in cache if (cc + 1) * ch > j0]: + del cache[cc] + outs = [] + for a in range(0, T, qb): + b = min(T, a + qb) + i0, i1 = (j0 + a, j0 + b) + lo = max(0, i0 - mem + 1) + dm, sm = self.V2.summary_mask(i1 - i0, i1, i0, mem, ch, G['slots']) + dm = dm[:, lo:] + cs = sm.any(0).nonzero().flatten().tolist() + Ks, Vs = ([G['Kr'][lo:i1]], [G['V'][lo:i1]]) + if cs: + for cc in cs: + if cc not in cache: + kc = G['K'][cc * ch:(cc + 1) * ch].mean(0, keepdim=True) + vc = G['V'][cc * ch:(cc + 1) * ch].mean(0, keepdim=True) + c1, s1 = self.V2.rope_tab(torch.tensor([cc * ch + ch // 2]), dh) + cache[cc] = (self.V2.rope(kc, c1, s1)[0], vc[0]) + Ks.append(torch.stack([cache[cc][0] for cc in cs])) + Vs.append(torch.stack([cache[cc][1] for cc in cs])) + dm = torch.cat([dm, sm[:, cs]], 1) + Kall = torch.cat(Ks, 0) + Vall = torch.cat(Vs, 0) + msk = dm[None, None] + if bi: + jp = torch.cat([torch.arange(lo, i1)] + ([torch.tensor([cc * ch + ch // 2 for cc in cs])] if cs else [])).float() + mj = torch.cat([G['M'][lo:i1]] + ([torch.stack([G['M'][cc * ch:(cc + 1) * ch].mean() for cc in cs])] if cs else [])) + ii = torch.arange(i0, i1).float() + bias = -G['slope'][:, None, None] * (ii[None, :, None] - jp[None, None, :]) + G['kb'][:, None, None] * mj[None, None, :] + msk = torch.where(dm[None], bias, torch.full_like(bias, float('-inf')))[None] + o = F.scaled_dot_product_attention(qr[a:b].transpose(0, 1)[None], Kall.transpose(0, 1)[None], Vall.transpose(0, 1)[None], attn_mask=msk) + outs.append(o[0].transpose(0, 1).reshape(b - a, H * dh)) + return self._mm(torch.cat(outs, 0), G['Wo']) + + def _snapshot(self): + return dict(state=self.state, carry=self.carry, ring=list(self.ring), slots=dict(self.slots), prev=self.prev, nu=self.next_ustart) + + def _restore(self, k): + if k >= self.n: + return True + sn = self.snap.get(k) + if sn is None: + return False + self.state, self.carry, self.ring, self.slots, self.prev, self.next_ustart = (sn['state'], sn['carry'], list(sn['ring']), dict(sn['slots']), sn['prev'], sn['nu']) + self.n = k + del self.words[k:], self.ustart[k:], self.spk[k:] + if self.mr is not None: + self._mk = min(self._mk, k) + self._mrc = {c: v for c, v in self._mrc.items() if max(c) < k} + for t in self.out.values(): + for j in [j for j, jr in t.items() if jr >= k]: + del t[j] + for j in [j for j in self.pooled if j >= k]: + del self.pooled[j] + for j in [j for j in self.snap if j > k]: + del self.snap[j] + return True + + def pieces(self, w): + x = self.cache.get(w) + if x is None: + ids = self.tok.encode_word(w) or [0] + x = self.cache[w] = ids[:P_MAX - 1] + ids[-1:] if len(ids) > P_MAX else ids + return x + + @torch.inference_mode() + def update(self, k, words): + k = min(k, self.n) + s0 = max([x for x in self.snap if x <= k], default=None) + if k < self.n and s0 is None: + ws = self.words[:k] + self.reset() + s0 = 0 + rep_ = ws + else: + s0 = k if k >= self.n else s0 + rep_ = self.words[s0:k] + self._restore(s0) + self._run(rep_ + [dict(w) for w in words]) + return self.due + + def _run(self, words): + if not words: + return + j0 = self.n + T = len(words) + self.snap[j0] = self._snapshot() + fes, fls, pcs, spks, uss = ([], [], [], [], []) + for i, w in enumerate(words): + fe, fl, spk = SA.ArcStream.feats(self, w) + fes.append(fe) + fls.append(fl) + pcs.append(self.pieces(w['w'])) + spks.append(spk) + uss.append(self.next_ustart or j0 + i == 0) + self.next_ustart = False + e = self._embed(pcs, fes, fls) + self.n_run = j0 + u = self._enc(e, j0) + h = self._trunk(u) + self._after(j0, words, u, h, uss, spks) + + def _after(self, j0, words, u, h, uss, spks): + T = len(words) + self.Hbuf[j0:j0 + T] = h + self.Ubuf[j0:j0 + T] = u + if self.mr is not None: + self._mk = min(self._mk, j0) + self._mrc = {c: v for c, v in self._mrc.items() if max(c) < j0} + self.Zw[j0:j0 + T] = h @ self.Mw + self.bw + if self.Zd is not None: + self.Zd[j0:j0 + T] = h @ self.Md + if self.sh is not None: + self.Zs[j0:j0 + T] = h @ self.sh[0] + self.sh[1] + z1 = F.gelu(self._mm(h, self.P1) + self.p1) + zs = [h + self._mm(z1[:, i * self.d:(i + 1) * self.d], W) + b for i, (W, b) in enumerate(self.P2)] + for i in range(T): + j = j0 + i + us = uss[i] + sp = spks[i] + cu, nu_ = self.carry['utt'] + ci, ni = self.carry['item'] + cs, ns = self.carry['speaker'] + w_ = 1.0 if self.ua is None else float(torch.exp((zs[0][i] @ self.ua[0] + self.ua[1]).clamp(-10, 10))) + cu = w_ * zs[0][i].double() + (0 if us else cu) + nu_ = w_ + (0 if us else nu_) + ci = ci + zs[2][i].double() + ni = ni + 1 + if sp > 0: + cs = cs.clone() + ns = ns.clone() + cs[sp - 1] += zs[1][i].double() + ns[sp - 1] += 1 + self.carry = dict(utt=(cu, nu_), item=(ci, ni), speaker=(cs, ns)) + self.pooled[j] = dict(utt=(cu / nu_).float(), item=(ci / ni).float(), speaker=(cs[sp - 1] / ns[sp - 1]).float() if sp > 0 else torch.zeros(self.d)) + self.words.append(words[i]) + self.ustart.append(us) + self.spk.append(sp) + self.n = j0 + T + for x in [x for x in self.snap if x < self.n - KEEP]: + del self.snap[x] + new1 = [i for i in range(max(0, j0 - D_MAX), self.n) if i not in self.out['T1']] + for j in range(j0, self.n): + self.out['T0'][j] = j + self._delayed(final=False) + self.due = {('T0', j): self.word_logits(j, 'T0') for j in range(j0, self.n)} + self.due.update({('T1', i): self.word_logits(i, 'T1') for i in new1 if i in self.out['T1']}) + + def _delayed(self, final, lo=None): + n = self.n + lo = max(0, n - 2 * D_MAX - 2) if lo is None else lo + starts = [i for i in range(max(0, min(lo, n - 3 * D_MAX)), n) if self.ustart[i]] + for tier, cap in (('T1', 3), ('T2', D_MAX)): + if tier == 'T2' and (not final): + continue + for i in range(lo, n): + if i in self.out[tier]: + continue + s_next = next((s for s in starts if s > i), None) + end = s_next - 1 if s_next is not None else n - 1 if final else None + jr = min(i + cap, end) if end is not None else i + cap if i + cap <= n - 1 else None + if jr is None: + continue + self.out[tier][i] = jr + + @torch.inference_mode() + def endpoint(self): + self.next_ustart = True + if not self.n: + return {} + s = max((i for i in range(self.n) if self.ustart[i])) + n1 = [i for i in range(s, self.n) if i not in self.out['T1']] + n2 = [i for i in range(s, self.n) if i not in self.out['T2']] + self._delayed(final=True, lo=s) + due = {('T1', i): self.word_logits(i, 'T1') for i in n1} + due.update({('T2', i): self.word_logits(i, 'T2') for i in n2}) + due['pooled', self.n - 1] = self.pooled_logits(self.n - 1) + return due + + def word_logits(self, i, tier): + jr = self.out[tier][i] + d = jr - i + z = self.Zw[jr] + self.dq[min(d, D_MAX)] + if d > 0 and self.Zd is not None: + z = z + self.Zd[i] + ctx = None + if d > 0 and self.da is not None: + A = self.da + Hs = self.Hbuf[i:jr + 1] + kk = Hs @ A['Wk'] + A['bk'] + A['ko'][:d + 1] + vv = Hs @ A['Wv'] + A['bv'] + qv = self.Hbuf[i] @ A['Wq'] + A['bq'] + A['qd'][min(d, D_MAX)] + ctx = (kk @ qv / math.sqrt(A['dk'])).softmax(0) @ vv @ A['Wo'] + z = z + ctx @ self.Mw + ymr = amr = None + if self.mr is not None: + ck_ = (i, jr) + if ck_ not in self._mrc: + self._mrc[ck_] = self._memread(i, self.Hbuf[jr] + self.dqv[min(d, D_MAX)] + (ctx if ctx is not None else 0)) + ymr, amr = self._mrc[ck_] + z = z + ymr @ self.Mw + out = {q: z[a:b] for q, (a, b) in self.wslices.items()} + if self.sh is not None: + zs_ = self.Zs[jr] + self.sh_dq[min(d, D_MAX)] + (ctx @ self.sh[0] if ctx is not None else 0) + (ymr @ self.sh[0] if ymr is not None else 0) + out['span_head'] = {f: (zs_[self.sh_sl[f, 't'][0]:self.sh_sl[f, 't'][1]], zs_[self.sh_sl[f, 'b'][0]:self.sh_sl[f, 'b'][1]]) for f in self.V2.SPAN_FAMS} + k = min(d, 2) + W = self.war + us = self.Ubuf[i + k] @ W['r'][0] + W['r'][1] if k else W['r'][1] + v = self._ln(self.Ubuf[i] + us + W['k'][k], W['ln']) + out['span_beg'] = v @ W['M'] + W['b'] + if amr is not None: + out['span_beg'] = out['span_beg'] + amr + return out + + @torch.inference_mode() + def _memread(self, i, hd): + R = self.mr + n = self.n + if self._mk < n: + hn = R.norm(self.Hbuf[self._mk:n]) + self.Kc[self._mk:n] = R.kn(R.k(hn)) + self.Vc[self._mk:n] = R.v(hn) + self.Bc[self._mk:n] = float(R.kb) * F.logsigmoid(R.kmark(hn)[:, 0]) + self._mk = n + q = R.qn(R.q(R.norm(hd))) + sc = torch.cat([torch.zeros(1), self.Kc[:i] @ q / math.sqrt(R.dk) + self.Bc[:i]]) + p = sc.softmax(0)[1:] + a = p @ self.Vc[:i] + return (self.mr_g * R.o(a), self.mr_gu * R.ou(a)) + + def pooled_logits(self, j): + return {q: self.pooled[j][q] @ self.pool[q][0] + self.pool[q][1] for q in POOLED} + +class QLin: + + def __init__(self, W_t, b=None): + W = W_t.t().contiguous() + self.p = torch.ops.quantized.linear_prepack(torch.quantize_per_channel(W, W.abs().amax(1).clamp(min=1e-08) / 127, torch.zeros(W.shape[0], dtype=torch.long), 0, torch.qint8), b) + + def mm(self, x): + return torch.ops.quantized.linear_dynamic(x.contiguous(), self.p) + +def quantize_fast(st): + for L in st.layers: + for k in ('Wf', 'Wout', 'M1', 'M2'): + L[k] = QLin(L[k]) + st.Winp = QLin(st.Winp) + for L in st.enc: + for k in ('Wqkv', 'Wo', 'F1', 'F2'): + L[k] = QLin(L[k]) + st.P1 = QLin(st.P1) + st.P2 = [(QLin(W), b) for W, b in st.P2] diff --git a/arctag/_core/kb.py b/arctag/_core/kb.py new file mode 100644 index 0000000000000000000000000000000000000000..3a92d734144297ff3d21c6384cdc98fcdc537ebb --- /dev/null +++ b/arctag/_core/kb.py @@ -0,0 +1,28 @@ +# Arc Tagger inference engine. Copyright (c) 2026 Whissle Inc. CC BY-NC 4.0 + Whissle terms, see LICENSE. +import math, re, zlib +import numpy as np +TYPES = ('name', 'number', 'keyphrase', 'term', 'product', 'legal_ref', 'slot_value', 'fact') + +def norm(t): + return ' '.join(re.findall('[a-z0-9]+', str(t).lower())) +_PH = [('ph', 'f'), ('gh(?=[^aeiou]|$)', ''), ('kn|gn|pn|wr|ps', lambda m: m.group(0)[1]), ('ck|q', 'k'), ('x', 'ks'), ('c(?=[iey])', 's'), ('c', 'k'), ('dg(?=[iey])', 'j'), ('g(?=[iey])', 'j'), ('sch', 'sk'), ('sh|ch|tch', 'x'), ('th', '0'), ('tion|sion', 'xn'), ('z', 's'), ('v', 'f'), ('w(?=[^aeiou]|$)', ''), ('h(?=[^aeiou]|$)', ''), ('b|p', 'p'), ('d|t', 't'), ('m', 'm'), ('(.)\\1+', '\\1')] +_DIG = {'0': 'zero', '1': 'one', '2': 'two', '3': 'three', '4': 'four', '5': 'five', '6': 'six', '7': 'seven', '8': 'eight', '9': 'nine'} +_PHC = [(re.compile(a), b) for a, b in _PH] +_NONA = re.compile('[^a-z]') +_VOW = re.compile('[aeiouy]') + +@__import__('functools').lru_cache(maxsize=1 << 18) +def phon_word(w): + w = _NONA.sub('', w.lower()) + if not w: + return '' + k = w + for a, b in _PHC: + k = a.sub(b, k) + return (k[0] + _VOW.sub('', k[1:]))[:6] if k else w[:1] + +def phon(t): + ws = [] + for w in re.findall('[a-z]+|[0-9]', str(t).lower()): + ws.append(phon_word(_DIG.get(w, w))) + return ' '.join((x for x in ws if x)) diff --git a/arctag/_core/kb_strfeat.py b/arctag/_core/kb_strfeat.py new file mode 100644 index 0000000000000000000000000000000000000000..3af4de66bfcdc6edc338453df374fd4e3d80c5a3 --- /dev/null +++ b/arctag/_core/kb_strfeat.py @@ -0,0 +1,30 @@ +# Arc Tagger inference engine. Copyright (c) 2026 Whissle Inc. CC BY-NC 4.0 + Whissle terms, see LICENSE. +import zlib, difflib, functools +import kb as KBM +NB = 4096 +NF = 8 + +def grams(t): + t = f'#{KBM.norm(t)}#' + return [zlib.crc32(t[i:i + 3].encode()) % NB for i in range(max(1, len(t) - 2))] + +def cng_batch(texts): + ids, off = ([], []) + for t in texts: + off.append(len(ids)) + ids += grams(t) + return (ids, off) + +def _g3(t): + t = f'#{t}#' + return {t[i:i + 3] for i in range(max(1, len(t) - 2))} + +@functools.lru_cache(maxsize=200000) +def sfeat(q, e): + qn, en = (KBM.norm(q), KBM.norm(e)) + if not qn or not en: + return [0.0] * NF + a, b = (_g3(qn), _g3(en)) + qt, et = (set(qn.split()), set(en.split())) + qd, ed = (''.join((c for c in qn if c.isdigit())), ''.join((c for c in en if c.isdigit()))) + return [len(a & b) / max(1, len(a | b)), len(qt & et) / max(1, len(qt | et)), difflib.SequenceMatcher(None, qn, en).ratio(), min(len(qn), len(en)) / max(len(qn), len(en)), float(qn == en), float(qn[:3] == en[:3]), float(qd == ed) if qd or ed else 0.5, float(KBM.phon(qn) == KBM.phon(en))] diff --git a/arctag/_core/losses.py b/arctag/_core/losses.py new file mode 100644 index 0000000000000000000000000000000000000000..4ba76e66dcc1711a050c06a11132fb1c97299252 --- /dev/null +++ b/arctag/_core/losses.py @@ -0,0 +1,35 @@ +# Arc Tagger inference engine. Copyright (c) 2026 Whissle Inc. CC BY-NC 4.0 + Whissle terms, see LICENSE. +import torch, torch.nn.functional as F +from tagtable import QUERIES + +class GroupIndex: + + def __init__(self, T, w_weak=0.3): + self.indep, self.groups, self.colw = ({}, {}, {}) + for q in QUERIES: + cols = T.cols[q] + self.indep[q] = torch.tensor([k for k, i in enumerate(cols) if i not in T.group_of], dtype=torch.long) + g = {} + for k, i in enumerate(cols): + if i in T.group_of: + g.setdefault(T.group_of[i], []).append(k) + self.groups[q] = {n: torch.tensor(v, dtype=torch.long) for n, v in g.items()} + self.colw[q] = torch.tensor([w_weak if i in T.weak else 1.0 for i in cols]) + self.posw = None + self.ls = {} + self.clsw = {} + self.typw = {} + self._dev = {} + + def on(self, dev): + dev = str(dev) + if dev not in self._dev: + self._dev[dev] = ({q: v.to(dev) for q, v in self.indep.items()}, {q: {n: m.to(dev) for n, m in g.items()} for q, g in self.groups.items()}, {q: v.to(dev) for q, v in self.colw.items()}, {q: {n: float(self.colw[q][m][0]) for n, m in g.items()} for q, g in self.groups.items()}) + return self._dev[dev] + + def probs(self, q, z): + p = torch.sigmoid(z.float()) + for n, m in self.groups[q].items(): + zz = torch.cat([z[..., m].float(), torch.zeros_like(z[..., :1]).float()], -1) + p[..., m] = torch.softmax(zz, -1)[..., :-1] + return p diff --git a/arctag/_core/speaker_link.py b/arctag/_core/speaker_link.py new file mode 100644 index 0000000000000000000000000000000000000000..66beccb32d2ffc0d33369a9e5676f34785bb1139 --- /dev/null +++ b/arctag/_core/speaker_link.py @@ -0,0 +1,87 @@ +# Arc Tagger inference engine. Copyright (c) 2026 Whissle Inc. CC BY-NC 4.0 + Whissle terms, see LICENSE. +import numpy as np +import torch, torch.nn as nn, torch.nn.functional as F +TIER_OFF = {'T0': 0, 'T1': 2, 'T2': 7} + +class SpkLink(nn.Module): + + def __init__(self, d, n_roles, dz=128): + super().__init__() + self.att = nn.Linear(d, 1) + self.zp = nn.Linear(d, dz) + self.qp = nn.Linear(d, dz) + self.tier = nn.Embedding(3, dz) + nn.init.zeros_(self.tier.weight) + self.new_v = nn.Parameter(torch.randn(dz) * 0.02) + self.b_new = nn.Parameter(torch.zeros(())) + self.scale = nn.Parameter(torch.tensor(np.log(10.0), dtype=torch.float32)) + self.w_same = nn.Parameter(torch.zeros(())) + self.w_newid = nn.Parameter(torch.zeros(())) + self.role = nn.Linear(dz, max(1, n_roles)) + self.n_roles = n_roles + + def turn_emb(self, h, turns): + dev = h.device + bi = torch.cat([torch.full((e - s,), b, dtype=torch.long) for b, s, e in turns]).to(dev) + wi = torch.cat([torch.arange(s, e) for b, s, e in turns]).to(dev) + ti = torch.cat([torch.full((e - s,), t, dtype=torch.long) for t, (b, s, e) in enumerate(turns)]).to(dev) + x = h[bi, wi].float() + a = self.att(x)[:, 0] + T = len(turns) + mx = torch.full((T,), -1e+30, device=dev).scatter_reduce(0, ti, a, 'amax') + ea = torch.exp(a - mx[ti]) + den = torch.zeros(T, device=dev).index_add(0, ti, ea) + pooled = torch.zeros(T, x.shape[1], device=dev).index_add(0, ti, x * (ea / den[ti])[:, None]) + return F.normalize(self.zp(pooled), dim=-1) + + def queries(self, h, turns, tier): + k = list(TIER_OFF).index(tier) + off = TIER_OFF[tier] + bi = torch.tensor([b for b, s, e in turns], device=h.device) + pi = torch.tensor([min(s + off, e - 1) for b, s, e in turns], device=h.device) + return F.normalize(self.qp(h[bi, pi].float()) + self.tier.weight[k], dim=-1) + + def logits(self, q, Z, C, A): + T = q.shape[0] + K = int(C.max().item()) + 1 if (C >= 0).any() else 1 + O = F.one_hot(C.clamp(min=0), K).float() * (C >= 0).float()[:, None] + low = torch.tril(torch.ones(T, T, device=q.device), -1) + n = low @ O + S = q @ Z.t() * low + sc = S @ O / n.clamp(min=1) + same = ((A[:, None] == A[None, :]) & (A[:, None] > 0)).float() * low + fs = same @ O / n.clamp(min=1) + newid = ((A > 0) & (same.sum(1) == 0)).float() + lc = self.scale.exp() * sc + self.w_same * fs + lc = lc.masked_fill(n == 0, -10000.0) + ln = self.scale.exp() * (q @ self.new_v) + self.b_new + self.w_newid * newid + return (torch.cat([ln[:, None], lc], 1), n) + + def role_logits(self, Z, C): + T = Z.shape[0] + same = ((C[:, None] == C[None, :]) & (C[:, None] >= 0)).float() * torch.tril(torch.ones(T, T, device=Z.device)) + m = same @ Z / same.sum(1, keepdim=True).clamp(min=1) + return self.role(m) + + @torch.no_grad() + def assign(self, q, Z, A, K_max=16): + T = q.shape[0] + pred = torch.full((T,), -1, dtype=torch.long, device=q.device) + nk = 0 + for j in range(T): + if j == 0: + pred[0] = 0 + nk = 1 + continue + lg, _ = self.logits(q[:j + 1], Z[:j + 1], torch.cat([pred[:j], pred.new_full((1,), -1)]), A[:j + 1]) + lg = lg[j] + if nk >= K_max: + lg = lg.clone() + lg[0] = -1000000000.0 + c = int(lg.argmax()) + if c == 0: + pred[j] = nk + nk += 1 + else: + pred[j] = c - 1 + return pred diff --git a/arctag/_core/stream_arc.py b/arctag/_core/stream_arc.py new file mode 100644 index 0000000000000000000000000000000000000000..0b4cc3bb9c80b2f46595a187ab809d91c1960347 --- /dev/null +++ b/arctag/_core/stream_arc.py @@ -0,0 +1,208 @@ +# Arc Tagger inference engine. Copyright (c) 2026 Whissle Inc. CC BY-NC 4.0 + Whissle terms, see LICENSE. +import argparse, bisect, json, os, sys, time +import numpy as np, torch, torch.nn.functional as F +HERE = os.path.dirname(os.path.abspath(__file__)) +sys.path.insert(0, HERE) +import arc_text as AT +from arc_text import WORD_Q, POOLED, D_MAX, P_MAX, zero_carry +from tagtable import TextTable, domain_index, asr_index, QUERIES +from losses import GroupIndex +GAP_EDGES = [0.0, 0.05, 0.15, 0.3, 0.6, 1.2, 3.0] +DUR_EDGES = [0.03, 0.05, 0.07, 0.09, 0.12, 0.18] +CONF_EDGES = [0.5, 0.7, 0.85, 0.95, 0.99] +KEEP = 64 +_bk = lambda x, e: 1 + bisect.bisect_right(e, x) + +class ArcStream: + + def __init__(self, model, table, domain=None, asr=None): + self.m = model.eval() + self.T = table + self.GI = GroupIndex(table) + self.dom = torch.tensor([domain_index(domain)]) + self.asr = asr_index(asr) + self.asr_name = asr or 'unknown' + self.tok = AT.ArcPieces(AT.BPE8K if getattr(model, 'scratch', None) else AT.TOK_PATH) + self.cache = {} + with torch.no_grad(): + self.K = self.m.keys() + self.reset() + + def reset(self): + self.words, self.hw, self.uw, self.ustart = ([], [], [], []) + self.enc_x = [[] for _ in self.m.word.enc] + self.snap = {} + self.state = None + self.carry = zero_carry(1, self.m.d, 'cpu') + self.slots = {} + self.prev = None + self.next_ustart = True + self.out = {'T0': {}, 'T1': {}, 'T2': {}} + self.pooled = {} + + def pieces(self, w): + x = self.cache.get(w) + if x is None: + ids = self.tok.encode_word(w) or [0] + x = self.cache[w] = ids[:P_MAX - 1] + ids[-1:] if len(ids) > P_MAX else ids + return x + + def feats(self, w): + t0, t1, prev = (w.get('t0'), w.get('t1'), self.prev) + last_t1 = prev['t1'] if prev else None + last_spk = prev['spk'] if prev else None + gap = dur = conf = chg = spk = 0 + if t0 is not None: + gap = 9 if last_t1 is None else 1 if t0 - last_t1 < 0 else _bk(t0 - last_t1, GAP_EDGES) + if t1 is not None: + dur = min(7, _bk((t1 - t0) / max(1, len(w['w'])), DUR_EDGES)) + fin = 1 if w.get('final', True) else 2 + if w.get('conf') is not None: + conf = _bk(float(w['conf']), CONF_EDGES) + s = w.get('spk') + if s is not None: + if s not in self.slots: + self.slots[s] = min(9, len(self.slots) + 1) + spk = self.slots[s] + chg = 0 if last_spk is None else 1 if s == last_spk else 2 + x = w['w'] + hd = any((ch.isdigit() for ch in x)) + if gap and t0 is not None and (t1 is not None): + fd = int(np.clip(round(t1 * 10) - round(t0 * 10) + 1, 1, 20)) + fg = 20 if prev is None or prev['t1'] is None else int(np.clip(round(t0 * 10) - round(prev['t1'] * 10) - 1, 0, 20)) + else: + fd, fg = (3, 0 if prev else 20) + self.prev = {'t1': t1 if t1 is not None else last_t1, 'spk': s if s is not None else last_spk} + return (dict(gap=gap, dur=dur, fin=fin, conf=conf, chg=chg, asr=self.asr), [int(hd), int(hd and x.isdigit()), 0, fd, fg], spk) + + def _snapshot(self): + return dict(state=self.state, carry=self.carry, slots=dict(self.slots), prev=self.prev, next_ustart=self.next_ustart, enc_len=[len(x) for x in self.enc_x]) + + def _restore(self, k): + if k == len(self.words): + return True + sn = self.snap.get(k) + if sn is None: + return False + self.state, self.carry, self.slots, self.prev, self.next_ustart = (sn['state'], sn['carry'], dict(sn['slots']), sn['prev'], sn['next_ustart']) + del self.words[k:], self.hw[k:], self.uw[k:], self.ustart[k:] + for L, x in zip(sn['enc_len'], self.enc_x): + del x[L:] + for tier, t in self.out.items(): + lo = k if tier == 'T0' else k - D_MAX + for j in [j for j in t if j >= lo]: + del t[j] + for j in [j for j in self.pooled if j >= k]: + del self.pooled[j] + for j in [j for j in self.snap if j > k]: + del self.snap[j] + return True + + @torch.no_grad() + def _step(self, w): + m = self.m + j = len(self.words) + self.snap[j] = self._snapshot() + for x in [x for x in self.snap if x < j - KEEP]: + del self.snap[x] + fe, fl, spk = self.feats(w) + p = self.pieces(w['w']) + npc = len(p) + pcs = torch.zeros(1, 1, P_MAX, dtype=torch.long) + pcs[0, 0, :npc] = torch.tensor(p) + fl[2] = npc + e = m.embed(pcs, torch.tensor([[npc]]), {k: torch.tensor([[v]]) for k, v in fe.items()}, torch.tensor([[fl]]), torch.tensor([[1 if self.asr_name == 'arc_transcriber' else 0]]))[0, 0] + x = e + for li, lay in enumerate(m.word.enc): + buf = self.enc_x[li] + buf.append(x) + win = torch.stack(buf[-lay.win:])[None] + x = lay(win, torch.ones(1, win.shape[1], dtype=torch.bool), pos0=len(buf) - win.shape[1])[0, -1] + u = m.word.enc_norm(x) + h, self.state = m.trunk.step(u[None, None], self.dom, torch.zeros(1, dtype=torch.long), self.state) + h = h[0, 0] + us = self.next_ustart or j == 0 + self.next_ustart = False + zin, self.carry = m.head.pooled(h[None, None], torch.tensor([[spk - 1 if spk > 0 else -1]]), torch.tensor([[us]]), self.carry) + self.words.append(w) + self.hw.append(h) + self.uw.append(u) + self.ustart.append(us) + self.pooled[j] = {q: m.head.logits_q(q, zin[q], self.dom, self.K)[0, 0] for q in POOLED} + self._emit(j, j, 'T0') + self._delayed(final=False) + + def _emit(self, i, jr, tier): + m = self.m + d = jr - i + x = self.hw[jr] + m.delay_q.weight[min(d, D_MAX)] + if d > 0 and hasattr(m, 'dmix'): + x = x + m.dmix(self.hw[i]) + lg = {q: m.head.logits_q(q, x[None, None], self.dom, self.K)[0, 0] for q in WORD_Q if q != 'span_beg'} + k = min(d, 2) + u = torch.stack(self.uw[i:i + k + 1])[None] if k else self.uw[i][None, None] + if k: + uu = torch.stack([self.uw[i], self.uw[i + k]])[None] + us = uu[:, 1:] + uu = uu[:, :1] + else: + uu = self.uw[i][None, None] + us = torch.zeros_like(uu) + w = m.word + c = m.cols_span_beg + v = w.war_ln(uu + w.war_r(us) + w.war_k.weight[k]) + zb = w.QB(v).float() @ self.K[c].T + m.bB_all[c] + lg['span_beg'] = zb.masked_fill(~m.head.valid[self.dom][:, c][:, None, :], AT.NEG)[0, 0] + self.out[tier][i] = lg + + def _delayed(self, final): + n = len(self.words) + starts = [i for i in range(n) if self.ustart[i]] + for tier, cap in (('T1', 3), ('T2', D_MAX)): + for i in range(max(0, n - 2 * D_MAX - 2), n): + if i in self.out[tier]: + continue + s_next = next((s for s in starts if s > i), None) + end = s_next - 1 if s_next is not None else n - 1 if final else None + if end is not None: + jr = min(i + cap, end) + elif i + cap <= n - 1: + jr = i + cap + else: + continue + self._emit(i, jr, tier) + + @torch.no_grad() + def update(self, k, words): + k = min(k, len(self.words)) + if not self._restore(k): + ws = self.words[:k] + self.reset() + for w in ws: + self._step(w) + for w in words: + self._step(dict(w)) + return {t: {i: v for i, v in d.items() if i >= k} for t, d in self.out.items()} + + def endpoint(self): + self.next_ustart = True + with torch.no_grad(): + self._delayed(final=True) + +def add_modules_for(m, sd): + if any((k.startswith('kb_spot') for k in sd)) and (not hasattr(m, 'kb_spot')): + m.add_kb_long() + if 'pii_imm.weight' in sd and (not hasattr(m, 'pii_imm')): + m.add_redact(n=sd['pii_imm.weight'].shape[0]) + if any((k.startswith('spk_link.') for k in sd)) and (not hasattr(m, 'spk_link')): + m.add_spk_link(flux='feat.turn.weight' in sd) + if 'dmix.weight' in sd and (not hasattr(m, 'dmix')): + m.add_delay_mix() + if any((k.startswith(('gat.', 'dattn.', 'span_head.', 'utt_att.')) for k in sd)) and (not hasattr(m, 'span_head')) and (not hasattr(m, 'gat')): + at = tuple(sorted({int(k.split('.')[1]) for k in sd if k.startswith('gat.')})) + m.add_v2(gattn_at=at, delay_attn=any((k.startswith('dattn.') for k in sd)), span_head=any((k.startswith('span_head.') for k in sd)), utt_att='utt_att.weight' in sd, gattn_bias=any((k.endswith('.lslope') for k in sd if k.startswith('gat.')))) + if any((k.startswith('memr.') for k in sd)) and (not hasattr(m, 'memr')): + m.add_memread() + if any((k.startswith(('kb_cng.', 'ver_s.')) for k in sd)) and (not hasattr(m, 'ver_s')) and (not hasattr(m, 'kb_cng')): + m.add_kb_str(cng=any((k.startswith('kb_cng.') for k in sd)), vstr=any((k.startswith('ver_s.') for k in sd))) + return m diff --git a/arctag/_core/tagtable.py b/arctag/_core/tagtable.py new file mode 100644 index 0000000000000000000000000000000000000000..ade16ba592ce7401e7349ba204a362b057d8f977 --- /dev/null +++ b/arctag/_core/tagtable.py @@ -0,0 +1,111 @@ +# Arc Tagger inference engine. Copyright (c) 2026 Whissle Inc. CC BY-NC 4.0 + Whissle terms, see LICENSE. +import hashlib, json, os +import numpy as np +HERE = os.path.dirname(os.path.abspath(__file__)) +B = os.path.dirname(HERE) +_HOME = os.environ.get('ARCTAG_HOME', os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +R8 = os.path.join(_HOME, 'ontology', 'tags_v1_r8.json') +R8_SHA = '2a2d312fcea8a6082fe8035e1e35d44ff425ed26bb14a7841205bb6054c845ea' +E_R8 = os.path.join(_HOME, 'ontology', 'head_E_r8_bge_small.npy') +TEXT_TAGS = os.path.join(_HOME, 'ontology', 'text_tags.json') +FINE_DOMAINS = ['nba', 'nhl', 'nfl', 'cricket', 'soccer', 'esports', 'news', 'talk', 'stream_chat', 'meeting', 'court_appellate', 'court_trial', 'assistant', 'assistant_noisy', 'short_clips', 'unknown'] +COARSE2FINE = {'sports': ['nba', 'nhl', 'nfl', 'cricket', 'soccer'], 'legal': ['court_appellate', 'court_trial']} +QUERIES = ('frame', 'span_beg', 'span_in', 'span_end', 'utt', 'speaker', 'ev_at', 'ev_imm', 'ev_rec', 'item') +POOLED = ('utt', 'speaker', 'item') +ASR_SOURCES = ['unknown', 'human', 'stock_rnnt', 'arc_transcriber', 'nemotron35', 'deepgram', 'final_only', 'web'] + +def domain_index(d): + d = (d or 'unknown').lower() + if d in FINE_DOMAINS: + return FINE_DOMAINS.index(d) + if d in COARSE2FINE: + return FINE_DOMAINS.index(COARSE2FINE[d][0]) + return FINE_DOMAINS.index('unknown') +ASR_ALIAS = {'whisper': 'final_only', 'parakeet': 'final_only'} + +def asr_index(a): + a = (a or 'unknown').lower() + a = ASR_ALIAS.get(a, a) + if a not in ASR_SOURCES and 'nemotron' in a and ('3' in a): + a = 'nemotron35' + return ASR_SOURCES.index(a) if a in ASR_SOURCES else 0 + +class TextTable: + + def __init__(self, r8=R8, text_tags=TEXT_TAGS, include_weak=True, ext=None): + raw = open(r8, 'rb').read() + self.sha = hashlib.sha256(raw).hexdigest() + j = json.loads(raw) + self.version = j['version'] + tt = json.load(open(text_tags)) + ext = ext if ext is not None else os.environ.get('ARCTAG_ONTO_EXT') or None + self.ext = ext + self._ext_E = None + self.domain_extra = {} + if ext: + x = json.load(open(ext)) + j = dict(j, tags=j['tags'] + x['tags']) + tt = dict(tt, tags=tt['tags'] + x['text_tags']) + self.domain_extra = x.get('domain_extra') or {} + self.version = f"{j['version']}+{x['version']}" + self.sha = hashlib.sha256(raw + open(ext, 'rb').read()).hexdigest() + _loc = os.path.join(os.path.dirname(os.path.abspath(ext)), os.path.basename(x['E'])) + self._ext_E = _loc if os.path.exists(_loc) else x['E'] + self.tag = {t['id']: t for t in j['tags']} + self.n = max((t['index'] for t in j['tags'])) + 1 + self.ids = [None] * self.n + for t in j['tags']: + self.ids[t['index']] = t['id'] + self.idx = {t: i for i, t in enumerate(self.ids)} + self.info = {r['id']: r for r in tt['tags']} + keep = {'yes', 'weak'} if include_weak else {'yes'} + self.active = [self.idx[r['id']] for r in tt['tags'] if r['text_derivable'] in keep and r['status'] != 'derived'] + self.active.sort() + self.weak = {self.idx[r['id']] for r in tt['tags'] if r['text_derivable'] == 'weak'} + ro = {} + for i in self.active: + r = self.info[self.ids[i]]['readouts'] + ro[i] = tuple(('span_beg' if q == 'bio' else q for q in r if q != 'norm')) + self.ro = ro + self.cols = {q: [i for i in self.active if q in ro[i]] for q in QUERIES} + self.colpos = {q: {i: k for k, i in enumerate(self.cols[q])} for q in QUERIES} + self.norm_tags = [i for i in self.active if 'norm' in self.info[self.ids[i]]['readouts']] + rg = {g['id']: g for g in j['groups']} + self.group_info = rg + self.members = {} + for i in self.active: + t = self.tag[self.ids[i]] + if t.get('exclusivity') not in (None, 'independent'): + self.members.setdefault(t['group'], []).append(i) + self.group_of = {i: g for g, m in self.members.items() for i in m} + self.family = {} + for i in self.active: + g = self.tag[self.ids[i]]['group'] + self.family.setdefault(g, []).append(i) + self.family[self.ids[i]] = [i] + parts = g.split('.') + for k in range(1, len(parts)): + self.family.setdefault('.'.join(parts[:k]) + '.*', []).append(i) + dg = j['domains'] if isinstance(j['domains'], dict) else {} + V = np.zeros((len(FINE_DOMAINS), self.n), bool) + for i in self.active: + keys = list(self.tag[self.ids[i]]['domains']) + [k for p, ks in self.domain_extra.items() if self.ids[i].startswith(p) for k in ks] + for d, fd in enumerate(FINE_DOMAINS): + V[d, i] = any((k == '*' or k == fd or fd in dg.get(k, []) or (fd in COARSE2FINE.get(k, [])) for k in keys)) + V[FINE_DOMAINS.index('unknown')] |= V.any(0) + self.valid = V + self.E = None + + def load_E(self, path=E_R8): + E = np.load(path).astype(np.float32) + if self._ext_E and E.shape[0] < self.n: + E = np.concatenate([E, np.load(self._ext_E).astype(np.float32)]) + assert E.shape[0] == self.n, (E.shape, self.n) + self.E = E + return E + + def col_names(self, q): + return [f'{self.ids[i]}@{q}' for i in self.cols[q]] + + def summary(self): + return {q: len(c) for q, c in self.cols.items()} | {'active': len(self.active), 'groups_excl': len(self.members), 'norm_tags': len(self.norm_tags)} diff --git a/arctag/_core/v2.py b/arctag/_core/v2.py new file mode 100644 index 0000000000000000000000000000000000000000..da61d7b0ddea30bd72bcded908167b013095935a --- /dev/null +++ b/arctag/_core/v2.py @@ -0,0 +1,303 @@ +# Arc Tagger inference engine. Copyright (c) 2026 Whissle Inc. CC BY-NC 4.0 + Whissle terms, see LICENSE. +import math +import numpy as np +import torch, torch.nn as nn, torch.nn.functional as F +MEM, CHUNK, SLOTS, ROPE_BASE = (2048, 64, 256, 50000.0) +QBLOCK = 512 +SPAN_FAMS = ('entity.number', 'entity.mention', 'safety.pii', 'safety.profanity', 'safety.toxicity') +BIO_O, BIO_B, BIO_I = (0, 1, 2) + +def rope_tab(pos, dh, base=ROPE_BASE): + inv = 1.0 / base ** (torch.arange(0, dh, 2, dtype=torch.float32, device=pos.device) / dh) + ang = pos.float()[..., None] * inv + return (torch.cos(ang), torch.sin(ang)) + +def rope(x, c, s): + c, s = (c[:, None, :].to(x.dtype), s[:, None, :].to(x.dtype)) + x1, x2 = (x[..., 0::2], x[..., 1::2]) + return torch.stack([x1 * c - x2 * s, x1 * s + x2 * c], -1).flatten(-2) + +def summary_mask(Wq, Wk, q0=0, mem=MEM, chunk=CHUNK, slots=SLOTS, device='cpu'): + i = torch.arange(q0, q0 + Wq, device=device)[:, None] + j = torch.arange(Wk, device=device)[None] + direct = (j <= i) & (j > i - mem) + C = Wk // chunk + c = torch.arange(C, device=device)[None] + cmax = torch.div(i - mem - chunk + 1, chunk, rounding_mode='floor') + summ = (c <= cmax) & (c > cmax - slots) + return (direct, summ) +BIAS_L0 = (32.0, 128.0, 1024.0, 8192.0) + +class GlobalAttn(nn.Module): + + def __init__(self, d, heads=4, mem=MEM, chunk=CHUNK, slots=SLOTS, biased=False): + super().__init__() + self.h, self.dh, self.mem, self.chunk, self.slots = (heads, d // heads, mem, chunk, slots) + self.norm = nn.RMSNorm(d) + self.qkv = nn.Linear(d, 3 * d, bias=False) + self.qn = nn.RMSNorm(self.dh) + self.kn = nn.RMSNorm(self.dh) + self.o = nn.Linear(d, d, bias=False) + nn.init.zeros_(self.o.weight) + self.biased = biased + if biased: + L0 = [BIAS_L0[i % len(BIAS_L0)] for i in range(heads)] + self.lslope = nn.Parameter(-torch.log(torch.tensor(L0))) + self.kmark = nn.Linear(d, 1) + nn.init.zeros_(self.kmark.weight) + nn.init.constant_(self.kmark.bias, -2.0) + self.kb = nn.Parameter(torch.zeros(heads)) + self.gate = nn.Parameter(torch.zeros(())) + nn.init.normal_(self.o.weight, std=0.02) + self._mlog = None + self.stats = None + + def key_bias(self, hn, W, pos=None): + ml = self.kmark(hn).squeeze(-1) + m = torch.sigmoid(ml.float()) + pos = torch.arange(W, device=hn.device) if pos is None else pos + sl = self.lslope.float().exp() + return (sl[None, None] * pos.float()[None, :, None] + self.kb.float()[None, None] * m[..., None], ml, m) + + def _aug(self, qr, kr, v, kbias): + B, W, H, dh = kr.shape + sq = math.sqrt(dh) + z = lambda x, n: torch.zeros(*x.shape[:-1], n, device=x.device, dtype=x.dtype) + qa = torch.cat([qr, torch.ones_like(qr[..., :1]), z(qr, 7)], -1) + ka = torch.cat([kr, (kbias * sq).to(kr.dtype)[..., None], z(kr, 7)], -1) + va = torch.cat([v, z(v, 8)], -1) + return (qa, ka, va) + + def proj(self, h): + B, W, _ = h.shape + q, k, v = self.qkv(self.norm(h)).view(B, W, 3, self.h, self.dh).unbind(2) + return (self.qn(q), self.kn(k), v) + + def summaries(self, k, v, C): + B = k.shape[0] + ch = self.chunk + ks = k[:, :C * ch].reshape(B, C, ch, self.h, self.dh).mean(2) + vs = v[:, :C * ch].reshape(B, C, ch, self.h, self.dh).mean(2) + c, s = rope_tab(torch.arange(C, device=k.device) * ch + ch // 2, self.dh) + return (rope(ks, c, s), vs) + + def forward(self, h): + B, W, _ = h.shape + q, k, v = self.proj(h) + c, s = rope_tab(torch.arange(W, device=h.device), self.dh) + qr, kr = (rope(q, c, s), rope(k, c, s)) + tq = lambda x: x.transpose(1, 2) + sc_ = None + dh = self.dh + if self.biased: + kbias, self._mlog, m_ = self.key_bias(self.norm(h), W) + qr, kr, v = self._aug(qr, kr, v, kbias) + sc_ = 1.0 / math.sqrt(dh) + if W <= self.mem: + a = F.scaled_dot_product_attention(tq(qr), tq(kr), tq(v), is_causal=True, scale=sc_) + else: + C = W // self.chunk + if self.biased: + ks, vs = self.summaries(k, v[..., :dh], C) + ctr = torch.arange(C, device=h.device) * self.chunk + self.chunk // 2 + mc = m_[:, :C * self.chunk].reshape(B, C, self.chunk).mean(-1) + kbs = self.lslope.float().exp()[None, None] * ctr.float()[None, :, None] + self.kb.float()[None, None] * mc[..., None] + _, ks, vs = self._aug(ks, ks, vs, kbs) + else: + ks, vs = self.summaries(k, v, C) + outs = [] + qb = QBLOCK + for a0 in range(0, W, qb): + a1 = min(W, a0 + qb) + lo = max(0, a0 - self.mem + 1) + dm, _ = summary_mask(a1 - a0, a1, a0, self.mem, self.chunk, self.slots, h.device) + _, sm = summary_mask(a1 - a0, W, a0, self.mem, self.chunk, self.slots, h.device) + M = torch.cat([dm[:, lo:], sm], 1) + + def blk(qq, kk, vv, kss, vss, M=M): + return F.scaled_dot_product_attention(tq(qq), tq(torch.cat([kk, kss], 1)), tq(torch.cat([vv, vss], 1)), attn_mask=M[None, None], scale=sc_) + args = (qr[:, a0:a1], kr[:, lo:a1], v[:, lo:a1], ks, vs) + outs.append(blk(*args)) + a = torch.cat(outs, 2) + if self.stats is not None: + self._stats(qr, kr, W, scale=sc_) + a = tq(a)[..., :dh] if self.biased else tq(a) + y = self.o(a.reshape(B, W, -1)) + return torch.tanh(self.gate) * y if self.biased else y + + @torch.no_grad() + def _stats(self, qr, kr, W, n=64, scale=None): + if W <= 512: + return + rows = torch.linspace(512, W - 1, min(n, W - 512), device=qr.device).long() + q = qr[:, rows].float() + sc = torch.einsum('bnhd,bkhd->bhnk', q, kr[:, :W].float()) * (scale or 1.0 / math.sqrt(self.dh)) + j = torch.arange(W, device=qr.device)[None] + i = rows[:, None] + ok = (j <= i) & (j > i - self.mem) + p = sc.masked_fill(~ok[None, None], -1000000000.0).softmax(-1) + dist = (i - j).clamp(min=0).float() + st = self.stats + st['n'] = st.get('n', 0) + p.shape[0] * p.shape[1] * p.shape[2] + st['dist'] = st.get('dist', 0.0) + float((p * dist[None, None]).sum()) + st['far'] = st.get('far', 0.0) + float((p * (dist > 256).float()[None, None]).sum()) + st['far1024'] = st.get('far1024', 0.0) + float((p * (dist > 1024).float()[None, None]).sum()) + key = torch.multinomial(p.reshape(-1, p.shape[-1]).float(), 1).view(p.shape[:3]) + st.setdefault('hist', []).append((rows[None, None] - key).flatten().float().cpu()) + +def trunk_forward(tr, gat, x, dom, delay): + from arc_audio.seqlayer.ssd import seq_layer_scan_state, _f + h, e = tr._cond(x, dom, delay) + for i, (lay, mlp) in enumerate(zip(tr.layers, tr.mlps)): + + def blk(h, e, i=i, lay=lay, mlp=mlp): + gb = tr.film[i](e)[:, None] + h = h * (1 + gb[..., :h.shape[-1]]) + gb[..., h.shape[-1]:] + s_, _ = seq_layer_scan_state(lay, _f(h), None, tr.chunk) + h = h + s_ + h = h + mlp(h) + if str(i) in gat: + h = h + gat[str(i)](h) + return h + h = blk(h, e) + return tr.out_norm(tr._adapt(h, dom)) + +class MemRead(nn.Module): + + def __init__(self, d, dk=64, d_ext=0, d_u=0): + super().__init__() + self.dk = dk + self.norm = nn.RMSNorm(d) + self.q = nn.Linear(d, dk, bias=False) + self.k = nn.Linear(d, dk, bias=False) + self.v = nn.Linear(d, dk, bias=False) + self.qn = nn.RMSNorm(dk) + self.kn = nn.RMSNorm(dk) + self.kmark = nn.Linear(d, 1) + nn.init.zeros_(self.kmark.weight) + nn.init.constant_(self.kmark.bias, -2.0) + self.kb = nn.Parameter(torch.ones(())) + self.kx = None + if d_ext: + self.kx = nn.Linear(d_ext, dk, bias=False) + nn.init.zeros_(self.kx.weight) + self.o = nn.Linear(dk, d, bias=False) + nn.init.normal_(self.o.weight, std=0.02) + self.gate = nn.Parameter(torch.zeros(())) + if d_u: + self.ou = nn.Linear(dk, d_u, bias=False) + nn.init.normal_(self.ou.weight, std=0.02) + self.gate_u = nn.Parameter(torch.zeros(())) + self._mlog = None + + def to_u(self, a): + return torch.tanh(self.gate_u) * self.ou(a) + + def keys(self, h, ext=None): + hn = self.norm(h) + ml = self.kmark(hn).squeeze(-1) + k = self.k(hn) + if self.kx is not None and ext is not None: + k = k + self.kx(ext.to(k.dtype)) + return (self.kn(k), self.v(hn), ml) + + def forward(self, hd, h, ext=None, kv=None): + B, W, _ = h.shape + dk = self.dk + k, v, ml = kv if kv is not None else self.keys(h, ext) + self._mlog = ml + bias = self.kb.float() * F.logsigmoid(ml.float()) + sh = lambda x: torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], 1) + k, v, bias = (sh(k), sh(v), sh(bias)) + q = self.qn(self.q(self.norm(hd))) + z = lambda x, n: torch.zeros(*x.shape[:-1], n, device=x.device, dtype=x.dtype) + sq = math.sqrt(dk) + qa = torch.cat([q, torch.ones_like(q[..., :1]), z(q, 7)], -1) + ka = torch.cat([k, (bias * sq).to(k.dtype)[..., None], z(k, 7)], -1) + va = torch.cat([v, z(v, 8)], -1) + a = F.scaled_dot_product_attention(qa[:, None], ka[:, None], va[:, None], is_causal=True, scale=1.0 / sq)[:, 0, :, :dk] + return (torch.tanh(self.gate) * self.o(a), a) + +class DelayAttn(nn.Module): + + def __init__(self, d, dmax, dk=64): + super().__init__() + self.dmax, self.dk = (dmax, dk) + self.q = nn.Linear(d, dk) + self.k = nn.Linear(d, dk) + self.v = nn.Linear(d, dk) + self.qd = nn.Embedding(dmax + 1, dk) + self.koff = nn.Embedding(dmax + 1, dk) + self.o = nn.Linear(dk, d, bias=False) + nn.init.zeros_(self.o.weight) + for m in (self.qd, self.koff): + nn.init.normal_(m.weight, std=0.02) + + def forward(self, h, d_i): + B, W, D = h.shape + K = self.dmax + 1 + off = torch.arange(K, device=h.device) + j = (torch.arange(W, device=h.device)[:, None] + off[None]).clamp(max=W - 1) + kk = self.k(h) + vv = self.v(h) + kg = kk[:, j] + self.koff(off)[None, None] + vg = vv[:, j] + q = self.q(h) + self.qd(d_i.clamp(0, self.dmax)) + sc = torch.einsum('bwd,bwkd->bwk', q, kg) / math.sqrt(self.dk) + sc = sc.masked_fill(off[None, None] > d_i[..., None], -10000.0) + ctx = torch.einsum('bwk,bwkd->bwd', sc.float().softmax(-1).to(vg.dtype), vg) + return self.o(ctx) * (d_i > 0)[..., None].to(h.dtype) + +def fam_members(T): + cin = list(T.cols['span_in']) + cb = {T.ids[i]: k for k, i in enumerate(T.cols['span_beg'])} + out = {} + for f in SPAN_FAMS: + ks = [k for k, i in enumerate(cin) if T.ids[i].startswith(f + '.')] + tags = [T.ids[cin[k]] for k in ks] + out[f] = (ks, [cb.get(t, -1) for t in tags], tags) + return out + +class SpanHead(nn.Module): + + def __init__(self, d, T): + super().__init__() + self.fm = fam_members(T) + self.typ = nn.ModuleDict({f.replace('.', '_'): nn.Linear(d, len(v[0]) + 1) for f, v in self.fm.items()}) + self.bio = nn.ModuleDict({f.replace('.', '_'): nn.Linear(d, 3) for f in self.fm}) + for m in self.bio.values(): + nn.init.zeros_(m.weight) + m.bias.data = torch.tensor([3.0, -3.0, -3.0]) + for m in self.typ.values(): + nn.init.zeros_(m.weight) + nn.init.zeros_(m.bias) + m.bias.data[-1] = 3.0 + fused = False + + def forward(self, hd): + if not self.fused: + return {f: (self.typ[f.replace('.', '_')](hd).float(), self.bio[f.replace('.', '_')](hd).float()) for f in self.fm} + ms = [m for f in self.fm for m in (self.typ[f.replace('.', '_')], self.bio[f.replace('.', '_')])] + z = F.linear(hd, torch.cat([m.weight for m in ms]), torch.cat([m.bias for m in ms])).float() + out = {} + o = 0 + for f in self.fm: + nt = self.typ[f.replace('.', '_')].out_features + out[f] = (z[..., o:o + nt], z[..., o + nt:o + nt + 3]) + o += nt + 3 + return out + +def decode(Pt, Pb, lab, th, tags): + pin = (1.0 - Pb[:, BIO_O] >= th) & lab + W = len(pin) + out = [] + a = None + lt = np.log(np.clip(Pt[:, :-1], 1e-09, 1.0)) + for i in range(W + 1): + start = i < W and pin[i] and (a is None or Pb[i, BIO_B] > Pb[i, BIO_I]) + if a is not None and (i == W or not pin[i] or start): + out.append((a, i, tags[int(lt[a:i].sum(0).argmax())])) + a = None + if i < W and pin[i] and (a is None): + a = i + return out diff --git a/arctag/asr.py b/arctag/asr.py new file mode 100644 index 0000000000000000000000000000000000000000..08806314e5614025b740e7db30fcfb163093531a --- /dev/null +++ b/arctag/asr.py @@ -0,0 +1,146 @@ +"""Live ASR -> Arc Tagger adapters (no network code here; see stream_deepgram.py / stream_speechmatics.py). + +Each adapter turns one provider message (a parsed JSON dict) into calls on an `arctag` stream: + + st.push_words(words, final=False, revise_from=k) # interim hypothesis: replaces words[k:] + st.push_words(words, final=True, revise_from=k) # final words: replace words[k:] and freeze them + st.endpoint() # end of utterance: utterance-level read-outs + +and returns the list of stream updates it produced (each is the dict push_words / endpoint returns). +`k` is the number of words already final, so an interim never rewrites final words. + +Deepgram live (/v1/listen, interim_results=true): + Results is_final=false -> push_words(words, final=False, revise_from=n_final) + Results is_final=true -> push_words(words, final=True, revise_from=n_final); n_final += len(words) + Results speech_final=true -> endpoint() (after the final words) + UtteranceEnd -> endpoint() (deferred to the next final if interim words are pending) + words[i].speaker (diarize=true) -> word "spk" + +Speechmatics real-time (v2): + AddPartialTranscript -> push_words(words, final=False, revise_from=n_final) + AddTranscript -> push_words(words, final=True, revise_from=n_final); n_final += len(words) + EndOfUtterance -> endpoint() (deferred to the next final if partial words are pending) + alternatives[0].speaker (diarization="speaker") -> word "spk" + punctuation results -> attached to the previous word ("attaches_to": "previous") or the next one +""" + + +class _Base: + def __init__(self, stream): + self.st = stream + self.n_final = 0 # words frozen by the provider + self.n_interim = 0 # interim words currently shown after them + self._ep_pending = False # an end-of-utterance arrived while interim words were pending + self._ep_at = 0 # n_final at the last endpoint + self.calls = [] # (method, kwargs) log, for tests and debugging + + def _push(self, ws, final): + if not ws and not self.n_interim and not final: + return [] # nothing to show and nothing to clear + if not ws and final and not self.n_interim: + return [] + self.calls.append(("push_words", {"words": [w["w"] for w in ws], "final": final, "revise_from": self.n_final})) + up = self.st.push_words(ws, final=final, revise_from=self.n_final) + if final: + self.n_final += len(ws); self.n_interim = 0 + else: + self.n_interim = len(ws) + return [up] + + def _endpoint(self): + if self.n_interim: # interim words pending: end the utterance after they are final + self._ep_pending = True + return [] + self._ep_pending = False + if not self.n_final or self.n_final == self._ep_at: + return [] # nothing new since the last endpoint + self._ep_at = self.n_final + self.calls.append(("endpoint", {})) + return [self.st.endpoint()] + + def _after_final(self, ups, ep=False): + if ep or self._ep_pending: + ups += self._endpoint() + return ups + + +class DeepgramAdapter(_Base): + """Deepgram live v1 messages (dicts) -> stream calls""" + + def __init__(self, stream, punctuated=True): + super().__init__(stream) + self.punctuated = punctuated + + def words(self, alt): + out = [] + for w in alt.get("words") or []: + tok = (w.get("punctuated_word") if self.punctuated else None) or w.get("word") or "" + if not tok.strip(): + continue + d = {"w": tok, "t0": w.get("start"), "t1": w.get("end"), "conf": w.get("confidence")} + if w.get("speaker") is not None: + d["spk"] = f"S{w['speaker']}" + out.append(d) + return out + + def __call__(self, m): + t = m.get("type", "Results") + if t == "UtteranceEnd": + return self._endpoint() + if t != "Results": + return [] # Metadata, SpeechStarted, ... + alt = ((m.get("channel") or {}).get("alternatives") or [{}])[0] + ws = self.words(alt) + if not m.get("is_final"): + return self._push(ws, final=False) + return self._after_final(self._push(ws, final=True), ep=bool(m.get("speech_final"))) + + +class SpeechmaticsAdapter(_Base): + """Speechmatics real-time v2 messages (dicts) -> stream calls""" + + def words(self, results): + out = []; glue_next = "" + for r in results or []: + alt = (r.get("alternatives") or [{}])[0] + tok = alt.get("content") or "" + if not tok.strip(): + continue + if r.get("type") == "punctuation": + if r.get("attaches_to", "previous") == "previous" and out: + out[-1]["w"] += tok + elif r.get("attaches_to") == "next": + glue_next += tok + elif out: + out[-1]["w"] += tok + continue + d = {"w": glue_next + tok, "t0": r.get("start_time"), "t1": r.get("end_time"), "conf": alt.get("confidence")} + glue_next = "" + spk = alt.get("speaker") + if spk is not None and spk != "UU": # UU = unknown speaker + d["spk"] = str(spk) + out.append(d) + return out + + def __call__(self, m): + t = m.get("message") + if t == "AddPartialTranscript": + return self._push(self.words(m.get("results")), final=False) + if t == "AddTranscript": + ws = self.words(m.get("results")) + if not ws and not self.n_interim: + return self._after_final([]) + return self._after_final(self._push(ws, final=True)) + if t == "EndOfUtterance": + return self._endpoint() + return [] # RecognitionStarted, AudioAdded, Info, Warning, EndOfTranscript + + +def summarize(up, stream): + """one stream update -> a compact JSON-able line for live display""" + return {"n_words": up.get("n_words"), "changed_from": up.get("changed_from"), + "spans": [{k: s[k] for k in ("tag", "start", "end", "text", "p")} for s in up.get("spans") or []], + "events": [{k: e[k] for k in ("tag", "at", "word", "p")} for e in up.get("events") or []], + "utterances": [{k: u[k] for k in ("start", "end", "intent", "act", "safety", "role", "state")} for u in up.get("utterances") or []], + "running": up.get("running") or {}, + "redacted_text": stream.redacted_text()} diff --git a/arctag/driver.py b/arctag/driver.py new file mode 100644 index 0000000000000000000000000000000000000000..29a97bc9775eb4b5f43457c0133b16838e380758 --- /dev/null +++ b/arctag/driver.py @@ -0,0 +1,222 @@ +"""Session driver: words in -> engine.ArcSession (arctag/_core/engine.py) -> structured JSON. + +This module has no package imports: it takes the engine module (`EN`) and a loaded model (`EN.Loaded`) as arguments. + +Message flow: + words -> {"type": "words", "from": k, "words": [...]} (append, or revise words[k:] for interim ASR hypotheses) + final -> {"type": "finalize", "upto": n} + end of utterance -> {"type": "endpoint", "at": n} + spans -> ArcSession.spans(tier) after every update; spans with end > a0 are replaced, older ones are kept + fine domain (sports league / court type) from lexicon cues every 25 words (engine.fine_hint) +""" +import re + +UI_DOMAINS = ("auto", "talk", "meeting", "court", "assistant", "sports", "news") +END_RE = re.compile(r"[.?!]+[\"')\]]*$") + +# redaction placeholders (public_contact is tag-only, so it is not masked) +REDACT = { + "safety.pii.phone_number": "PHONE", "safety.pii.email": "EMAIL", "safety.pii.street_address": "ADDRESS", + "safety.pii.card_account_number": "CARD", "safety.pii.gov_id": "GOV_ID", "safety.pii.date_of_birth": "DOB", + "safety.pii.private_name": "NAME", "safety.pii.url_handle": "HANDLE", + "safety.profanity.mild": "PROFANITY", "safety.profanity.strong": "PROFANITY", + "safety.toxicity.insult_harassment": "TOXIC", "safety.toxicity.hate_identity": "TOXIC", + "safety.toxicity.threat_violence": "TOXIC", "safety.toxicity.sexual_explicit": "TOXIC", "safety.toxicity.self_harm": "TOXIC", +} + + +def thr_label(th): + """which threshold tables are active: display / events tables, model defaults""" + parts = [] + if th.pref_step is not None: + parts.append("display") + if th.ev_step is not None: + parts.append("events") + if th.own: + parts.append("default") + return " + ".join(parts) or "default 0.5" + + +def as_words(x, final=True): + """str | [str] | [dict] -> word dicts {"w", "final", optional "t0", "t1", "conf", "spk"}""" + if isinstance(x, str): + x = x.split() + out = [] + for w in x: + if isinstance(w, str): + if w.strip(): + out.append({"w": w.strip(), "final": final}) + else: + d = {k: w[k] for k in ("w", "t0", "t1", "conf", "spk", "final") if k in w and w[k] is not None} + d.setdefault("final", final) + if str(d.get("w", "")).strip(): + d["w"] = str(d["w"]).strip() + out.append(d) + return out + + +def split_utterances(words): + """utterance boundaries after words ending in . ? ! (and at the end)""" + utts, cur = [], [] + for w in words: + cur.append(w) + if END_RE.search(w["w"]): + utts.append(cur); cur = [] + if cur: + utts.append(cur) + return utts + + +class Stream: + """one streaming session. push_words() / endpoint() return incremental updates; finalize() returns the full result.""" + + def __init__(self, EN, loaded, domain="auto", tier="T0", meta=None): + if domain not in UI_DOMAINS: + raise ValueError(f"domain must be one of {UI_DOMAINS}") + if tier not in ("T0", "T1", "T2"): + raise ValueError("tier must be T0, T1 or T2") + self.EN = EN; self.tier = tier; self.meta = dict(meta or {}) + self.s = EN.ArcSession(loaded, ui_domain=domain, asr="unknown") + self._spans = {} # (tag, a, b) -> span (display tier) + self._n_ev = 0; self._fine_n = -1 + self.events = []; self.utterances = {} # w0 -> utterance dict (built at its endpoint) + + # ------------------------------------------------------------------ input + def push_words(self, words, final=True, revise_from=None, endpoint=False): + """append words (or, with revise_from=k, replace words[k:] by them: interim ASR revisions). Returns the update.""" + ws = as_words(words, final=final) + k = len(self.s.words) if revise_from is None else int(revise_from) + evs = [{"type": "words", "from": k, "words": ws}] + if final: + evs.append({"type": "finalize", "upto": k + len(ws)}) + if endpoint: + evs.append({"type": "endpoint", "at": k + len(ws)}) + return self._apply(evs) + + def endpoint(self): + """end of the current utterance (ASR speech_final / end-of-turn / VAD): utterance-level read-outs are emitted here""" + n = len(self.s.words) + return self._apply([{"type": "finalize", "upto": n}, {"type": "endpoint", "at": n}]) + + def finalize(self): + """close the open utterance (if any) and return the full structured result""" + if self.s.e.n and (not self.s.utt_bounds or self.s.utt_bounds[-1] < self.s.e.n): + self.endpoint() + return self.result() + + # ------------------------------------------------------------------ engine + def _apply(self, evs): + s = self.s; EN = self.EN + lo, utt_done = s.apply(evs) + if EN.FINE_REDOMAIN and s.ui_domain in EN.FINE_IX and len(s.words) // 25 != self._fine_n: + self._fine_n = len(s.words) // 25 + fh = EN.fine_hint(s.ui_domain, s.words) + if fh and fh != s.fine: + s.set_domain(s.ui_domain, fine=fh) + a0, sp = s.spans(self.tier) + for key in [key for key, x in self._spans.items() if x["b"] > a0]: + del self._spans[key] + for x in sp: + self._spans[(x["tag"], x["a"], x["b"])] = x + ev_new = [self._event(e) for e in s.events[self._n_ev:]] + self._n_ev = len(s.events) + ev_new = [e for e in ev_new if e] + self.events.extend(ev_new) + utts = [self._utt(u) for u in utt_done] + for u in utts: + self.utterances[u["start"]] = u + return {"n_words": len(s.words), "changed_from": lo, "spans": [self._span(x) for x in sp], "events": ev_new, + "utterances": utts, "running": self._running()} + + def _event(self, e): + th = self.s.L.thr + if th.ev_hidden(e["tag"]) or e["tier"] != th.ev_tier(e["tag"]): + return None + return {"tag": e["tag"], "at": e["i"], "word": e["word"], "p": e["p"], "thr": e["thr"], "tier": e["tier"], "read": e["q"]} + + def _span(self, x): + out = {"tag": x["tag"], "family": x["fam"], "start": x["a"], "end": x["b"], "text": x["text"], "p": x["p"], "thr": x["thr"]} + for k in ("alt", "ver", "kb", "src"): + if k in x: + out[k] = x[k] + return out + + @staticmethod + def _on(rows): + return [r["tag"] for r in rows if r["on"]] + + AFFECT_GROUPS = ("affect.emotion.", "affect.arousal.", "affect.valence.", "affect.intensity.") + + def _emotion(self, j): + """affect.* utt read-outs (text-only, experimental): the top tag of each affect group in the ontology + (emotion, arousal, valence, intensity)""" + s = self.s + if j is None or j not in s.e.pooled: + return [] + import torch + with torch.inference_mode(): + U = s.L.GI.probs("utt", s.e.pooled_logits(j)["utt"][None])[0].numpy() + return [r for g in self.AFFECT_GROUPS for r in s._top("utt", U, g, 1)] + + def _utt(self, u): + ro = u["readouts"] + r = {"start": u["w0"], "end": u["w1"], "text": u["text"], + "intent": self._on(ro.get("intent", [])), "act": self._on(ro.get("act", [])), "safety": self._on(ro.get("safety", [])), + "role": self._on(ro.get("role", [])), "item": self._on(ro.get("item", [])), + "state": {g: self._on(v) for g, v in (ro.get("state") or {}).items() if self._on(v)}, + "emotion": self._emotion(ro.get("j")), + "scores": {k: ro.get(k) for k in ("intent", "act", "safety", "role", "item", "state")}} + if ro.get("role_note"): + r["role_note"] = ro["role_note"] + return r + + def _running(self): + ro = self.s.readouts(tier=self.tier if self.tier != "T2" else "T1") + if not ro: + return {} + return {"intent": self._on(ro.get("intent", [])), "act": self._on(ro.get("act", [])), "safety": self._on(ro.get("safety", [])), + "state": {g: self._on(v) for g, v in (ro.get("state") or {}).items() if self._on(v)}} + + # ------------------------------------------------------------------ output + def spans(self): + return [self._span(x) for x in sorted(self._spans.values(), key=lambda x: (x["a"], -x["b"], x["tag"]))] + + def redacted_text(self, spans=None): + ws = [w["w"] for w in self.s.words] + lab = [None] * len(ws) + for x in spans if spans is not None else self.spans(): + L = REDACT.get(x["tag"]) + if L: + for i in range(x["start"], min(x["end"], len(ws))): + lab[i] = lab[i] or (L, x["start"]) + out = []; i = 0 + while i < len(ws): + if lab[i] is None: + out.append(ws[i]); i += 1; continue + j = i + while j < len(ws) and lab[j] == lab[i]: + j += 1 + out.append(f"[{lab[i][0]}]"); i = j + return " ".join(out) + + def result(self): + sp = self.spans() + utts = [self.utterances[k] for k in sorted(self.utterances)] + return {"meta": dict(self.meta, domain=self.s.ui_domain, fine_domain=self.s.fine, tier=self.tier, + thresholds=thr_label(self.s.L.thr)), + "words": [w["w"] for w in self.s.words], + "spans": sp, + "entities": [x for x in sp if x["family"] in ("entity", "number")], + "pii": [x for x in sp if x["family"] == "pii"], + "profanity_toxicity": [x for x in sp if x["family"] == "profanity"], + "redacted_text": self.redacted_text(sp), + "utterances": utts, + "events": list(self.events)} + + +def tag(EN, loaded, x, domain="auto", tier="T0", meta=None): + """offline tagging: text / words -> utterances (split after . ? !) -> one streaming pass -> result""" + st = Stream(EN, loaded, domain=domain, tier=tier, meta=meta) + for u in split_utterances(as_words(x)): + st.push_words(u, final=True, endpoint=True) + return st.finalize() diff --git a/arctag/live.py b/arctag/live.py new file mode 100644 index 0000000000000000000000000000000000000000..6621bdc5083c204bb9d302172f267ee47d8e5c74 --- /dev/null +++ b/arctag/live.py @@ -0,0 +1,97 @@ +"""Shared plumbing for the live ASR scripts: audio sources (wav file or microphone), JSON-lines output, key handling. + +Audio is 16-bit PCM, mono, sent in 100 ms chunks. A wav file is paced in real time by default (live-like latency); --fast sends +it as quickly as the provider accepts it. +""" +import asyncio, json, os, sys, time + +CHUNK_S = 0.1 + + +def api_key(var): + """the API key from the environment only (never from files, never printed)""" + k = os.environ.get(var, "").strip() + if not k: + sys.exit(f"set {var} in the environment (the key is read from the environment only)") + return k + + +def scrub(s, key): + """remove the key from any text before it is printed""" + s = str(s) + return s.replace(key, "***") if key else s + + +def wav_info(path): + import wave + with wave.open(path, "rb") as w: + if w.getsampwidth() != 2: + sys.exit("the wav file must be 16-bit PCM") + return w.getframerate(), w.getnchannels() + + +async def wav_chunks(path, pace=True): + """yields 100 ms chunks of mono int16 PCM bytes from a 16-bit wav (multi-channel audio is down-mixed)""" + import wave + import numpy as np + with wave.open(path, "rb") as w: + sr, ch = w.getframerate(), w.getnchannels() + n = int(sr * CHUNK_S); t0 = time.monotonic(); sent = 0.0 + while True: + b = w.readframes(n) + if not b: + break + if ch > 1: + x = np.frombuffer(b, np.int16).reshape(-1, ch).astype(np.int32).mean(axis=1) + b = x.astype(np.int16).tobytes() + yield b + sent += len(b) / 2 / sr + if pace: + await asyncio.sleep(max(0.0, t0 + sent - time.monotonic())) + else: + await asyncio.sleep(0) + + +async def mic_chunks(sr=16000, seconds=None): + """yields 100 ms chunks from the default microphone (pip install sounddevice); Ctrl-C or `seconds` stops""" + try: + import sounddevice as sd + except ImportError: + sys.exit("microphone input needs: pip install sounddevice") + q = asyncio.Queue(); loop = asyncio.get_running_loop() + + def cb(data, frames, t, status): + loop.call_soon_threadsafe(q.put_nowait, bytes(data)) + + t_end = None if seconds is None else time.monotonic() + seconds + with sd.RawInputStream(samplerate=sr, channels=1, dtype="int16", blocksize=int(sr * CHUNK_S), callback=cb): + while t_end is None or time.monotonic() < t_end: + yield await q.get() + + +def source(args): + """(sample_rate, async chunk iterator) for --wav PATH or --mic""" + if args.wav: + sr, _ = wav_info(args.wav) + return sr, wav_chunks(args.wav, pace=not args.fast) + return args.sample_rate, mic_chunks(args.sample_rate, args.seconds) + + +def add_common_args(ap): + g = ap.add_mutually_exclusive_group(required=True) + g.add_argument("--wav", help="16-bit PCM wav file") + g.add_argument("--mic", action="store_true", help="default microphone (needs sounddevice)") + ap.add_argument("--fast", action="store_true", help="wav: do not pace in real time") + ap.add_argument("--seconds", type=float, default=None, help="mic: stop after this many seconds") + ap.add_argument("--sample-rate", type=int, default=16000, help="mic sample rate") + ap.add_argument("--model-dir", default=os.path.dirname(os.path.dirname(os.path.abspath(__file__))), + help="Arc Tagger dir, or the Hugging Face repo id WhissleAI/arc-tagger (default: this downloaded repo)") + ap.add_argument("--domain", default="auto", choices=["auto", "talk", "meeting", "court", "assistant", "sports", "news"]) + ap.add_argument("--tier", default="T0", choices=["T0", "T1", "T2"]) + ap.add_argument("--no-interim", action="store_true", help="print only updates caused by final words / endpoints") + ap.add_argument("--result", default=None, help="write the full result JSON here at the end") + return ap + + +def emit(obj, out=sys.stdout): + out.write(json.dumps(obj, ensure_ascii=False) + "\n"); out.flush() diff --git a/config.json b/config.json new file mode 100644 index 0000000000000000000000000000000000000000..a12666d1410d32c1b4fec939406ac17bac5fb35e --- /dev/null +++ b/config.json @@ -0,0 +1,2618 @@ +{ + "name": "arc-tagger", + "version": "0.4.0", + "ontology": "ontology_v1.0-r8+ext_v7_aff1", + "ontology_sha": "b346f896ba5bab9d1a9a7ea10c8b2ce980aa13c136e15b6602c43d6505c559f8", + "onto_ext": "ontology/ext_v7_aff.json", + "queries": [ + "frame", + "span_beg", + "span_in", + "span_end", + "utt", + "speaker", + "ev_at", + "ev_imm", + "ev_rec", + "item" + ], + "tiers": { + "T0": 1, + "T1": 4, + "T2": "utterance" + }, + "columns": { + "frame": [ + "meta.lang.en@frame", + "meta.lang.non_en@frame", + "meta.lang.mixed@frame", + "content.mode.live_action@frame", + "content.mode.replay_highlight@frame", + "content.mode.studio_talk@frame", + "content.mode.field_report@frame", + "content.mode.interview@frame", + "content.mode.call_in@frame", + "content.mode.live_event_feed@frame", + "content.mode.ad_promo@frame", + "content.game_state.in_play@frame", + "content.game_state.stoppage@frame", + "content.game_state.timeout@frame", + "content.game_state.review@frame", + "content.game_state.period_break@frame", + "content.game_state.pre_post@frame", + "content.legal_phase.argument_petitioner@frame", + "content.legal_phase.argument_respondent@frame", + "content.legal_phase.rebuttal@frame", + "content.legal_phase.opening_statement@frame", + "content.legal_phase.direct_exam@frame", + "content.legal_phase.cross_exam@frame", + "content.legal_phase.redirect_recross@frame", + "content.legal_phase.closing_argument@frame", + "content.legal_phase.bench_procedure@frame", + "content.meeting_phase.opening@frame", + "content.meeting_phase.agenda_admin@frame", + "content.meeting_phase.presentation@frame", + "content.meeting_phase.discussion@frame", + "content.meeting_phase.chitchat@frame", + "content.meeting_phase.closing@frame", + "content.subject.politics_government@frame", + "content.subject.economy_business@frame", + "content.subject.crime_justice@frame", + "content.subject.conflict_security@frame", + "content.subject.disaster_accident@frame", + "content.subject.weather_climate@frame", + "content.subject.science_tech@frame", + "content.subject.health_medicine@frame", + "content.subject.education_society@frame", + "content.subject.sports@frame", + "content.subject.arts_entertainment@frame", + "content.subject.lifestyle_personal@frame", + "content.breaking_news@frame", + "scene.laughter@frame", + "scene.distress_call@frame", + "meta.people_present@frame" + ], + "span_beg": [ + "safety.pii.phone_number@span_beg", + "safety.pii.email@span_beg", + "safety.pii.street_address@span_beg", + "safety.pii.private_name@span_beg", + "safety.pii.card_account_number@span_beg", + "safety.pii.gov_id@span_beg", + "safety.pii.url_handle@span_beg", + "safety.pii.date_of_birth@span_beg", + "entity.mention.person@span_beg", + "entity.mention.team@span_beg", + "entity.mention.organization@span_beg", + "entity.mention.place@span_beg", + "entity.mention.work_product@span_beg", + "entity.mention.event_name@span_beg", + "entity.mention.legal_ref@span_beg", + "entity.mention.game_entity@span_beg", + "entity.mention.game_map@span_beg", + "entity.numeral@span_beg", + "entity.number.score@span_beg", + "entity.number.clock@span_beg", + "entity.number.time_of_day@span_beg", + "entity.number.date@span_beg", + "entity.number.duration@span_beg", + "entity.number.money@span_beg", + "entity.number.percent@span_beg", + "entity.number.ordinal@span_beg", + "entity.number.count@span_beg", + "entity.number.stat@span_beg", + "entity.number.measure@span_beg", + "entity.number.jersey_number@span_beg", + "entity.number.field_position@span_beg", + "entity.number.ratio_odds@span_beg", + "entity.number.ref_number@span_beg", + "entity.number.id_pii@span_beg", + "safety.pii.request@span_beg", + "safety.pii.public_contact@span_beg", + "entity.mention.term@span_beg", + "safety.pii.leak@span_beg" + ], + "span_in": [ + "safety.toxicity.insult_harassment@span_in", + "safety.toxicity.hate_identity@span_in", + "safety.toxicity.threat_violence@span_in", + "safety.toxicity.sexual_explicit@span_in", + "safety.toxicity.self_harm@span_in", + "safety.profanity.mild@span_in", + "safety.profanity.strong@span_in", + "safety.pii.phone_number@span_in", + "safety.pii.email@span_in", + "safety.pii.street_address@span_in", + "safety.pii.private_name@span_in", + "safety.pii.card_account_number@span_in", + "safety.pii.gov_id@span_in", + "safety.pii.url_handle@span_in", + "safety.pii.date_of_birth@span_in", + "entity.mention.person@span_in", + "entity.mention.team@span_in", + "entity.mention.organization@span_in", + "entity.mention.place@span_in", + "entity.mention.work_product@span_in", + "entity.mention.event_name@span_in", + "entity.mention.legal_ref@span_in", + "entity.mention.game_entity@span_in", + "entity.mention.game_map@span_in", + "entity.numeral@span_in", + "entity.number.score@span_in", + "entity.number.clock@span_in", + "entity.number.time_of_day@span_in", + "entity.number.date@span_in", + "entity.number.duration@span_in", + "entity.number.money@span_in", + "entity.number.percent@span_in", + "entity.number.ordinal@span_in", + "entity.number.count@span_in", + "entity.number.stat@span_in", + "entity.number.measure@span_in", + "entity.number.jersey_number@span_in", + "entity.number.field_position@span_in", + "entity.number.ratio_odds@span_in", + "entity.number.ref_number@span_in", + "entity.number.id_pii@span_in", + "safety.pii.public_contact@span_in", + "entity.mention.term@span_in" + ], + "span_end": [ + "safety.toxicity.insult_harassment@span_end", + "safety.toxicity.hate_identity@span_end", + "safety.toxicity.threat_violence@span_end", + "safety.toxicity.sexual_explicit@span_end", + "safety.toxicity.self_harm@span_end", + "safety.profanity.mild@span_end", + "safety.profanity.strong@span_end", + "safety.pii.phone_number@span_end", + "safety.pii.email@span_end", + "safety.pii.street_address@span_end", + "safety.pii.private_name@span_end", + "safety.pii.card_account_number@span_end", + "safety.pii.gov_id@span_end", + "safety.pii.url_handle@span_end", + "safety.pii.date_of_birth@span_end", + "entity.mention.person@span_end", + "entity.mention.team@span_end", + "entity.mention.organization@span_end", + "entity.mention.place@span_end", + "entity.mention.work_product@span_end", + "entity.mention.event_name@span_end", + "entity.mention.legal_ref@span_end", + "entity.mention.game_entity@span_end", + "entity.mention.game_map@span_end", + "entity.numeral@span_end", + "entity.number.score@span_end", + "entity.number.clock@span_end", + "entity.number.time_of_day@span_end", + "entity.number.date@span_end", + "entity.number.duration@span_end", + "entity.number.money@span_end", + "entity.number.percent@span_end", + "entity.number.ordinal@span_end", + "entity.number.count@span_end", + "entity.number.stat@span_end", + "entity.number.measure@span_end", + "entity.number.jersey_number@span_end", + "entity.number.field_position@span_end", + "entity.number.ratio_odds@span_end", + "entity.number.ref_number@span_end", + "entity.number.id_pii@span_end", + "safety.pii.public_contact@span_end", + "entity.mention.term@span_end" + ], + "utt": [ + "discourse.act.statement@utt", + "discourse.act.question@utt", + "discourse.act.opinion_assessment@utt", + "discourse.act.suggestion_proposal@utt", + "discourse.act.commitment_offer@utt", + "discourse.act.command_request@utt", + "discourse.act.ritual_procedural@utt", + "discourse.answer@utt", + "discourse.backchannel@utt", + "discourse.assistant.device_directed@utt", + "discourse.intent.alarm_query@utt", + "discourse.intent.alarm_remove@utt", + "discourse.intent.alarm_set@utt", + "discourse.intent.audio_volume_down@utt", + "discourse.intent.audio_volume_mute@utt", + "discourse.intent.audio_volume_other@utt", + "discourse.intent.audio_volume_up@utt", + "discourse.intent.calendar_query@utt", + "discourse.intent.calendar_remove@utt", + "discourse.intent.calendar_set@utt", + "discourse.intent.cooking_query@utt", + "discourse.intent.cooking_recipe@utt", + "discourse.intent.datetime_convert@utt", + "discourse.intent.datetime_query@utt", + "discourse.intent.email_addcontact@utt", + "discourse.intent.email_query@utt", + "discourse.intent.email_querycontact@utt", + "discourse.intent.email_sendemail@utt", + "discourse.intent.general_greet@utt", + "discourse.intent.general_joke@utt", + "discourse.intent.general_quirky@utt", + "discourse.intent.iot_cleaning@utt", + "discourse.intent.iot_coffee@utt", + "discourse.intent.iot_hue_lightchange@utt", + "discourse.intent.iot_hue_lightdim@utt", + "discourse.intent.iot_hue_lightoff@utt", + "discourse.intent.iot_hue_lighton@utt", + "discourse.intent.iot_hue_lightup@utt", + "discourse.intent.iot_wemo_off@utt", + "discourse.intent.iot_wemo_on@utt", + "discourse.intent.lists_createoradd@utt", + "discourse.intent.lists_query@utt", + "discourse.intent.lists_remove@utt", + "discourse.intent.music_dislikeness@utt", + "discourse.intent.music_likeness@utt", + "discourse.intent.music_query@utt", + "discourse.intent.music_settings@utt", + "discourse.intent.news_query@utt", + "discourse.intent.play_audiobook@utt", + "discourse.intent.play_game@utt", + "discourse.intent.play_music@utt", + "discourse.intent.play_podcasts@utt", + "discourse.intent.play_radio@utt", + "discourse.intent.qa_currency@utt", + "discourse.intent.qa_definition@utt", + "discourse.intent.qa_factoid@utt", + "discourse.intent.qa_maths@utt", + "discourse.intent.qa_stock@utt", + "discourse.intent.recommendation_events@utt", + "discourse.intent.recommendation_locations@utt", + "discourse.intent.recommendation_movies@utt", + "discourse.intent.social_post@utt", + "discourse.intent.social_query@utt", + "discourse.intent.takeaway_order@utt", + "discourse.intent.takeaway_query@utt", + "discourse.intent.transport_query@utt", + "discourse.intent.transport_taxi@utt", + "discourse.intent.transport_ticket@utt", + "discourse.intent.transport_traffic@utt", + "discourse.intent.weather_query@utt", + "content.sports_focus.game_action@utt", + "content.sports_focus.analysis_tactics@utt", + "content.sports_focus.player_team_background@utt", + "content.sports_focus.stats_records_standings@utt", + "content.sports_focus.injury_health@utt", + "content.sports_focus.officiating_rules@utt", + "content.sports_focus.off_game_banter@utt", + "affect.emotion.neutral@utt", + "affect.emotion.happy@utt", + "affect.emotion.angry@utt", + "affect.emotion.sad@utt", + "affect.emotion.surprised@utt", + "affect.arousal.low@utt", + "affect.arousal.medium@utt", + "affect.arousal.high@utt", + "safety.toxicity.illegal_regulated@utt", + "safety.solicitation.dating_romantic@utt", + "safety.pii.request@utt", + "content.promo_redirect@utt", + "intent.request_information@utt", + "intent.request_action@utt", + "intent.request_permission@utt", + "intent.offer_help@utt", + "intent.propose_plan@utt", + "intent.schedule_arrange@utt", + "intent.assign_task@utt", + "intent.decide_agree@utt", + "intent.disagree_object@utt", + "intent.confirm@utt", + "intent.deny_refuse@utt", + "intent.clarify_correct@utt", + "intent.complain_criticize@utt", + 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"fam:entity.mention": 0.3, + "famL50:entity.mention": 0.3, + "famL30:entity.mention": 0.3, + "head:entity.mention": 0.6, + "safety.toxicity.threat_violence": 0.1, + "safety.toxicity.self_harm": 0.1, + "fam:safety.toxicity": 0.1, + "head:safety.toxicity": 0.1, + "safety.pii.private_name": 0.8, + "safety.pii.street_address": 0.2, + "safety.pii.public_contact": 0.7, + "fam:safety.pii": 0.6, + "head:safety.pii": 0.5 + } + }, + "span_head_families": [ + "entity.mention", + "entity.number", + "safety.pii", + "safety.profanity" + ] +} \ No newline at end of file diff --git a/engine_settings.json b/engine_settings.json new file mode 100644 index 0000000000000000000000000000000000000000..bf17dd89802af444d6b83d7d4cb7714385aa0169 --- /dev/null +++ b/engine_settings.json @@ -0,0 +1,56 @@ +{ + "applied_by": "arctag/_core/engine.py (for reference; not read by the code)", + "ontology": "ontology_v1.0-r8+ext_v7_aff1 (ontology/tags_v1_r8.json + ontology/ext_v7_aff.json; text tags ontology/text_tags.json)", + "thresholds": { + "display": "thresholds/display.json: T0 display thresholds per tag@readout and fine domain, '_all' fallback; 1.01 = hidden", + "span_head_families": "config.json span_head_families: span families decoded from the span head", + "default": "config.json thresholds: used where display.json has no value", + "fallback": "domain -> same-vertical sibling -> _all -> 0.5" + }, + "engine_floors": { + "CLAMP_SPAN": 0.3, + "CLAMP_TOX": 0.5, + "CLAMP_TOX_applies_outside": [ + "talk", + "stream_chat", + "short_clips", + "esports" + ], + "span_head_threshold_default": 0.5, + "display_tier": "T0", + "span_window_words": 96, + "revision_keep_words": 64 + }, + "ui_domains": { + "auto": [ + "unknown" + ], + "talk": [ + "talk", + "stream_chat", + "news" + ], + "meeting": [ + "meeting" + ], + "court": [ + "court_appellate", + "court_trial" + ], + "assistant": [ + "assistant", + "assistant_noisy" + ], + "sports": [ + "nba", + "nhl", + "nfl", + "soccer", + "cricket" + ], + "news": [ + "news", + "talk" + ] + } +} \ No newline at end of file diff --git a/example.py b/example.py new file mode 100644 index 0000000000000000000000000000000000000000..bec39303f11ce20edd2f743703e05163b8bce28b --- /dev/null +++ b/example.py @@ -0,0 +1,29 @@ +"""Arc Tagger quickstart. Run from the downloaded repo: python example.py [local dir or repo id, default: this dir] +(the repo id is WhissleAI/arc-tagger)""" +import json, os, sys, time +HERE = os.path.dirname(os.path.abspath(__file__)); sys.path.insert(0, HERE) +import torch +torch.set_num_threads(int(os.environ.get("ARCTAG_THREADS", "2"))) +from arctag import ArcTagger + +tg = ArcTagger.from_pretrained(sys.argv[1] if len(sys.argv) > 1 else HERE) + +# 1) offline: plain text (utterances end after . ? !) +out = tg.tag("Hi, thanks for calling Northwind Bank, this is Maria. My name is John Patel and my number is " + "five five five, two one three, four four eight nine.", domain="talk") +print("redacted:", out["redacted_text"]) +for s in out["spans"]: + print(f" span {s['tag']:<34} {s['text']!r} p={s['p']}") +for u in out["utterances"]: + print(f" utt [{u['start']}:{u['end']}] act={u['act']} intent={u['intent']} safety={u['safety']}") + +# 2) streaming: push ASR words as they arrive (interim -> revised final), endpoint at end of utterance +st = tg.stream(domain="assistant") +st.push_words(["set", "an", "alarm", "for"], final=False) # interim hypothesis +up = st.push_words(["set", "an", "alarm", "for", "seven", "thirty."], revise_from=0, final=True) # final replaces words[0:] +print("live spans:", [(s["tag"], s["text"]) for s in up["spans"]]) +up = st.endpoint() +print("utterance:", up["utterances"][0]["intent"], up["utterances"][0]["act"]) +res = st.finalize() +print("latency:", st.s.stats()) +print(json.dumps({k: res[k] for k in ("meta", "spans", "events")}, indent=1)[:1500]) diff --git a/model.safetensors b/model.safetensors new file mode 100644 index 0000000000000000000000000000000000000000..09dd2e039f758b0a5e78929838670347b2750161 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d738a97a12beba8752435937d8f72c967c484ab8a659bf891881d528545b0632 +size 45368568 diff --git a/onnx/SHA256SUMS b/onnx/SHA256SUMS new file mode 100644 index 0000000000000000000000000000000000000000..95bda6f6c857d58d3816da3183898ee86d42d75f --- /dev/null +++ b/onnx/SHA256SUMS @@ -0,0 +1,4 @@ +9f464b8142c0557ce66f653f137acfdcf90908c692ccb761b176e68524226247 arctag.onnx +0c265f76f842e24b13da8fcff30f4dc683c3676e60d2aec9ff1bf5a1e1e0ed9c arctag_int8.onnx +ca2d012fe35bfad1863f49c1cd7fa7a41ff628e4d0e47c497e0f6dea15a89314 arctag_meta.json +2ae4623fc283d9b25b83e9efb9bf376f24c8974a01a2bbbe6cf8b0b29ac6fc35 tokenizer.json diff --git a/onnx/arctag.onnx b/onnx/arctag.onnx new file mode 100644 index 0000000000000000000000000000000000000000..9316ce07e3d7be819e24f42256c0e123937c36b2 --- /dev/null +++ b/onnx/arctag.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9f464b8142c0557ce66f653f137acfdcf90908c692ccb761b176e68524226247 +size 34023441 diff --git a/onnx/arctag_int8.onnx b/onnx/arctag_int8.onnx new file mode 100644 index 0000000000000000000000000000000000000000..c945ef1394a2ad98372033122a80ef63f95093e7 --- /dev/null +++ b/onnx/arctag_int8.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c265f76f842e24b13da8fcff30f4dc683c3676e60d2aec9ff1bf5a1e1e0ed9c +size 11453067 diff --git a/onnx/arctag_meta.json b/onnx/arctag_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..89eafbdb36439ff50ed42af9c30d680d2382db99 --- /dev/null +++ b/onnx/arctag_meta.json @@ -0,0 +1 @@ +{"name":"arc-tagger","version":"0.4.0","format":2,"model":"Arc Tagger (text-only streaming tagger, ~9.8M params), T0 read-outs","ontology":"ontology_v1.0-r8+ext_v7_aff1","ontology_sha":"b346f896ba5bab9d1a9a7ea10c8b2ce980aa13c136e15b6602c43d6505c559f8","thresholds_source":"package engine thresholds (display + events + defaults) with the engine's T0 display 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insult, sadness, criticism).", + "description": "The speaker's stance in this utterance is negative (complaint, anger, insult, sadness, criticism).", + "paraphrases": [ + "The speaker complains or criticises.", + "An angry or upset remark.", + "The speaker sounds unhappy about something." + ] + }, + { + "id": "affect.valence.neu", + "index": 332, + "facet": "AFFECT", + "group": "affect.valence", + "exclusivity": "affect.valence", + "unit": "U", + "domains": [ + "*" + ], + "status": "proposed_v7", + "definition": "The utterance is neutral in stance: factual, procedural or flat, with no clear positive or negative feeling.", + "description": "The utterance is neutral in stance: factual, procedural or flat, with no clear positive or negative feeling.", + "paraphrases": [ + "A factual statement with no emotion.", + "A neutral procedural remark.", + "Plain information, no stance." + ] + }, + { + "id": "affect.valence.pos", + "index": 333, + "facet": "AFFECT", + "group": "affect.valence", + 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bad together.", + "Praise and complaint blended." + ] + }, + { + "id": "affect.intensity.1", + "index": 335, + "facet": "AFFECT", + "group": "affect.intensity", + "exclusivity": "affect.intensity", + "unit": "U", + "domains": [ + "*" + ], + "status": "proposed_v7", + "definition": "The positive or negative stance is mild.", + "description": "The positive or negative stance is mild.", + "paraphrases": [ + "A slight, mild feeling.", + "Mildly pleased or mildly annoyed.", + "Low-strength emotion." + ] + }, + { + "id": "affect.intensity.2", + "index": 336, + "facet": "AFFECT", + "group": "affect.intensity", + "exclusivity": "affect.intensity", + "unit": "U", + "domains": [ + "*" + ], + "status": "proposed_v7", + "definition": "The positive or negative stance is clear and moderate.", + "description": "The positive or negative stance is clear and moderate.", + "paraphrases": [ + "A clear, moderate feeling.", + "Noticeably happy or noticeably upset.", + "Medium-strength emotion." + ] + }, + { + "id": "affect.intensity.3", + "index": 337, + "facet": "AFFECT", + "group": "affect.intensity", + "exclusivity": "affect.intensity", + "unit": "U", + "domains": [ + "*" + ], + "status": "proposed_v7", + "definition": "The positive or negative stance is strong or extreme.", + "description": "The positive or negative stance is strong or extreme.", + "paraphrases": [ + "A very strong feeling.", + "Furious or ecstatic.", + "Extreme emotion, abuse or rapture." + ] + } + ], + "text_tags": [ + { + "index": 328, + "id": "speaker.role.agent", + "facet": "SPEAKER", + "group": "speaker.role", + "unit": "S", + "status": "core", + "exclusivity": "speaker.role", + "text_derivable": "yes", + "readouts": [ + "speaker" + ], + "tier": "T3" + }, + { + "index": 329, + "id": "speaker.role.customer", + "facet": "SPEAKER", + "group": "speaker.role", + "unit": "S", + "status": "core", + "exclusivity": "speaker.role", + "text_derivable": "yes", + "readouts": [ + "speaker" + ], + "tier": "T3" + }, + { + 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"gate": "meta.speech", + "null_allowed": false, + "none_member": null, + "status": "experimental" + }, + { + "id": "safety.threat", + "facet": "SAFETY", + "kind": "I", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "gate": "meta.speech", + "null_allowed": false, + "none_member": null, + "status": "experimental" + }, + { + "id": "safety.child", + "facet": "SAFETY", + "kind": "I", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "gate": "meta.speech", + "null_allowed": false, + "none_member": null, + "status": "experimental" + } + ], + "tags": [ + { + "id": "meta.speech", + "index": 0, + "facet": "META", + "group": "meta", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "At least one human voice is producing speech (words, not laughter/cough/crowd chant) in the frame.", + "description": "Someone is talking right now.", + "paraphrases": [ + "A person's voice is speaking words in this moment.", + "There is spoken language here, not just noise or music.", + "You can hear a human speaking." + ] + }, + { + "id": "meta.overlap", + "index": 1, + "facet": "META", + "group": "meta", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Two or more people speak at the same time for >= 200 ms (overlapping speech, not speech over crowd/music).", + "description": "Two people are talking over each other at the same time.", + "paraphrases": [ + "Several voices speak simultaneously and overlap.", + "Crosstalk: more than one speaker at once.", + "People interrupt and talk on top of one another." + ] + }, + { + "id": "meta.degraded_audio", + "index": 2, + "facet": "META", + "group": "meta", + "exclusivity": "independent", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The channel is degraded enough to affect intelligibility: telephone band (< 4 kHz), SNR < 5 dB, clipping or heavy codec artefacts.", + "description": "The audio sounds bad: thin phone-line sound, heavy noise or distortion.", + "paraphrases": [ + "Poor channel quality, like a telephone call or a clipped, noisy recording.", + "The voice is muffled, band-limited or buried in noise.", + "Low-quality, hard-to-understand audio." + ] + }, + { + "id": "meta.synthetic_voice", + "index": 3, + "facet": "META", + "group": "meta", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Speech in the frame was produced or transformed by a machine: text-to-speech, voice cloning, voice conversion, or neural vocoder re-synthesis of a human recording.", + "description": "The voice is computer-generated text-to-speech.", + "paraphrases": [ + "A synthetic, cloned or voice-converted voice is speaking.", + "This speech was produced by a machine, not a person.", + "AI-generated deepfake speech." + ] + }, + { + "id": "meta.lang.en", + "index": 4, + "facet": "META", + "group": "meta.lang", + "exclusivity": "meta.lang", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Speech is English.", + "description": "The speech is in English.", + "paraphrases": [ + "Hello, how are you doing today?", + "Words like the, and, you are spoken.", + "An English speaker talking." + ] + }, + { + "id": "meta.lang.non_en", + "index": 5, + "facet": "META", + "group": "meta.lang", + "exclusivity": "meta.lang", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Speech is a language other than English.", + "description": "The speech is in a foreign language such as Spanish, Hindi or French.", + "paraphrases": [ + "Hola, ¿cómo estás?", + "Namaste, aap kaise hain?", + "Bonjour, comment ça va?" + ] + }, + { + "id": "meta.lang.mixed", + "index": 6, + "facet": "META", + "group": "meta.lang", + "exclusivity": "meta.lang", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "English and another language alternate within the segment (code-switching).", + "description": "The speaker switches between English and another language.", + "paraphrases": [ + "Code-switching: English mixed with another language in the same stretch.", + "Bilingual talk alternating languages.", + "Half English, half another language." + ] + }, + { + "id": "speaker.role.play_by_play", + "index": 7, + "facet": "SPEAKER", + "group": "speaker.role", + "exclusivity": "speaker.role", + "unit": "S", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports" + ], + "status": "core", + "definition": "Narrates live action as it happens.", + "description": "This speaker calls the live action as it happens.", + "paraphrases": [ + "The play-by-play announcer describing each play in real time.", + "The caster narrating the game moment by moment.", + "Voice that tells you who has the ball and what just happened." + ] + }, + { + "id": "speaker.role.analyst", + "index": 8, + "facet": "SPEAKER", + "group": "speaker.role", + "exclusivity": "speaker.role", + "unit": "S", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk" + ], + "status": "core", + "definition": "Gives expert analysis/opinion as a regular part of the broadcast (colour, desk analyst, pundit).", + "description": "This speaker is the expert who explains and evaluates, not the one calling the plays.", + "paraphrases": [ + "The colour commentator or pundit giving analysis.", + "A desk analyst breaking down why something worked.", + "Former player offering opinion and insight on the broadcast." + ] + }, + { + "id": "speaker.role.host", + "index": 9, + "facet": "SPEAKER", + "group": "speaker.role", + "exclusivity": "speaker.role", + "unit": "S", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "meeting" + ], + "status": "core", + "definition": "Presents, moderates and hands off (studio host, anchor, talk host, meeting chair).", + "description": "This speaker presents the show and hands off to others.", + "paraphrases": [ + "The anchor or host who introduces segments and guests.", + "The person running the programme or chairing the meeting.", + "Lead presenter who throws to reporters and breaks." + ] + }, + { + "id": "speaker.role.co_host", + "index": 10, + "facet": "SPEAKER", + "group": "speaker.role", + "exclusivity": "speaker.role", + "unit": "S", + "domains": [ + "news", + "talk" + ], + "status": "core", + "definition": "Second regular presenter sharing hosting duties.", + "description": "This speaker is the second regular presenter alongside the host.", + "paraphrases": [ + "Co-anchor or podcast co-host sharing the presenting.", + "A regular presenter who banters with the host but does not lead.", + "The other host of the show, not a guest." + ] + }, + { + "id": "speaker.role.reporter", + "index": 11, + "facet": "SPEAKER", + "group": "speaker.role", + "exclusivity": "speaker.role", + "unit": "S", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk" + ], + "status": "core", + "definition": "Field/sideline/courtside reporter, correspondent, meteorologist delivering a report.", + "description": "This speaker is a journalist reporting from the field or sideline.", + "paraphrases": [ + "A correspondent delivering a report on location.", + "Sideline or courtside reporter with an update.", + "Field reporter or meteorologist filing a story." + ] + }, + { + "id": "speaker.role.guest", + "index": 12, + "facet": "SPEAKER", + "group": "speaker.role", + "exclusivity": "speaker.role", + "unit": "S", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk" + ], + "status": "core", + "definition": "Invited or quoted person in news/talk (interviewee, panelist, official, expert).", + "description": "This speaker is an invited guest or interviewee on a news or talk show.", + "paraphrases": [ + "An expert, official or celebrity being interviewed.", + "A soundbite from a named person who is not part of the programme staff.", + "The panelist or interview subject, not the host." + ] + }, + { + "id": "speaker.role.athlete", + "index": 13, + "facet": "SPEAKER", + "group": "speaker.role", + "exclusivity": "speaker.role", + "unit": "S", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports" + ], + "status": "core", + "definition": "Competitor speaking inside a sports/esports broadcast.", + "description": "This speaker is a player or competitor talking during a sports or esports broadcast.", + "paraphrases": [ + "An athlete giving an interview or mic'd up during the game.", + "A player answering questions after the match.", + "Team comms from a competitor in the game." + ] + }, + { + "id": "speaker.role.coach_staff", + "index": 14, + "facet": "SPEAKER", + "group": "speaker.role", + "exclusivity": "speaker.role", + "unit": "S", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports" + ], + "status": "core", + "definition": "Coach/manager/team staff speaking inside a sports/esports broadcast.", + "description": "This speaker is a coach, manager or team staff member, not a player.", + "paraphrases": [ + "The head coach talking about the game plan.", + "A manager interviewed between periods.", + "Team staff speaking on the broadcast." + ] + }, + { + "id": "speaker.role.game_official", + "index": 15, + "facet": "SPEAKER", + "group": "speaker.role", + "exclusivity": "speaker.role", + "unit": "S", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports" + ], + "status": "core", + "definition": "Referee/umpire/video ref speaking.", + "description": "This speaker is the referee, umpire or match official.", + "paraphrases": [ + "The ref announcing a penalty over the stadium mic.", + "An umpire or video referee giving a decision.", + "The official explains the call." + ] + }, + { + "id": "speaker.role.caller_audience", + "index": 16, + "facet": "SPEAKER", + "group": "speaker.role", + "exclusivity": "speaker.role", + "unit": "S", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk" + ], + "status": "core", + "definition": "Member of the public (caller, audience question, vox-pop, fan).", + "description": "This speaker is a member of the public: a caller, fan or eyewitness.", + "paraphrases": [ + "A listener calling in to the show.", + "A fan or bystander interviewed on the spot.", + "An audience member asking a question." + ] + }, + { + "id": "speaker.role.announcer_voiceover", + "index": 17, + "facet": "SPEAKER", + "group": "speaker.role", + "exclusivity": "speaker.role", + "unit": "S", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk" + ], + "status": "core", + "definition": "Non-presenting voice: PA announcer, ad/promo read, station ID, package narrator.", + "description": "This voice is an announcer or voice-over, not a presenter: ads, promos, PA or narration.", + "paraphrases": [ + "The stadium PA announcer.", + "A commercial or promo voice reading scripted copy.", + "Narrator of a packaged segment or trailer." + ] + }, + { + "id": "speaker.role.participant", + "index": 18, + "facet": "SPEAKER", + "group": "speaker.role", + "exclusivity": "speaker.role", + "unit": "S", + "domains": [ + "meeting" + ], + "status": "core", + "definition": "Peer member of a meeting without a presenting function.", + "description": "This speaker is an ordinary meeting participant, not the chair.", + "paraphrases": [ + "A team member contributing in a meeting.", + "One of the peers in the discussion.", + "A meeting attendee without a leading role." + ] + }, + { + "id": "speaker.role.judge", + "index": 19, + "facet": "SPEAKER", + "group": "speaker.role", + "exclusivity": "speaker.role", + "unit": "S", + "domains": [ + "court_appellate", + "court_trial" + ], + "status": "core", + "definition": "Presiding judicial officer.", + "description": "This speaker is the judge or justice presiding.", + "paraphrases": [ + "The judge ruling or questioning from the bench.", + "A Supreme Court justice asking the advocate a question.", + "The presiding judicial officer." + ] + }, + { + "id": "speaker.role.counsel", + "index": 20, + "facet": "SPEAKER", + "group": "speaker.role", + "exclusivity": "speaker.role", + "unit": "S", + "domains": [ + "court_appellate", + "court_trial" + ], + "status": "core", + "definition": "Attorney/advocate (questioning vs opposing dropped: not decidable).", + "description": "This speaker is a lawyer for one of the parties.", + "paraphrases": [ + "An attorney examining a witness or arguing the case.", + "Counsel addressing the court.", + "The advocate making legal arguments." + ] + }, + { + "id": "speaker.role.witness", + "index": 21, + "facet": "SPEAKER", + "group": "speaker.role", + "exclusivity": "speaker.role", + "unit": "S", + "domains": [ + "court_trial" + ], + "status": "core", + "definition": "Sworn witness / party testifying.", + "description": "This speaker is a witness testifying under oath.", + "paraphrases": [ + "The witness answering the lawyer's questions.", + "A person giving testimony on the stand.", + "Testimony from the witness box." + ] + }, + { + "id": "speaker.role.device_user", + "index": 22, + "facet": "SPEAKER", + "group": "speaker.role", + "exclusivity": "speaker.role", + "unit": "S", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Person addressing a voice assistant.", + "description": "This speaker is giving a command to a voice assistant.", + "paraphrases": [ + "A person talking to their smart speaker.", + "The user asking the assistant to do something.", + "Someone addressing the device." + ] + }, + { + "id": "speaker.role.other", + "index": 23, + "facet": "SPEAKER", + "group": "speaker.role", + "exclusivity": "speaker.role", + "unit": "S", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "meeting", + "court_appellate", + "court_trial", + "assistant" + ], + "status": "core", + "definition": "Speech whose function is none of the above (unidentifiable soundbite, clerk, juror, video-game voice lines, background TV voice).", + "description": "This speaker has none of the usual broadcast, meeting, court or assistant roles.", + "paraphrases": [ + "An unidentified voice such as a clerk, juror or background TV voice.", + "Scripted voice lines from a video game.", + "A speaker whose function cannot be any of the listed roles." + ] + }, + { + "id": "speaker.change", + "index": 24, + "facet": "SPEAKER", + "group": "speaker.change", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "A different speaker starts talking (turn start whose speaker differs from the previous turn's speaker).", + "description": "A different person just started speaking.", + "paraphrases": [ + "The speaker changes here.", + "A new voice takes over from the previous one.", + "Turn change to another speaker." + ] + }, + { + "id": "discourse.act.statement", + "index": 25, + "facet": "DISCOURSE", + "group": "discourse.act", + "exclusivity": "discourse.act", + "unit": "U", + "domains": [ + "news", + "talk", + "meeting", + "court_appellate", + "court_trial", + "assistant" + ], + "status": "core", + "definition": "Conveys information, narration or a factual claim.", + "description": "The speaker states a fact or tells what happened.", + "paraphrases": [ + "An informative statement, not a question or opinion.", + "Narration or reporting of information.", + "A plain declarative giving information." + ] + }, + { + "id": "discourse.act.question", + "index": 26, + "facet": "DISCOURSE", + "group": "discourse.act", + "exclusivity": "discourse.act", + "unit": "U", + "domains": [ + "news", + "talk", + "meeting", + "court_appellate", + "court_trial", + "assistant" + ], + "status": "core", + "definition": "Requests information or a judgement from the addressee (wh-, yes/no, elicitation).", + "description": "The speaker asks a question.", + "paraphrases": [ + "Someone requests information or an answer.", + "A question directed at the listener.", + "Asking: what, when, why, did you..." + ] + }, + { + "id": "discourse.act.opinion_assessment", + "index": 27, + "facet": "DISCOURSE", + "group": "discourse.act", + "exclusivity": "discourse.act", + "unit": "U", + "domains": [ + "news", + "talk", + "meeting", + "court_appellate", + "court_trial", + "assistant" + ], + "status": "core", + "definition": "Evaluates or expresses an opinion/judgement.", + "description": "The speaker gives an opinion or judges something.", + "paraphrases": [ + "An evaluation: good, bad, likely, wrong.", + "Expressing what they think about something.", + "A subjective assessment, not a neutral fact." + ] + }, + { + "id": "discourse.act.suggestion_proposal", + "index": 28, + "facet": "DISCOURSE", + "group": "discourse.act", + "exclusivity": "discourse.act", + "unit": "U", + "domains": [ + "news", + "talk", + "meeting", + "court_appellate", + "court_trial", + "assistant" + ], + "status": "core", + "definition": "Proposes a course of action for the group/addressee.", + "description": "The speaker proposes what the group could do.", + "paraphrases": [ + "A suggestion such as let's do this or why don't we.", + "Proposing a course of action.", + "Putting forward an idea for the team to adopt." + ] + }, + { + "id": "discourse.act.commitment_offer", + "index": 29, + "facet": "DISCOURSE", + "group": "discourse.act", + "exclusivity": "discourse.act", + "unit": "U", + "domains": [ + "news", + "talk", + "meeting", + "court_appellate", + "court_trial", + "assistant" + ], + "status": "core", + "definition": "Speaker commits to or offers a future action.", + "description": "The speaker promises or offers to do something.", + "paraphrases": [ + "I will take care of it.", + "Committing to a future action.", + "Volunteering to do a task." + ] + }, + { + "id": "discourse.act.command_request", + "index": 30, + "facet": "DISCOURSE", + "group": "discourse.act", + "exclusivity": "discourse.act", + "unit": "U", + "domains": [ + "news", + "talk", + "meeting", + "court_appellate", + "court_trial", + "assistant" + ], + "status": "core", + "definition": "Directs the addressee to perform an action now.", + "description": "The speaker tells someone to do something now.", + "paraphrases": [ + "An instruction or command.", + "A request for action, like turn it off or cancel that.", + "Imperative directing the listener." + ] + }, + { + "id": "discourse.act.ritual_procedural", + "index": 31, + "facet": "DISCOURSE", + "group": "discourse.act", + "exclusivity": "discourse.act", + "unit": "U", + "domains": [ + "news", + "talk", + "meeting", + "court_appellate", + "court_trial", + "assistant" + ], + "status": "core", + "definition": "Conventional formula: greeting, thanks, sign-off, oath, courtroom procedure.", + "description": "A conventional formula: greeting, thanks, goodbye, oath or courtroom formality.", + "paraphrases": [ + "Social niceties like hello and thank you.", + "May it please the Court, or the case is submitted.", + "Procedural or ritual phrase with no new content." + ] + }, + { + "id": "discourse.answer", + "index": 32, + "facet": "DISCOURSE", + "group": "discourse", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "news", + "talk", + "meeting", + "court_appellate", + "court_trial" + ], + "status": "core", + "definition": "Utterance that responds to a question asked by another speaker immediately before.", + "description": "The speaker is answering a question someone just asked.", + "paraphrases": [ + "A reply to the previous speaker's question.", + "Response to a question, e.g. the witness answers.", + "Giving the answer that was asked for." + ] + }, + { + "id": "discourse.backchannel", + "index": 33, + "facet": "DISCOURSE", + "group": "discourse", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "news", + "talk", + "meeting" + ], + "status": "core", + "definition": "Short listener response that does not claim the floor (yeah, mm-hmm, right).", + "description": "A short listener response like mm-hmm, yeah or right.", + "paraphrases": [ + "Feedback noise from a listener who is not taking the floor.", + "Quick acknowledgement while someone else talks.", + "Uh-huh, okay, sure, while listening." + ] + }, + { + "id": "discourse.interruption", + "index": 34, + "facet": "DISCOURSE", + "group": "discourse", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "news", + "talk", + "meeting", + "court_appellate", + "court_trial" + ], + "status": "experimental", + "definition": "A speaker starts while another is mid-utterance and the first stops within 1 s (floor taken).", + "description": "Someone cuts in and the current speaker stops.", + "paraphrases": [ + "An interruption takes the floor mid-sentence.", + "The speaker is cut off by another person.", + "Barging in before the other finished." + ] + }, + { + "id": "discourse.turn_yield", + "index": 35, + "facet": "DISCOURSE", + "group": "discourse", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "news", + "talk", + "meeting", + "court_appellate", + "court_trial" + ], + "status": "experimental", + "definition": "The current speaker ends their turn and another speaker takes the floor next (end-of-turn).", + "description": "The speaker finishes their turn and hands the floor to someone else.", + "paraphrases": [ + "End of turn: another person will speak next.", + "The current speaker is done and yields.", + "A natural point where the next speaker takes over." + ] + }, + { + "id": "discourse.assistant.device_directed", + "index": 36, + "facet": "DISCOURSE", + "group": "discourse.assistant", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Speech addressed to the voice assistant.", + "description": "This speech is addressed to the voice assistant.", + "paraphrases": [ + "Someone is talking to the smart speaker, not to a person.", + "A command meant for the device.", + "Directed at the assistant, not background TV." + ] + }, + { + "id": "discourse.assistant.command_complete", + "index": 37, + "facet": "DISCOURSE", + "group": "discourse.assistant", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "The directed command has just ended (endpoint).", + "description": "The voice command has just ended.", + "paraphrases": [ + "The user finished speaking the request.", + "End of the command: time to respond.", + "The device-directed utterance is complete." + ] + }, + { + "id": "discourse.intent.alarm_query", + "index": 38, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'alarm_query'.", + "description": "The user asks what alarms are set.", + "paraphrases": [ + "'what alarms do I have tomorrow'", + "'is my wake-up alarm still on'", + "'list my alarms'" + ] + }, + { + "id": "discourse.intent.alarm_remove", + "index": 39, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'alarm_remove'.", + "description": "The user asks to delete an alarm.", + "paraphrases": [ + "'delete my six am alarm'", + "'cancel all alarms'", + "'remove the alarm for Monday'" + ] + }, + { + "id": "discourse.intent.alarm_set", + "index": 40, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'alarm_set'.", + "description": "The user asks to set an alarm.", + "paraphrases": [ + "'wake me up at seven'", + "'set an alarm for six thirty'", + "'alarm in twenty minutes'" + ] + }, + { + "id": "discourse.intent.audio_volume_down", + "index": 41, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'audio_volume_down'.", + "description": "The user asks to turn the volume down.", + "paraphrases": [ + "'turn it down'", + "'lower the volume a bit'", + "'too loud, quieter please'" + ] + }, + { + "id": "discourse.intent.audio_volume_mute", + "index": 42, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'audio_volume_mute'.", + "description": "The user asks to mute the sound.", + "paraphrases": [ + "'mute'", + "'silence the speaker'", + "'no sound for a while'" + ] + }, + { + "id": "discourse.intent.audio_volume_other", + "index": 43, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'audio_volume_other'.", + "description": "The user asks to change some other audio setting.", + "paraphrases": [ + "'change the equaliser'", + "'set volume to level five'", + "'balance the speakers'" + ] + }, + { + "id": "discourse.intent.audio_volume_up", + "index": 44, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'audio_volume_up'.", + "description": "The user asks to turn the volume up.", + "paraphrases": [ + "'louder please'", + "'turn the volume up'", + "'I can't hear, raise it'" + ] + }, + { + "id": "discourse.intent.calendar_query", + "index": 45, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'calendar_query'.", + "description": "The user asks what is on their calendar.", + "paraphrases": [ + "'what's on my schedule today'", + "'do I have meetings tomorrow'", + "'when is my dentist appointment'" + ] + }, + { + "id": "discourse.intent.calendar_remove", + "index": 46, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'calendar_remove'.", + "description": "The user asks to delete a calendar event.", + "paraphrases": [ + "'cancel my meeting with John'", + "'delete tomorrow's appointment'", + "'clear my calendar for Friday'" + ] + }, + { + "id": "discourse.intent.calendar_set", + "index": 47, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'calendar_set'.", + "description": "The user asks to add an event or reminder to the calendar.", + "paraphrases": [ + "'remind me to call mom at five'", + "'add lunch with Sara on Tuesday'", + "'schedule a meeting at noon'" + ] + }, + { + "id": "discourse.intent.cooking_query", + "index": 48, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'cooking_query'.", + "description": "The user asks a cooking question.", + "paraphrases": [ + "'how long do I boil an egg'", + "'what temperature for roast chicken'", + "'can I freeze cooked rice'" + ] + }, + { + "id": "discourse.intent.cooking_recipe", + "index": 49, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'cooking_recipe'.", + "description": "The user asks for a recipe.", + "paraphrases": [ + "'give me a recipe for pancakes'", + "'how do I make lasagna'", + "'find a vegan curry recipe'" + ] + }, + { + "id": "discourse.intent.datetime_convert", + "index": 50, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'datetime_convert'.", + "description": "The user asks to convert a time between time zones.", + "paraphrases": [ + "'what time is it in Tokyo now'", + "'convert three pm London to New York time'", + "'time difference with Sydney'" + ] + }, + { + "id": "discourse.intent.datetime_query", + "index": 51, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'datetime_query'.", + "description": "The user asks for the date or time.", + "paraphrases": [ + "'what time is it'", + "'what's today's date'", + "'which day is it'" + ] + }, + { + "id": "discourse.intent.email_addcontact", + "index": 52, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'email_addcontact'.", + "description": "The user asks to add an email contact.", + "paraphrases": [ + "'save this email address to my contacts'", + "'add Mike's email'", + "'new contact from this sender'" + ] + }, + { + "id": "discourse.intent.email_query", + "index": 53, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'email_query'.", + "description": "The user asks to check their emails.", + "paraphrases": [ + "'do I have new emails'", + "'read my inbox'", + "'any mail from my boss'" + ] + }, + { + "id": "discourse.intent.email_querycontact", + "index": 54, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'email_querycontact'.", + "description": "The user asks for a contact's details.", + "paraphrases": [ + "'what's Anna's phone number'", + "'find the email of my landlord'", + "'show contact details for Tom'" + ] + }, + { + "id": "discourse.intent.email_sendemail", + "index": 55, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'email_sendemail'.", + "description": "The user asks to send an email.", + "paraphrases": [ + "'email my boss that I'm late'", + "'send a mail to the team'", + "'reply to Lisa's email'" + ] + }, + { + "id": "discourse.intent.general_greet", + "index": 56, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'general_greet'.", + "description": "The user greets the assistant.", + "paraphrases": [ + "'hello there'", + "'good morning'", + "'hi, how are you'" + ] + }, + { + "id": "discourse.intent.general_joke", + "index": 57, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'general_joke'.", + "description": "The user asks the assistant to tell a joke.", + "paraphrases": [ + "'tell me a joke'", + "'make me laugh'", + "'do you know any funny jokes'" + ] + }, + { + "id": "discourse.intent.general_quirky", + "index": 58, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'general_quirky'.", + "description": "The user says something playful or odd to the assistant.", + "paraphrases": [ + "'do you dream'", + "'are you single'", + "'sing me something silly'" + ] + }, + { + "id": "discourse.intent.iot_cleaning", + "index": 59, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'iot_cleaning'.", + "description": "The user asks the robot vacuum to clean.", + "paraphrases": [ + "'start the vacuum'", + "'clean the living room'", + "'send the robot cleaner to the kitchen'" + ] + }, + { + "id": "discourse.intent.iot_coffee", + "index": 60, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'iot_coffee'.", + "description": "The user asks the coffee machine to make coffee.", + "paraphrases": [ + "'make me a coffee'", + "'brew an espresso'", + "'start the coffee machine'" + ] + }, + { + "id": "discourse.intent.iot_hue_lightchange", + "index": 61, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'iot_hue_lightchange'.", + "description": "The user asks to change the light colour.", + "paraphrases": [ + "'make the lights blue'", + "'change the light colour to warm white'", + "'set the bedroom lamp to red'" + ] + }, + { + "id": "discourse.intent.iot_hue_lightdim", + "index": 62, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'iot_hue_lightdim'.", + "description": "The user asks to dim the lights.", + "paraphrases": [ + "'dim the lights'", + "'lights down to thirty percent'", + "'make it darker in here'" + ] + }, + { + "id": "discourse.intent.iot_hue_lightoff", + "index": 63, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'iot_hue_lightoff'.", + "description": "The user asks to switch the lights off.", + "paraphrases": [ + "'lights off'", + "'switch off the kitchen light'", + "'turn out the lamp'" + ] + }, + { + "id": "discourse.intent.iot_hue_lighton", + "index": 64, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'iot_hue_lighton'.", + "description": "The user asks to switch the lights on.", + "paraphrases": [ + "'lights on'", + "'switch on the hallway light'", + "'turn the lamp on'" + ] + }, + { + "id": "discourse.intent.iot_hue_lightup", + "index": 65, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'iot_hue_lightup'.", + "description": "The user asks to make the lights brighter.", + "paraphrases": [ + "'brighter please'", + "'increase the brightness'", + "'turn the lights up to full'" + ] + }, + { + "id": "discourse.intent.iot_wemo_off", + "index": 66, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'iot_wemo_off'.", + "description": "The user asks to switch a smart plug off.", + "paraphrases": [ + "'turn off the smart plug'", + "'switch the heater socket off'", + "'power off the fan plug'" + ] + }, + { + "id": "discourse.intent.iot_wemo_on", + "index": 67, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'iot_wemo_on'.", + "description": "The user asks to switch a smart plug on.", + "paraphrases": [ + "'turn on the smart plug'", + "'switch the heater socket on'", + "'power on the fan plug'" + ] + }, + { + "id": "discourse.intent.lists_createoradd", + "index": 68, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'lists_createoradd'.", + "description": "The user asks to create a list or add an item to it.", + "paraphrases": [ + "'add milk to my shopping list'", + "'make a new to-do list'", + "'put batteries on the list'" + ] + }, + { + "id": "discourse.intent.lists_query", + "index": 69, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'lists_query'.", + "description": "The user asks what is on a list.", + "paraphrases": [ + "'what's on my shopping list'", + "'read my to-do list'", + "'do I have eggs on the list'" + ] + }, + { + "id": "discourse.intent.lists_remove", + "index": 70, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'lists_remove'.", + "description": "The user asks to remove an item or list.", + "paraphrases": [ + "'take bread off the list'", + "'delete my grocery list'", + "'remove the last item'" + ] + }, + { + "id": "discourse.intent.music_dislikeness", + "index": 71, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'music_dislikeness'.", + "description": "The user says they dislike the current song.", + "paraphrases": [ + "'I hate this song'", + "'skip, I don't like this track'", + "'never play this artist again'" + ] + }, + { + "id": "discourse.intent.music_likeness", + "index": 72, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'music_likeness'.", + "description": "The user says they like a song or artist.", + "paraphrases": [ + "'I love this song'", + "'remember that I like jazz'", + "'this track is great'" + ] + }, + { + "id": "discourse.intent.music_query", + "index": 73, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'music_query'.", + "description": "The user asks what song is playing.", + "paraphrases": [ + "'what song is this'", + "'who sings this'", + "'name of the current track'" + ] + }, + { + "id": "discourse.intent.music_settings", + "index": 74, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'music_settings'.", + "description": "The user asks to change music playback settings like shuffle or repeat.", + "paraphrases": [ + "'shuffle my playlist'", + "'repeat this song'", + "'turn on crossfade'" + ] + }, + { + "id": "discourse.intent.news_query", + "index": 75, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'news_query'.", + "description": "The user asks for the news.", + "paraphrases": [ + "'what's the latest news'", + "'headlines from the BBC'", + "'any news about the election'" + ] + }, + { + "id": "discourse.intent.play_audiobook", + "index": 76, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'play_audiobook'.", + "description": "The user asks to play an audiobook.", + "paraphrases": [ + "'resume my audiobook'", + "'play Harry Potter audiobook'", + "'continue the book from chapter three'" + ] + }, + { + "id": "discourse.intent.play_game", + "index": 77, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'play_game'.", + "description": "The user asks to play a game.", + "paraphrases": [ + "'let's play a quiz'", + "'start a trivia game'", + "'play twenty questions with me'" + ] + }, + { + "id": "discourse.intent.play_music", + "index": 78, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'play_music'.", + "description": "The user asks to play music.", + "paraphrases": [ + "'play some rock'", + "'put on Taylor Swift'", + "'play my workout playlist'" + ] + }, + { + "id": "discourse.intent.play_podcasts", + "index": 79, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'play_podcasts'.", + "description": "The user asks to play a podcast.", + "paraphrases": [ + "'play the latest episode of my podcast'", + "'next podcast episode'", + "'start a true crime podcast'" + ] + }, + { + "id": "discourse.intent.play_radio", + "index": 80, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'play_radio'.", + "description": "The user asks to play a radio station.", + "paraphrases": [ + "'play BBC Radio 2'", + "'tune in to the jazz station'", + "'turn on the radio'" + ] + }, + { + "id": "discourse.intent.qa_currency", + "index": 81, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'qa_currency'.", + "description": "The user asks an exchange rate question.", + "paraphrases": [ + "'how many euros is a hundred dollars'", + "'dollar to yen rate'", + "'pound exchange rate today'" + ] + }, + { + "id": "discourse.intent.qa_definition", + "index": 82, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'qa_definition'.", + "description": "The user asks what a word means.", + "paraphrases": [ + "'what does ubiquitous mean'", + "'define serendipity'", + "'meaning of the word lucid'" + ] + }, + { + "id": "discourse.intent.qa_factoid", + "index": 83, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'qa_factoid'.", + "description": "The user asks a general knowledge question.", + "paraphrases": [ + "'how tall is Mount Everest'", + "'who wrote Hamlet'", + "'what is the capital of Peru'" + ] + }, + { + "id": "discourse.intent.qa_maths", + "index": 84, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'qa_maths'.", + "description": "The user asks a maths question.", + "paraphrases": [ + "'what's twelve times fifteen'", + "'square root of 144'", + "'add 37 and 58'" + ] + }, + { + "id": "discourse.intent.qa_stock", + "index": 85, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'qa_stock'.", + "description": "The user asks about a stock price.", + "paraphrases": [ + "'how is Apple stock doing'", + "'price of Tesla shares'", + "'Dow Jones today'" + ] + }, + { + "id": "discourse.intent.recommendation_events", + "index": 86, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'recommendation_events'.", + "description": "The user asks for events to go to.", + "paraphrases": [ + "'what's happening in town this weekend'", + "'any concerts nearby'", + "'events for kids on Saturday'" + ] + }, + { + "id": "discourse.intent.recommendation_locations", + "index": 87, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'recommendation_locations'.", + "description": "The user asks for a place recommendation like a restaurant.", + "paraphrases": [ + "'find a good Italian restaurant near me'", + "'best coffee shop around here'", + "'where can I buy shoes nearby'" + ] + }, + { + "id": "discourse.intent.recommendation_movies", + "index": 88, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'recommendation_movies'.", + "description": "The user asks for a movie recommendation.", + "paraphrases": [ + "'recommend a good comedy movie'", + "'what should I watch tonight'", + "'suggest a thriller film'" + ] + }, + { + "id": "discourse.intent.social_post", + "index": 89, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'social_post'.", + "description": "The user asks to post something on social media.", + "paraphrases": [ + "'tweet that I'm on holiday'", + "'post this photo to Facebook'", + "'complain to the company on Twitter'" + ] + }, + { + "id": "discourse.intent.social_query", + "index": 90, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'social_query'.", + "description": "The user asks about social media updates.", + "paraphrases": [ + "'any new notifications on Facebook'", + "'what are my friends posting'", + "'check my Instagram updates'" + ] + }, + { + "id": "discourse.intent.takeaway_order", + "index": 91, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'takeaway_order'.", + "description": "The user asks to order takeaway food.", + "paraphrases": [ + "'order a pizza'", + "'get me sushi delivered'", + "'order my usual from the Chinese place'" + ] + }, + { + "id": "discourse.intent.takeaway_query", + "index": 92, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'takeaway_query'.", + "description": "The user asks about a takeaway order or menu.", + "paraphrases": [ + "'is my food order on its way'", + "'does the Thai place deliver'", + "'what's on the burger menu'" + ] + }, + { + "id": "discourse.intent.transport_query", + "index": 93, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'transport_query'.", + "description": "The user asks about train or bus times.", + "paraphrases": [ + "'when is the next train to Leeds'", + "'bus times to the airport'", + "'is the subway running'" + ] + }, + { + "id": "discourse.intent.transport_taxi", + "index": 94, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'transport_taxi'.", + "description": "The user asks to book a taxi.", + "paraphrases": [ + "'book me an uber'", + "'call a taxi to the station'", + "'get a cab for eight pm'" + ] + }, + { + "id": "discourse.intent.transport_ticket", + "index": 95, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'transport_ticket'.", + "description": "The user asks to book a travel ticket.", + "paraphrases": [ + "'buy a train ticket to Paris'", + "'book a flight to Rome'", + "'get two bus tickets'" + ] + }, + { + "id": "discourse.intent.transport_traffic", + "index": 96, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'transport_traffic'.", + "description": "The user asks about the traffic.", + "paraphrases": [ + "'how's the traffic on the motorway'", + "'any traffic jams on my way to work'", + "'is the bridge congested'" + ] + }, + { + "id": "discourse.intent.weather_query", + "index": 97, + "facet": "DISCOURSE", + "group": "discourse.intent", + "exclusivity": "discourse.intent", + "unit": "U", + "domains": [ + "assistant" + ], + "status": "core", + "definition": "Command intent 'weather_query'.", + "description": "The user asks about the weather.", + "paraphrases": [ + "'will it rain today'", + "'what's the weather tomorrow'", + "'how cold is it outside'" + ] + }, + { + "id": "content.mode.live_action", + "index": 98, + "facet": "CONTENT", + "group": "content.mode", + "exclusivity": "content.mode", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports" + ], + "status": "core", + "definition": "Live coverage of competition play in progress (incl. dead-ball moments inside live coverage).", + "description": "The broadcast is covering live game play right now.", + "paraphrases": [ + "Live match coverage with commentary on the action.", + "The game is on and being called live.", + "Live esports or sports play in progress on air." + ] + }, + { + "id": "content.mode.replay_highlight", + "index": 99, + "facet": "CONTENT", + "group": "content.mode", + "exclusivity": "content.mode", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news" + ], + "status": "core", + "definition": "Replay, recap or highlight package of earlier action.", + "description": "A replay or highlight of earlier action is being shown.", + "paraphrases": [ + "Let's take another look: replay segment.", + "Recap or highlight package.", + "Reviewing a play that already happened." + ] + }, + { + "id": "content.mode.studio_talk", + "index": 100, + "facet": "CONTENT", + "group": "content.mode", + "exclusivity": "content.mode", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "court_trial" + ], + "status": "core", + "definition": "Presenters/analysts/hosts talking from a studio, desk or booth outside live action (pre/halftime show, news desk, talk-show/podcast conversation, panel).", + "description": "Presenters and analysts talk in the studio, away from live action.", + "paraphrases": [ + "A news desk, talk show or podcast conversation.", + "Halftime or pre-game studio discussion.", + "Panel or desk segment in the studio." + ] + }, + { + "id": "content.mode.field_report", + "index": 101, + "facet": "CONTENT", + "group": "content.mode", + "exclusivity": "content.mode", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "news", + "talk" + ], + "status": "core", + "definition": "Correspondent report from a location (live stand-up or package).", + "description": "A reporter files a report from a location.", + "paraphrases": [ + "Correspondent live on the scene.", + "A packaged news report narrated from the field.", + "On-location reporting segment." + ] + }, + { + "id": "content.mode.interview", + "index": 102, + "facet": "CONTENT", + "group": "content.mode", + "exclusivity": "content.mode", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "court_trial" + ], + "status": "core", + "definition": "Structured Q&A between an interviewer and an interviewee is the segment's main content.", + "description": "The segment is an interview: questions and answers with one guest.", + "paraphrases": [ + "Sideline or post-game interview.", + "A host interviews a guest.", + "Q and A with an interviewee is the main content." + ] + }, + { + "id": "content.mode.call_in", + "index": 103, + "facet": "CONTENT", + "group": "content.mode", + "exclusivity": "content.mode", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "news", + "talk" + ], + "status": "core", + "definition": "Open-line listener calls aired on a show.", + "description": "Listeners phone in to the show.", + "paraphrases": [ + "Call-in segment with members of the public.", + "Open phone lines on talk radio.", + "Taking calls from listeners." + ] + }, + { + "id": "content.mode.live_event_feed", + "index": 104, + "facet": "CONTENT", + "group": "content.mode", + "exclusivity": "content.mode", + "unit": "G", + "domains": [ + "news", + "talk", + "court_trial" + ], + "status": "core", + "definition": "Carried live feed of a non-competition event: press conference, speech, hearing, courtroom proceedings.", + "description": "The broadcast carries a live event feed such as a press conference or courtroom.", + "paraphrases": [ + "Live coverage of a speech or hearing in its own audio.", + "Courtroom proceedings aired live.", + "A press conference carried in full." + ] + }, + { + "id": "content.mode.ad_promo", + "index": 105, + "facet": "CONTENT", + "group": "content.mode", + "exclusivity": "content.mode", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "court_trial" + ], + "status": "core", + "definition": "Commercial, sponsor read, programme/network promo.", + "description": "A commercial, sponsor message or promo is airing.", + "paraphrases": [ + "Advertisement break or sponsored read.", + "Promo for an upcoming show.", + "Brought to you by our sponsor." + ] + }, + { + "id": "content.mode.bumper_jingle", + "index": 106, + "facet": "CONTENT", + "group": "content.mode", + "exclusivity": "content.mode", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk" + ], + "status": "core", + "definition": "Short station/programme identity music or transition sting (< ~20 s).", + "description": "A short station jingle or show theme sting plays.", + "paraphrases": [ + "Transition music bumper between segments.", + "Branded sounder or intro theme.", + "The show's ident jingle." + ] + }, + { + "id": "content.mode.music_program", + "index": 107, + "facet": "CONTENT", + "group": "content.mode", + "exclusivity": "content.mode", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "court_trial" + ], + "status": "core", + "definition": "Music played as content (song, performance, walk-out music) for >= 10 s.", + "description": "Music is being played as content, like a song on the radio.", + "paraphrases": [ + "A song or performance airs.", + "The anthem or walk-out music plays.", + "Music segment rather than talk." + ] + }, + { + "id": "content.mode.break_intermission", + "index": 108, + "facet": "CONTENT", + "group": "content.mode", + "exclusivity": "content.mode", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports" + ], + "status": "core", + "definition": "Scheduled pause of the event with no programme talk (intermission, standby screen).", + "description": "The event is on a scheduled break or intermission with no programme talk.", + "paraphrases": [ + "Standby screen music during an esports break.", + "Intermission between periods with no show.", + "The broadcast idles during the break." + ] + }, + { + "id": "content.mode.no_commentary", + "index": 109, + "facet": "CONTENT", + "group": "content.mode", + "exclusivity": "content.mode", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "court_trial" + ], + "status": "core", + "definition": "Event audio continues with no commentary or programme speech for >= 3 s.", + "description": "The event continues but nobody is commentating.", + "paraphrases": [ + "Only crowd and game sounds, no announcers.", + "Commentators are silent while play goes on.", + "Natural sound with no commentary." + ] + }, + { + "id": "content.game_state.in_play", + "index": 110, + "facet": "CONTENT", + "group": "content.game_state", + "exclusivity": "content.game_state", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "soccer" + ], + "status": "core", + "definition": "Ball/puck live, clock running or play in progress.", + "description": "The ball or puck is live and play is running.", + "paraphrases": [ + "Action is in progress with the clock running.", + "Live play, not a stoppage.", + "The game is being played right now." + ] + }, + { + "id": "content.game_state.stoppage", + "index": 111, + "facet": "CONTENT", + "group": "content.game_state", + "exclusivity": "content.game_state", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "soccer" + ], + "status": "core", + "definition": "Short dead-ball stoppage (foul, out of bounds, between plays).", + "description": "Play is briefly stopped between plays or after a whistle.", + "paraphrases": [ + "Dead ball after a foul or out of bounds.", + "A short stoppage in play.", + "The whistle has gone; play will resume soon." + ] + }, + { + "id": "content.game_state.timeout", + "index": 112, + "facet": "CONTENT", + "group": "content.game_state", + "exclusivity": "content.game_state", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "soccer" + ], + "status": "core", + "definition": "Team/TV timeout.", + "description": "A team or TV timeout is in effect.", + "paraphrases": [ + "Teams are in a timeout huddle.", + "The game is paused for a timeout.", + "Commercial timeout in the game." + ] + }, + { + "id": "content.game_state.review", + "index": 113, + "facet": "CONTENT", + "group": "content.game_state", + "exclusivity": "content.game_state", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "soccer" + ], + "status": "core", + "definition": "Official video review/challenge in progress.", + "description": "Officials are reviewing a play on video.", + "paraphrases": [ + "A replay review or challenge is underway.", + "Waiting on the video review decision.", + "VAR check in progress." + ] + }, + { + "id": "content.game_state.period_break", + "index": 114, + "facet": "CONTENT", + "group": "content.game_state", + "exclusivity": "content.game_state", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "soccer" + ], + "status": "core", + "definition": "Between periods/quarters/halves.", + "description": "It is the break between periods, quarters or halves.", + "paraphrases": [ + "Halftime or intermission.", + "Between periods of the game.", + "End of the quarter break." + ] + }, + { + "id": "content.game_state.pre_post", + "index": 115, + "facet": "CONTENT", + "group": "content.game_state", + "exclusivity": "content.game_state", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "soccer" + ], + "status": "core", + "definition": "Before start or after end of the game.", + "description": "The game has not started yet or is already over.", + "paraphrases": [ + "Pre-game or post-game time.", + "Before tip-off or after the final whistle.", + "Outside regulation play." + ] + }, + { + "id": "content.legal_phase.argument_petitioner", + "index": 116, + "facet": "CONTENT", + "group": "content.legal_phase", + "exclusivity": "content.legal_phase", + "unit": "G", + "domains": [ + "court_appellate" + ], + "status": "core", + "definition": "Petitioner-side advocate holds the floor (appellate argument).", + "description": "The petitioner's lawyer is arguing before the appellate court.", + "paraphrases": [ + "Oral argument for the petitioner side.", + "Counsel for the petitioner has the floor.", + "Petitioner's opening argument at the Supreme Court." + ] + }, + { + "id": "content.legal_phase.argument_respondent", + "index": 117, + "facet": "CONTENT", + "group": "content.legal_phase", + "exclusivity": "content.legal_phase", + "unit": "G", + "domains": [ + "court_appellate" + ], + "status": "core", + "definition": "Respondent-side advocate holds the floor.", + "description": "The respondent's lawyer is arguing before the appellate court.", + "paraphrases": [ + "Oral argument for the respondent side.", + "Counsel for the respondent has the floor.", + "The other side's argument in the appeal." + ] + }, + { + "id": "content.legal_phase.rebuttal", + "index": 118, + "facet": "CONTENT", + "group": "content.legal_phase", + "exclusivity": "content.legal_phase", + "unit": "G", + "domains": [ + "court_appellate" + ], + "status": "core", + "definition": "Petitioner's rebuttal section.", + "description": "The petitioner's lawyer is giving rebuttal at the end.", + "paraphrases": [ + "Rebuttal argument after the respondent.", + "Reserved rebuttal time.", + "Final rebuttal by the petitioner." + ] + }, + { + "id": "content.legal_phase.opening_statement", + "index": 119, + "facet": "CONTENT", + "group": "content.legal_phase", + "exclusivity": "content.legal_phase", + "unit": "G", + "domains": [ + "court_trial" + ], + "status": "core", + "definition": "Opening statement to the jury.", + "description": "A lawyer gives an opening statement to the jury.", + "paraphrases": [ + "Opening statements at the start of trial.", + "Counsel previews the evidence for the jury.", + "The trial's opening remarks." + ] + }, + { + "id": "content.legal_phase.direct_exam", + "index": 120, + "facet": "CONTENT", + "group": "content.legal_phase", + "exclusivity": "content.legal_phase", + "unit": "G", + "domains": [ + "court_trial" + ], + "status": "core", + "definition": "Direct examination of a witness.", + "description": "A lawyer questions their own witness on direct examination.", + "paraphrases": [ + "Direct examination of the witness.", + "Friendly questioning by the side that called the witness.", + "Open questions on direct." + ] + }, + { + "id": "content.legal_phase.cross_exam", + "index": 121, + "facet": "CONTENT", + "group": "content.legal_phase", + "exclusivity": "content.legal_phase", + "unit": "G", + "domains": [ + "court_trial" + ], + "status": "core", + "definition": "Cross-examination.", + "description": "The opposing lawyer cross-examines the witness.", + "paraphrases": [ + "Cross-examination with leading questions.", + "The other side challenges the witness.", + "Hostile questioning on cross." + ] + }, + { + "id": "content.legal_phase.redirect_recross", + "index": 122, + "facet": "CONTENT", + "group": "content.legal_phase", + "exclusivity": "content.legal_phase", + "unit": "G", + "domains": [ + "court_trial" + ], + "status": "core", + "definition": "Redirect or recross examination.", + "description": "Redirect or recross examination after cross.", + "paraphrases": [ + "Follow-up questioning on redirect.", + "Recross of the witness.", + "Second round of witness questioning." + ] + }, + { + "id": "content.legal_phase.closing_argument", + "index": 123, + "facet": "CONTENT", + "group": "content.legal_phase", + "exclusivity": "content.legal_phase", + "unit": "G", + "domains": [ + "court_trial" + ], + "status": "core", + "definition": "Closing argument to the jury.", + "description": "A lawyer gives the closing argument to the jury.", + "paraphrases": [ + "Closing statements summing up the case.", + "Final argument before deliberation.", + "Counsel's summation." + ] + }, + { + "id": "content.legal_phase.bench_procedure", + "index": 124, + "facet": "CONTENT", + "group": "content.legal_phase", + "exclusivity": "content.legal_phase", + "unit": "G", + "domains": [ + "court_appellate", + "court_trial" + ], + "status": "core", + "definition": "Judge-led matters: rulings discussion, sidebars, jury instructions, verdict reading, scheduling.", + "description": "The judge handles procedure: rulings, sidebars, instructions, scheduling or the verdict.", + "paraphrases": [ + "Jury instructions from the judge.", + "A sidebar or procedural discussion at the bench.", + "The court addresses scheduling or reads the verdict." + ] + }, + { + "id": "content.meeting_phase.opening", + "index": 125, + "facet": "CONTENT", + "group": "content.meeting_phase", + "exclusivity": "content.meeting_phase", + "unit": "G", + "domains": [ + "meeting" + ], + "status": "core", + "definition": "Opening: greetings, introductions, agenda announcement.", + "description": "The meeting is opening with greetings and the agenda.", + "paraphrases": [ + "Start of the meeting and introductions.", + "Kick-off of the meeting.", + "Welcome and agenda overview." + ] + }, + { + "id": "content.meeting_phase.agenda_admin", + "index": 126, + "facet": "CONTENT", + "group": "content.meeting_phase", + "exclusivity": "content.meeting_phase", + "unit": "G", + "domains": [ + "meeting" + ], + "status": "core", + "definition": "Administrative items: agenda, roles, budget, requirements, process.", + "description": "The meeting handles admin items: roles, budget, requirements, process.", + "paraphrases": [ + "Going through administrative agenda points.", + "Discussing budget and project requirements.", + "Housekeeping items in the meeting." + ] + }, + { + "id": "content.meeting_phase.presentation", + "index": 127, + "facet": "CONTENT", + "group": "content.meeting_phase", + "exclusivity": "content.meeting_phase", + "unit": "G", + "domains": [ + "meeting" + ], + "status": "core", + "definition": "One participant presents prepared material.", + "description": "One participant presents prepared material.", + "paraphrases": [ + "A slide presentation in the meeting.", + "Someone walks the group through their findings.", + "Prepared presentation segment." + ] + }, + { + "id": "content.meeting_phase.discussion", + "index": 128, + "facet": "CONTENT", + "group": "content.meeting_phase", + "exclusivity": "content.meeting_phase", + "unit": "G", + "domains": [ + "meeting" + ], + "status": "core", + "definition": "Open discussion / evaluation of options.", + "description": "The group openly discusses and evaluates options.", + "paraphrases": [ + "Free discussion among participants.", + "Debating the design options.", + "Group brainstorming and evaluation." + ] + }, + { + "id": "content.meeting_phase.chitchat", + "index": 129, + "facet": "CONTENT", + "group": "content.meeting_phase", + "exclusivity": "content.meeting_phase", + "unit": "G", + "domains": [ + "meeting" + ], + "status": "core", + "definition": "Off-task social talk.", + "description": "Off-topic social chat in the meeting.", + "paraphrases": [ + "Small talk not about the task.", + "Joking around before getting back to work.", + "Social conversation in the meeting." + ] + }, + { + "id": "content.meeting_phase.closing", + "index": 130, + "facet": "CONTENT", + "group": "content.meeting_phase", + "exclusivity": "content.meeting_phase", + "unit": "G", + "domains": [ + "meeting" + ], + "status": "core", + "definition": "Wrap-up, next steps, goodbyes.", + "description": "The meeting is wrapping up with next steps and goodbyes.", + "paraphrases": [ + "End of meeting summary.", + "Closing the meeting.", + "Wrap-up and action recap." + ] + }, + { + "id": "content.sports_focus.game_action", + "index": 131, + "facet": "CONTENT", + "group": "content.sports_focus", + "exclusivity": "content.sports_focus", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports" + ], + "status": "core", + "definition": "Calls or describes the current/just-happened play.", + "description": "The commentary describes the play happening now.", + "paraphrases": [ + "Calling the current action on the field.", + "What just happened in the game.", + "Live description of the play." + ] + }, + { + "id": "content.sports_focus.analysis_tactics", + "index": 132, + "facet": "CONTENT", + "group": "content.sports_focus", + "exclusivity": "content.sports_focus", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports" + ], + "status": "core", + "definition": "Explains why/how play works: tactics, technique, strategy.", + "description": "The commentary explains tactics and why a play worked.", + "paraphrases": [ + "Breaking down strategy and technique.", + "Tactical analysis of the team's approach.", + "Explaining the scheme rather than calling it." + ] + }, + { + "id": "content.sports_focus.player_team_background", + "index": 133, + "facet": "CONTENT", + "group": "content.sports_focus", + "exclusivity": "content.sports_focus", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports" + ], + "status": "core", + "definition": "Background, form, personal story, history of players/teams.", + "description": "The commentary tells background stories about players or teams.", + "paraphrases": [ + "A player's history, form or personal story.", + "Background on the team's season and journey.", + "Biographical talk about an athlete." + ] + }, + { + "id": "content.sports_focus.stats_records_standings", + "index": 134, + "facet": "CONTENT", + "group": "content.sports_focus", + "exclusivity": "content.sports_focus", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports" + ], + "status": "core", + "definition": "Statistics, records, standings, schedule, playoff picture.", + "description": "The commentary cites statistics, records or standings.", + "paraphrases": [ + "Numbers: averages, records, league table.", + "Where the team sits in the standings.", + "Statistical context about players." + ] + }, + { + "id": "content.sports_focus.injury_health", + "index": 135, + "facet": "CONTENT", + "group": "content.sports_focus", + "exclusivity": "content.sports_focus", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports" + ], + "status": "core", + "definition": "Injury or health status.", + "description": "The commentary is about an injury or a player's health.", + "paraphrases": [ + "A player is hurt and heading to the locker room.", + "Injury update.", + "Talking about who is out injured." + ] + }, + { + "id": "content.sports_focus.officiating_rules", + "index": 136, + "facet": "CONTENT", + "group": "content.sports_focus", + "exclusivity": "content.sports_focus", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports" + ], + "status": "core", + "definition": "Officials' decisions, rules, reviews.", + "description": "The commentary discusses referees' calls or the rules.", + "paraphrases": [ + "Debating whether that should be a foul.", + "Explaining a rule or a review decision.", + "Criticising the officiating." + ] + }, + { + "id": "content.sports_focus.off_game_banter", + "index": 137, + "facet": "CONTENT", + "group": "content.sports_focus", + "exclusivity": "content.sports_focus", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports" + ], + "status": "core", + "definition": "Talk unrelated to the game or sport (personal chat, jokes, other events). Strict: anything about the game is NOT this class.", + "description": "The commentators chat about something unrelated to the game or sport.", + "paraphrases": [ + "Off-topic banter in the booth.", + "Personal jokes and chat with nothing about the game.", + "Talking about dinner plans instead of the match." + ] + }, + { + "id": "content.subject.politics_government", + "index": 138, + "facet": "CONTENT", + "group": "content.subject", + "exclusivity": "content.subject", + "unit": "G", + "domains": [ + "news", + "talk" + ], + "status": "core", + "definition": "Elections, government, legislation, diplomacy.", + "description": "The story is about politics or government.", + "paraphrases": [ + "Elections, Congress and legislation.", + "Political news.", + "Government policy and politicians." + ] + }, + { + "id": "content.subject.economy_business", + "index": 139, + "facet": "CONTENT", + "group": "content.subject", + "exclusivity": "content.subject", + "unit": "G", + "domains": [ + "news", + "talk" + ], + "status": "core", + "definition": "Economy, markets, companies, personal finance.", + "description": "The story is about the economy or business.", + "paraphrases": [ + "Markets, companies and money.", + "Business news and finance.", + "Inflation, jobs and the stock market." + ] + }, + { + "id": "content.subject.crime_justice", + "index": 140, + "facet": "CONTENT", + "group": "content.subject", + "exclusivity": "content.subject", + "unit": "G", + "domains": [ + "news", + "talk" + ], + "status": "core", + "definition": "Crime, policing, courts, trials.", + "description": "The story is about crime, police or the courts.", + "paraphrases": [ + "A criminal case or police investigation.", + "Crime news.", + "Trial and justice system coverage." + ] + }, + { + "id": "content.subject.conflict_security", + "index": 141, + "facet": "CONTENT", + "group": "content.subject", + "exclusivity": "content.subject", + "unit": "G", + "domains": [ + "news", + "talk" + ], + "status": "core", + "definition": "War, terrorism, military, armed conflict.", + "description": "The story is about war, terrorism or the military.", + "paraphrases": [ + "Armed conflict coverage.", + "Security and defence news.", + "Fighting and military operations abroad." + ] + }, + { + "id": "content.subject.disaster_accident", + "index": 142, + "facet": "CONTENT", + "group": "content.subject", + "exclusivity": "content.subject", + "unit": "G", + "domains": [ + "news", + "talk" + ], + "status": "core", + "definition": "Natural disasters, fires, crashes, accidents.", + "description": "The story is about a disaster or accident.", + "paraphrases": [ + "Fires, floods, earthquakes or crashes.", + "Emergency and accident news.", + "A deadly accident report." + ] + }, + { + "id": "content.subject.weather_climate", + "index": 143, + "facet": "CONTENT", + "group": "content.subject", + "exclusivity": "content.subject", + "unit": "G", + "domains": [ + "news", + "talk" + ], + "status": "core", + "definition": "Weather forecasts and climate/environment.", + "description": "The story is about the weather or climate.", + "paraphrases": [ + "Weather forecast.", + "Storms, temperatures and climate change.", + "The forecast for the week." + ] + }, + { + "id": "content.subject.science_tech", + "index": 144, + "facet": "CONTENT", + "group": "content.subject", + "exclusivity": "content.subject", + "unit": "G", + "domains": [ + "news", + "talk" + ], + "status": "core", + "definition": "Science, technology, space, internet.", + "description": "The story is about science or technology.", + "paraphrases": [ + "Tech and gadgets news.", + "Space, research and discoveries.", + "AI and the internet." + ] + }, + { + "id": "content.subject.health_medicine", + "index": 145, + "facet": "CONTENT", + "group": "content.subject", + "exclusivity": "content.subject", + "unit": "G", + "domains": [ + "news", + "talk" + ], + "status": "core", + "definition": "Health, medicine, disease, public health.", + "description": "The story is about health or medicine.", + "paraphrases": [ + "Disease, doctors and public health.", + "Medical news.", + "Health advice and hospitals." + ] + }, + { + "id": "content.subject.education_society", + "index": 146, + "facet": "CONTENT", + "group": "content.subject", + "exclusivity": "content.subject", + "unit": "G", + "domains": [ + "news", + "talk" + ], + "status": "core", + "definition": "Education, social issues, religion, demographics.", + "description": "The story is about education or social issues.", + "paraphrases": [ + "Schools, religion, immigration and society.", + "Social issues and communities.", + "Education policy and students." + ] + }, + { + "id": "content.subject.sports", + "index": 147, + "facet": "CONTENT", + "group": "content.subject", + "exclusivity": "content.subject", + "unit": "G", + "domains": [ + "news", + "talk" + ], + "status": "core", + "definition": "Sport as a subject (news sports desk, sports talk).", + "description": "The story is about sports.", + "paraphrases": [ + "Sports news segment.", + "Talking about games and athletes.", + "The sports desk." + ] + }, + { + "id": "content.subject.arts_entertainment", + "index": 148, + "facet": "CONTENT", + "group": "content.subject", + "exclusivity": "content.subject", + "unit": "G", + "domains": [ + "news", + "talk" + ], + "status": "core", + "definition": "Film, music, TV, celebrities, games as culture.", + "description": "The story is about entertainment or culture.", + "paraphrases": [ + "Movies, music, TV and celebrities.", + "Showbiz news.", + "Arts and pop culture." + ] + }, + { + "id": "content.subject.lifestyle_personal", + "index": 149, + "facet": "CONTENT", + "group": "content.subject", + "exclusivity": "content.subject", + "unit": "G", + "domains": [ + "news", + "talk" + ], + "status": "core", + "definition": "Personal life, relationships, food, travel, hobbies.", + "description": "The talk is about personal life or lifestyle.", + "paraphrases": [ + "Relationships, food, travel and hobbies.", + "Personal stories and everyday life.", + "Lifestyle chat." + ] + }, + { + "id": "content.story_start", + "index": 150, + "facet": "CONTENT", + "group": "content", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "meeting" + ], + "status": "core", + "definition": "A new editorial unit starts: new news story, talk topic, sports coverage segment, esports game/map, meeting topic.", + "description": "A new story or topic begins here.", + "paraphrases": [ + "Moving on to the next story.", + "Start of a new segment or topic.", + "In other news..." + ] + }, + { + "id": "content.breaking_news", + "index": 151, + "facet": "CONTENT", + "group": "content", + "exclusivity": "independent", + "unit": "G", + "domains": [ + "news" + ], + "status": "experimental", + "definition": "The presenter announces breaking/developing news for this story.", + "description": "The presenter announces breaking news.", + "paraphrases": [ + "This just in: a developing story.", + "Breaking news alert.", + "Urgent developing news." + ] + }, + { + "id": "item.esports_title.league_of_legends", + "index": 152, + "facet": "CONTENT", + "group": "item.esports_title", + "exclusivity": "item.esports_title", + "unit": "D", + "domains": [ + "esports" + ], + "status": "core", + "definition": "The item covers league of legends.", + "description": "The esports broadcast is League of Legends.", + "paraphrases": [ + "LoL match coverage.", + "Summoner's Rift, champions and Baron.", + "League of Legends esports." + ] + }, + { + "id": "item.esports_title.valorant", + "index": 153, + "facet": "CONTENT", + "group": "item.esports_title", + "exclusivity": "item.esports_title", + "unit": "D", + "domains": [ + "esports" + ], + "status": "core", + "definition": "The item covers valorant.", + "description": "The esports broadcast is Valorant.", + "paraphrases": [ + "Valorant match with agents like Jett and Sova.", + "VCT coverage.", + "A Valorant game." + ] + }, + { + "id": "item.esports_title.counter_strike", + "index": 154, + "facet": "CONTENT", + "group": "item.esports_title", + "exclusivity": "item.esports_title", + "unit": "D", + "domains": [ + "esports" + ], + "status": "core", + "definition": "The item covers counter strike.", + "description": "The esports broadcast is Counter-Strike.", + "paraphrases": [ + "CS match on maps like Mirage and Inferno.", + "Counter-Strike with AWPs and bomb plants.", + "A CS tournament." + ] + }, + { + "id": "item.esports_title.dota2", + "index": 155, + "facet": "CONTENT", + "group": "item.esports_title", + "exclusivity": "item.esports_title", + "unit": "D", + "domains": [ + "esports" + ], + "status": "core", + "definition": "The item covers dota2.", + "description": "The esports broadcast is Dota 2.", + "paraphrases": [ + "Dota match with Roshan.", + "Dota 2 tournament coverage.", + "A Dota game." + ] + }, + { + "id": "item.esports_title.rocket_league", + "index": 156, + "facet": "CONTENT", + "group": "item.esports_title", + "exclusivity": "item.esports_title", + "unit": "D", + "domains": [ + "esports" + ], + "status": "core", + "definition": "The item covers rocket league.", + "description": "The esports broadcast is Rocket League.", + "paraphrases": [ + "Rocket League cars and goals.", + "RLCS coverage.", + "A Rocket League match." + ] + }, + { + "id": "item.esports_title.call_of_duty", + "index": 157, + "facet": "CONTENT", + "group": "item.esports_title", + "exclusivity": "item.esports_title", + "unit": "D", + "domains": [ + "esports" + ], + "status": "core", + "definition": "The item covers call of duty.", + "description": "The esports broadcast is Call of Duty.", + "paraphrases": [ + "CoD league match.", + "Call of Duty esports.", + "A Call of Duty game." + ] + }, + { + "id": "item.esports_title.other_title", + "index": 158, + "facet": "CONTENT", + "group": "item.esports_title", + "exclusivity": "item.esports_title", + "unit": "D", + "domains": [ + "esports" + ], + "status": "core", + "definition": "The item covers a game outside the listed titles (defined, not a catch-all for unknown).", + "description": "The esports broadcast is a game other than the main listed titles.", + "paraphrases": [ + "A different video game tournament.", + "Some other esports title.", + "An unlisted game being cast." + ] + }, + { + "id": "item.case_area.criminal", + "index": 159, + "facet": "CONTENT", + "group": "item.case_area", + "exclusivity": "item.case_area", + "unit": "D", + "domains": [ + "court_appellate", + "court_trial" + ], + "status": "core", + "definition": "Criminal law and procedure (incl. violent and non-violent crimes, sentencing).", + "description": "The case is about criminal law.", + "paraphrases": [ + "A criminal prosecution or sentencing.", + "Criminal procedure case.", + "A crime and its trial." + ] + }, + { + "id": "item.case_area.civil_rights_liberties", + "index": 160, + "facet": "CONTENT", + "group": "item.case_area", + "exclusivity": "item.case_area", + "unit": "D", + "domains": [ + "court_appellate", + "court_trial" + ], + "status": "core", + "definition": "Civil rights, First Amendment, due process, privacy, equal protection.", + "description": "The case is about civil rights or civil liberties.", + "paraphrases": [ + "Free speech, due process or equal protection.", + "A constitutional rights case.", + "Privacy and First Amendment issues." + ] + }, + { + "id": "item.case_area.economic_regulatory", + "index": 161, + "facet": "CONTENT", + "group": "item.case_area", + "exclusivity": "item.case_area", + "unit": "D", + "domains": [ + "court_appellate", + "court_trial" + ], + "status": "core", + "definition": "Business, economic activity, labour, tax, government regulation.", + "description": "The case is about business, labour, tax or regulation.", + "paraphrases": [ + "An economic or regulatory dispute.", + "Securities, antitrust or tax law.", + "Government regulation of business." + ] + }, + { + "id": "item.case_area.government_powers", + "index": 162, + "facet": "CONTENT", + "group": "item.case_area", + "exclusivity": "item.case_area", + "unit": "D", + "domains": [ + "court_appellate", + "court_trial" + ], + "status": "core", + "definition": "Federalism, separation of powers, judicial power, jurisdiction.", + "description": "The case is about powers of government and courts.", + "paraphrases": [ + "Federalism and separation of powers.", + "Jurisdiction of the courts.", + "Which government body has authority." + ] + }, + { + "id": "item.case_area.private_civil", + "index": 163, + "facet": "CONTENT", + "group": "item.case_area", + "exclusivity": "item.case_area", + "unit": "D", + "domains": [ + "court_appellate", + "court_trial" + ], + "status": "core", + "definition": "Private disputes: torts, contracts, family, property, attorney matters.", + "description": "The case is a private civil dispute.", + "paraphrases": [ + "Contracts, torts, property or family law.", + "One private party suing another.", + "A civil lawsuit between individuals or companies." + ] + }, + { + "id": "event.sports.score.recent", + "index": 164, + "facet": "EVENTS", + "group": "event.sports", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "nba", + "nhl", + "nfl", + "soccer" + ], + "status": "core", + "definition": "A scoring play changes the score (NBA basket/three/free throw, NHL goal, NFL TD/FG/safety, soccer goal; not cricket runs). .recent: the audio instant is within the last 10 s.", + "description": "A team scores - it just happened seconds ago.", + "paraphrases": [ + "Moments ago: points go on the board.", + "Right after this: a goal or basket is scored.", + "Reaction to what just happened: the score changes." + ] + }, + { + "id": "event.sports.score.imminent", + "index": 165, + "facet": "EVENTS", + "group": "event.sports", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "nba", + "nhl", + "nfl", + "soccer" + ], + "status": "experimental", + "definition": "A scoring play changes the score (NBA basket/three/free throw, NHL goal, NFL TD/FG/safety, soccer goal; not cricket runs). .imminent: the instant is within the next 30 s.", + "description": "Before it happens: a scoring chance is building up; a team scores may follow within half a minute.", + "paraphrases": [ + "Tension rises as a scoring chance is building up.", + "Commentators anticipate: a scoring chance is building up.", + "Not yet: a scoring chance is building up, outcome still open." + ] + }, + { + "id": "event.sports.touchdown.recent", + "index": 166, + "facet": "EVENTS", + "group": "event.sports", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "nfl" + ], + "status": "core", + "definition": "NFL touchdown (refines score). .recent: the audio instant is within the last 10 s.", + "description": "An NFL touchdown is scored - it just happened seconds ago.", + "paraphrases": [ + "Moments ago: touchdown!", + "Right after this: the ball crosses the goal line for six.", + "Reaction to what just happened: a touchdown play." + ] + }, + { + "id": "event.sports.touchdown.imminent", + "index": 167, + "facet": "EVENTS", + "group": "event.sports", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "nfl" + ], + "status": "experimental", + "definition": "NFL touchdown (refines score). .imminent: the instant is within the next 30 s.", + "description": "Before it happens: the offense is driving deep toward the end zone; an NFL touchdown is scored may follow within half a minute.", + "paraphrases": [ + "Tension rises as the offense is driving deep toward the end zone.", + "Commentators anticipate: the offense is driving deep toward the end zone.", + "Not yet: the offense is driving deep toward the end zone, outcome still open." + ] + }, + { + "id": "event.sports.field_goal.recent", + "index": 168, + "facet": "EVENTS", + "group": "event.sports", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "nfl" + ], + "status": "core", + "definition": "NFL field goal made (refines score). .recent: the audio instant is within the last 10 s.", + "description": "An NFL field goal is made - it just happened seconds ago.", + "paraphrases": [ + "Moments ago: the kick is good for three.", + "Right after this: field goal through the uprights.", + "Reaction to what just happened: a made field goal." + ] + }, + { + "id": "event.sports.field_goal.imminent", + "index": 169, + "facet": "EVENTS", + "group": "event.sports", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "nfl" + ], + "status": "experimental", + "definition": "NFL field goal made (refines score). .imminent: the instant is within the next 30 s.", + "description": "Before it happens: the kicking unit lines up for an attempt; an NFL field goal is made may follow within half a minute.", + "paraphrases": [ + "Tension rises as the kicking unit lines up for an attempt.", + "Commentators anticipate: the kicking unit lines up for an attempt.", + "Not yet: the kicking unit lines up for an attempt, outcome still open." + ] + }, + { + "id": "event.sports.wicket.recent", + "index": 170, + "facet": "EVENTS", + "group": "event.sports", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "cricket" + ], + "status": "core", + "definition": "Cricket dismissal. .recent: the audio instant is within the last 10 s.", + "description": "A cricket batter is out - it just happened seconds ago.", + "paraphrases": [ + "Moments ago: wicket falls.", + "Right after this: bowled, caught or lbw.", + "Reaction to what just happened: a dismissal in cricket." + ] + }, + { + "id": "event.sports.wicket.imminent", + "index": 171, + "facet": "EVENTS", + "group": "event.sports", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "cricket" + ], + "status": "experimental", + "definition": "Cricket dismissal. .imminent: the instant is within the next 30 s.", + "description": "Before it happens: the bowler is building pressure on the batter; a cricket batter is out may follow within half a minute.", + "paraphrases": [ + "Tension rises as the bowler is building pressure on the batter.", + "Commentators anticipate: the bowler is building pressure on the batter.", + "Not yet: the bowler is building pressure on the batter, outcome still open." + ] + }, + { + "id": "event.sports.boundary.recent", + "index": 172, + "facet": "EVENTS", + "group": "event.sports", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "cricket" + ], + "status": "core", + "definition": "Cricket four or six. .recent: the audio instant is within the last 10 s.", + "description": "A cricket boundary: four or six runs - it just happened seconds ago.", + "paraphrases": [ + "Moments ago: that's four!", + "Right after this: hit over the rope for six.", + "Reaction to what just happened: a boundary is struck." + ] + }, + { + "id": "event.sports.boundary.imminent", + "index": 173, + "facet": "EVENTS", + "group": "event.sports", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "cricket" + ], + "status": "experimental", + "definition": "Cricket four or six. .imminent: the instant is within the next 30 s.", + "description": "Before it happens: the batter is shaping to attack the bowling; a cricket boundary: four or six runs may follow within half a minute.", + "paraphrases": [ + "Tension rises as the batter is shaping to attack the bowling.", + "Commentators anticipate: the batter is shaping to attack the bowling.", + "Not yet: the batter is shaping to attack the bowling, outcome still open." + ] + }, + { + "id": "event.sports.turnover.recent", + "index": 174, + "facet": "EVENTS", + "group": "event.sports", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "nba", + "nfl" + ], + "status": "core", + "definition": "Possession lost by steal/turnover (NBA), interception/fumble lost/turnover on downs (NFL). .recent: the audio instant is within the last 10 s.", + "description": "Possession is lost to the other team - it just happened seconds ago.", + "paraphrases": [ + "Moments ago: a steal or interception.", + "Right after this: turnover: they give the ball away.", + "Reaction to what just happened: fumble lost." + ] + }, + { + "id": "event.sports.penalty_foul.recent", + "index": 175, + "facet": "EVENTS", + "group": "event.sports", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "nba", + "nhl", + "nfl", + "soccer" + ], + "status": "core", + "definition": "Official penalty call: NFL accepted penalty, NHL penalty, NBA technical/flagrant, soccer card. .recent: the audio instant is within the last 10 s.", + "description": "The official calls a penalty or foul - it just happened seconds ago.", + "paraphrases": [ + "Moments ago: flag on the play.", + "Right after this: a player gets a penalty or card.", + "Reaction to what just happened: technical foul called." + ] + }, + { + "id": "event.sports.timeout_called.recent", + "index": 176, + "facet": "EVENTS", + "group": "event.sports", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "nba", + "nhl", + "nfl" + ], + "status": "core", + "definition": "Team or TV timeout called. .recent: the audio instant is within the last 10 s.", + "description": "A timeout is called - it just happened seconds ago.", + "paraphrases": [ + "Moments ago: the coach calls time.", + "Right after this: tV timeout.", + "Reaction to what just happened: teams take a timeout." + ] + }, + { + "id": "event.sports.review_challenge.recent", + "index": 177, + "facet": "EVENTS", + "group": "event.sports", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "nba", + "nhl", + "nfl", + "soccer" + ], + "status": "core", + "definition": "Official video review / VAR check starts. .recent: the audio instant is within the last 10 s.", + "description": "Officials start a video review or challenge - it just happened seconds ago.", + "paraphrases": [ + "Moments ago: the play is under review.", + "Right after this: coach's challenge.", + "Reaction to what just happened: vAR check begins." + ] + }, + { + "id": "event.sports.injury_stoppage.recent", + "index": 178, + "facet": "EVENTS", + "group": "event.sports", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "nfl" + ], + "status": "core", + "definition": "Play stops for an injury. .recent: the audio instant is within the last 10 s.", + "description": "Play stops because a player is injured - it just happened seconds ago.", + "paraphrases": [ + "Moments ago: injury timeout on the field.", + "Right after this: a player is down and play halts.", + "Reaction to what just happened: trainers come out for an injury." + ] + }, + { + "id": "event.sports.substitution.recent", + "index": 179, + "facet": "EVENTS", + "group": "event.sports", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "soccer" + ], + "status": "core", + "definition": "Player substitution (soccer). .recent: the audio instant is within the last 10 s.", + "description": "A soccer substitution is made - it just happened seconds ago.", + "paraphrases": [ + "Moments ago: a player comes off and another comes on.", + "Right after this: change of personnel.", + "Reaction to what just happened: substitute enters the match." + ] + }, + { + "id": "event.sports.period_end.recent", + "index": 180, + "facet": "EVENTS", + "group": "event.sports", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket" + ], + "status": "core", + "definition": "End of a period/quarter/half/innings. .recent: the audio instant is within the last 10 s.", + "description": "A period, quarter, half or innings ends - it just happened seconds ago.", + "paraphrases": [ + "Moments ago: end of the quarter.", + "Right after this: the buzzer sounds to end the half.", + "Reaction to what just happened: innings complete." + ] + }, + { + "id": "event.sports.shot_on_target.recent", + "index": 181, + "facet": "EVENTS", + "group": "event.sports", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "soccer" + ], + "status": "core", + "definition": "Shot on target without a goal (soccer). .recent: the audio instant is within the last 10 s.", + "description": "A soccer shot on target is saved or blocked - it just happened seconds ago.", + "paraphrases": [ + "Moments ago: shot on goal but no goal.", + "Right after this: the keeper makes a save.", + "Reaction to what just happened: a good chance on target." + ] + }, + { + "id": "event.legal.objection.recent", + "index": 182, + "facet": "EVENTS", + "group": "event.legal", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "court_trial" + ], + "status": "core", + "definition": "Counsel objects. .recent: within the last 10 s.", + "description": "A lawyer stands and objects to a question or evidence - it just happened seconds ago.", + "paraphrases": [ + "Moments ago: objection, Your Honor, hearsay.", + "Right after this: counsel protests the question as leading.", + "Reaction to what just happened: the attorney challenges the testimony." + ] + }, + { + "id": "event.legal.ruling_sustained.recent", + "index": 183, + "facet": "EVENTS", + "group": "event.legal", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "court_trial" + ], + "status": "core", + "definition": "Judge sustains an objection. .recent: within the last 10 s.", + "description": "The judge agrees with the lawyer and blocks the question - it just happened seconds ago.", + "paraphrases": [ + "Moments ago: sustained; rephrase your question, counsel.", + "Right after this: the jury is told to disregard the answer.", + "Reaction to what just happened: the court upholds the challenge." + ] + }, + { + "id": "event.legal.ruling_overruled.recent", + "index": 184, + "facet": "EVENTS", + "group": "event.legal", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "court_trial" + ], + "status": "core", + "definition": "Judge overrules an objection. .recent: within the last 10 s.", + "description": "The judge rejects the lawyer's challenge and lets the question stand - it just happened seconds ago.", + "paraphrases": [ + "Moments ago: overruled; the witness may answer.", + "Right after this: the court denies the objection.", + "Reaction to what just happened: go ahead and answer the question." + ] + }, + { + "id": "event.meeting.decision.recent", + "index": 185, + "facet": "EVENTS", + "group": "event.meeting", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "meeting" + ], + "status": "experimental", + "definition": "The group has just reached a decision.", + "description": "The meeting reaches a decision - it just happened seconds ago.", + "paraphrases": [ + "Moments ago: the group agrees on what to do.", + "Right after this: decision made.", + "Reaction to what just happened: so we go with this option." + ] + }, + { + "id": "event.meeting.action_item.recent", + "index": 186, + "facet": "EVENTS", + "group": "event.meeting", + "exclusivity": "independent", + "unit": "E", + "domains": [ + "meeting" + ], + "status": "experimental", + "definition": "A task was just assigned/accepted.", + "description": "A task is assigned to someone in the meeting - it just happened seconds ago.", + "paraphrases": [ + "Moments ago: action item: someone will do this.", + "Right after this: giving a person a to-do.", + "Reaction to what just happened: assigning responsibility for a task." + ] + }, + { + "id": "scene.cheer", + "index": 187, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "meeting", + "court_appellate", + "court_trial", + "assistant" + ], + "status": "core", + "definition": "Crowd cheering/roar (excited crowd vocalisation).", + "description": "The crowd cheers and roars.", + "paraphrases": [ + "Fans erupt in cheering.", + "Loud crowd excitement.", + "The arena roars." + ] + }, + { + "id": "scene.applause", + "index": 188, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "meeting", + "court_appellate", + "court_trial", + "assistant" + ], + "status": "core", + "definition": "Clapping by many people.", + "description": "People clap their hands.", + "paraphrases": [ + "Applause from the audience.", + "A round of clapping.", + "Hand clapping from many people." + ] + }, + { + "id": "scene.crowd_ambience", + "index": 189, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "meeting", + "court_appellate", + "court_trial", + "assistant" + ], + "status": "core", + "definition": "Background crowd noise without cheering (murmur, hubbub).", + "description": "Background crowd murmur without cheering.", + "paraphrases": [ + "The hum of a large crowd.", + "Ambient stadium noise.", + "Crowd chatter in the background." + ] + }, + { + "id": "scene.boo_jeer", + "index": 190, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "meeting", + "court_appellate", + "court_trial", + "assistant" + ], + "status": "experimental", + "definition": "Crowd booing/jeering.", + "description": "The crowd boos.", + "paraphrases": [ + "Fans jeer and boo.", + "Booing in the stadium.", + "Disapproving crowd." + ] + }, + { + "id": "scene.chant", + "index": 191, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "meeting", + "court_appellate", + "court_trial", + "assistant" + ], + "status": "experimental", + "definition": "Rhythmic crowd chanting/singing.", + "description": "The crowd chants in rhythm.", + "paraphrases": [ + "Fans singing a chant together.", + "Rhythmic crowd chant.", + "Supporters chanting the team's name." + ] + }, + { + "id": "scene.music", + "index": 192, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "meeting", + "court_appellate", + "court_trial", + "assistant" + ], + "status": "core", + "definition": "Music is audible (foreground or bed).", + "description": "Music is playing.", + "paraphrases": [ + "You can hear music, in the foreground or as a bed.", + "A song or instrumental plays.", + "Background music under the talk." + ] + }, + { + "id": "scene.laughter", + "index": 193, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "meeting", + "court_appellate", + "court_trial", + "assistant" + ], + "status": "core", + "definition": "Human laughter.", + "description": "People laugh.", + "paraphrases": [ + "Laughter from the speakers or audience.", + "Someone bursts out laughing.", + "Laughing." + ] + }, + { + "id": "scene.horn", + "index": 194, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer" + ], + "status": "core", + "definition": "Goal horn / air horn / vehicle horn.", + "description": "A horn sounds, like a goal horn or air horn.", + "paraphrases": [ + "The goal horn blares.", + "Air horn blast.", + "Loud horn." + ] + }, + { + "id": "scene.whistle", + "index": 195, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer" + ], + "status": "core", + "definition": "Referee or other whistle.", + "description": "A whistle blows.", + "paraphrases": [ + "The referee's whistle.", + "Sharp whistle sound.", + "Whistle stops play." + ] + }, + { + "id": "scene.buzzer", + "index": 196, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer" + ], + "status": "core", + "definition": "Shot-clock / period buzzer.", + "description": "A buzzer sounds.", + "paraphrases": [ + "The shot-clock or end-of-period buzzer.", + "Electronic buzzer in the arena.", + "Buzzer beater sound." + ] + }, + { + "id": "scene.gunshot_explosion", + "index": 197, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "esports", + "news" + ], + "status": "experimental", + "definition": "Gunfire or explosion sounds (incl. video-game audio).", + "description": "Gunfire or an explosion is heard.", + "paraphrases": [ + "In-game gunshots in a shooter.", + "Explosion sounds.", + "Shots fired." + ] + }, + { + "id": "scene.silence", + "index": 198, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "meeting", + "court_appellate", + "court_trial", + "assistant" + ], + "status": "core", + "definition": "Near-silence: level < -50 dBFS for >= 0.5 s.", + "description": "It is nearly silent.", + "paraphrases": [ + "Dead air with almost no sound.", + "Quiet with no talking or noise.", + "Silence." + ] + }, + { + "id": "affect.emotion.neutral", + "index": 199, + "facet": "AFFECT", + "group": "affect.emotion", + "exclusivity": "affect.emotion", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "No marked emotion.", + "description": "The voice sounds emotionally neutral.", + "paraphrases": [ + "Calm, matter-of-fact tone.", + "No particular emotion in the voice.", + "Neutral delivery." + ] + }, + { + "id": "affect.emotion.happy", + "index": 200, + "facet": "AFFECT", + "group": "affect.emotion", + "exclusivity": "affect.emotion", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Joy/excitement-positive voice.", + "description": "The voice sounds happy or joyful.", + "paraphrases": [ + "Cheerful, upbeat tone.", + "The speaker sounds pleased.", + "Joy in the voice." + ] + }, + { + "id": "affect.emotion.angry", + "index": 201, + "facet": "AFFECT", + "group": "affect.emotion", + "exclusivity": "affect.emotion", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Anger/irritation in the voice.", + "description": "The voice sounds angry.", + "paraphrases": [ + "Irritated, annoyed tone.", + "The speaker sounds mad.", + "Anger in the voice." + ] + }, + { + "id": "affect.emotion.sad", + "index": 202, + "facet": "AFFECT", + "group": "affect.emotion", + "exclusivity": "affect.emotion", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Sadness.", + "description": "The voice sounds sad.", + "paraphrases": [ + "Downcast, sorrowful tone.", + "The speaker sounds unhappy.", + "Sadness in the voice." + ] + }, + { + "id": "affect.emotion.surprised", + "index": 203, + "facet": "AFFECT", + "group": "affect.emotion", + "exclusivity": "affect.emotion", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Surprise.", + "description": "The voice sounds surprised.", + "paraphrases": [ + "Astonished tone.", + "The speaker is caught off guard.", + "Surprise in the voice." + ] + }, + { + "id": "affect.arousal.low", + "index": 204, + "facet": "AFFECT", + "group": "affect.arousal", + "exclusivity": "affect.arousal", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Calm, low-energy delivery.", + "description": "The speaker sounds calm and low-energy.", + "paraphrases": [ + "Relaxed, quiet delivery.", + "Subdued voice.", + "Low vocal activation." + ] + }, + { + "id": "affect.arousal.medium", + "index": 205, + "facet": "AFFECT", + "group": "affect.arousal", + "exclusivity": "affect.arousal", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Engaged, normal broadcast energy.", + "description": "The speaker sounds engaged at a normal energy.", + "paraphrases": [ + "Typical broadcast energy.", + "Moderately animated voice.", + "Normal activation." + ] + }, + { + "id": "affect.arousal.high", + "index": 206, + "facet": "AFFECT", + "group": "affect.arousal", + "exclusivity": "affect.arousal", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Excited, shouting, highly activated voice.", + "description": "The speaker sounds very excited or agitated.", + "paraphrases": [ + "Shouting, high-energy voice.", + "Highly animated, intense delivery.", + "Screaming with excitement." + ] + }, + { + "id": "safety.toxicity.insult_harassment", + "index": 207, + "facet": "SAFETY", + "group": "safety.toxicity", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_trial", + "short_clips" + ], + "status": "core", + "definition": "Insult, demeaning or harassing language at a person.", + "description": "The speaker insults or harasses someone.", + "paraphrases": [ + "Demeaning, abusive words at a person.", + "Name-calling and bullying.", + "Harassing language." + ] + }, + { + "id": "safety.toxicity.hate_identity", + "index": 208, + "facet": "SAFETY", + "group": "safety.toxicity", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_trial", + "short_clips" + ], + "status": "core", + "definition": "Attack on a protected identity group (slurs, dehumanisation).", + "description": "The speaker attacks a group based on identity.", + "paraphrases": [ + "A slur or hateful remark about race, religion or gender.", + "Hate speech.", + "Dehumanising language about a group." + ] + }, + { + "id": "safety.toxicity.threat_violence", + "index": 209, + "facet": "SAFETY", + "group": "safety.toxicity", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_trial", + "short_clips" + ], + "status": "core", + "definition": "Threat or incitement of violence.", + "description": "The speaker threatens violence.", + "paraphrases": [ + "I'm going to hurt you.", + "Inciting violence.", + "A violent threat." + ] + }, + { + "id": "safety.toxicity.sexual_explicit", + "index": 210, + "facet": "SAFETY", + "group": "safety.toxicity", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_trial", + "short_clips" + ], + "status": "core", + "definition": "Sexually explicit or harassing sexual content.", + "description": "The speaker says something sexually explicit.", + "paraphrases": [ + "Sexual harassment or explicit sexual talk.", + "Lewd sexual content.", + "Explicit sexual remarks." + ] + }, + { + "id": "safety.toxicity.self_harm", + "index": 211, + "facet": "SAFETY", + "group": "safety.toxicity", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_trial", + "short_clips" + ], + "status": "core", + "definition": "Encouragement or glorification of self-harm.", + "description": "The speaker encourages self-harm or suicide.", + "paraphrases": [ + "Glorifying self-harm.", + "Telling someone to hurt themselves.", + "Self-harm encouragement." + ] + }, + { + "id": "safety.toxicity.toxic_any", + "index": 212, + "facet": "SAFETY", + "group": "safety.toxicity", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_trial", + "short_clips" + ], + "status": "derived", + "definition": "Any toxicity category is present.", + "description": "Any toxic speech.", + "paraphrases": [ + "Abusive content of any category.", + "Toxic language.", + "Harmful speech." + ] + }, + { + "id": "safety.profanity.mild", + "index": 213, + "facet": "SAFETY", + "group": "safety.profanity", + "exclusivity": "safety.profanity", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Mild profanity (damn, hell, crap).", + "description": "A mild swear word like damn or hell.", + "paraphrases": [ + "Light profanity.", + "Mild cursing.", + "Crap, damn, hell." + ] + }, + { + "id": "safety.profanity.strong", + "index": 214, + "facet": "SAFETY", + "group": "safety.profanity", + "exclusivity": "safety.profanity", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Strong profanity / obscenity.", + "description": "A strong swear word or obscenity.", + "paraphrases": [ + "The f-word or similar.", + "Strong profanity that would be bleeped.", + "Obscene language." + ] + }, + { + "id": "safety.pii.phone_number", + "index": 215, + "facet": "SAFETY", + "group": "safety.pii", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Spoken phone number of a private person.", + "description": "A private person says a phone number.", + "paraphrases": [ + "Someone reads out their phone number.", + "Personal telephone number spoken.", + "Call me at this number." + ] + }, + { + "id": "safety.pii.email", + "index": 216, + "facet": "SAFETY", + "group": "safety.pii", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Spoken email of a private person.", + "description": "A private email address is spoken.", + "paraphrases": [ + "Someone spells out their email.", + "Personal email address.", + "My email is..." + ] + }, + { + "id": "safety.pii.street_address", + "index": 217, + "facet": "SAFETY", + "group": "safety.pii", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Spoken street address of a private person.", + "description": "A private street address is spoken.", + "paraphrases": [ + "Someone gives their home address.", + "House number and street.", + "Where I live is..." + ] + }, + { + "id": "safety.pii.private_name", + "index": 218, + "facet": "SAFETY", + "group": "safety.pii", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Spoken private name of a private person.", + "description": "The name of a private, non-public person is spoken.", + "paraphrases": [ + "A caller's or witness's personal name.", + "Name of an ordinary private individual.", + "Private person's full name." + ] + }, + { + "id": "safety.pii.card_account_number", + "index": 219, + "facet": "SAFETY", + "group": "safety.pii", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Spoken card account number of a private person.", + "description": "A card or account number is spoken.", + "paraphrases": [ + "Credit card digits read out.", + "Bank account number.", + "Account number disclosed." + ] + }, + { + "id": "safety.pii.gov_id", + "index": 220, + "facet": "SAFETY", + "group": "safety.pii", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Spoken gov id of a private person.", + "description": "A government ID number is spoken.", + "paraphrases": [ + "Social security or passport number.", + "National ID digits.", + "Driver's licence number." + ] + }, + { + "id": "safety.pii.url_handle", + "index": 221, + "facet": "SAFETY", + "group": "safety.pii", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Spoken url handle of a private person.", + "description": "A private social media handle or URL is spoken.", + "paraphrases": [ + "Someone's personal username.", + "Find me at my handle.", + "Personal web address." + ] + }, + { + "id": "safety.pii.date_of_birth", + "index": 222, + "facet": "SAFETY", + "group": "safety.pii", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Spoken date of birth of a private person.", + "description": "A private person's date of birth is spoken.", + "paraphrases": [ + "My birthday is...", + "Date of birth disclosed.", + "Born on this date." + ] + }, + { + "id": "entity.mention.person", + "index": 223, + "facet": "ENTITIES", + "group": "entity.mention", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "A named individual (real or fictional), incl. nicknames and possessives.", + "description": "A person is mentioned by name.", + "paraphrases": [ + "Someone's name or nickname is said.", + "A named individual: player, politician, justice.", + "Steph hits the three." + ] + }, + { + "id": "entity.mention.team", + "index": 224, + "facet": "ENTITIES", + "group": "entity.mention", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "A sports/esports team or national side as a competitor.", + "description": "A sports or esports team is named.", + "paraphrases": [ + "The Celtics, the Warriors, a national side.", + "Team name mentioned as a competitor.", + "The city name meaning the team." + ] + }, + { + "id": "entity.mention.organization", + "index": 225, + "facet": "ENTITIES", + "group": "entity.mention", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Company, institution, league, agency, court, party, media outlet, school.", + "description": "An organisation is named: company, league, agency, court or party.", + "paraphrases": [ + "The NHL, the IRS, Congress.", + "A named institution.", + "Mention of a company or agency." + ] + }, + { + "id": "entity.mention.place", + "index": 226, + "facet": "ENTITIES", + "group": "entity.mention", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Geopolitical entity, location, venue, street.", + "description": "A place is named: country, city, venue or street.", + "paraphrases": [ + "Location mentioned by name.", + "We're live from Anfield.", + "A geographic name." + ] + }, + { + "id": "entity.mention.work_product", + "index": 227, + "facet": "ENTITIES", + "group": "entity.mention", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Product, brand, app, vehicle, title of a book/film/song/show/podcast.", + "description": "A product, brand, app or title of a work is named.", + "paraphrases": [ + "A movie, song, book or podcast title.", + "Brand or product name.", + "The name of an app or vehicle." + ] + }, + { + "id": "entity.mention.event_name", + "index": 228, + "facet": "ENTITIES", + "group": "entity.mention", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Named event.", + "description": "A named event is mentioned.", + "paraphrases": [ + "The Champions League or the 2024 election.", + "Tournament name.", + "A specific named event." + ] + }, + { + "id": "entity.mention.legal_ref", + "index": 229, + "facet": "ENTITIES", + "group": "entity.mention", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Case name, statute, act, rule, constitutional clause.", + "description": "A legal reference: case name, statute or constitutional clause.", + "paraphrases": [ + "The Fourth Amendment.", + "A cited case like Smith v. Jones.", + "The name of an act or rule." + ] + }, + { + "id": "entity.mention.game_entity", + "index": 230, + "facet": "ENTITIES", + "group": "entity.mention", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Video-game character, hero, champion, item, ability, objective.", + "description": "A video-game character, item or objective is named.", + "paraphrases": [ + "A champion or hero name.", + "The AWP or Baron.", + "In-game character mentioned." + ] + }, + { + "id": "entity.mention.game_map", + "index": 231, + "facet": "ENTITIES", + "group": "entity.mention", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Video-game map.", + "description": "A video-game map is named.", + "paraphrases": [ + "Mirage or Summoner's Rift.", + "The map being played.", + "Name of an esports map." + ] + }, + { + "id": "entity.numeral", + "index": 232, + "facet": "ENTITIES", + "group": "entity.numeral", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Word is inside a numeral mention (number words + their unit/qualifier words).", + "description": "A number is being spoken.", + "paraphrases": [ + "Someone says a numeral with its unit.", + "Spoken number like three minutes left.", + "Numbers in speech." + ] + }, + { + "id": "entity.number.score", + "index": 233, + "facet": "ENTITIES", + "group": "entity.number", + "exclusivity": "entity.number", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Game score (pair or one side / margin).", + "description": "A game score is spoken.", + "paraphrases": [ + "102 to 98.", + "The score is two-one.", + "Cricket 101 for 6." + ] + }, + { + "id": "entity.number.clock", + "index": 234, + "facet": "ENTITIES", + "group": "entity.number", + "exclusivity": "entity.number", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Game clock with direction (remaining/elapsed) and period.", + "description": "The game clock is spoken.", + "paraphrases": [ + "Nine thirty-six left in the third.", + "Time remaining in the period.", + "Two minutes into the half." + ] + }, + { + "id": "entity.number.time_of_day", + "index": 235, + "facet": "ENTITIES", + "group": "entity.number", + "exclusivity": "entity.number", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Clock time of day.", + "description": "A time of day is spoken.", + "paraphrases": [ + "Five pm.", + "At seven thirty tonight.", + "Clock time." + ] + }, + { + "id": "entity.number.date", + "index": 236, + "facet": "ENTITIES", + "group": "entity.number", + "exclusivity": "entity.number", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Calendar date, year, decade, weekday or relative date (REL form).", + "description": "A date or year is spoken.", + "paraphrases": [ + "Twenty twenty-three.", + "On Tuesday the fourteenth.", + "In two days." + ] + }, + { + "id": "entity.number.duration", + "index": 237, + "facet": "ENTITIES", + "group": "entity.number", + "exclusivity": "entity.number", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Length of time.", + "description": "A length of time is spoken.", + "paraphrases": [ + "Seven and a half minutes.", + "For two weeks.", + "A duration." + ] + }, + { + "id": "entity.number.money", + "index": 238, + "facet": "ENTITIES", + "group": "entity.number", + "exclusivity": "entity.number", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Monetary amount (with currency).", + "description": "An amount of money is spoken.", + "paraphrases": [ + "Four dollars a gallon.", + "Two and a half million dollars.", + "A price." + ] + }, + { + "id": "entity.number.percent", + "index": 239, + "facet": "ENTITIES", + "group": "entity.number", + "exclusivity": "entity.number", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Percentage.", + "description": "A percentage is spoken.", + "paraphrases": [ + "Fifty-nine percent.", + "Shooting 45 percent.", + "A percent figure." + ] + }, + { + "id": "entity.number.ordinal", + "index": 240, + "facet": "ENTITIES", + "group": "entity.number", + "exclusivity": "entity.number", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Ordinal / rank.", + "description": "An ordinal or rank is spoken.", + "paraphrases": [ + "First pick, second half.", + "Third place.", + "An ordinal number." + ] + }, + { + "id": "entity.number.count", + "index": 241, + "facet": "ENTITIES", + "group": "entity.number", + "exclusivity": "entity.number", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Count of things.", + "description": "A count of things is spoken.", + "paraphrases": [ + "Three titles.", + "Five people.", + "How many times." + ] + }, + { + "id": "entity.number.stat", + "index": 242, + "facet": "ENTITIES", + "group": "entity.number", + "exclusivity": "entity.number", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Statistic with a stat unit from the closed list.", + "description": "A player or team statistic is spoken.", + "paraphrases": [ + "24.6 points per game.", + "Three kills.", + "Stat line numbers." + ] + }, + { + "id": "entity.number.measure", + "index": 243, + "facet": "ENTITIES", + "group": "entity.number", + "exclusivity": "entity.number", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Physical measure (distance, height, weight...).", + "description": "A physical measurement is spoken.", + "paraphrases": [ + "Six foot ten.", + "A yard and a half.", + "Distance or weight." + ] + }, + { + "id": "entity.number.jersey_number", + "index": 244, + "facet": "ENTITIES", + "group": "entity.number", + "exclusivity": "entity.number", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Player jersey number.", + "description": "A jersey number is spoken.", + "paraphrases": [ + "Number 10.", + "Wearing number 23.", + "Uniform number." + ] + }, + { + "id": "entity.number.field_position", + "index": 245, + "facet": "ENTITIES", + "group": "entity.number", + "exclusivity": "entity.number", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Field position / yard line / cricket over.", + "description": "A field position or yard line is spoken.", + "paraphrases": [ + "First and goal at the one.", + "At the 47-yard line.", + "Over 12.3 in cricket." + ] + }, + { + "id": "entity.number.ratio_odds", + "index": 246, + "facet": "ENTITIES", + "group": "entity.number", + "exclusivity": "entity.number", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Ratio or betting odds.", + "description": "A ratio or betting odds are spoken.", + "paraphrases": [ + "Seven to one.", + "Plus 150.", + "Odds on the game." + ] + }, + { + "id": "entity.number.ref_number", + "index": 247, + "facet": "ENTITIES", + "group": "entity.number", + "exclusivity": "entity.number", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Reference number: page, exhibit, docket, statute section, channel, flight.", + "description": "A reference number is spoken: page, exhibit, statute section, channel.", + "paraphrases": [ + "Exhibit 524.", + "Page 38, line 4.", + "Channel 4." + ] + }, + { + "id": "entity.number.id_pii", + "index": 248, + "facet": "ENTITIES", + "group": "entity.number", + "exclusivity": "entity.number", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "core", + "definition": "Identifying number of a private person (phone, card/account, government id); value stored only as a hash.", + "description": "A private identifying number is spoken.", + "paraphrases": [ + "A phone or card number.", + "Personal ID digits.", + "Account number digits." + ] + }, + { + "id": "scene.background_speech", + "index": 249, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Speech from people other than the foreground talker(s) in the background (chatter, babble, a second distant talker), not overlapping foreground turns.", + "description": "Other people are chatting in the background behind the main speaker.", + "paraphrases": [ + "Background babble of distant voices.", + "A murmur of conversations far behind the talker.", + "Second, distant voices you can hear but are not part of the conversation." + ] + }, + { + "id": "scene.siren", + "index": 250, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "An emergency-vehicle or civil-defence siren.", + "description": "A siren wails, like a police car or ambulance.", + "paraphrases": [ + "Emergency vehicle siren going past.", + "The rising and falling sound of a siren.", + "Civil-defence or fire-engine siren." + ] + }, + { + "id": "scene.phone_ring", + "index": 251, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "A telephone or ringtone rings.", + "description": "A telephone is ringing.", + "paraphrases": [ + "A ringtone goes off.", + "Phone ringing in the room.", + "The ring of a mobile phone or desk phone." + ] + }, + { + "id": "scene.typing", + "index": 252, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Keyboard typing.", + "description": "Someone is typing on a keyboard.", + "paraphrases": [ + "Clicking keyboard keys.", + "Keyboard tapping in the background.", + "The clatter of typing." + ] + }, + { + "id": "scene.door_knock", + "index": 253, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "A door opens / closes / slams, or a knock.", + "description": "A door opens, closes or slams, or someone knocks.", + "paraphrases": [ + "Knocking on a door.", + "A door bangs shut.", + "Door creak and slam." + ] + }, + { + "id": "scene.beep", + "index": 254, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "A short electronic beep or bleep (incl. reversing beeps, censor bleeps).", + "description": "A short electronic beep.", + "paraphrases": [ + "A bleep, like a censor bleep or a reversing truck.", + "Electronic beeping tone.", + "Beep beep." + ] + }, + { + "id": "scene.alarm", + "index": 255, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "An alarm (alarm clock, smoke / fire alarm).", + "description": "An alarm is going off.", + "paraphrases": [ + "Smoke or fire alarm sounding.", + "An alarm clock ringing.", + "Loud repeating alarm tone." + ] + }, + { + "id": "scene.wind", + "index": 256, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Wind or wind noise on the microphone.", + "description": "Wind is blowing into the microphone.", + "paraphrases": [ + "Wind noise rumbling on the mic.", + "Gusts of wind outdoors.", + "Whooshing wind sound." + ] + }, + { + "id": "scene.traffic", + "index": 257, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Road traffic / passing vehicles.", + "description": "Road traffic and passing cars.", + "paraphrases": [ + "Vehicles driving by.", + "The noise of a busy road.", + "Cars and trucks passing." + ] + }, + { + "id": "scene.noise_other", + "index": 258, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Audible non-speech noise that matches no other scene tag (hum, static, machinery, broadband noise).", + "description": "Some other noise: hum, static, machinery or hiss.", + "paraphrases": [ + "Mains hum or electrical buzz.", + "Static and broadband noise.", + "Machine noise that is not speech or music." + ] + }, + { + "id": "scene.ambience.cafe", + "index": 259, + "facet": "SCENE", + "group": "scene.ambience", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Cafe / restaurant / cafeteria room ambience (murmur, dishes).", + "description": "It sounds like a cafe or restaurant around the speaker.", + "paraphrases": [ + "Murmur and clinking dishes of a busy cafe.", + "Cafeteria background ambience.", + "Restaurant chatter and cutlery." + ] + }, + { + "id": "scene.ambience.office", + "index": 260, + "facet": "SCENE", + "group": "scene.ambience", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Indoor office / meeting-room / hallway / living-room ambience.", + "description": "It sounds like an indoor office or meeting room.", + "paraphrases": [ + "Quiet office room tone with distant activity.", + "Indoor room ambience, hallway or living room.", + "Meeting-room background." + ] + }, + { + "id": "scene.ambience.street", + "index": 261, + "facet": "SCENE", + "group": "scene.ambience", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Outdoor urban ambience (street, square, station, bus, metro).", + "description": "It sounds like being outdoors in a city street.", + "paraphrases": [ + "Urban outdoor ambience: street, square or station.", + "Busy street noise around the speaker.", + "On a bus or metro platform." + ] + }, + { + "id": "meta.clipping", + "index": 262, + "facet": "META", + "group": "meta", + "exclusivity": "independent", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Clipping / saturation distortion of the waveform.", + "description": "The audio is clipped and distorted from being too loud.", + "paraphrases": [ + "Saturated, crunchy overdriven voice.", + "The recording distorts on loud parts.", + "Clipping distortion." + ] + }, + { + "id": "meta.channel.telephone", + "index": 263, + "facet": "META", + "group": "meta.channel", + "exclusivity": "independent", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Telephone channel: ~300-3400 Hz band-pass at 8 kHz.", + "description": "The voice sounds like it is coming through a telephone line.", + "paraphrases": [ + "Thin, band-passed phone-call audio.", + "A phone-in caller's tinny voice.", + "Landline telephone sound." + ] + }, + { + "id": "meta.channel.narrowband", + "index": 264, + "facet": "META", + "group": "meta.channel", + "exclusivity": "independent", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Audio band-limited below 4 kHz (8 kHz sampling anywhere in the chain; implied by telephone).", + "description": "The audio is narrowband with no high frequencies.", + "paraphrases": [ + "Muffled, band-limited sound like 8 kHz audio.", + "Missing treble, low-bandwidth recording.", + "Narrowband audio." + ] + }, + { + "id": "meta.channel.mulaw", + "index": 265, + "facet": "META", + "group": "meta.channel", + "exclusivity": "independent", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "8-bit G.711 mu-law companding.", + "description": "The audio has 8-bit mu-law telephone companding.", + "paraphrases": [ + "Grainy G.711 phone codec sound.", + "Companded telephone audio with quantisation hiss.", + "Mu-law coded voice." + ] + }, + { + "id": "meta.channel.mp3", + "index": 266, + "facet": "META", + "group": "meta.channel", + "exclusivity": "independent", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Low-bitrate MP3 artefacts (16-40 kbps).", + "description": "The audio has low-bitrate MP3 artefacts.", + "paraphrases": [ + "Swirly, watery compression sound of a low-quality MP3.", + "Heavily compressed MP3 audio.", + "MP3 codec artefacts." + ] + }, + { + "id": "meta.channel.opus", + "index": 267, + "facet": "META", + "group": "meta.channel", + "exclusivity": "independent", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Low-bitrate Opus artefacts.", + "description": "The audio has low-bitrate Opus codec artefacts.", + "paraphrases": [ + "Voice-chat style Opus compression.", + "Compressed internet-call audio.", + "Opus codec sound." + ] + }, + { + "id": "meta.room.small", + "index": 268, + "facet": "META", + "group": "meta.room", + "exclusivity": "meta.room", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Room reverberation with RT60 < 0.35 s.", + "description": "The voice has a little reverb from a small room.", + "paraphrases": [ + "Short echo of a small room.", + "Slightly roomy sound, like a small office.", + "Small-room reverberation." + ] + }, + { + "id": "meta.room.medium", + "index": 269, + "facet": "META", + "group": "meta.room", + "exclusivity": "meta.room", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Room reverberation with 0.35 s <= RT60 < 0.7 s.", + "description": "The voice has noticeable reverb from a medium-sized room.", + "paraphrases": [ + "Echo of a classroom or meeting hall.", + "Moderately reverberant room.", + "Medium-room reverberation." + ] + }, + { + "id": "meta.room.large", + "index": 270, + "facet": "META", + "group": "meta.room", + "exclusivity": "meta.room", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Room reverberation with RT60 >= 0.7 s.", + "description": "The voice echoes strongly as in a large hall.", + "paraphrases": [ + "Long reverb like a church or gym.", + "Very reverberant large space.", + "Large-hall reverberation." + ] + }, + { + "id": "meta.snr.high", + "index": 271, + "facet": "META", + "group": "meta.snr", + "exclusivity": "meta.snr", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Foreground-to-noise ratio >= 20 dB.", + "description": "The voice is clean and well above the background noise.", + "paraphrases": [ + "Clear speech with little noise.", + "High signal-to-noise ratio.", + "Quiet background, voice stands out." + ] + }, + { + "id": "meta.snr.mid", + "index": 272, + "facet": "META", + "group": "meta.snr", + "exclusivity": "meta.snr", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Foreground-to-noise ratio 10-20 dB.", + "description": "The voice is clear but there is noticeable background noise.", + "paraphrases": [ + "Moderate noise behind the speaker.", + "Medium signal-to-noise ratio.", + "Speech over some noise." + ] + }, + { + "id": "meta.snr.low", + "index": 273, + "facet": "META", + "group": "meta.snr", + "exclusivity": "meta.snr", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Foreground-to-noise ratio < 10 dB.", + "description": "The voice is buried in loud background noise.", + "paraphrases": [ + "Very noisy, hard to hear the speaker.", + "Low signal-to-noise ratio.", + "Noise almost drowns out the speech." + ] + }, + { + "id": "safety.toxicity.illegal_regulated", + "index": 274, + "facet": "SAFETY", + "group": "safety.toxicity", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_trial", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker solicits, sells, arranges or instructs illegal or regulated goods or activities (drugs, weapons, fraud, hacking, underage alcohol or gambling).", + "description": "The speaker offers, sells or arranges something illegal like drugs, weapons or fraud.", + "paraphrases": [ + "Hit me up if you want to buy some pills.", + "Someone explains how to scam or hack people.", + "Soliciting illegal goods or services, not reporting on them." + ] + }, + { + "id": "safety.solicitation.dating_romantic", + "index": 275, + "facet": "SAFETY", + "group": "safety.solicitation", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker solicits a romantic or sexual relationship with the listener ('go out with me', 'be my girlfriend').", + "description": "The speaker asks the listener out or for a romantic relationship.", + "paraphrases": [ + "Wanna be my girlfriend?", + "Come on, go out with me, give me your number.", + "Romantic or sexual solicitation aimed at the listener." + ] + }, + { + "id": "safety.pii.request", + "index": 276, + "facet": "SAFETY", + "group": "safety.pii", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker asks another person for that person's (or a third private person's) personal information: name, address, phone, email, password, age, school.", + "description": "The speaker asks someone for their personal information.", + "paraphrases": [ + "What's your address? What school do you go to?", + "Tell me your phone number and your real name.", + "Asking another person for private details like a password or age." + ] + }, + { + "id": "safety.pii.public_contact", + "index": 277, + "facet": "SAFETY", + "group": "safety.pii", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "A public or business contact detail: station call-in line, sponsor website, company IR email or business address.", + "description": "A public business contact is spoken: a call-in number, website or office address.", + "paraphrases": [ + "Call the station at this number.", + "Visit our website at example dot com.", + "A sponsor's or company's public contact details." + ] + }, + { + "id": "content.promo_redirect", + "index": 278, + "facet": "CONTENT", + "group": "content", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker directs the audience to an external website, app, phone line, social account, channel, QR code or sponsor offer.", + "description": "The speaker sends the audience somewhere else: a website, app, channel or sponsor offer.", + "paraphrases": [ + "Download the app and use code SAVE twenty.", + "Follow us on social media and subscribe to the channel.", + "Go to our website for the full offer." + ] + }, + { + "id": "entity.mention.term", + "index": 279, + "facet": "ENTITIES", + "group": "entity.mention", + "exclusivity": "independent", + "unit": "W", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "A domain key term: finance (EBITDA, basis points), legal (habeas corpus, summary judgment), sports (pick and roll, power play), esports (eco round), parliament (quorum, plenary).", + "description": "A domain key term is used, like EBITDA, summary judgment or pick and roll.", + "paraphrases": [ + "Technical jargon of the field.", + "Free cash flow, basis points, habeas corpus, power play.", + "A specialised term of finance, law, sport or parliament." + ] + }, + { + "id": "intent.request_information", + "index": 280, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker asks for information, an answer, an opinion or a judgement from the addressee.", + "description": "The speaker asks a question to get information or an answer.", + "paraphrases": [ + "What time does it start?", + "Can you tell us where you were that night?", + "Asking someone for facts or their opinion." + ] + }, + { + "id": "intent.request_action", + "index": 281, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker asks or directs the addressee (or a device / the audience) to do something now or soon.", + "description": "The speaker asks or tells someone to do something.", + "paraphrases": [ + "Please close the door.", + "Turn the lights off.", + "Download the app and subscribe to the channel." + ] + }, + { + "id": "intent.request_permission", + "index": 282, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker asks to be allowed to do something.", + "description": "The speaker asks to be allowed to do something.", + "paraphrases": [ + "May I approach the witness?", + "Can I jump in here?", + "Is it okay if I ask a follow-up?" + ] + }, + { + "id": "intent.offer_help", + "index": 283, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker offers or commits to do something for the addressee or the group.", + "description": "The speaker offers or promises to do something.", + "paraphrases": [ + "I'll send the notes tonight.", + "Let me check and get back to you.", + "I can take that one." + ] + }, + { + "id": "intent.propose_plan", + "index": 284, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker proposes a course of action or an option for the group to consider.", + "description": "The speaker proposes an idea or course of action.", + "paraphrases": [ + "Let's move it to Thursday.", + "Why don't we split it into phases?", + "I suggest an amendment." + ] + }, + { + "id": "intent.schedule_arrange", + "index": 285, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker sets, moves, cancels or asks about a time / date / place for an event.", + "description": "The speaker sets or changes a time, date or place.", + "paraphrases": [ + "Let's meet at ten tomorrow.", + "We'll resume at one thirty.", + "Set an alarm for six." + ] + }, + { + "id": "intent.assign_task", + "index": 286, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker assigns a task or responsibility to a specific person or team.", + "description": "The speaker gives someone a task or responsibility.", + "paraphrases": [ + "Maria, you'll prepare the costs.", + "Action item for the design team.", + "You're in charge of the prototype." + ] + }, + { + "id": "intent.decide_agree", + "index": 287, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker agrees, accepts, or announces a decision / ruling.", + "description": "The speaker agrees or announces a decision or ruling.", + "paraphrases": [ + "Agreed, let's go with that.", + "Sustained.", + "The amendment is adopted." + ] + }, + { + "id": "intent.disagree_object", + "index": 288, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker disagrees with, objects to, or rejects a claim, proposal or question.", + "description": "The speaker disagrees or objects.", + "paraphrases": [ + "Objection, Your Honor, hearsay.", + "I'm not convinced that works.", + "That's just not true." + ] + }, + { + "id": "intent.confirm", + "index": 289, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker confirms, verifies or acknowledges a fact or an understanding asked about.", + "description": "The speaker confirms that something is correct.", + "paraphrases": [ + "Yes, that's correct.", + "I do.", + "Right, that's the final number." + ] + }, + { + "id": "intent.deny_refuse", + "index": 290, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker denies a claim about themselves or refuses a request / offer.", + "description": "The speaker denies something or refuses a request.", + "paraphrases": [ + "No, I never said that.", + "We can't do that.", + "No, don't send it." + ] + }, + { + "id": "intent.clarify_correct", + "index": 291, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker clarifies, rephrases or corrects something said earlier (own or another's).", + "description": "The speaker clarifies or corrects what was said.", + "paraphrases": [ + "Sorry, I meant Tuesday, not Thursday.", + "Let me rephrase the question.", + "To be clear, that's per unit." + ] + }, + { + "id": "intent.complain_criticize", + "index": 292, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker complains about or criticizes a person, thing or situation (without a directed threat).", + "description": "The speaker complains or criticizes.", + "paraphrases": [ + "That was a terrible call.", + "This game is so broken.", + "I hate this song." + ] + }, + { + "id": "intent.thank_apologize", + "index": 293, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker thanks or apologizes.", + "description": "The speaker thanks someone or apologizes.", + "paraphrases": [ + "Thanks for joining us.", + "Sorry to interrupt.", + "Thank you, Your Honor." + ] + }, + { + "id": "intent.greet_close", + "index": 294, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker greets, introduces, welcomes, or closes / signs off.", + "description": "The speaker greets, welcomes or signs off.", + "paraphrases": [ + "Good evening and welcome.", + "May it please the Court.", + "That's all for tonight, goodbye." + ] + }, + { + "id": "intent.purchase_order", + "index": 295, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker buys, orders, books or sells something (or asks to).", + "description": "The speaker buys, orders, books or sells something.", + "paraphrases": [ + "Order me a pizza.", + "Book a taxi to the station.", + "I'll sell you the skin for twenty." + ] + }, + { + "id": "intent.report_inform", + "index": 296, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker states information, narrates or describes (default for informative statements).", + "description": "The speaker states information or narrates what happened.", + "paraphrases": [ + "Revenue grew eight percent.", + "He drives baseline and scores.", + "Police arrested a suspect." + ] + }, + { + "id": "intent.predict_speculate", + "index": 297, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker predicts, forecasts or speculates about the future or the unknown.", + "description": "The speaker predicts or speculates about what will happen.", + "paraphrases": [ + "We expect margins to improve next year.", + "This could be the turning point.", + "It'll probably slip a week." + ] + }, + { + "id": "intent.persuade_argue", + "index": 298, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker argues for a position or tries to persuade the addressee / audience.", + "description": "The speaker argues for a position or tries to persuade.", + "paraphrases": [ + "The evidence clearly shows my client was not there.", + "We must act now.", + "Trust me, this build is better." + ] + }, + { + "id": "intent.warn_threaten", + "index": 299, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker warns of a risk or consequence, or threatens.", + "description": "The speaker warns of a risk or threatens a consequence.", + "paraphrases": [ + "Watch out, the floor is wet.", + "Officials warn residents to stay indoors.", + "Do that again and you're banned." + ] + }, + { + "id": "intent.offer_admit_evidence", + "index": 300, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Counsel offers / moves an exhibit or item into evidence, or the court admits / receives it.", + "description": "Counsel offers an exhibit into evidence, or the court admits it.", + "paraphrases": [ + "Your Honor, we move to admit this exhibit.", + "The exhibit is received.", + "Offering the document into evidence." + ] + }, + { + "id": "intent.segue_handoff", + "index": 301, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker manages the programme flow: segue / tease to the next item, hand-off to another presenter, guest or caller.", + "description": "The presenter moves the programme on or hands over to someone else.", + "paraphrases": [ + "Coming up after the break.", + "Let's go live to our reporter.", + "Caller, you're on the air." + ] + }, + { + "id": "intent.call_to_action", + "index": 302, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker directs the audience to act off-air: subscribe, follow, visit, call, download, donate, use a code.", + "description": "The speaker tells the audience to subscribe, follow, visit, call or download.", + "paraphrases": [ + "Download the app and use the code.", + "Follow us on social media.", + "Visit our website today." + ] + }, + { + "id": "intent.shout_out", + "index": 303, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker publicly acknowledges / gives a shout-out to someone (often for a sub, gift, raid, or in a greeting).", + "description": "The speaker gives a public shout-out to someone.", + "paraphrases": [ + "Shout out to everyone who subscribed.", + "Thanks for the raid!", + "Big shout out to the mods." + ] + }, + { + "id": "intent.elicit_input", + "index": 304, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker invites contributions from the group / audience (open floor), not a specific information question.", + "description": "The speaker invites the group or audience to contribute.", + "paraphrases": [ + "Any thoughts on that?", + "Chat, what do you think?", + "Are there any questions?" + ] + }, + { + "id": "intent.advise_recommend", + "index": 305, + "facet": "DISCOURSE", + "group": "intent", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker gives advice or a recommendation for the addressee's own benefit.", + "description": "The speaker gives advice for the listener's own benefit.", + "paraphrases": [ + "You should get that checked by a doctor.", + "My advice is to pay off the card first.", + "I'd recommend testing it with real users." + ] + }, + { + "id": "meta.replayed_voice", + "index": 306, + "facet": "META", + "group": "meta", + "exclusivity": "independent", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The voice was played through a loudspeaker and re-recorded by a microphone (replay attack, speaker-phone playback, TV / radio speech in the room), human or synthetic.", + "description": "The voice is played back through a loudspeaker and re-recorded.", + "paraphrases": [ + "Speech replayed from a speaker into a microphone.", + "A recording of a voice being played in the room.", + "Loudspeaker playback of speech." + ] + }, + { + "id": "meta.spliced_voice", + "index": 307, + "facet": "META", + "group": "meta", + "exclusivity": "independent", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The clip mixes real human speech and synthetic speech of the same apparent speaker (partial deepfake: inserted / replaced words or phrases).", + "description": "Part of this speech is synthetic and spliced into a real recording.", + "paraphrases": [ + "Real speech with inserted AI-generated words.", + "A partially faked recording.", + "Human speech edited with synthetic segments." + ] + }, + { + "id": "safety.scam.attempt", + "index": 308, + "facet": "SAFETY", + "group": "safety.scam", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker is trying to defraud the listener (fraudulent call or message): any scam technique below, or a known scam script.", + "description": "The caller is trying to scam or defraud the listener.", + "paraphrases": [ + "A fraudulent call asking for money or codes.", + "This is a scam call.", + "Someone posing as the bank to steal from you." + ] + }, + { + "id": "safety.scam.impersonation", + "index": 309, + "facet": "SAFETY", + "group": "safety.scam", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The caller falsely claims to be an authority or trusted party (bank, tax office, police, tech support, a relative).", + "description": "The caller pretends to be a bank, government office, police or tech support.", + "paraphrases": [ + "I'm calling from the tax office about your account.", + "A fake official on the phone.", + "Posing as a trusted company." + ] + }, + { + "id": "safety.scam.urgency", + "index": 310, + "facet": "SAFETY", + "group": "safety.scam", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The caller creates false urgency or threatens consequences to rush the listener.", + "description": "The caller pressures the listener to act immediately or face consequences.", + "paraphrases": [ + "You must pay today or you'll be arrested.", + "Act now before your account is closed.", + "False urgency to rush a decision." + ] + }, + { + "id": "safety.scam.payment_request", + "index": 311, + "facet": "SAFETY", + "group": "safety.scam", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The caller asks for money in an unusual, untraceable form (gift cards, wire, crypto, cash courier).", + "description": "The caller asks for payment by gift cards, wire transfer or crypto.", + "paraphrases": [ + "Buy gift cards and read me the numbers.", + "Send the money by wire right now.", + "Pay in bitcoin to fix the problem." + ] + }, + { + "id": "safety.scam.credential_request", + "index": 312, + "facet": "SAFETY", + "group": "safety.scam", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The caller asks for secrets: passwords, PINs, one-time codes, full card numbers, security answers.", + "description": "The caller asks for a password, PIN or one-time code.", + "paraphrases": [ + "Read me the code we just texted you.", + "What's your online banking password?", + "Tell me your card PIN to verify." + ] + }, + { + "id": "safety.scam.remote_access", + "index": 313, + "facet": "SAFETY", + "group": "safety.scam", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The caller asks the listener to install software or grant remote access to a device or account.", + "description": "The caller asks the listener to install software or give remote access.", + "paraphrases": [ + "Download this app so I can fix your computer.", + "Let me connect to your screen.", + "Grant me remote access to your device." + ] + }, + { + "id": "safety.pii.leak", + "index": 314, + "facet": "SAFETY", + "group": "safety.pii", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Sensitive personal data is spoken aloud in the clip (the speaker's own or a third party's): card / account / ID numbers, passwords, codes, addresses, dates of birth.", + "description": "Sensitive personal information is spoken aloud.", + "paraphrases": [ + "Someone reads out their card number or password.", + "A private address and date of birth are said.", + "Personal data leaking in conversation." + ] + }, + { + "id": "scene.scream", + "index": 315, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "A human scream or shriek (fear, pain, alarm), not cheering or singing.", + "description": "Someone screams.", + "paraphrases": [ + "A loud scream of fear or pain.", + "A shriek.", + "A person screaming." + ] + }, + { + "id": "scene.crying", + "index": 316, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Crying, sobbing or wailing.", + "description": "Someone is crying or sobbing.", + "paraphrases": [ + "Loud sobbing.", + "A person wailing in distress.", + "Crying sounds." + ] + }, + { + "id": "scene.glass_break", + "index": 317, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Glass breaking or shattering.", + "description": "Glass breaks.", + "paraphrases": [ + "The sound of shattering glass.", + "A window smashing.", + "Breaking glass." + ] + }, + { + "id": "scene.distress_call", + "index": 318, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "A person urgently calls for help or emergency services ('help!', 'somebody help me', 'call nine one one') in a real situation.", + "description": "Someone urgently calls for help.", + "paraphrases": [ + "Help! Somebody help me!", + "Call nine one one!", + "A cry for emergency help." + ] + }, + { + "id": "scene.distress", + "index": 319, + "facet": "SCENE", + "group": "scene", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Umbrella: an acoustic or spoken sign of distress is present (scream, crying, gunshot / explosion, glass breaking, a call for help). Sirens and alarms alone do NOT fire it.", + "description": "There are sounds of distress: screaming, crying, gunshots, breaking glass or a call for help.", + "paraphrases": [ + "An emergency is happening.", + "Screams and breaking glass.", + "Someone in danger." + ] + }, + { + "id": "meta.people_present", + "index": 320, + "facet": "META", + "group": "meta", + "exclusivity": "independent", + "unit": "G", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Someone other than the main / device-directed talker is audibly present in the clip (recording-consent awareness).", + "description": "Several people are present and talking around the device.", + "paraphrases": [ + "More than one person can be heard nearby.", + "Two people talking next to the phone.", + "Others are in the room." + ] + }, + { + "id": "meta.bystander_speech", + "index": 321, + "facet": "META", + "group": "meta", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Intelligible speech from a nearby person other than the main talker / device owner (someone who may not have consented to recording).", + "description": "Another person nearby is speaking clearly, not the main speaker.", + "paraphrases": [ + "A bystander talking next to the speaker.", + "Someone else in the room speaks.", + "Nearby second voice." + ] + }, + { + "id": "safety.crisis.self_harm", + "index": 322, + "facet": "SAFETY", + "group": "safety.crisis", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker expresses thoughts, intent or plans of self-harm or suicide, in the first person.", + "description": "The speaker expresses thoughts of harming themselves.", + "paraphrases": [ + "I don't want to be here anymore.", + "I've been thinking about ending it.", + "First-person crisis language." + ] + }, + { + "id": "safety.threat.escalation", + "index": 323, + "facet": "SAFETY", + "group": "safety.threat", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "The speaker's threats or harassment toward the listener intensify (repeated, more specific or more violent than before in the same conversation).", + "description": "The speaker's threats are getting more serious.", + "paraphrases": [ + "I warned you once, next time I'll come to your house.", + "Repeated, intensifying threats.", + "The harassment is escalating." + ] + }, + { + "id": "safety.child.grooming_cue", + "index": 324, + "facet": "SAFETY", + "group": "safety.child", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "An adult uses known grooming tactics toward a child: secrecy requests, isolation, unusual gifts or favours, moving to a private channel, asking for photos or personal details, testing boundaries.", + "description": "An adult tries to get a child to keep secrets or talk privately.", + "paraphrases": [ + "This is our secret, don't tell your parents.", + "Let's move to a private chat, just us.", + "Asking a child for personal details or photos." + ] + }, + { + "id": "safety.child.inappropriate", + "index": 325, + "facet": "SAFETY", + "group": "safety.child", + "exclusivity": "independent", + "unit": "U", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "Content not appropriate for children in the clip (strong profanity, graphic violence, sexual references, drugs) - the child-mode filter flag.", + "description": "The content is not suitable for children.", + "paraphrases": [ + "Strong swearing and graphic violence.", + "Adult content in the audio.", + "Not appropriate for a child to hear." + ] + }, + { + "id": "meta.media_command", + "index": 326, + "facet": "META", + "group": "meta", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "An assistant-style command (wake word or device instruction) is spoken by media playback (TV, radio, video, ad), not by a live user.", + "description": "A voice assistant command is spoken by the TV, radio or a video, not the user.", + "paraphrases": [ + "A commercial says: hey assistant, order more.", + "A command coming from media playback.", + "The TV is talking to the smart speaker." + ] + }, + { + "id": "meta.hidden_command", + "index": 327, + "facet": "META", + "group": "meta", + "exclusivity": "independent", + "unit": "F", + "domains": [ + "nba", + "nhl", + "nfl", + "cricket", + "soccer", + "esports", + "news", + "talk", + "stream_chat", + "meeting", + "court_appellate", + "court_trial", + "assistant", + "short_clips" + ], + "status": "experimental", + "definition": "A command is deliberately hidden in the audio: very low level under music / noise, time-compressed, whispered under speech, or band-shifted.", + "description": "A command is hidden in the audio, quiet or masked under music.", + "paraphrases": [ + "A whispered instruction buried under sound.", + "An obscured device command.", + "A concealed voice command." + ] + } + ] +} \ No newline at end of file diff --git a/ontology/text_tags.json b/ontology/text_tags.json new file mode 100644 index 0000000000000000000000000000000000000000..0168fd8e37e1c4dcaa4b00a3c92b502b3f49049f --- /dev/null +++ b/ontology/text_tags.json @@ -0,0 +1,4619 @@ +{ + "version": "arctag-text-tags-v1", + "ontology": "tags_v1_r8.json", + "ontology_version": "ontology_v1.0-r8", + "tiers": { + "T0": "lookahead 0 words", + "T1": "lookahead 3 words", + "T2": "utterance endpoint", + "T3": "long-context (session) state, served at endpoint cadence" + }, + "tags": [ + { + "index": 0, + "id": "meta.speech", + "facet": "META", + "group": "meta", + "unit": "F", + "status": "core", + "exclusivity": "independent", + "text_derivable": "no", + "readouts": [], + "tier": null + }, + { + "index": 1, + "id": "meta.overlap", + "facet": "META", + "group": "meta", + "unit": "F", + "status": "experimental", + "exclusivity": "independent", + "text_derivable": "no", + "readouts": [], + "tier": null + }, + { + "index": 2, + "id": "meta.degraded_audio", + "facet": "META", + "group": "meta", + "unit": "G", + "status": "experimental", + "exclusivity": "independent", + "text_derivable": "no", + "readouts": [], + "tier": null + }, + { + "index": 3, + "id": "meta.synthetic_voice", + "facet": "META", + "group": "meta", + "unit": "F", + "status": "experimental", + "exclusivity": "independent", + "text_derivable": "no", + "readouts": [], + "tier": null + }, + { + "index": 4, + "id": "meta.lang.en", + "facet": "META", + "group": "meta.lang", + "unit": "G", + "status": "experimental", + "exclusivity": "meta.lang", + "text_derivable": "weak", + "readouts": [ + "frame" + ], + "tier": "T1" + }, + { + "index": 5, + "id": "meta.lang.non_en", + "facet": "META", + "group": "meta.lang", + "unit": "G", + "status": "experimental", + "exclusivity": "meta.lang", + "text_derivable": "weak", + "readouts": [ + "frame" + ], + "tier": "T1" + }, + { + "index": 6, + "id": "meta.lang.mixed", + "facet": "META", + "group": "meta.lang", + "unit": "G", + "status": "experimental", + "exclusivity": "meta.lang", + "text_derivable": "weak", + "readouts": [ + "frame" + ], + "tier": "T1" + }, + { + "index": 7, + "id": "speaker.role.play_by_play", + "facet": "SPEAKER", + "group": "speaker.role", + "unit": "S", + "status": "core", + "exclusivity": "speaker.role", + "text_derivable": "yes", + "readouts": [ + "speaker" + ], + "tier": "T3" + }, + { + "index": 8, + "id": "speaker.role.analyst", + "facet": "SPEAKER", + "group": "speaker.role", + "unit": "S", + "status": "core", + "exclusivity": "speaker.role", + "text_derivable": "yes", + "readouts": [ + "speaker" + ], + "tier": "T3" + }, + { + "index": 9, + "id": "speaker.role.host", + "facet": "SPEAKER", + "group": "speaker.role", + "unit": "S", + "status": "core", + "exclusivity": "speaker.role", + "text_derivable": "yes", + "readouts": [ + "speaker" + ], + "tier": "T3" + }, + { + "index": 10, + "id": "speaker.role.co_host", + "facet": "SPEAKER", + "group": "speaker.role", + "unit": "S", + "status": "core", + "exclusivity": "speaker.role", + "text_derivable": "yes", + "readouts": [ + "speaker" + ], + "tier": "T3" + }, + { + "index": 11, + "id": "speaker.role.reporter", + "facet": "SPEAKER", + "group": "speaker.role", + "unit": "S", + "status": "core", + "exclusivity": "speaker.role", + "text_derivable": "yes", + "readouts": [ + "speaker" + ], + "tier": "T3" + }, + { + "index": 12, + "id": "speaker.role.guest", + "facet": "SPEAKER", + "group": "speaker.role", + "unit": "S", + "status": "core", + "exclusivity": "speaker.role", + "text_derivable": "yes", + "readouts": [ + "speaker" + ], + "tier": "T3" + }, + { + "index": 13, + "id": "speaker.role.athlete", + "facet": "SPEAKER", + "group": "speaker.role", + "unit": "S", + "status": "core", + "exclusivity": "speaker.role", + "text_derivable": "yes", + "readouts": [ + "speaker" + ], + "tier": "T3" + }, + { + "index": 14, + "id": "speaker.role.coach_staff", + "facet": "SPEAKER", + "group": "speaker.role", + "unit": "S", + "status": "core", + "exclusivity": "speaker.role", + "text_derivable": "yes", + "readouts": [ + "speaker" + ], + "tier": "T3" + }, + { + "index": 15, + "id": "speaker.role.game_official", + "facet": "SPEAKER", + "group": "speaker.role", + "unit": "S", + "status": "core", + "exclusivity": "speaker.role", + "text_derivable": "yes", + "readouts": [ + "speaker" + ], + "tier": "T3" + }, + { + "index": 16, + "id": "speaker.role.caller_audience", + "facet": "SPEAKER", + "group": "speaker.role", + "unit": "S", + "status": "core", + "exclusivity": "speaker.role", + "text_derivable": "yes", + "readouts": [ + "speaker" + ], + "tier": "T3" + }, + { + "index": 17, + "id": "speaker.role.announcer_voiceover", + "facet": "SPEAKER", + "group": "speaker.role", + "unit": "S", + "status": "core", + "exclusivity": "speaker.role", + "text_derivable": "yes", + "readouts": [ + "speaker" + ], + "tier": "T3" + }, + { + "index": 18, + "id": "speaker.role.participant", + "facet": "SPEAKER", + "group": "speaker.role", + "unit": "S", + "status": "core", + "exclusivity": "speaker.role", + "text_derivable": "yes", + "readouts": [ + "speaker" + ], + "tier": "T3" + }, + { + "index": 19, + "id": "speaker.role.judge", + "facet": "SPEAKER", + "group": "speaker.role", + "unit": "S", + "status": "core", + "exclusivity": "speaker.role", + "text_derivable": "yes", + "readouts": [ + "speaker" + ], + "tier": "T3" + }, + { + "index": 20, + "id": "speaker.role.counsel", + "facet": "SPEAKER", + "group": "speaker.role", + "unit": "S", + "status": "core", + "exclusivity": "speaker.role", + "text_derivable": "yes", + "readouts": [ + "speaker" + ], + "tier": "T3" + }, + { + "index": 21, + "id": "speaker.role.witness", + "facet": "SPEAKER", + "group": "speaker.role", + "unit": "S", + "status": "core", + "exclusivity": "speaker.role", + "text_derivable": "yes", + "readouts": [ + "speaker" + ], + "tier": "T3" + }, + { + "index": 22, + "id": "speaker.role.device_user", + "facet": "SPEAKER", + "group": "speaker.role", + "unit": "S", + "status": "core", + "exclusivity": "speaker.role", + "text_derivable": "yes", + "readouts": [ + "speaker" + ], + "tier": "T3" + }, + { + "index": 23, + "id": "speaker.role.other", + "facet": "SPEAKER", + "group": "speaker.role", + "unit": "S", + "status": "core", + "exclusivity": "speaker.role", + "text_derivable": "yes", + "readouts": [ + "speaker" + ], + "tier": "T3" + }, + { + "index": 24, + "id": "speaker.change", + "facet": "SPEAKER", + "group": "speaker.change", + "unit": "E", + "status": "core", + "exclusivity": "independent", + "text_derivable": "weak", + "readouts": [ + "ev_at" + ], + "tier": "T0" + }, + { + "index": 25, + "id": "discourse.act.statement", + "facet": "DISCOURSE", + "group": "discourse.act", + "unit": "U", + "status": "core", + "exclusivity": "discourse.act", + "text_derivable": "yes", + "readouts": [ + "utt" + ], + "tier": "T2" + }, + { + "index": 26, + "id": "discourse.act.question", + "facet": "DISCOURSE", + "group": "discourse.act", + "unit": "U", + "status": "core", + "exclusivity": "discourse.act", + "text_derivable": "yes", + "readouts": [ + "utt" + ], + "tier": "T2" + }, + { + "index": 27, + "id": "discourse.act.opinion_assessment", + "facet": "DISCOURSE", + "group": "discourse.act", + "unit": "U", + "status": "core", + "exclusivity": "discourse.act", + "text_derivable": "yes", + "readouts": [ + "utt" + ], + "tier": "T2" + }, + { + "index": 28, + "id": "discourse.act.suggestion_proposal", + "facet": "DISCOURSE", + "group": "discourse.act", + "unit": "U", + "status": "core", + "exclusivity": "discourse.act", + "text_derivable": "yes", + "readouts": [ + "utt" + ], + "tier": "T2" + }, + { + "index": 29, + "id": "discourse.act.commitment_offer", + "facet": "DISCOURSE", + "group": "discourse.act", + "unit": "U", + "status": "core", + "exclusivity": "discourse.act", + "text_derivable": "yes", + "readouts": [ + "utt" + ], + "tier": "T2" + }, + { + "index": 30, + "id": "discourse.act.command_request", + "facet": "DISCOURSE", + "group": "discourse.act", + "unit": "U", + "status": "core", + "exclusivity": "discourse.act", + "text_derivable": "yes", + "readouts": [ + "utt" + ], + "tier": "T2" + }, + { + "index": 31, + "id": "discourse.act.ritual_procedural", + "facet": "DISCOURSE", + "group": "discourse.act", + "unit": "U", + "status": "core", + "exclusivity": "discourse.act", + "text_derivable": "yes", + "readouts": [ + "utt" + ], + "tier": "T2" + }, + { + "index": 32, + "id": "discourse.answer", + "facet": "DISCOURSE", + "group": "discourse", + "unit": "U", + "status": "core", + "exclusivity": "independent", + "text_derivable": "yes", + "readouts": [ + "utt" + ], + "tier": "T2" + }, + { + "index": 33, + "id": "discourse.backchannel", + "facet": "DISCOURSE", + "group": "discourse", + "unit": "U", + "status": "core", + "exclusivity": "independent", + "text_derivable": "yes", + "readouts": [ + "utt" + ], + "tier": "T2" + }, + { + "index": 34, + "id": "discourse.interruption", + "facet": "DISCOURSE", + "group": "discourse", + "unit": "E", + "status": "experimental", + "exclusivity": "independent", + "text_derivable": "weak", + "readouts": [ + "ev_at" + ], + "tier": "T0" + }, + { + "index": 35, + "id": "discourse.turn_yield", + "facet": "DISCOURSE", + "group": "discourse", + "unit": "E", + "status": "experimental", + "exclusivity": "independent", + "text_derivable": "weak", + "readouts": [ + "ev_at" + ], + "tier": "T0" + }, + { + "index": 36, + "id": "discourse.assistant.device_directed", + "facet": "DISCOURSE", + "group": "discourse.assistant", + "unit": "U", + "status": "core", + "exclusivity": "independent", + "text_derivable": "yes", + "readouts": [ + "utt" + ], + "tier": "T2" + }, + { + "index": 37, + "id": "discourse.assistant.command_complete", + "facet": "DISCOURSE", + "group": "discourse.assistant", + "unit": "E", + "status": "core", + "exclusivity": "independent", + "text_derivable": "yes", + "readouts": [ + "ev_at" + ], + "tier": "T0" + }, + { + "index": 38, + "id": "discourse.intent.alarm_query", + "facet": "DISCOURSE", + "group": "discourse.intent", + "unit": "U", + "status": "core", + "exclusivity": "discourse.intent", + "text_derivable": "yes", + "readouts": [ + "utt" + ], + "tier": "T2" + }, + { + "index": 39, + "id": "discourse.intent.alarm_remove", + "facet": "DISCOURSE", + "group": "discourse.intent", + "unit": "U", + "status": "core", + "exclusivity": "discourse.intent", + "text_derivable": "yes", + "readouts": [ + "utt" + ], + "tier": "T2" + }, + { + "index": 40, + "id": 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"status": "experimental", + "exclusivity": "independent", + "text_derivable": "weak", + "readouts": [ + "frame" + ], + "tier": "T1" + }, + { + "index": 321, + "id": "meta.bystander_speech", + "facet": "META", + "group": "meta", + "unit": "F", + "status": "experimental", + "exclusivity": "independent", + "text_derivable": "no", + "readouts": [], + "tier": null + }, + { + "index": 322, + "id": "safety.crisis.self_harm", + "facet": "SAFETY", + "group": "safety.crisis", + "unit": "U", + "status": "experimental", + "exclusivity": "independent", + "text_derivable": "yes", + "readouts": [ + "utt" + ], + "tier": "T2" + }, + { + "index": 323, + "id": "safety.threat.escalation", + "facet": "SAFETY", + "group": "safety.threat", + "unit": "U", + "status": "experimental", + "exclusivity": "independent", + "text_derivable": "yes", + "readouts": [ + "utt" + ], + "tier": "T2" + }, + { + "index": 324, + "id": "safety.child.grooming_cue", + "facet": "SAFETY", + "group": "safety.child", + "unit": "U", + "status": "experimental", + "exclusivity": "independent", + "text_derivable": "yes", + "readouts": [ + "utt" + ], + "tier": "T2" + }, + { + "index": 325, + "id": "safety.child.inappropriate", + "facet": "SAFETY", + "group": "safety.child", + "unit": "U", + "status": "experimental", + "exclusivity": "independent", + "text_derivable": "yes", + "readouts": [ + "utt" + ], + "tier": "T2" + }, + { + "index": 326, + "id": "meta.media_command", + "facet": "META", + "group": "meta", + "unit": "F", + "status": "experimental", + "exclusivity": "independent", + "text_derivable": "no", + "readouts": [], + "tier": null + }, + { + "index": 327, + "id": "meta.hidden_command", + "facet": "META", + "group": "meta", + "unit": "F", + "status": "experimental", + "exclusivity": "independent", + "text_derivable": "no", + "readouts": [], + "tier": null + } + ] +} \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..2d1a31dc7793670955b7f1d67ae04838c69b9724 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,9 @@ +# CPU torch is enough: pip install torch --index-url https://download.pytorch.org/whl/cpu +torch>=2.1 +numpy>=1.24 +safetensors>=0.4 +tokenizers>=0.15 +huggingface_hub>=0.20 +# live ASR scripts (stream_deepgram.py / stream_speechmatics.py): +# websockets>=12 +# sounddevice>=0.4 (only for --mic) diff --git a/stream_deepgram.py b/stream_deepgram.py new file mode 100644 index 0000000000000000000000000000000000000000..0a75698bf9a1b2addc5ce2d7b0b887d2e9e8e960 --- /dev/null +++ b/stream_deepgram.py @@ -0,0 +1,96 @@ +"""Deepgram live ASR -> Arc Tagger, printed as JSON lines. + + export DEEPGRAM_API_KEY=... # read from the environment only + pip install websockets # + sounddevice for --mic + python stream_deepgram.py --wav call.wav --domain talk + python stream_deepgram.py --mic --domain assistant --seconds 30 + +Interim results (is_final=false) are tagged immediately and revised when Deepgram revises them; is_final freezes the words; +speech_final / UtteranceEnd end the utterance (utterance-level intent, act, safety and role read-outs). With --diarize +(default on) Deepgram speaker ids are passed to the tagger, which enables speaker roles. + +Each output line is {"type": "update", "source": "interim"|"final"|"endpoint", ...summary} (see arctag.asr.summarize); +the last line is {"type": "done", ...}. --result writes the full result (same schema as ArcTagger.tag()). +""" +import argparse, asyncio, json, os, sys, urllib.parse + +HERE = os.path.dirname(os.path.abspath(__file__)); sys.path.insert(0, HERE) +from arctag import live # noqa: E402 +from arctag.asr import DeepgramAdapter, summarize # noqa: E402 + +URL = "wss://api.deepgram.com/v1/listen" + + +def dg_url(a, sr): + q = dict(model=a.dg_model, language="en", encoding="linear16", sample_rate=sr, channels=1, interim_results="true", + punctuate="true", smart_format="true", diarize="true" if a.diarize else "false", vad_events="true", + utterance_end_ms=a.utterance_end_ms, endpointing=a.endpointing) + return URL + "?" + urllib.parse.urlencode(q) + + +async def connect(url, key): + import websockets + hdr = {"Authorization": "Token " + key} + try: + return await websockets.connect(url, additional_headers=hdr, max_size=None) + except TypeError: # websockets < 14 + return await websockets.connect(url, extra_headers=hdr, max_size=None) + + +async def run(a): + key = live.api_key("DEEPGRAM_API_KEY") + from arctag import ArcTagger + tg = ArcTagger.from_pretrained(a.model_dir) + st = tg.stream(domain=a.domain, tier=a.tier) + ad = DeepgramAdapter(st) + sr, chunks = live.source(a) + try: + ws = await connect(dg_url(a, sr), key) + except Exception as e: + sys.exit("Deepgram connect failed: " + live.scrub(e, key)) + + async def send(): + async for b in chunks: + await ws.send(b) + await ws.send(json.dumps({"type": "CloseStream"})) + + async def recv(): + async for raw in ws: + if isinstance(raw, bytes): + continue + m = json.loads(raw) + if m.get("type") == "Error": + live.emit({"type": "error", "message": live.scrub(m.get("description") or m.get("message") or "", key)}) + continue + src = ("endpoint" if m.get("type") == "UtteranceEnd" else "final" if m.get("is_final") else "interim") + for up in ad(m): + if a.no_interim and src == "interim": + continue + live.emit(dict(type="update", source="endpoint" if up.get("utterances") else src, **summarize(up, st))) + + tx = asyncio.create_task(send()) + try: + await recv() + finally: + tx.cancel() + res = st.finalize() + live.emit({"type": "done", "n_words": len(res["words"]), "redacted_text": res["redacted_text"], + "latency_ms": st.s.stats()}) + if a.result: + json.dump(res, open(a.result, "w"), indent=1, default=str) + + +def main(): + ap = live.add_common_args(argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)) + ap.add_argument("--dg-model", default="nova-3") + ap.add_argument("--no-diarize", dest="diarize", action="store_false") + ap.add_argument("--endpointing", type=int, default=300, help="ms of silence for speech_final") + ap.add_argument("--utterance-end-ms", type=int, default=1000) + try: + asyncio.run(run(ap.parse_args())) + except KeyboardInterrupt: + pass + + +if __name__ == "__main__": + main() diff --git a/stream_speechmatics.py b/stream_speechmatics.py new file mode 100644 index 0000000000000000000000000000000000000000..05d792c43a64e6a28fe20bbb37c0df7dedce97ba --- /dev/null +++ b/stream_speechmatics.py @@ -0,0 +1,110 @@ +"""Speechmatics real-time ASR -> Arc Tagger, printed as JSON lines. + +Status: tested against the documented message format (offline fixtures in tests/); not yet tested live. + + export SPEECHMATICS_API_KEY=... # read from the environment only + pip install websockets # + sounddevice for --mic + python stream_speechmatics.py --wav call.wav --domain talk + python stream_speechmatics.py --mic --domain meeting --seconds 30 + +AddPartialTranscript is tagged immediately and replaced by the next partial; AddTranscript freezes the words; EndOfUtterance +(conversation_config.end_of_utterance_silence_trigger) ends the utterance (utterance-level intent, act, safety and role +read-outs). With diarization (default "speaker") the speaker labels are passed to the tagger, which enables speaker roles. + +Each output line is {"type": "update", "source": "interim"|"final"|"endpoint", ...summary} (see arctag.asr.summarize); +the last line is {"type": "done", ...}. --result writes the full result (same schema as ArcTagger.tag()). +""" +import argparse, asyncio, json, os, sys + +HERE = os.path.dirname(os.path.abspath(__file__)); sys.path.insert(0, HERE) +from arctag import live # noqa: E402 +from arctag.asr import SpeechmaticsAdapter, summarize # noqa: E402 + +URL = "wss://eu2.rt.speechmatics.com/v2" + + +def start_msg(a, sr): + tc = {"language": "en", "enable_partials": True, "max_delay": a.max_delay, "operating_point": a.operating_point, + "conversation_config": {"end_of_utterance_silence_trigger": a.eou_silence}} + if a.diarization != "none": + tc["diarization"] = a.diarization + return {"message": "StartRecognition", "audio_format": {"type": "raw", "encoding": "pcm_s16le", "sample_rate": sr}, + "transcription_config": tc} + + +async def connect(url, key): + import websockets + hdr = {"Authorization": "Bearer " + key} + try: + return await websockets.connect(url, additional_headers=hdr, max_size=None) + except TypeError: # websockets < 14 + return await websockets.connect(url, extra_headers=hdr, max_size=None) + + +async def run(a): + key = live.api_key("SPEECHMATICS_API_KEY") + from arctag import ArcTagger + tg = ArcTagger.from_pretrained(a.model_dir) + st = tg.stream(domain=a.domain, tier=a.tier) + ad = SpeechmaticsAdapter(st) + sr, chunks = live.source(a) + try: + ws = await connect(a.url, key) + except Exception as e: + sys.exit("Speechmatics connect failed: " + live.scrub(e, key)) + await ws.send(json.dumps(start_msg(a, sr))) + started = asyncio.Event() + + async def send(): + await started.wait() + n = 0 + async for b in chunks: + await ws.send(b); n += 1 # binary frame = AddAudio + await ws.send(json.dumps({"message": "EndOfStream", "last_seq_no": n})) + + async def recv(): + async for raw in ws: + if isinstance(raw, bytes): + continue + m = json.loads(raw); t = m.get("message") + if t == "RecognitionStarted": + started.set(); continue + if t == "Error": + live.emit({"type": "error", "message": live.scrub(m.get("reason") or m.get("type") or "", key)}) + break + if t == "EndOfTranscript": + break + src = {"AddPartialTranscript": "interim", "AddTranscript": "final", "EndOfUtterance": "endpoint"}.get(t) + for up in ad(m): + if a.no_interim and src == "interim": + continue + live.emit(dict(type="update", source="endpoint" if up.get("utterances") else src, **summarize(up, st))) + + tx = asyncio.create_task(send()) + try: + await recv() + finally: + tx.cancel() + await ws.close() + res = st.finalize() + live.emit({"type": "done", "n_words": len(res["words"]), "redacted_text": res["redacted_text"], + "latency_ms": st.s.stats()}) + if a.result: + json.dump(res, open(a.result, "w"), indent=1, default=str) + + +def main(): + ap = live.add_common_args(argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)) + ap.add_argument("--url", default=URL, help="real-time endpoint for your region") + ap.add_argument("--max-delay", type=float, default=1.0, help="seconds before a word is final (lower = faster finals)") + ap.add_argument("--operating-point", default="enhanced", choices=["standard", "enhanced"]) + ap.add_argument("--eou-silence", type=float, default=0.5, help="seconds of silence for EndOfUtterance") + ap.add_argument("--diarization", default="speaker", choices=["speaker", "none"]) + try: + asyncio.run(run(ap.parse_args())) + except KeyboardInterrupt: + pass + + +if __name__ == "__main__": + main() diff --git a/tests/fixtures/deepgram_live.jsonl b/tests/fixtures/deepgram_live.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..bd871552866a24e23bf7b288a6b2355291c70133 --- /dev/null +++ b/tests/fixtures/deepgram_live.jsonl @@ -0,0 +1,17 @@ +{"type": "Metadata", "transaction_key": "deprecated", "request_id": "00000000-0000-0000-0000-000000000000", "sha256": "0000000000000000000000000000000000000000000000000000000000000000", "created": "2026-01-01T00:00:00.000Z", "duration": 0.0, "channels": 1} +{"type": "SpeechStarted", "channel": [0], "timestamp": 0.1} +{"type": "Results", "channel_index": [0, 1], "duration": 1.05, "start": 0.1, "is_final": false, "speech_final": false, "from_finalize": false, "channel": {"alternatives": [{"transcript": "hi this is", "confidence": 0.96, "words": [{"word": "hi", "start": 0.1, "end": 0.4, "confidence": 0.97, "punctuated_word": "hi", "speaker": 0, "speaker_confidence": 0.9}, {"word": "this", "start": 0.45, "end": 0.75, "confidence": 0.97, "punctuated_word": "this", "speaker": 0, "speaker_confidence": 0.9}, {"word": "is", "start": 0.8, "end": 1.1, "confidence": 0.97, "punctuated_word": "is", "speaker": 0, "speaker_confidence": 0.9}]}]}, "metadata": {"request_id": "00000000-0000-0000-0000-000000000000", "model_info": {"name": "general-nova-3", "version": "x", "arch": "nova-3"}, "model_uuid": "00000000-0000-0000-0000-000000000000"}} +{"type": "Results", "channel_index": [0, 1], "duration": 1.75, "start": 0.1, "is_final": false, "speech_final": false, "from_finalize": false, "channel": {"alternatives": [{"transcript": "hi this is dana from", "confidence": 0.96, "words": [{"word": "hi", "start": 0.1, "end": 0.4, "confidence": 0.97, "punctuated_word": "hi", "speaker": 0, "speaker_confidence": 0.9}, {"word": "this", "start": 0.45, "end": 0.75, "confidence": 0.97, "punctuated_word": "this", "speaker": 0, "speaker_confidence": 0.9}, {"word": "is", "start": 0.8, "end": 1.1, "confidence": 0.97, "punctuated_word": "is", "speaker": 0, "speaker_confidence": 0.9}, {"word": "dana", "start": 1.15, "end": 1.45, "confidence": 0.97, "punctuated_word": "dana", "speaker": 0, "speaker_confidence": 0.9}, {"word": "from", "start": 1.5, "end": 1.8, "confidence": 0.97, "punctuated_word": "from", "speaker": 0, "speaker_confidence": 0.9}]}]}, "metadata": {"request_id": "00000000-0000-0000-0000-000000000000", "model_info": {"name": "general-nova-3", "version": "x", "arch": "nova-3"}, "model_uuid": "00000000-0000-0000-0000-000000000000"}} +{"type": "Results", "channel_index": [0, 1], "duration": 2.45, "start": 0.1, "is_final": true, "speech_final": false, "from_finalize": false, "channel": {"alternatives": [{"transcript": "Hi, this is Dana from Acme Telecom.", "confidence": 0.96, "words": [{"word": "hi", "start": 0.1, "end": 0.4, "confidence": 0.97, "punctuated_word": "Hi,", "speaker": 0, "speaker_confidence": 0.9}, {"word": "this", "start": 0.45, "end": 0.75, "confidence": 0.97, "punctuated_word": "this", "speaker": 0, "speaker_confidence": 0.9}, {"word": "is", "start": 0.8, "end": 1.1, "confidence": 0.97, "punctuated_word": "is", "speaker": 0, "speaker_confidence": 0.9}, {"word": "dana", "start": 1.15, "end": 1.45, "confidence": 0.97, 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1, "speaker_confidence": 0.9}, {"word": "is", "start": 8.25, "end": 8.55, "confidence": 0.97, "punctuated_word": "is", "speaker": 1, "speaker_confidence": 0.9}, {"word": "555-213-4489", "start": 8.6, "end": 8.9, "confidence": 0.97, "punctuated_word": "555-213-4489.", "speaker": 1, "speaker_confidence": 0.9}]}]}, "metadata": {"request_id": "00000000-0000-0000-0000-000000000000", "model_info": {"name": "general-nova-3", "version": "x", "arch": "nova-3"}, "model_uuid": "00000000-0000-0000-0000-000000000000"}} +{"type": "Results", "channel_index": [0, 1], "duration": 0.0, "start": 9.0, "is_final": true, "speech_final": true, "from_finalize": false, "channel": {"alternatives": [{"transcript": "", "confidence": 0.96, "words": []}]}, "metadata": {"request_id": "00000000-0000-0000-0000-000000000000", "model_info": {"name": "general-nova-3", "version": "x", "arch": "nova-3"}, "model_uuid": "00000000-0000-0000-0000-000000000000"}} +{"type": "Results", "channel_index": [0, 1], "duration": 0.7, "start": 10.0, "is_final": false, "speech_final": false, "from_finalize": false, "channel": {"alternatives": [{"transcript": "thanks john", "confidence": 0.96, "words": [{"word": "thanks", "start": 10.0, "end": 10.3, "confidence": 0.97, "punctuated_word": "thanks", "speaker": 0, "speaker_confidence": 0.9}, {"word": "john", "start": 10.35, "end": 10.65, "confidence": 0.97, "punctuated_word": "john", "speaker": 0, "speaker_confidence": 0.9}]}]}, "metadata": {"request_id": "00000000-0000-0000-0000-000000000000", "model_info": {"name": "general-nova-3", "version": "x", "arch": "nova-3"}, "model_uuid": "00000000-0000-0000-0000-000000000000"}} +{"type": "Results", "channel_index": [0, 1], "duration": 3.15, "start": 10.0, "is_final": true, "speech_final": true, "from_finalize": false, "channel": {"alternatives": [{"transcript": "Thanks, John. I'll send a text to that number.", "confidence": 0.96, "words": [{"word": "thanks", "start": 10.0, "end": 10.3, "confidence": 0.97, "punctuated_word": "Thanks,", "speaker": 0, "speaker_confidence": 0.9}, {"word": "john", "start": 10.35, "end": 10.65, "confidence": 0.97, "punctuated_word": "John.", "speaker": 0, "speaker_confidence": 0.9}, {"word": "i'll", "start": 10.7, "end": 11.0, "confidence": 0.97, "punctuated_word": "I'll", "speaker": 0, "speaker_confidence": 0.9}, {"word": "send", "start": 11.05, "end": 11.35, "confidence": 0.97, "punctuated_word": "send", "speaker": 0, "speaker_confidence": 0.9}, {"word": "a", "start": 11.4, "end": 11.7, "confidence": 0.97, "punctuated_word": "a", "speaker": 0, "speaker_confidence": 0.9}, {"word": "text", "start": 11.75, "end": 12.05, "confidence": 0.97, "punctuated_word": "text", "speaker": 0, "speaker_confidence": 0.9}, {"word": "to", "start": 12.1, "end": 12.4, "confidence": 0.97, "punctuated_word": "to", "speaker": 0, "speaker_confidence": 0.9}, {"word": "that", "start": 12.45, "end": 12.75, "confidence": 0.97, "punctuated_word": "that", "speaker": 0, "speaker_confidence": 0.9}, {"word": "number", "start": 12.8, "end": 13.1, "confidence": 0.97, "punctuated_word": "number.", "speaker": 0, "speaker_confidence": 0.9}]}]}, "metadata": {"request_id": "00000000-0000-0000-0000-000000000000", "model_info": {"name": "general-nova-3", "version": "x", "arch": "nova-3"}, "model_uuid": "00000000-0000-0000-0000-000000000000"}} +{"type": "UtteranceEnd", "channel": [0, 1], "last_word_end": 13.2} diff --git a/tests/fixtures/speechmatics_rt.jsonl b/tests/fixtures/speechmatics_rt.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..b3ed2523fb6bc1ccb03eb0f32f941a96c1d1f93e --- /dev/null +++ b/tests/fixtures/speechmatics_rt.jsonl @@ -0,0 +1,20 @@ +{"message": "RecognitionStarted", "id": "00000000-0000-0000-0000-000000000000", "language_pack_info": {"language_description": "English", "word_delimiter": " ", "writing_direction": "left-to-right", "itn": true, "adapted": false}} +{"message": "Info", "type": "recognition_quality", "quality": "telephony", "reason": "Running recognition using a telephony model quality."} +{"message": "AudioAdded", "seq_no": 1} +{"message": "AddPartialTranscript", "format": "2.9", "metadata": {"start_time": 0.1, "end_time": 0.8, "transcript": "Hi this"}, "results": [{"type": "word", "start_time": 0.1, "end_time": 0.4, "alternatives": [{"content": "Hi", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 0.45, "end_time": 0.75, "alternatives": [{"content": "this", "confidence": 0.95, "language": "en", "speaker": "S1"}]}]} +{"message": "AddPartialTranscript", "format": "2.9", "metadata": {"start_time": 0.1, "end_time": 1.85, "transcript": "Hi, this is Dana"}, "results": [{"type": "word", "start_time": 0.1, "end_time": 0.4, "alternatives": [{"content": "Hi", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "punctuation", "start_time": 0.45, "end_time": 0.45, "attaches_to": "previous", "is_eos": false, "alternatives": [{"content": ",", "confidence": 1.0, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 0.45, "end_time": 0.75, "alternatives": [{"content": "this", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 0.8, "end_time": 1.1, "alternatives": [{"content": "is", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 1.15, "end_time": 1.45, "alternatives": [{"content": "Dana", "confidence": 0.95, "language": "en", "speaker": "S1"}]}]} +{"message": "AddTranscript", "format": "2.9", "metadata": {"start_time": 0.1, "end_time": 3.25, "transcript": "Hi, this is Dana from Acme Telecom."}, "results": [{"type": "word", "start_time": 0.1, "end_time": 0.4, "alternatives": [{"content": "Hi", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "punctuation", "start_time": 0.45, "end_time": 0.45, "attaches_to": "previous", "is_eos": false, "alternatives": [{"content": ",", "confidence": 1.0, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 0.45, "end_time": 0.75, "alternatives": [{"content": "this", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 0.8, "end_time": 1.1, "alternatives": [{"content": "is", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 1.15, "end_time": 1.45, "alternatives": [{"content": "Dana", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 1.5, "end_time": 1.8, "alternatives": [{"content": "from", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 1.85, "end_time": 2.15, "alternatives": [{"content": "Acme", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 2.2, "end_time": 2.5, "alternatives": [{"content": "Telecom", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "punctuation", "start_time": 2.55, "end_time": 2.55, "attaches_to": "previous", "is_eos": true, "alternatives": [{"content": ".", "confidence": 1.0, "language": "en", "speaker": "S1"}]}]} +{"message": "AddPartialTranscript", "format": "2.9", "metadata": {"start_time": 2.6, "end_time": 3.65, "transcript": "How can I"}, "results": [{"type": "word", "start_time": 2.6, "end_time": 2.9, "alternatives": [{"content": "How", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 2.95, "end_time": 3.25, "alternatives": [{"content": "can", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 3.3, "end_time": 3.6, "alternatives": [{"content": "I", "confidence": 0.95, "language": "en", "speaker": "S1"}]}]} +{"message": "AddTranscript", "format": "2.9", "metadata": {"start_time": 2.6, "end_time": 5.05, "transcript": "How can I help you today?"}, "results": [{"type": "word", "start_time": 2.6, "end_time": 2.9, "alternatives": [{"content": "How", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 2.95, "end_time": 3.25, "alternatives": [{"content": "can", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 3.3, "end_time": 3.6, "alternatives": [{"content": "I", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 3.65, "end_time": 3.95, "alternatives": [{"content": "help", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 4.0, "end_time": 4.3, "alternatives": [{"content": "you", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 4.35, "end_time": 4.65, "alternatives": [{"content": "today", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "punctuation", "start_time": 4.7, "end_time": 4.7, "attaches_to": "previous", "is_eos": true, "alternatives": [{"content": "?", "confidence": 1.0, "language": "en", "speaker": "S1"}]}]} +{"message": "EndOfUtterance", "format": "2.9", "metadata": {"start_time": 4.7, "end_time": 4.7}} +{"message": "AudioAdded", "seq_no": 60} +{"message": "AddPartialTranscript", "format": "2.9", "metadata": {"start_time": 5.0, "end_time": 6.05, "transcript": "Yeah my name"}, "results": [{"type": "word", "start_time": 5.0, "end_time": 5.3, "alternatives": [{"content": "Yeah", "confidence": 0.95, "language": "en", "speaker": "S2"}]}, {"type": "word", "start_time": 5.35, "end_time": 5.65, "alternatives": [{"content": "my", "confidence": 0.95, "language": "en", "speaker": "S2"}]}, {"type": "word", "start_time": 5.7, "end_time": 6.0, "alternatives": [{"content": "name", "confidence": 0.95, "language": "en", "speaker": "S2"}]}]} +{"message": "AddTranscript", "format": "2.9", "metadata": {"start_time": 5.0, "end_time": 7.8, "transcript": "Yeah, my name is John Patel."}, "results": [{"type": "word", "start_time": 5.0, "end_time": 5.3, "alternatives": [{"content": "Yeah", "confidence": 0.95, "language": "en", "speaker": "S2"}]}, {"type": "punctuation", "start_time": 5.35, "end_time": 5.35, "attaches_to": "previous", "is_eos": false, "alternatives": [{"content": ",", "confidence": 1.0, "language": "en", "speaker": "S2"}]}, {"type": "word", "start_time": 5.35, "end_time": 5.65, "alternatives": [{"content": "my", "confidence": 0.95, "language": "en", "speaker": "S2"}]}, {"type": "word", "start_time": 5.7, "end_time": 6.0, "alternatives": [{"content": "name", "confidence": 0.95, "language": "en", "speaker": "S2"}]}, {"type": "word", "start_time": 6.05, "end_time": 6.35, "alternatives": [{"content": "is", "confidence": 0.95, "language": "en", "speaker": "S2"}]}, {"type": "word", "start_time": 6.4, "end_time": 6.7, "alternatives": [{"content": "John", "confidence": 0.95, "language": "en", "speaker": "S2"}]}, {"type": "word", "start_time": 6.75, "end_time": 7.05, "alternatives": [{"content": "Patel", "confidence": 0.95, "language": "en", "speaker": "S2"}]}, {"type": "punctuation", "start_time": 7.1, "end_time": 7.1, "attaches_to": "previous", "is_eos": true, "alternatives": [{"content": ".", "confidence": 1.0, "language": "en", "speaker": "S2"}]}]} +{"message": "AddPartialTranscript", "format": "2.9", "metadata": {"start_time": 7.2, "end_time": 8.95, "transcript": "And my number is 555"}, "results": [{"type": "word", "start_time": 7.2, "end_time": 7.5, "alternatives": [{"content": "And", "confidence": 0.95, "language": "en", "speaker": "S2"}]}, {"type": "word", "start_time": 7.55, "end_time": 7.85, "alternatives": [{"content": "my", "confidence": 0.95, "language": "en", "speaker": "S2"}]}, {"type": "word", "start_time": 7.9, "end_time": 8.2, "alternatives": [{"content": "number", "confidence": 0.95, "language": "en", "speaker": "S2"}]}, {"type": "word", "start_time": 8.25, "end_time": 8.55, "alternatives": [{"content": "is", "confidence": 0.95, "language": "en", "speaker": "S2"}]}, {"type": "word", "start_time": 8.6, "end_time": 8.9, "alternatives": [{"content": "555", "confidence": 0.95, "language": "en", "speaker": "S2"}]}]} +{"message": "EndOfUtterance", "format": "2.9", "metadata": {"start_time": 7.0, "end_time": 7.0}} +{"message": "AddTranscript", "format": "2.9", "metadata": {"start_time": 7.2, "end_time": 9.3, "transcript": "And my number is 555-213-4489."}, "results": [{"type": "word", "start_time": 7.2, "end_time": 7.5, "alternatives": [{"content": "And", "confidence": 0.95, "language": "en", "speaker": "S2"}]}, {"type": "word", "start_time": 7.55, "end_time": 7.85, "alternatives": [{"content": "my", "confidence": 0.95, "language": "en", "speaker": "S2"}]}, {"type": "word", "start_time": 7.9, "end_time": 8.2, "alternatives": [{"content": "number", "confidence": 0.95, "language": "en", "speaker": "S2"}]}, {"type": "word", "start_time": 8.25, "end_time": 8.55, "alternatives": [{"content": "is", "confidence": 0.95, "language": "en", "speaker": "S2"}]}, {"type": "word", "start_time": 8.6, "end_time": 8.9, "alternatives": [{"content": "555-213-4489", "confidence": 0.95, "language": "en", "speaker": "S2"}]}, {"type": "punctuation", "start_time": 8.95, "end_time": 8.95, "attaches_to": "previous", "is_eos": true, "alternatives": [{"content": ".", "confidence": 1.0, "language": "en", "speaker": "S2"}]}]} +{"message": "AddTranscript", "format": "2.9", "metadata": {"start_time": 9.0, "end_time": 9.0, "transcript": ""}, "results": []} +{"message": "AddPartialTranscript", "format": "2.9", "metadata": {"start_time": 10.0, "end_time": 10.7, "transcript": "Thanks John"}, "results": [{"type": "word", "start_time": 10.0, "end_time": 10.3, "alternatives": [{"content": "Thanks", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 10.35, "end_time": 10.65, "alternatives": [{"content": "John", "confidence": 0.95, "language": "en", "speaker": "S1"}]}]} +{"message": "AddTranscript", "format": "2.9", "metadata": {"start_time": 10.0, "end_time": 14.2, "transcript": "Thanks, John. I'll send a text to that number."}, "results": [{"type": "word", "start_time": 10.0, "end_time": 10.3, "alternatives": [{"content": "Thanks", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "punctuation", "start_time": 10.35, "end_time": 10.35, "attaches_to": "previous", "is_eos": false, "alternatives": [{"content": ",", "confidence": 1.0, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 10.35, "end_time": 10.65, "alternatives": [{"content": "John", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "punctuation", "start_time": 10.7, "end_time": 10.7, "attaches_to": "previous", "is_eos": true, "alternatives": [{"content": ".", "confidence": 1.0, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 10.7, "end_time": 11.0, "alternatives": [{"content": "I'll", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 11.05, "end_time": 11.35, "alternatives": [{"content": "send", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 11.4, "end_time": 11.7, "alternatives": [{"content": "a", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 11.75, "end_time": 12.05, "alternatives": [{"content": "text", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 12.1, "end_time": 12.4, "alternatives": [{"content": "to", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 12.45, "end_time": 12.75, "alternatives": [{"content": "that", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "word", "start_time": 12.8, "end_time": 13.1, "alternatives": [{"content": "number", "confidence": 0.95, "language": "en", "speaker": "S1"}]}, {"type": "punctuation", "start_time": 13.15, "end_time": 13.15, "attaches_to": "previous", "is_eos": true, "alternatives": [{"content": ".", "confidence": 1.0, "language": "en", "speaker": "S1"}]}]} +{"message": "EndOfUtterance", "format": "2.9", "metadata": {"start_time": 13.2, "end_time": 13.2}} +{"message": "EndOfTranscript"} diff --git a/tests/test_adapters.py b/tests/test_adapters.py new file mode 100644 index 0000000000000000000000000000000000000000..afb468ac7de2b1daa3f891a6f707387cf71e3d79 --- /dev/null +++ b/tests/test_adapters.py @@ -0,0 +1,121 @@ +"""Offline tests of the live ASR adapters against recorded-format provider messages (tests/fixtures, made-up content). + + python tests/test_adapters.py # or: pytest tests/ + +1. call sequence: a recording fake stream checks the exact push_words / endpoint calls each adapter makes; +2. end to end: the same messages drive a real ArcTagger stream; checks words, speakers, utterances and JSON-able output. +""" +import json, os, sys + +HERE = os.path.dirname(os.path.abspath(__file__)); ROOT = os.path.dirname(HERE); sys.path.insert(0, ROOT) +from arctag.asr import DeepgramAdapter, SpeechmaticsAdapter, summarize # noqa: E402 + +FIX = os.path.join(HERE, "fixtures") +U1 = ["Hi,", "this", "is", "Dana", "from", "Acme", "Telecom."] +U2 = ["How", "can", "I", "help", "you", "today?"] +U3 = ["Yeah,", "my", "name", "is", "John", "Patel."] +U4 = ["And", "my", "number", "is", "555-213-4489."] +U5 = ["Thanks,", "John.", "I'll", "send", "a", "text", "to", "that", "number."] +ALL = U1 + U2 + U3 + U4 + U5 + + +def P(words, final, k): + return ("push_words", {"words": words, "final": final, "revise_from": k}) + + +E = ("endpoint", {}) +EXPECT = { + "deepgram": [P(["hi", "this", "is"], False, 0), P(["hi", "this", "is", "dana", "from"], False, 0), P(U1, True, 0), + P(["how", "can", "i"], False, 7), P(U2, True, 7), E, + P(["yeah", "my", "name"], False, 13), P(["yeah", "my", "name", "is", "john"], False, 13), P(U3, True, 13), + P(["and", "my", "number", "is", "five", "five", "five"], False, 19), + P(U4, True, 19), E, # UtteranceEnd arrived with interim words pending: endpoint after the final + P(["thanks", "john"], False, 24), P(U5, True, 24), E], + "speechmatics": [P(["Hi", "this"], False, 0), P(["Hi,", "this", "is", "Dana"], False, 0), P(U1, True, 0), + P(["How", "can", "I"], False, 7), P(U2, True, 7), E, + P(["Yeah", "my", "name"], False, 13), P(U3, True, 13), + P(["And", "my", "number", "is", "555"], False, 19), + P(U4, True, 19), E, # EndOfUtterance arrived with partial words pending + P(["Thanks", "John"], False, 24), P(U5, True, 24), E], +} +ADAPTERS = {"deepgram": (DeepgramAdapter, "deepgram_live.jsonl"), "speechmatics": (SpeechmaticsAdapter, "speechmatics_rt.jsonl")} +SPK = {"deepgram": ("S0", "S1"), "speechmatics": ("S1", "S2")} + + +def messages(name): + return [json.loads(l) for l in open(os.path.join(FIX, ADAPTERS[name][1])) if l.strip()] + + +class FakeStream: + """records calls; keeps the word list like the real stream does""" + def __init__(self): + self.words = []; self.n_endpoints = 0; self.spk = [] + + def push_words(self, words, final=True, revise_from=None, endpoint=False): + k = len(self.words) if revise_from is None else revise_from + assert 0 <= k <= len(self.words) + self.words = self.words[:k] + [dict(w, final=final) for w in words] + return {"n_words": len(self.words), "spans": [], "events": [], "utterances": [], "running": {}} + + def endpoint(self): + assert all(w["final"] for w in self.words), "endpoint with non-final words" + self.n_endpoints += 1 + return {"n_words": len(self.words), "spans": [], "events": [], "utterances": [{}], "running": {}} + + def redacted_text(self): + return " ".join(w["w"] for w in self.words) + + +def check_sequence(name): + cls, _ = ADAPTERS[name] + fs = FakeStream(); ad = cls(fs) + for m in messages(name): + ad(m) + assert ad.calls == EXPECT[name], json.dumps(ad.calls, indent=0) + assert [w["w"] for w in fs.words] == ALL and all(w["final"] for w in fs.words) + spk = [w.get("spk") for w in fs.words] + a, b = SPK[name] + assert spk == [a] * 13 + [b] * 11 + [a] * 9, spk + assert fs.n_endpoints == 3 + return len(ad.calls) + + +def check_end_to_end(name, tg): + cls, _ = ADAPTERS[name] + st = tg.stream(domain="talk"); ad = cls(st); lines = [] + for m in messages(name): + for up in ad(m): + lines.append(json.dumps(summarize(up, st))) # must be JSON-serialisable + res = st.finalize() + assert res["words"] == ALL, res["words"] + assert [(u["start"], u["end"]) for u in res["utterances"]] == [(0, 13), (13, 24), (24, 33)], res["utterances"] + assert [w.get("spk") for w in st.s.words][:2] == [SPK[name][0]] * 2 + return {"updates": len(lines), "redacted_text": res["redacted_text"], + "spans": [(s["tag"], s["text"]) for s in res["spans"]], + "acts": [u["act"] for u in res["utterances"]], "roles": [u["role"] for u in res["utterances"]]} + + +def test_deepgram_sequence(): + check_sequence("deepgram") + + +def test_speechmatics_sequence(): + check_sequence("speechmatics") + + +def test_end_to_end(): + from arctag import ArcTagger + tg = ArcTagger.from_pretrained(ROOT) + for name in ADAPTERS: + check_end_to_end(name, tg) + + +if __name__ == "__main__": + for name in ADAPTERS: + print(f"{name}: call sequence OK ({check_sequence(name)} calls)") + import torch + torch.set_num_threads(1) + from arctag import ArcTagger + tg = ArcTagger.from_pretrained(ROOT) + for name in ADAPTERS: + print(f"{name}: end to end OK", json.dumps(check_end_to_end(name, tg))) diff --git a/thresholds/display.json b/thresholds/display.json new file mode 100644 index 0000000000000000000000000000000000000000..74090e9c415276d5613f89110c836c595baf9a8d --- /dev/null +++ b/thresholds/display.json @@ -0,0 +1,174 @@ +{ +"tier": "T0", +"about": "display threshold per tag@readout and fine domain, '_all' = fallback for all domains; 1.01 = hidden", +"thresholds": { +"safety.toxicity.illegal_regulated@utt": { +"_all": 0.07, +"assistant": 0.07, +"court_appellate": 0.07, +"court_trial": 0.07, +"meeting": 0.07, +"nba": 0.07, +"stream_chat": 0.07, +"talk": 0.04, +"unknown": 0.34 +}, +"safety.solicitation.dating_romantic@utt": { +"_all": 0.07, +"assistant": 0.07, +"court_appellate": 0.07, +"court_trial": 0.07, +"meeting": 0.07, +"nba": 0.07, +"stream_chat": 0.02, +"talk": 0.02, +"unknown": 0.07 +}, +"safety.pii.request@utt": { +"_all": 0.97, +"assistant": 0.97, +"court_appellate": 0.97, +"court_trial": 0.97, +"meeting": 0.97, +"nba": 0.97, +"stream_chat": 0.64, +"talk": 0.67, +"unknown": 0.97 +}, +"safety.scam.attempt@utt": { +"_all": 0.87, +"assistant": 0.87, +"court_appellate": 0.99, +"court_trial": 0.87, +"meeting": 0.87, +"nba": 0.87, +"stream_chat": 0.43, +"talk": 0.3, +"unknown": 0.87 +}, +"safety.scam.impersonation@utt": { +"_all": 0.41, +"assistant": 0.41, +"court_appellate": 0.85, +"court_trial": 0.41, +"meeting": 0.41, +"nba": 0.45, +"stream_chat": 0.15, +"talk": 0.06, +"unknown": 0.41 +}, +"safety.scam.urgency@utt": { +"_all": 0.6, +"assistant": 0.6, +"court_appellate": 0.95, +"court_trial": 0.6, +"meeting": 0.65, +"nba": 0.61, +"stream_chat": 0.57, +"talk": 0.1, +"unknown": 0.6 +}, +"safety.scam.payment_request@utt": { +"_all": 0.6, +"assistant": 0.6, +"court_appellate": 0.81, +"court_trial": 0.6, +"meeting": 0.6, +"nba": 0.74, +"stream_chat": 0.33, +"talk": 0.22, +"unknown": 0.6 +}, +"safety.scam.credential_request@utt": { +"_all": 0.24, +"assistant": 0.24, +"court_appellate": 0.6, +"court_trial": 0.24, +"meeting": 0.24, +"nba": 0.24, +"stream_chat": 0.03, +"talk": 0.03, +"unknown": 0.24 +}, +"safety.scam.remote_access@utt": { +"_all": 0.27, +"assistant": 0.27, +"court_appellate": 0.61, +"court_trial": 0.27, +"meeting": 0.41, +"nba": 0.27, +"stream_chat": 0.02, +"talk": 0.03, +"unknown": 0.27 +}, +"safety.pii.leak@utt": { +"_all": 1.01, +"assistant": 1.01, +"court_appellate": 1.01, +"court_trial": 1.01, +"meeting": 1.01, +"nba": 1.01, +"unknown": 1.01, +"talk": 0.81, +"stream_chat": 0.99, +"short_clips": 0.99, +"esports": 0.99 +}, +"safety.crisis.self_harm@utt": { +"_all": 0.44, +"assistant": 0.44, +"court_appellate": 0.65, +"court_trial": 0.44, +"meeting": 0.49, +"nba": 0.45, +"stream_chat": 0.47, +"talk": 0.09, +"unknown": 0.44 +}, +"safety.threat.escalation@utt": { +"_all": 0.02, +"assistant": 0.02, +"court_appellate": 0.02, +"court_trial": 0.02, +"meeting": 0.02, +"nba": 0.02, +"stream_chat": 0.02, +"talk": 0.02, +"unknown": 0.02 +}, +"safety.child.grooming_cue@utt": { +"_all": 0.73, +"assistant": 0.73, +"court_appellate": 0.96, +"court_trial": 0.73, +"meeting": 0.73, +"nba": 0.73, +"stream_chat": 0.12, +"talk": 0.03, +"unknown": 0.73 +}, +"safety.child.inappropriate@utt": { +"_all": 0.96, +"assistant": 0.96, +"court_appellate": 0.96, +"court_trial": 0.96, +"meeting": 0.96, +"nba": 0.96, +"stream_chat": 0.95, +"talk": 0.02, +"unknown": 0.96 +}, +"safety.jailbreak.attempt@utt": { +"_all": 0.99, +"assistant": 0.99, +"court_appellate": 0.99, +"court_trial": 0.99, +"meeting": 0.99, +"nba": 0.99, +"stream_chat": 0.76, +"talk": 0.76, +"unknown": 0.99 +} +}, +"hide": [] +} \ No newline at end of file diff --git a/tok/bpe8k/tokenizer.json 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