Apache-2.0 licence for the weights; banner

The weights (cue.safetensors, config.json) are now Apache-2.0, like the encoder. NOTICE keeps the CC BY 4.0 attributions for the training data.

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* 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
+banner.png filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,202 @@
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NOTICE

@@ -1,4 +1,7 @@
-Cue v5 - third-party components and data
+Cue v5
+Copyright 2026 IOTEverythin. Licensed under the Apache License, Version 2.0 (see LICENSE).
+
+Third-party components and data
 
 encoder/  The encoder of OpenAI Whisper small (openai/whisper-small), Apache License 2.0.
           Modification: only the encoder is kept and its positional-embedding table is cut to the

README.md

@@ -1,141 +1,143 @@
----
-license: other
-license_name: ioteverythin-research
-library_name: transformers
-pipeline_tag: audio-classification
-base_model: openai/whisper-small
-tags:
-- voice-agent
-- turn-taking
-- barge-in
-- endpointing
-- full-duplex
-- indian-english
----
-
-# Cue v5: turn-taking for voice agents, trained on real conversation
-
-Cue listens to a call and decides, every 160 ms, what a voice agent should do: keep talking,
-**PAUSE** or **STOP** while it speaks; **RESPOND** or keep waiting after the caller stops.
-v5 is Cue v4 retrained on real human conversation as well as synthetic calls, and it can hear
-the agent's own audio.
-
-**What changed from v4**
-
-- **Real conversation.** Besides the synthetic Indian English calls, v5 was trained on the AMI and
-  ICSI meeting corpora (CC BY 4.0): real backchannels, overlaps, interruptions and hesitations.
-  On held-out real meetings it catches about twice as many interruptions as v4.
-- **The agent's own audio as a second input.** A live agent knows what it is playing; v5 hears it
-  through a second encoder pass, which helps it tell the agent's own voice (echo) from the caller.
-  It is optional: without it the input is zero, a condition v5 was also trained on.
-- **Phone conditions.** Meetings and calls were trained clean and through echo, reverb, recorded
-  noise (MUSAN) and an 8 kHz phone codec.
-- **No shortcut through the agent's reply.** In recordings the other side starts talking right after
-  a turn ends; a live agent only does after Cue decides. The agent audio is hidden in that window
-  and meetings are trained without the other side's audio, so v5 does not learn that shortcut.
-
-**Status: research model.** Real meetings are not phone calls, and v5 has not been tested on
-production call traffic.
-
-## Use
-
-```python
-import cue_turn                                   # pip install "cue-turn[full]"
-cue = cue_turn.load("v5")                         # or a folder with this repository's files
-stream = cue.stream(sample_rate=8000, agent_sample_rate=24000)
-for caller_chunk, agent_chunk in call:            # the agent chunk: what it played over the same span
-    for d in stream.feed(caller_chunk, assistant_speaking=bot_is_playing(), agent_audio=agent_chunk):
-        handle(d)                                 # {"t_ms": ..., "decision": "STOP" | "PAUSE" | "RESPOND"}
-```
-
-In Pipecat, add `CueAgentAudioTap(session)` just before `transport.output()` so the session sees the
-bot's audio; see the cue-turn README. A GPU is recommended (two encoder passes while the agent
-speaks; one while it is quiet).
-
-## Decision settings
-
-| Profile | Barge-in (stop_p, hold) | End of turn (p_done, turn_hold) |
-|---|---|---|
-| responsive | 0.7, 4 | 0.5, 6 |
-| **balanced (default)** | **0.8, 6** | **0.9, 12** |
-| cautious | 0.95, 6 | 0.95, 16 |
-
-All with cooldown 1500 ms, min_speech 10 frames, fallback 2000 ms. Defaults were chosen on AMI
-validation meetings and the synthetic test sets together: real speech wants lower thresholds for
-interruptions and longer holds for end of turn than synthetic speech does.
-
-| Setting | Synthetic barge-in, clean: hard / takeover / stops on backchannel | AMI validation: interruptions caught / false alarms |
-|---|---|---|
-| 0.7, 4 | 85.5% / 96.9% / 11.4% | 63% / 7.8% |
-| **0.8, 6** | **90.3% / 93.8% / 8.8%** | **39% / 4.1%** |
-| 0.95, 6 | 91.9% / 87.5% / 3.5% | 2% / 0% |
-
-| Setting | Synthetic end of turn, clean: mid-thought / answered / delay | AMI validation: turn ends caught / answers into pauses |
-|---|---|---|
-| 0.5, 6 | 4.5% / 95.6% / 137 ms | 76% / 37% |
-| **0.9, 12** | **3.7% / 96.7% / 256 ms** | **63% / 25%** |
-| 0.95, 16 | 3.0% / 96.6% / 336 ms | 51% / 14% |
-
-## Results
-
-### Real meetings (held-out AMI and ICSI test meetings; best setting with at most 10% false alarms)
-
-| | v4 | **v5** |
-|---|---|---|
-| AMI interruptions caught | 24% | **61%** |
-| ICSI interruptions caught | 37% | **63%** |
-| AMI end of turn | none under 10% false alarms | **27%** (1.2 s) |
-| ICSI end of turn | none under 10% false alarms | **59%** (1.06 s) |
-
-### Synthetic test calls (each model's best setting under the v4 card's matched rules)
-
-| | Hard | Takeovers | Soft | Stops on backchannel | Stops on noise | Reaction p50 |
-|---|---|---|---|---|---|---|
-| v4, clean | 85.5% | 90.6% | 93.8% | 3.5% | 5.6% | 243 ms |
-| v5, clean | 90.3% | 93.8% | 92.5% | 7.0% | 7.4% | 241 ms |
-| v4, realistic | 75.8% | 75.0% | 91.2% | 3.5% | 8.0% | 311 ms |
-| v5, realistic | 85.5% | 81.2% | 93.8% | 4.4% | 11.7% | 281 ms |
-
-| End of turn | Answers mid-thought | Turn ends answered | Delay p50 |
-|---|---|---|---|
-| v4, clean / realistic | 5.2% / 7.5% | 95.5% / 94.4% | 177 / 185 ms |
-| v5, clean / realistic | 4.5% / 7.5% | 95.6% / 94.1% | 137 / 154 ms |
-
-### TurnBench (dev set, cross-validated settings, official scorer)
-
-Both models without any TurnBench training data. Recall / false alarms / median delay.
-
-| | End of turn | End of turn, Cue's end-of-turn judgement | Interruption |
-|---|---|---|---|
-| v4 | 0.772 / 7.7% / 1.41 s | 0.608 / 8.7% / 1.16 s | 0.614 / 8.8% / 583 ms |
-| v5, without the other speaker's audio | 0.779 / 8.9% / 1.38 s | 0.605 / 7.0% / 947 ms | **0.804 / 7.9% / 547 ms** |
-| v5, with the other speaker's audio | **0.800 / 7.0% / 1.16 s** | **0.686 / 8.1% / 908 ms** | 0.712 / 9.2% / 653 ms |
-
-The best end-of-turn rule on TurnBench is a silence timer on Cue's speech detection (about 1 s of
-quiet): the benchmark ranks recall within a false-alarm budget and does not count delay. Published
-systems on the same dev set include VAP (0.841 / 0.957) and Kyutai's semantic VAD (0.803 / 0.934).
-
-## Training
-
-| | |
-|---|---|
-| Start | Cue v4 (the agent-audio weights start at zero, so step 0 is v4) |
-| Data | 2,600 synthetic call tracks (1,300 calls, clean + realistic; 32 with Dia-1.6B caller voices), AMI 20 meetings (80 speaker tracks), ICSI 10 meetings (30 speaker tracks); sampled 50 / 30 / 20% |
-| Labels | synthetic: exact event timings from the generator; meetings: from the corpus dialogue acts (backchannels, floor-taking, interrupted utterances), word timings and vocal sounds |
-| Augmentation | echo, room reverb, MUSAN noise, 8 kHz mu-law codec, level, on half the tracks |
-| Agent input | the agent's audio, kept only while the agent is playing and hidden in the 1.5 s decision window; zeroed for 15% of synthetic segments and for all meetings |
-| Optimiser | AdamW, lr 1e-4, one-cycle, batch 16 x 20 s, 8,000 steps, bfloat16 on an RTX 4060 laptop GPU (79 min) |
-
-## Limitations
-
-- Real meetings are not phone calls; v5 has not been validated on production calls.
-- End of turn on real speech is still slow (about 1 s) and catches about half of turn ends.
-- The default settings trade synthetic false stops for real-speech recall; tune per deployment.
-- Indian-English and Hinglish backchannels ("haan", "achha") are only in the synthetic data.
-
-## Licences
-
-- `encoder/`: Whisper small's encoder, Apache-2.0 (position table shortened; see NOTICE).
-- `cue.safetensors`, `config.json`: IOTEverythin, research use.
-- Training data: AMI and ICSI (CC BY 4.0), MUSAN (CC BY 4.0), Chatterbox (MIT), Dia-1.6B (Apache-2.0),
-  GLOBE (CC0), Svarah (CC BY 4.0). See NOTICE.
+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: audio-classification
+base_model: openai/whisper-small
+tags:
+- voice-agent
+- turn-taking
+- barge-in
+- endpointing
+- full-duplex
+- indian-english
+---
+
+<p align="center"><img src="https://huggingface.co/IOTEverythin/cue-v5/resolve/main/banner.png" alt="Cue v5: caller audio and the agent's own audio in; STOP, PAUSE or RESPOND out. 2.5x more real interruptions caught than Cue v4 on AMI meetings; 0.80 interruption recall on TurnBench." width="100%"></p>
+
+# Cue v5: turn-taking for voice agents, trained on real conversation
+
+Cue listens to a call and decides, every 160 ms, what a voice agent should do: keep talking,
+**PAUSE** or **STOP** while it speaks; **RESPOND** or keep waiting after the caller stops.
+v5 is Cue v4 retrained on real human conversation as well as synthetic calls, and it can hear
+the agent's own audio.
+
+**What changed from v4**
+
+- **Real conversation.** Besides the synthetic Indian English calls, v5 was trained on the AMI and
+  ICSI meeting corpora (CC BY 4.0): real backchannels, overlaps, interruptions and hesitations.
+  On held-out real meetings it catches about twice as many interruptions as v4.
+- **The agent's own audio as a second input.** A live agent knows what it is playing; v5 hears it
+  through a second encoder pass, which helps it tell the agent's own voice (echo) from the caller.
+  It is optional: without it the input is zero, a condition v5 was also trained on.
+- **Phone conditions.** Meetings and calls were trained clean and through echo, reverb, recorded
+  noise (MUSAN) and an 8 kHz phone codec.
+- **No shortcut through the agent's reply.** In recordings the other side starts talking right after
+  a turn ends; a live agent only does after Cue decides. The agent audio is hidden in that window
+  and meetings are trained without the other side's audio, so v5 does not learn that shortcut.
+
+**Status: early release.** Real meetings are not phone calls, and v5 has not been tested on
+production call traffic.
+
+## Use
+
+```python
+import cue_turn                                   # pip install "cue-turn[full]"
+cue = cue_turn.load("v5")                         # or a folder with this repository's files
+stream = cue.stream(sample_rate=8000, agent_sample_rate=24000)
+for caller_chunk, agent_chunk in call:            # the agent chunk: what it played over the same span
+    for d in stream.feed(caller_chunk, assistant_speaking=bot_is_playing(), agent_audio=agent_chunk):
+        handle(d)                                 # {"t_ms": ..., "decision": "STOP" | "PAUSE" | "RESPOND"}
+```
+
+In Pipecat, add `CueAgentAudioTap(session)` just before `transport.output()` so the session sees the
+bot's audio; see the cue-turn README. A GPU is recommended (two encoder passes while the agent
+speaks; one while it is quiet).
+
+## Decision settings
+
+| Profile | Barge-in (stop_p, hold) | End of turn (p_done, turn_hold) |
+|---|---|---|
+| responsive | 0.7, 4 | 0.5, 6 |
+| **balanced (default)** | **0.8, 6** | **0.9, 12** |
+| cautious | 0.95, 6 | 0.95, 16 |
+
+All with cooldown 1500 ms, min_speech 10 frames, fallback 2000 ms. Defaults were chosen on AMI
+validation meetings and the synthetic test sets together: real speech wants lower thresholds for
+interruptions and longer holds for end of turn than synthetic speech does.
+
+| Setting | Synthetic barge-in, clean: hard / takeover / stops on backchannel | AMI validation: interruptions caught / false alarms |
+|---|---|---|
+| 0.7, 4 | 85.5% / 96.9% / 11.4% | 63% / 7.8% |
+| **0.8, 6** | **90.3% / 93.8% / 8.8%** | **39% / 4.1%** |
+| 0.95, 6 | 91.9% / 87.5% / 3.5% | 2% / 0% |
+
+| Setting | Synthetic end of turn, clean: mid-thought / answered / delay | AMI validation: turn ends caught / answers into pauses |
+|---|---|---|
+| 0.5, 6 | 4.5% / 95.6% / 137 ms | 76% / 37% |
+| **0.9, 12** | **3.7% / 96.7% / 256 ms** | **63% / 25%** |
+| 0.95, 16 | 3.0% / 96.6% / 336 ms | 51% / 14% |
+
+## Results
+
+### Real meetings (held-out AMI and ICSI test meetings; best setting with at most 10% false alarms)
+
+| | v4 | **v5** |
+|---|---|---|
+| AMI interruptions caught | 24% | **61%** |
+| ICSI interruptions caught | 37% | **63%** |
+| AMI end of turn | none under 10% false alarms | **27%** (1.2 s) |
+| ICSI end of turn | none under 10% false alarms | **59%** (1.06 s) |
+
+### Synthetic test calls (each model's best setting under the v4 card's matched rules)
+
+| | Hard | Takeovers | Soft | Stops on backchannel | Stops on noise | Reaction p50 |
+|---|---|---|---|---|---|---|
+| v4, clean | 85.5% | 90.6% | 93.8% | 3.5% | 5.6% | 243 ms |
+| v5, clean | 90.3% | 93.8% | 92.5% | 7.0% | 7.4% | 241 ms |
+| v4, realistic | 75.8% | 75.0% | 91.2% | 3.5% | 8.0% | 311 ms |
+| v5, realistic | 85.5% | 81.2% | 93.8% | 4.4% | 11.7% | 281 ms |
+
+| End of turn | Answers mid-thought | Turn ends answered | Delay p50 |
+|---|---|---|---|
+| v4, clean / realistic | 5.2% / 7.5% | 95.5% / 94.4% | 177 / 185 ms |
+| v5, clean / realistic | 4.5% / 7.5% | 95.6% / 94.1% | 137 / 154 ms |
+
+### TurnBench (dev set, cross-validated settings, official scorer)
+
+Both models without any TurnBench training data. Recall / false alarms / median delay.
+
+| | End of turn | End of turn, Cue's end-of-turn judgement | Interruption |
+|---|---|---|---|
+| v4 | 0.772 / 7.7% / 1.41 s | 0.608 / 8.7% / 1.16 s | 0.614 / 8.8% / 583 ms |
+| v5, without the other speaker's audio | 0.779 / 8.9% / 1.38 s | 0.605 / 7.0% / 947 ms | **0.804 / 7.9% / 547 ms** |
+| v5, with the other speaker's audio | **0.800 / 7.0% / 1.16 s** | **0.686 / 8.1% / 908 ms** | 0.712 / 9.2% / 653 ms |
+
+The best end-of-turn rule on TurnBench is a silence timer on Cue's speech detection (about 1 s of
+quiet): the benchmark ranks recall within a false-alarm budget and does not count delay. Published
+systems on the same dev set include VAP (0.841 / 0.957) and Kyutai's semantic VAD (0.803 / 0.934).
+
+## Training
+
+| | |
+|---|---|
+| Start | Cue v4 (the agent-audio weights start at zero, so step 0 is v4) |
+| Data | 2,600 synthetic call tracks (1,300 calls, clean + realistic; 32 with Dia-1.6B caller voices), AMI 20 meetings (80 speaker tracks), ICSI 10 meetings (30 speaker tracks); sampled 50 / 30 / 20% |
+| Labels | synthetic: exact event timings from the generator; meetings: from the corpus dialogue acts (backchannels, floor-taking, interrupted utterances), word timings and vocal sounds |
+| Augmentation | echo, room reverb, MUSAN noise, 8 kHz mu-law codec, level, on half the tracks |
+| Agent input | the agent's audio, kept only while the agent is playing and hidden in the 1.5 s decision window; zeroed for 15% of synthetic segments and for all meetings |
+| Optimiser | AdamW, lr 1e-4, one-cycle, batch 16 x 20 s, 8,000 steps, bfloat16 on an RTX 4060 laptop GPU (79 min) |
+
+## Limitations
+
+- Real meetings are not phone calls; v5 has not been validated on production calls.
+- End of turn on real speech is still slow (about 1 s) and catches about half of turn ends.
+- The default settings trade synthetic false stops for real-speech recall; tune per deployment.
+- Indian-English and Hinglish backchannels ("haan", "achha") are only in the synthetic data.
+
+## Licences
+
+- `encoder/`: Whisper small's encoder, Apache-2.0 (position table shortened; see NOTICE).
+- `cue.safetensors`, `config.json`: IOTEverythin, Apache-2.0 (see LICENSE). Commercial use is allowed;
+  keep the NOTICE file, which carries the attributions the CC BY 4.0 training data requires.
+- Training data: AMI and ICSI (CC BY 4.0), MUSAN (CC BY 4.0), Chatterbox (MIT), Dia-1.6B (Apache-2.0),
+  GLOBE (CC0), Svarah (CC BY 4.0). See NOTICE.