| --- |
| pretty_name: Webinars — Russian speech and ASR transcripts |
| language: |
| - ru |
| license: mit |
| task_categories: |
| - automatic-speech-recognition |
| - text-to-speech |
| size_categories: |
| - 100K<n<1M |
| annotations_creators: |
| - machine-generated |
| multilinguality: monolingual |
| source_datasets: |
| - ZeroAgency/shkolkovo-bobr.video-webinars-audio |
| tags: |
| - audio |
| - russian |
| - speech |
| - webinars |
| - asr |
| - tts |
| - flac |
| - parquet |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| dataset_info: |
| features: |
| - name: audio |
| dtype: audio |
| - name: text |
| dtype: string |
| - name: id |
| dtype: string |
| - name: duration |
| dtype: float64 |
| - name: language |
| dtype: string |
| - name: recording_id |
| dtype: string |
| - name: sample_rate |
| dtype: int32 |
| - name: start_seconds |
| dtype: float64 |
| - name: end_seconds |
| dtype: float64 |
| - name: source_spans |
| list: |
| - name: start_sample |
| dtype: int64 |
| - name: end_sample |
| dtype: int64 |
| - name: removed_pause_seconds |
| dtype: float64 |
| splits: |
| - name: train |
| num_examples: 313655 |
| num_bytes: 273662271879 |
| download_size: 273381108048 |
| dataset_size: 273662271879 |
| --- |
| |
| ## 💜 Support TeraTTS |
|
|
| If this dataset is useful to you, consider supporting TeraTTS development. Thank you for your support! |
|
|
| | Currency | Donation address | |
| | :--- | :--- | |
| | **ETH** | `0x19766c207F82303c3998976d852DB82e8Dcc5d77` | |
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| | **Tether USDt (TRON / TRC20)** | `TZ6QeYZSr2h22rzGqQZQZTMzWYVPdUej1y` | |
| | **BTC** | `bc1q2l0xdlylqpweu4sm3y53f95g0nazcm8yu42gkq` | |
|
|
| --- |
|
|
| # Webinars |
|
|
| **313,655 audio segments with ASR text · 2,055.08 hours · 2,319 source recordings.** |
|
|
| Russian educational webinars, filtered for language and detected music, prepared as self-contained |
| Parquet shards for speech recognition and speech synthesis research. Each row |
| contains an audio segment and its automatically recognized text. Audio players |
| are available in the dataset viewer above. |
|
|
| Source audio: [ZeroAgency/shkolkovo-bobr.video-webinars-audio](https://huggingface.co/datasets/ZeroAgency/shkolkovo-bobr.video-webinars-audio), |
| from [bobr.video](https://bobr.video) and [Школково](https://shkolkovo.online). |
| All credit for the original recordings and course materials goes to their creators. |
|
|
| ## Contents |
|
|
| | Property | Value | |
| | --- | --- | |
| | Split | `train` | |
| | Audio/text pairs | 313,655 | |
| | Audio hours | 2,055.08 | |
| | Source recordings represented | 2,319 | |
| | Shards | 965 Parquet files | |
| | Download size | 254.61 GiB | |
| | Audio | Mono FLAC, 16-bit PCM, native 44.1 or 48 kHz | |
| | ASR language label | `ru` for all 313,655 segments | |
|
|
| Audio bytes are embedded in Parquet; no source MP3 downloads or local paths are needed. |
| The `train` split includes all published pairs. This release does not define a |
| held-out evaluation set. Split by `recording_id` when constructing your own validation set. |
|
|
| ## Load from Hugging Face |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("TeraTTS/webinars", split="train", streaming=True) |
| sample = next(iter(ds)) |
| print(sample["text"]) |
| print(sample["duration"], sample["sample_rate"]) |
| |
| # Current datasets versions expose a TorchCodec decoder: |
| audio = sample["audio"].get_all_samples() |
| waveform = audio.data |
| sample_rate = audio.sample_rate |
| ``` |
|
|
| For access to the embedded FLAC without an audio decoder: |
|
|
| ```python |
| from datasets import Audio, load_dataset |
| import io |
| import soundfile as sf |
| |
| ds = load_dataset("TeraTTS/webinars", split="train", streaming=True) |
| ds = ds.cast_column("audio", Audio(decode=False)) |
| sample = next(iter(ds)) |
| waveform, sample_rate = sf.read(io.BytesIO(sample["audio"]["bytes"])) |
| print(sample["text"]) |
| ``` |
|
|
| ## Columns |
|
|
| | Column | Meaning | |
| | --- | --- | |
| | `audio` | Embedded FLAC, represented by Hugging Face's `Audio` feature | |
| | `text` | Original Whisper ASR text for the segment | |
| | `id` | Stable segment identifier | |
| | `duration` | Duration of the published audio in seconds | |
| | `language` | Automatic Whisper language label; may be incorrect | |
| | `recording_id` | Upstream webinar UUID, shared by segments from one recording | |
| | `sample_rate` | Native audio sample rate | |
| | `start_seconds`, `end_seconds` | Segment's enclosing interval in the original MP3 | |
| | `source_spans` | Ordered native sample intervals retained from the original MP3 | |
| | `removed_pause_seconds` | Time removed from internal quiet pauses | |
|
|
| Each `source_spans` entry has `start_sample` and `end_sample`, with an exclusive |
| end. Decode the original stereo MP3, take the arithmetic channel mean, then |
| concatenate these intervals to reproduce the segment before PCM16 conversion. |
| The published duration can be shorter than `end_seconds - start_seconds`. |
| The original webinar can be looked up at `https://bobr.video/watch/<recording_id>`. |
|
|
| ## Preparation |
|
|
| 1. Original audio was downloaded from `ZeroAgency/shkolkovo-bobr.video-webinars-audio` at revision |
| `c35640ea983658baa75047ad0d058c7912ff08ce`. This release uses the recordings, with newly generated |
| local transcripts; it does not reuse the source repository's TXT transcripts. |
| 2. FireRed VAD located speech and grouped it into segments targeting 20–30 seconds. |
| Short tails remain; some boundaries were forced to satisfy the maximum duration. |
| 3. ASR used `Systran/faster-whisper-large-v3`, revision |
| `edaa852ec7e145841d8ffdb056a99866b5f0a478`, with FP16 inference, beam size 5, |
| automatic language detection and temperature fallback. |
| 4. Long internal quiet pauses were shortened using the saved, audited edit maps. |
| Eligible quiet runs of at least 0.8 s retained 0.3 s, protecting VAD speech with |
| 60 ms margins and trusted Unity2 word alignments with 80 ms margins. Untrusted |
| alignments used a more conservative energy threshold. Edge silence was retained. |
| No new alignment or transcription was run for this publication. |
| 5. The ASR text was preserved verbatim. The 130 segments with empty or |
| whitespace-only transcripts were omitted. The language/music filters below were |
| then applied. No stress marks or prosody tags were added to the transcripts. |
| 6. Audio was reconstructed at its native sample rate, converted to mono PCM16 and |
| encoded as FLAC. Every original and pause-edited float32 chunk was checked |
| against the waveform hashes saved during data preparation. FLAC headers, |
| lengths and representative decode round trips were checked before upload. |
|
|
| FLAC stores the exported PCM16 signal losslessly; the source recordings were |
| already MP3. This export does not restore frequencies or detail lost by MP3. |
| Full shard checksums and measured counts are in [manifest.json](manifest.json). |
|
|
| ## Language and music filtering |
|
|
| This filtered release retains only segments whose existing ASR language tag is |
| `ru`. It removes 3,526 other-tagged segments |
| and 8,643 additional Russian-tagged segments |
| flagged for music, background music, singing, songs or musical instruments. |
|
|
| [EfficientAT mn10_as](https://github.com/fschmid56/EfficientAT) scanned every |
| Russian-tagged segment using 10-second windows with a 5-second hop, including the |
| final window. Short clips were zero-padded. Analysis used temporary 32 kHz mono |
| waveforms reconstructed from the same saved pause-edit spans as the release. |
| [AST](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) independently |
| checked all primary candidates (score >= 0.05 or background-music score >= 0.005) |
| and 1,000 deterministic low-score |
| audit samples at 16 kHz. Where these two detectors disagreed at a maximum |
| target-class/window score of 0.15, EfficientAT mn40_as resolved the decision. |
| A segment was removed when at least two detectors exceeded that threshold. |
| The low-score audit found no additional music decisions under this rule. |
| |
| All shards were rebuilt from local source recordings and audited ASR/edit records. |
| Retained audio signals, transcripts, identifiers and source spans are unchanged. |
| Detector revisions, thresholds and counts are in [filtering.json](filtering.json); |
| per-sample decisions and scores are in [filter_scores.parquet](filter_scores.parquet). |
| The unfiltered version remains reproducible at revision `83c2459778926a7db6144abd552b084de1ac95e9`. |
| Filtering is automatic; quiet music can remain and false exclusions are possible. |
| Detector scores are not calibrated probabilities of a clean recording. |
| |
| ## Transcript-quality filtering (2026-11-14) |
| |
| This revision additionally excludes **1,430 audio/text pairs** |
| (9.53 audio hours) from the preceding Russian/no-music |
| release. Reference transcripts are kept exactly as they were; independent ASR |
| text is used only as filtering evidence and does not replace our Whisper output. |
| |
| The screen combines source-specific extreme letter rates, severe anomalies in |
| the existing Qwen word alignments, and large unaligned leading/tail spans. |
| Unicode letter counts exclude whitespace, punctuation and stress markup. Audio |
| duration is the exact released duration, including existing webinar pause edits. |
| The duration-weighted mean is 10.39 |
| letters/sec. Rates alone never establish that a transcript is wrong. |
| |
| 2,813 selected pairs, including fixed duration-stratified controls, |
| were independently checked with Qwen3-ASR-1.7B and Silero speech detection. |
| ASR received only audio, with automatic language detection and no reference text |
| or hotwords. Comparison normalizes punctuation, case, ё and integer spellings. |
| Qwen forced alignment places supplied words; timestamp margins and isolated |
| zero-duration words are not evidence that the words were spoken or missing. |
| |
| Conservative exclusions require independent mismatch evidence. **714 |
| uncertain cases are retained and marked for review**, including numeric ambiguity. |
| The remainder of the corpus has not undergone exhaustive independent ASR checks; |
| automatic filtering can miss errors and can exclude usable pairs. |
| |
| See [quality/report.json](quality/report.json), [quality/policy.py](quality/policy.py), |
| [quality/decisions.parquet](quality/decisions.parquet), and |
| [quality/exclusions.jsonl](quality/exclusions.jsonl). Integrated language, music |
| and final transcript decisions are in [filter_scores.parquet](filter_scores.parquet). |
| The preceding release remains available at revision `9c0dc32fc92ef6aa336783e2c838b1c0e3051ae9`. |
| |
| ## Transcript quality |
| |
| These are automatic transcripts, without manual correction or a measured corpus WER. |
| Retained recordings can contain multiple speakers, background sounds, filler |
| words, course-specific names or formulas, and music missed by the automatic filter. ASR may hallucinate, omit |
| words or misidentify a language. Speech-aware segmenting and pause checks do |
| not establish transcript correctness. This release preserves those limitations |
| so users can apply task-specific filtering. |
| |
| ## Source and license |
| |
| The [upstream dataset card](https://huggingface.co/datasets/ZeroAgency/shkolkovo-bobr.video-webinars-audio) declares |
| the MIT license and states that copyright for the course materials belongs to |
| Shkolkovo.online. It describes the intended use as LLM and STT/TTS training. |
| That attribution is retained here. See [UPSTREAM_README.md](UPSTREAM_README.md) |
| for the original card and [NOTICE](NOTICE) for provenance. |
| |
| Please cite both the original dataset and this prepared release: |
| |
| ```bibtex |
| @misc{zeroagency_webinars, |
| author = {ZeroAgency}, |
| title = {shkolkovo-bobr.video-webinars-audio}, |
| howpublished = {Hugging Face dataset}, |
| url = {https://huggingface.co/datasets/ZeroAgency/shkolkovo-bobr.video-webinars-audio} |
| } |
|
|
| @misc{teratts_webinars_2026, |
| author = {TeraTTS}, |
| title = {Webinars: segmented audio with ASR transcripts}, |
| year = {2026}, |
| howpublished = {Hugging Face dataset}, |
| url = {https://huggingface.co/datasets/TeraTTS/webinars} |
| } |
| ``` |
| |
