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--- |
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tags: |
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- audio |
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- speech |
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- whisper |
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- dataset |
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--- |
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# atc-validation-v2 |
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Speech dataset prepared with Trelis Studio. |
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## Statistics |
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| Metric | Value | |
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|--------|-------| |
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| Source files | 1 | |
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| Validation samples | 13 | |
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| Total duration | 5.0 minutes | |
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## Columns |
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| Column | Type | Description | |
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|--------|------|-------------| |
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| `audio` | Audio | Audio segment (16kHz) - speech only, silence stripped via VAD | |
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| `text` | string | Plain transcription (no timestamps) - backwards compatible | |
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| `text_ts` | string | Transcription WITH Whisper timestamp tokens (e.g., `<|0.00|>Hello<|0.50|>`) | |
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| `start_time` | string | Segment start in original audio (HH:MM:SS.mmm) | |
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| `end_time` | string | Segment end in original audio (HH:MM:SS.mmm) | |
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| `speech_duration` | float | Duration of speech in segment (excluding silence) | |
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| `word_timestamps` | list | Word-level timestamps (relative to speech-only audio) | |
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| `source_file` | string | Original audio filename | |
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## VAD Processing |
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Audio segments are processed with Silero VAD to match faster-whisper inference: |
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- Silence is stripped from audio (only speech regions remain) |
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- Timestamps are relative to the concatenated speech audio |
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- This ensures training data matches inference behavior |
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## Training Usage |
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For Whisper timestamp training, use the two-bucket approach: |
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- **Bucket A (50%)**: Use `text` - plain transcription without timestamps |
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- **Bucket B (50%)**: Use `text_ts` - transcription with Whisper timestamp tokens |
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## Usage |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("Trelis/atc-validation-v2") |
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``` |
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--- |
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*Prepared with [Trelis Studio](https://studio.trelis.com)* |
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