| | --- |
| | tags: |
| | - audio |
| | - speech |
| | - whisper |
| | - dataset |
| | --- |
| | # test-draft-asr-e2e |
| |
|
| | Speech dataset prepared with Trelis Studio. |
| |
|
| | ## Statistics |
| |
|
| | | Metric | Value | |
| | |--------|-------| |
| | | Source files | 1 | |
| | | Train samples | 4 | |
| | | Total duration | 2.1 minutes | |
| |
|
| | ## Columns |
| |
|
| | | Column | Type | Description | |
| | |--------|------|-------------| |
| | | `audio` | Audio | Audio segment (16kHz) - speech only, extracted from aligned regions | |
| | | `text` | string | Plain transcription (no timestamps) | |
| | | `text_ts` | string | Transcription with Whisper timestamp tokens | |
| | | `preconditioning` | string | Previous segment's plain text (empty for first segment of each source file) | |
| | | `start_time` | string | Segment start in original audio (HH:MM:SS.mmm) | |
| | | `end_time` | string | Segment end in original audio (HH:MM:SS.mmm) | |
| | | `speech_duration` | float | Duration of speech in segment (excluding silence) | |
| | | `word_timestamps` | string | Word-level timestamps as JSON (relative to speech-only audio) | |
| | | `source_file` | string | Original audio filename | |
| | | `language` | string | ISO 639-1 language code for Whisper language token (e.g., `en`, `el`) | |
| |
|
| | ## Speech Segmentation |
| |
|
| | For `.txt` transcripts, CTC alignment produces word-level timestamps that define speech |
| | boundaries. The full audio is treated as a single speech region and split at word boundaries |
| | into ~20s chunks. For `.srt`/`.vtt` files, transcript timestamps define segments. |
| | Timestamps are relative to the extracted speech audio within each chunk. |
| |
|
| | ## Training Usage |
| |
|
| | ### 2-bucket approach (default) |
| | - **Bucket A (50%)**: Use `text` column - plain transcription without timestamps |
| | - **Bucket B (50%)**: Use `text_ts` column - transcription with Whisper timestamp tokens |
| |
|
| | ### 4-bucket approach (with preconditioning) |
| | - **Bucket A (25%)**: `text` only (no timestamps, no preconditioning) |
| | - **Bucket A' (25%)**: `preconditioning` + `text` (previous segment context, no timestamps) |
| | - **Bucket B (25%)**: `text_ts` only (timestamps, no preconditioning) |
| | - **Bucket B' (25%)**: `preconditioning` + `text_ts` (previous segment context + timestamps) |
| |
|
| | Preconditioning prepends the previous segment's text to teach the model conversational continuity. |
| | Bucket ratios are configurable at training time. |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | from datasets import load_dataset |
| | |
| | dataset = load_dataset("Trelis/test-draft-asr-e2e") |
| | ``` |
| |
|
| | --- |
| | *Prepared with [Trelis Studio](https://studio.trelis.com)* |
| |
|
