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README.md

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+---
+tags:
+- audio
+- speech
+- whisper
+- dataset
+---
+# pilotgpt-test
+
+Speech dataset prepared with Trelis Studio.
+
+## Statistics
+
+| Metric | Value |
+|--------|-------|
+| Source files | 2 |
+| Validation samples | 22 |
+| Total duration | 4.6 minutes |
+
+## Columns
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `audio` | Audio | Audio segment (16kHz) - speech only, silence stripped via VAD |
+| `text` | string | Plain transcription (no timestamps) - backwards compatible |
+| `text_ts` | string | Transcription WITH Whisper timestamp tokens (e.g., `<|0.00|>Hello<|0.50|>`) |
+| `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` | list | Word-level timestamps (relative to speech-only audio) |
+| `source_file` | string | Original audio filename |
+
+## VAD Processing
+
+Audio segments are processed with Silero VAD to match faster-whisper inference:
+- Silence is stripped from audio (only speech regions remain)
+- Timestamps are relative to the concatenated speech audio
+- This ensures training data matches inference behavior
+
+## Training Usage
+
+For Whisper timestamp training, use the two-bucket approach:
+- **Bucket A (50%)**: Use `text` - plain transcription without timestamps
+- **Bucket B (50%)**: Use `text_ts` - transcription with Whisper timestamp tokens
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("Trelis/pilotgpt-test")
+```
+
+---
+*Prepared with [Trelis Studio](https://studio.trelis.com)*