---
tags:
- audio
- speech
- whisper
- dataset
---
# pilotgpt-test

Speech dataset prepared with Trelis Studio.

## Statistics

| Metric | Value |
|--------|-------|
| Source files | 2 |
| Validation samples | 47 |
| 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)*