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README.md

@@ -1,40 +1,56 @@
 ---
-dataset_info:
-  features:
-  - name: audio
-    dtype:
-      audio:
-        sampling_rate: 16000
-  - name: text
-    dtype: string
-  - name: text_ts
-    dtype: string
-  - name: preconditioning
-    dtype: string
-  - name: start_time
-    dtype: string
-  - name: end_time
-    dtype: string
-  - name: speech_duration
-    dtype: float32
-  - name: word_timestamps
-    dtype: string
-  - name: source_file
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 98343002
-    num_examples: 136
-  - name: validation
-    num_bytes: 10466530
-    num_examples: 15
-  download_size: 107661781
-  dataset_size: 108809532
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
+tags:
+- audio
+- speech
+- whisper
+- dataset
 ---
+# sample-dataset-test-neural-pack
+
+Speech dataset prepared with Trelis Studio.
+
+## Statistics
+
+| Metric | Value |
+|--------|-------|
+| Source files | 6 |
+| Train samples | 136 |
+| Validation samples | 15 |
+| Total duration | 62.6 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/sample-dataset-test-neural-pack")
+```
+
+---
+*Prepared with [Trelis Studio](https://studio.trelis.com)*