---
title: Audio Dataset Explorer for TTS
emoji: 🎙️
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.44.0
python_version: "3.10"
app_file: app.py
pinned: false
license: mit
---

# 🎙️ Audio Dataset Explorer for TTS

Interactive tool for exploring audio datasets, analyzing speakers, and selecting training data for TTS models.

## Features

- 📊 **Overview Statistics** - Analyze all speakers in a dataset
- 🎯 **Speaker Details** - Deep dive into individual speaker statistics
- 📈 **Interactive Charts** - Duration distributions, word counts, sample distributions
- 📥 **Export Instructions** - Step-by-step guide to create your own filtered dataset fork
- 🔄 **Multi-Dataset Support** - Works with any HuggingFace audio dataset with speaker_id field

## Usage

1. **Load Dataset**: Enter dataset name and config (e.g., `ylacombe/cml-tts` + `polish`)
2. **Overview**: Check statistics for all speakers
3. **Select Speaker**: Choose a speaker from the dropdown
4. **Analyze**: View detailed statistics and audio samples
5. **Export**: Get instructions to create your own filtered dataset

## Supported Datasets

The tool works with any HuggingFace dataset that has:
- Audio data
- `speaker_id` field
- `duration` and `text` fields (optional but recommended)

### Tested Datasets

- `ylacombe/cml-tts` - Multi-lingual TTS (Dutch, French, German, Italian, Polish, Portuguese, Spanish)
- `facebook/voxpopuli` - European Parliament speeches
- `mozilla-foundation/common_voice_*` - Community-contributed voices

## Why This Tool?

When training TTS models, you often want to:
- Select a single speaker for consistency
- Understand data distribution before training
- Create filtered subsets for experiments
- Add custom columns (emotion, quality scores, etc.)

This tool helps you make informed decisions about your training data.

## Creating Your Own Dataset Fork

After selecting a speaker, use the "Pobierz & Fork" tab to get instructions for:
1. Downloading the full dataset
2. Filtering to your chosen speaker
3. Adding custom columns
4. Pushing to HuggingFace Hub as a new dataset

## Local Development

```bash
# Install dependencies
pip install -r requirements.txt

# Run locally
python app.py
```

## Credits

Built for the TTS training workflow. Designed to work with HuggingFace Datasets ecosystem.

## License

MIT License - Feel free to use and modify for your projects!