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---
title: TTS Samples
emoji: 🚀
colorFrom: red
colorTo: red
sdk: docker
app_port: 8501
tags:
- streamlit
pinned: false
short_description: TTS Samples
license: mit
---
# TTS Samples Player (Streamlit)
This Streamlit app lets you play pre-generated TTS WAV samples in a Hugging Face Space.
## Folder structure
- `app.py`: Streamlit application code.
- `requirements.txt`: dependencies.
- `tts_smaples/`: folder containing subfolders per TTS provider, each with `.wav` files.
- Example:
```
tts_smaples/
├── azure/
│ ├── HsiaoChen.wav
│ ├── HsiaoYu.wav
│ └── YunJhe.wav
├── google/
│ └── Sulafat.wav
├── openai/
│ └── Nova.wav
└── yating/
└── Yating.wav
```
## Deployment in Hugging Face Spaces
1. Create a new Space using the **Streamlit** SDK.
2. Commit the repository with:
- `app.py`
- `requirements.txt` (containing `streamlit`)
- `tts_smaples/` folder with your `.wav` samples.
3. HF Spaces will install dependencies and serve the Streamlit app automatically.
4. Open the Space URL; you should see a dropdown to select provider/voice and play the audio.
## Extending
- **Generate TTS on the fly**: add a `st.text_input` or `st.text_area` and a “Generate” button; in its callback, run your TTS engine, save to a temp file or return raw bytes, and pass to `st.audio(...)`.
- **Styling/Layout**: use `st.sidebar` for selections, or columns/containers for layout.
- **Additional formats**: you can support `.mp3` or other audio formats; adjust the file extension check and `st.audio` format argument accordingly.
- **Caching**: if generation is expensive, cache results with `@st.cache_data` or `@st.cache_resource`.
- **Large files**: if your WAV files are large, consider their size for client bandwidth; you may offer compressed versions or stream.
---
### 5. Testing locally
Before pushing:
1. Ensure you have Python installed and a virtualenv.
2. `pip install -r requirements.txt`
3. Ensure your `tts_smaples/` folder is in the same directory.
4. Run:
```bash
streamlit run app.py
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