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Soprano-Factory: Train your own 2000x realtime text-to-speech model
| Date | Stars |
|---|---|
| 2026-07-24 | 251 |
| 2026-07-25 | 252 |
| 2026-07-28 | 252 |
| 2026-07-30 | 252 |
| 2026-07-31 | 254 |
| 2026-08-06 | 254 |
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<div align="center"> # Soprano-Factory: Train your own 2000x realtime text-to-speech model [](https://huggingface.co/ekwek/Soprano-80M) [](https://github.com/ekwek1/soprano) [](https://huggingface.co/spaces/ekwek/Soprano-TTS) <img width="640" height="320" alt="soprano-github" src="https://github.com/user-attachments/assets/f929e484-7a1e-43ea-ba75-83bc79b559de" /> </div> ### 📰 News **2026.01.13 - [Soprano-Factory](https://github.com/ekwek1/soprano-factory) released! You can now train/fine-tune your own Soprano models.** --- ## Overview **Soprano-Factory** is a 600-LOC training script allowing anyone to train/fine-tune Soprano models with their **own data**, on their **own hardware**. You can use Soprano-Factory to add new voices, styles, and languages to the original Soprano model. ## About Soprano **[Soprano](https://github.com/ekwek1/soprano)** is an ultra‑lightweight, on-device text‑to‑speech (TTS) model designed for expressive, high‑fidelity speech synthesis at unprecedented speed. Soprano was designed with the following features: - Up to **20x** real-time generation on CPU and **2000x** real-time on GPU - **Lossless streaming** with **<250 ms** latency on CPU, **<15 ms** on GPU - **<1 GB** memory usage with a compact 80M parameter architecture - **Infinite generation length** with automatic text splitting - Highly expressive, crystal clear audio generation at **32kHz** - Widespread support for CUDA, CPU, and MPS devices on Windows, Linux, and Mac - Supports WebUI, CLI, and OpenAI-compatible endpoint for easy and production-ready inference --- ## Installation ```bash git clone https://github.com/ekwek1/soprano-factory.git cd soprano-factory pip install -r requirements.txt ``` If using Windows you may need to reinstall Pytorch to have CUDA support. --- ## Usage ### 1. Prepare Dataset Soprano-Factory expects input data in LJSpeech format. Please see `example_dataset` for how to structure your dataset. The wav files can be in any sample rate; they will be automatically resampled to 32 kHz. ### 2. Dataset Preprocessing ``` python generate_dataset.py --input-dir path/to/files Args: --input-dir: Path to directory of LJSpeech-style dataset. If none is provided this defaults to the provided example dataset. ``` ### 3. Model Training ``` python train.py --input-dir path/to/files --save-dir path/to/weights Args: --input-dir: Path to directory of LJSpeech-style dataset. If none is provided this defaults to the provided example dataset. --save-dir: Path to directory to save weights ``` ### 4. Inference Once the model has been trained, you can then use the custom weights in the [Soprano repository](https://github.com/ekwek1/soprano)! Just pass your save directory into model_path. --- ## Disclaimer I did not originally design Soprano with finetuning in mind. As a result, I cannot guarantee that you will see good results after training. --- ## License This project is licensed under the **Apache-2.0** license. See `LICENSE` for details.
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matched fp:5bc397367f98de64, topic:text-to-speech, topic:tts, desc:text-to-speech