Soprano-1.1-80M

[](https://github.com/ekwek1/soprano) [](https://huggingface.co/spaces/ekwek/Soprano-TTS) **2026.01.14 - [**Soprano-1.1-80M**](https://huggingface.co/ekwek/Soprano-1.1-80M) released! 95% fewer hallucinations and a 63% preference rate over Soprano-80M.** 2026.01.13 - [Soprano-Factory](https://github.com/ekwek1/soprano-factory) released! You can now train/fine-tune your own Soprano models. 2025.12.22 - Soprano-80M released! [Code](https://github.com/ekwek1/soprano) | [Demo](https://huggingface.co/spaces/ekwek/Soprano-TTS) **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 **2000x** real-time generation on GPU and **20x** real-time on CPU - **Lossless streaming** with **<15 ms** latency on GPU, **<250 ms** on CPU - **<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 pip install soprano-tts To get the latest features, you can install from source instead. git clone https://github.com/ekwek1/soprano.git cd soprano pip install -e .[lmdeploy] git clone https://github.com/ekwek1/soprano.git cd soprano pip install -e . > ### ⚠️ Warning: Windows CUDA users > > On Windows with CUDA, `pip` will install a CPU-only PyTorch build. To ensure CUDA support works as expected, reinstall PyTorch explicitly with the correct CUDA wheel **after** installing Soprano: > > ```bash > pip uninstall -y torch > pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cu128 > ``` Start WebUI: soprano-webui # hosted on http://127.0.0.1:7860 by default > **Tip:** You can increase cache size and decoder batch size to increase inference speed at the cost of higher memory usage. For example: > ```bash > soprano-webui --cache-size 1000 --decoder-batch-size 4 > ``` soprano "Soprano is an extremely lightweight text to speech model." optional arguments: --output, -o Output audio file path (non-streaming only). Defaults to 'output.wav' --model-path, -m Path to local model directory (optional) --device, -d Device to use for inference. Supported: auto, cuda, cpu, mps. Defaults to 'auto' --backend, -b Backend to use for inference. Supported: auto, transformers, lmdeploy. Defaults to 'auto' --cache-size, -c Cache size in MB (for lmdeploy backend). Defaults to 100 --decoder-batch-size, -bs Decoder batch size. Defaults to 1 --streaming, -s Enable streaming playback to speakers > **Tip:** You can increase cache size and decoder batch size to increase inference speed at the cost of higher memory usage. > **Note:** The CLI will reload the model every time it is called. As a result, inference speed will be slower than other methods. Start server: uvicorn soprano.server:app --host 0.0.0.0 --port 8000 Use the endpoint like this: curl http://localhost:8000/v1/audio/speech \ -H "Content-Type: application/json" \ -d '{ "input": "Soprano is an extremely lightweight text to speech model." }' \ --output speech.wav > **Note:** Currently, this endpoint only supports nonstreaming output. from soprano import SopranoTTS model = SopranoTTS(backend='auto', device='auto', cache_size_mb=100, decoder_batch_size=1) > **Tip:** You can increase cache_size_mb and decoder_batch_size to increase inference speed at the cost of higher memory usage. out = model.infer("Soprano is an extremely lightweight text to speech model.") # can achieve 2000x real-time with sufficiently long input! out = model.infer("Soprano is an extremely lightweight text to speech model.", "out.wav") out = model.infer( "Soprano is an extremely lightweight text to speech model.", temperature=0.3, top_p=0.95, repetition_penalty=1.2, ) out = model.infer_batch(["Soprano is an extremely lightweight text to speech model."] * 10) # can achieve 2000x real-time with sufficiently large input size! out = model.infer_batch(["Soprano is an extremely lightweight text to speech model."] * 10, "/dir") from soprano.utils.streaming import play_stream stream = model.infer_stream("Soprano is an extremely lightweight text to speech model.", chunk_size=1) play_stream(stream) # plays audio with <15 ms latency! * Soprano works best when each sentence is between 2 and 15 seconds long. * Although Soprano recognizes numbers and some special characters, it occasionally mispronounces them. Best results can be achieved by converting these into their phonetic form. (1+1 -> one plus one, etc) * If Soprano produces unsatisfactory results, you can easily regenerate it for a new, potentially better generation. You may also change the sampling settings for more varied results. * Avoid improper grammar such as not using contractions, multiple spaces, etc. Soprano is currently English-only and does not support voice cloning. In addition, Soprano was trained on only 1,000 hours of audio (~100x less than other TTS models), so mispronunciation of uncommon words may occur. This is expected to diminish as Soprano is trained on more data. This project is licensed under the **Apache-2.0** license. See `LICENSE` for details.