sopro-v2-turbo

[](https://research.haloneuro.ai/posts/sopro-v2) [](https://github.com/samuel-vitorino/sopro) [](https://samuel-vitorino.github.io/sopro/) > **Compatibility:** Current model artifacts require `sopro>=2.2.0` or `@soprotts/onnx-web>=0.3.0`; upgrade with `pip install -U sopro` or `npm install @soprotts/onnx-web@latest`. Sopro (from the Portuguese word for "breath/blow") is a lightweight voice-cloning text-to-speech model family. This repo ships **sopro-v2-turbo**, a 120M-parameter open model that streams, runs comfortably on a laptop CPU or in the browser, and reaches SOTA-level intelligibility against much larger systems. The full story, evaluations, and audio samples are in the [blog post](https://research.haloneuro.ai/posts/sopro-v2). Main features: - **120M parameters** - **English, European Portuguese, French, and German** - **Streaming** with ~300 ms time-to-first-audio on a laptop CPU - **Zero-shot voice cloning** from 5-20 seconds of reference audio - **0.24 RTF offline / 0.21 RTF streaming** on an M3 CPU, 0.07 RTF on an H100 - **Runs in the browser** via an ONNX runtime Run the model and open the demo with one command: uvx --from sopro soprotts serve If Sopro is already installed: soprotts serve Then navigate to http://localhost:7860. The model downloads on first use and stays in the Hugging Face cache. Sopro selects CUDA or CPU automatically and defaults to CPU on macOS (pass `--device mps` explicitly to use MPS). Use `soprotts serve --help` for model, device, port, and CPU int8 options. There is also a fully in-browser demo with no server involved: https://samuel-vitorino.github.io/sopro/. On mobile the model is quantized, so results can be slightly below the local demo, and devices with low memory may crash. The dependency-isolated ONNX runtime and exporter are documented in [`web/README.md`](web/README.md). Both demos use the frontend in [`demos/web`](demos/web). pip install -U sopro git clone https://github.com/samuel-vitorino/sopro cd sopro pip install -e . soprotts "Sopro is a lightweight 120 million parameter text-to-speech model that streams and runs on device." --ref ref.wav --out out.wav Add `--stream` for the streaming path. You have the expected `--temperature`, `--top-p`, and `--top-k` parameters, alongside: - `--lang` (`en`, `pt`, `fr`, `de`; optional, helps pronunciation on ambiguous text) - `--int8` (int8 AR weights on CPU) - `--steps` (acoustic solver steps; default 2) - `--max-seconds` (cap per generated segment; long text is split into segments, so total length is unbounded) from sopro import SoproTTS tts = SoproTTS.from_pretrained("samuel-vitorino/sopro-v2-turbo", device="cpu") wav = tts.synthesize( "Hello! This is a non-streaming Sopro TTS example.", ref_audio_path="ref.wav", ) tts.save_wav("out.wav", wav) import torch from sopro import SoproTTS tts = SoproTTS.from_pretrained("samuel-vitorino/sopro-v2-turbo", device="cpu") chunks = [] for chunk in tts.stream( "Hello! This is a streaming Sopro TTS example.", ref_audio_path="ref.mp3", ): chunks.append(chunk.cpu()) wav = torch.cat(chunks, dim=-1) tts.save_wav("out_stream.wav", wav) You can also precalculate the reference to reduce time-to-first-audio: import torch from sopro import SoproTTS tts = SoproTTS.from_pretrained("samuel-vitorino/sopro-v2-turbo", device="cpu") ref = tts.prepare_reference(ref_audio_path="ref.mp3", stream=True) chunks = [] for chunk in tts.stream( "Hello! This is a streaming Sopro TTS example.", ref=ref, ): chunks.append(chunk.cpu()) wav = torch.cat(chunks, dim=-1) tts.save_wav("out_stream.wav", wav) - We did not add watermarking: with an open-source inference pipeline it would be trivial to remove, so it would only provide a false sense of safety. Please use the model for good: do not impersonate people. - The text frontend is deliberately minimal, so some abbreviations, numbers, and symbols may not be pronounced correctly. Prefer words: `1 + 2` should be written `one plus two`. That said, Sopro generally reads common abbreviations like "CPU" or "TTS" fine, and you can put a language-specific normalizer in front of it. - Mixed-language text is a weak spot: words from one language inside a sentence of another (an English product name in a Portuguese sentence, for example) can be mispronounced. - The streaming path (chunked attention and the causal vocoder) is not bit-exact with the offline path. For best quality, prefer the offline path. - We are not planning to release the training code in the near future due to its complexity. - [Emilia YODAS](https://huggingface.co/datasets/amphion/Emilia-Dataset) - [LibriTTS-R](https://huggingface.co/datasets/mythicinfinity/libritts_r) - [FalAR](https://huggingface.co/datasets/inesc-id/FalAR) - [CSM](https://github.com/SesameAILabs/csm) - [F5-TTS](https://github.com/SWivid/F5-TTS) - [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) - [Vocos](https://github.com/gemelo-ai/vocos) - [Whisper](https://github.com/openai/whisper)