Clone a specific person's voice from a short recording and narrate any new text in that voice
Generate multilingual speech for an app covering 600 languages without recording each one separately
Describe a voice in plain words like 'female, British accent, low pitch' and generate audio without any recording
Insert natural non-verbal sounds like laughter or sighs into synthesized speech using inline tags
| k2-fsa/omnivoice | jamwithai/production-agentic-rag-course | al-one/hass-xiaomi-miot | |
|---|---|---|---|
| Stars | 5,867 | 5,869 | 5,863 |
| Language | Python | Python | Python |
| Setup difficulty | hard | hard | moderate |
| Complexity | 3/5 | 4/5 | 2/5 |
| Audience | developer | developer | general |
Figures from each repo's GitHub metadata at analysis time.
Requires an NVIDIA GPU or Apple Silicon, model weights must be downloaded from HuggingFace before first use.
OmniVoice is a text-to-speech system that converts written text into spoken audio while supporting over 600 languages. Its main capability is zero-shot voice cloning: you provide a short audio recording (3 to 10 seconds) of someone speaking, and OmniVoice will generate new speech in that same person's voice without any additional training. There are three ways to use it. Voice cloning requires a reference audio clip and an optional transcript of what is being said in that clip, the model reads the new text in the cloned voice. Voice design lets you describe the voice you want using text attributes (female, low pitch, British accent, child, whisper, and so on) without needing a reference recording at all. The third mode, called Auto Voice, picks a voice on its own if you provide neither a reference clip nor a description. Beyond basic voice generation, OmniVoice supports inline non-verbal sounds inserted directly into the text. You can write tags like [laughter] or [sigh] inside a sentence, and the model will produce those sounds at the appropriate moment. There is also pronunciation control for Chinese text using pinyin notation. The system is built on a diffusion language model architecture. The README states inference runs at a real-time factor of 0.025, meaning it can produce 40 seconds of audio for every second of compute time. Output audio is at 24 kHz. Installation is via pip or uv. For people who prefer not to write code, there is a local web interface you can launch with a single command, a hosted demo on HuggingFace Spaces, and a Google Colab notebook. The pretrained model weights are available on HuggingFace. The package runs on NVIDIA GPUs and Apple Silicon.
A text-to-speech tool that clones any voice from a 3, 10 second audio clip and speaks in over 600 languages, with no extra training required, or lets you describe the voice you want in plain words.
Mainly Python. The stack also includes Python, HuggingFace, CUDA.
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.