Instructions to use mlx-community/ZONOS3-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/ZONOS3-Mini with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download mlx-community/ZONOS3-Mini --local-dir ZONOS3-Mini
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
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Download README.md from mlx-community/ZONOS3-Mini: direct link, hf CLI and curl.
- Browser
- Download file 2.69 kB
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https://huggingface.co/mlx-community/ZONOS3-Mini/resolve/main/README.md
- Command line
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hf download hf://mlx-community/ZONOS3-Mini/README.md
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curl -L -o README.md https://huggingface.co/mlx-community/ZONOS3-Mini/resolve/main/README.md
2.69 kB
| license: apache-2.0 | |
| library_name: mlx-audio | |
| pipeline_tag: text-to-speech | |
| tags: | |
| - mlx | |
| base_model: Zyphra/ZONOS3-Mini | |
| # ZONOS3-Mini | |
| [ZONOS3-Mini](https://huggingface.co/Zyphra/ZONOS3-Mini) is Zyphra's multilingual | |
| text-to-speech model with 100 million active parameters. It generates 44.1 kHz | |
| mono audio with 27 built-in voices, streaming, and voice cloning from reference | |
| audio without a transcript. | |
| ## Supported Models | |
| - [mlx-community/ZONOS3-Mini](https://huggingface.co/mlx-community/ZONOS3-Mini) | |
| ## CLI Example | |
| ```bash | |
| python -m mlx_audio.tts.generate \ | |
| --model mlx-community/ZONOS3-Mini \ | |
| --text "Hello from ZONOS3 Mini!" \ | |
| --voice jett | |
| ``` | |
| Use `--ref_audio reference.wav` to clone a voice, or add `--stream` for | |
| streaming playback. | |
| ## Python Example | |
| ```python | |
| from mlx_audio.audio_io import write as audio_write | |
| from mlx_audio.tts import load | |
| model = load("mlx-community/ZONOS3-Mini") | |
| result = next(model.generate( | |
| text="Hello from ZONOS3 Mini!", | |
| voice="jett", | |
| )) | |
| audio_write("speech.wav", result.audio, result.sample_rate) | |
| # Voice cloning does not require a reference transcript. | |
| result = next(model.generate( | |
| text="Welcome back.", | |
| ref_audio="reference.wav", | |
| )) | |
| audio_write("cloned.wav", result.audio, result.sample_rate) | |
| ``` | |
| Use `model.voices` to list the included voices. `voice="none"` generates without | |
| a speaker prompt. Reference audio paths are resampled automatically; array | |
| inputs must be mono PCM at 44,100 Hz. `speaker_embedding` accepts a | |
| 192-dimensional vector or a NumPy file, and `speaker_cfg_scale` controls | |
| speaker guidance (default `1.0`). | |
| Set `stream=True` for incremental synthesis, with `streaming_interval` in | |
| seconds. `speed` controls speaking rate. Acoustic controls are available through | |
| `condition_values={"loudness": -20, "fmax": 20000, "speaking_rate": 16}` or | |
| `condition_buckets`. `max_tokens` limits generated delayed audio rows at 15 Hz. | |
| Lightweight English number normalization is enabled by default; other languages | |
| pass through as written. Set `text_normalization=False` for text already | |
| normalized by another frontend. Speech editing, accent adapters, hidden-state | |
| steering, and raw reference-code encoding are not exposed by this implementation. | |
| ## License | |
| Model weights are released under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0). | |
| The backbone, codec, and speaker encoder are adapted from | |
| [Zyphra's MIT-licensed reference implementation](https://github.com/Zyphra/ZONOS3-Mini/blob/6ff97cac0c684732de5f2bc6e3671aa783c15054/LICENSE). | |
| The Gated DeltaNet kernel includes MIT-licensed code by Apple, also available | |
| in [mlx-lm](https://github.com/ml-explore/mlx-lm/blob/main/LICENSE). | |