Instructions to use vanch007/mlx-pocket-tts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use vanch007/mlx-pocket-tts with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir mlx-pocket-tts vanch007/mlx-pocket-tts
- Pocket-TTS
How to use vanch007/mlx-pocket-tts with Pocket-TTS:
from pocket_tts import TTSModel import scipy.io.wavfile tts_model = TTSModel.load_model("vanch007/mlx-pocket-tts") voice_state = tts_model.get_state_for_audio_prompt( "hf://kyutai/tts-voices/alba-mackenna/casual.wav" ) audio = tts_model.generate_audio(voice_state, "Hello world, this is a test.") # Audio is a 1D torch tensor containing PCM data. scipy.io.wavfile.write("output.wav", tts_model.sample_rate, audio.numpy()) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
MLX Pocket TTS 8-bit
Ready-to-run Apple Silicon MLX weights for Pocket TTS. The generation FlowLM is dynamically quantized to 8-bit with group size 64; Mimi remains at source precision. The artifact includes 26 preset voice embeddings and supports arbitrary reference-audio voice cloning through the companion MLX implementation.
- Source code and complete usage guide: vanch007/mlx-pocket-tts
- This model: vanch007/mlx-pocket-tts
- Upstream code: kyutai-labs/pocket-tts
- Upstream weights: kyutai/pocket-tts
- Voice provenance: kyutai/tts-voices
Install and generate
Python 3.10+ and an Apple Silicon Mac are required.
git clone https://github.com/vanch007/mlx-pocket-tts.git
cd mlx-pocket-tts
uv sync
uv run mlx-pocket-tts generate \
--model vanch007/mlx-pocket-tts \
--voice alba \
--text "Hello from Pocket TTS on Apple Silicon." \
--output outputs/hello.wav
Use --ref-audio reference.wav for voice cloning, --stream for incremental generation, or
mlx-pocket-tts serve --model vanch007/mlx-pocket-tts for the Web UI and streaming HTTP API. The
GitHub README documents the Python API, reusable voice export, conversion, training, checkpointing
and distillation surfaces.
Artifact contents
| File | Purpose |
|---|---|
model.safetensors |
Strict-load MLX model weights; 8-bit FlowLM and source-precision Mimi |
config.json |
Architecture and quantization configuration |
tokenizer.model |
SentencePiece tokenizer |
embeddings/*.safetensors |
26 bundled preset voice states |
quantization.json |
Reproducible quantization manifest |
Local M3 Max verification
The release artifact was tested directly after quantization:
| Check | Result |
|---|---|
| Strict weight audit | pass: 316 parameter groups; no missing, extra or mismatched tensors |
| Generated audio | pass: finite 24 kHz waveform, 3.52 seconds |
| Real-time factor | 0.338 |
| Peak memory | 0.355 GB |
| Whisper replay | WER 0.0 |
The source repository contains 33 automated tests and the full feature/evidence matrix. Metrics are single-machine release checks, not universal quality claims.
Provenance, license and responsible use
This conversion is derived from kyutai/pocket-tts model revision
492522650173a0653b7575cdc25ae09810e5d741. Model weights, tokenizer and bundled voice assets are
distributed under their upstream terms; the Hugging Face metadata declares CC BY 4.0. Attribute
Kyutai and the upstream Pocket TTS project when redistributing or publishing outputs where the
license requires it. The MLX implementation itself is MIT licensed in the GitHub repository.
Use voice cloning only with the speaker's explicit authorization. Do not use this model for impersonation, deception, fraud, harassment, privacy violations, unlawful activity or other harmful purposes. Users are responsible for complying with applicable laws and the upstream terms.
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