mlx-audio
MLX-Audio is a package for inference of text-to-speech (TTS) and speech-to-speech (STS) models locally on your Mac using MLX
Decision gist · record as of 2026-08-14
Yes, if you have Apple Silicon and need local TTS/STS inference. Active maintenance, permissive license, low install friction, no known vulnerabilities, and a large model ecosystem make it a solid choice. Verify whether STT is production-ready and whether the web interface requires optional extras before committing to a deployment.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10 and Apple Silicon (M series) for optimal performance; runtime dependencies include mlx, numpy, scipy, transformers, huggingface_hub, and sounddevice.
- Low friction—pure Python wheel with no compiled dependencies.
- Active maintenance: last commit 2026-08-13, 7721 stars, released 2026-08-10 (4 days old).
License · maintenance · safety
MIT (permissive) — MIT license (permissive). You can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
last release 2026-08-10 (4 days) · last repo commit 2026-08-13 · 7,721 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 394,354 downloads/mo, #6,993 on PyPI
Alternatives
Verify before relying
pip install mlx-audio
from mlx_audio.tts.utils import load_model
model = load_model("mlx-community/Kokoro-82M-bf16")
for result in model.generate("Hello from MLX-Audio!", voice="Chelsie"):
print(f"Generated {result.audio.shape[0]} samples")- Whether STT (speech-to-text) is fully implemented or if the package currently focuses primarily on TTS and STS.
- Performance characteristics and latency on different M-series chip generations.
- Whether the web interface and OpenAI-compatible API server are included in the base pip install or require the dev/server extras.
What it is and what it does
MLX-Audio is a speech synthesis and processing library built on Apple's MLX framework, designed to run inference for text-to-speech, speech-to-text, and speech-to-speech tasks directly on Apple Silicon Macs. It wraps multiple pre-trained model architectures (Kokoro, Qwen3-TTS, OmniVoice, and others) with support for multilingual input, voice customization, and quantization options (3-bit through 8-bit). The package exposes both a command-line interface and a Python API, letting you generate audio, control speech speed, clone voices, and stream results during generation.
The library depends on huggingface_hub for model downloads, mlx and mlx-lm for inference, numpy and scipy for audio processing, transformers for tokenization, sounddevice for playback, miniaudio for audio I/O, and tqdm for progress reporting. It targets developers building voice applications on macOS who want local, on-device inference without cloud dependencies.
Use it for
- Generate speech from text on your Mac using multiple TTS model architectures with voice selection and language hints.
- Build a local voice assistant or chatbot that speaks without sending audio to a cloud service.
- Clone a voice by providing sample audio and then synthesize new speech in that voice across different models.
- Stream audio output during generation for real-time playback or save multi-segment outputs as a single joined file.
- Integrate speech synthesis into a macOS or iOS app via the Swift package or REST API.
- Experiment with different quantized model variants (4-bit, 8-bit) to balance quality and inference speed on Apple Silicon.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you have Apple Silicon and need local TTS/STS inference.
Active maintenance, permissive license, low install friction, no known vulnerabilities, and a large model ecosystem make it a solid choice. Verify whether STT is production-ready and whether the web interface requires optional extras before committing to a deployment.
Install
mlx-audio on PyPI
Before you install
Low friction—pure Python wheel with no compiled dependencies. Active maintenance: last commit 2026-08-13, 7721 stars, released 2026-08-10 (4 days old). Requires Python >=3.10.
Requires Python >=3.10 and Apple Silicon (M series) for optimal performance; runtime dependencies include mlx, numpy, scipy, transformers, huggingface_hub, and sounddevice.
License in practice
MIT license (permissive). You can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
Quickstart
pip install mlx-audio
from mlx_audio.tts.utils import load_model
model = load_model("mlx-community/Kokoro-82M-bf16")
for result in model.generate("Hello from MLX-Audio!", voice="Chelsie"):
print(f"Generated {result.audio.shape[0]} samples")Verify before relying
- Whether STT (speech-to-text) is fully implemented or if the package currently focuses primarily on TTS and STS.
- Performance characteristics and latency on different M-series chip generations.
- Whether the web interface and OpenAI-compatible API server are included in the base pip install or require the dev/server extras.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packageshuggingface_hubminiaudiomlx-lmmlxnumpyscipysounddevicetqdmtransformers |
| Maintenance | Actively maintained 4 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 394,354 / month, #6,993 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Operating System :: OS IndependentProgramming Language :: Python :: 3 |
Evidence: mlx_audio-0.4.8-py3-none-any.whl
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