MLX-Audio
The best audio processing library built on Apple's MLX framework, providing fast and efficient text-to-speech (TTS), speech-to-text (STT), and speech-to-speech (STS) on Apple Silicon.
Features
Installation
Using pip
pip install mlx-audioUsing uv to install only the command line tools
Latest release from pypi:
uv tool install --force mlx-audio --prerelease=allowLatest code from github:
uv tool install --force git+https://github.com/Blaizzy/mlx-audio.git --prerelease=allowFor development or web interface:
git clone https://github.com/Blaizzy/mlx-audio.git
cd mlx-audio pip install -e ".[dev]"
Quick Start
Command Line
# Basic TTS generation
mlx_audio.tts.generate --model mlx-community/Kokoro-82M-bf16 --text "Hello, world!" --lang_code a
# With voice selection and speed adjustment mlx_audio.tts.generate --model mlx-community/Kokoro-82M-bf16 --text "Hello!" --voice af_heart --speed 1.2 --lang_code a
# Play audio immediately mlx_audio.tts.generate --model mlx-community/Kokoro-82M-bf16 --text "Hello!" --play --lang_code a
# Save to a specific directory mlx_audio.tts.generate --model mlx-community/Kokoro-82M-bf16 --text "Hello!" --output_path ./my_audio --lang_code a
Python API
from mlx_audio.tts.utils import load_model
# Load model model = load_model("mlx-community/Kokoro-82M-bf16")
# Generate speech for result in model.generate("Hello from MLX-Audio!", voice="af_heart"): print(f"Generated {result.audio.shape[0]} samples") # result.audio contains the waveform as mx.array
Supported Models
Text-to-Speech (TTS)
| Model | Description | Languages | Repo | |-------|-------------|-----------|------| | Kokoro | Fast, high-quality multilingual TTS | EN, JA, ZH, FR, ES, IT, PT, HI | mlx-community/Kokoro-82M-bf16 | | Qwen3-TTS | Alibaba's multilingual TTS with voice design | ZH, EN, JA, KO, + more | mlx-community/Qwen3-TTS-12Hz-1.7B-VoiceDesign-bf16 | | CSM | Conversational Speech Model with voice cloning | EN | mlx-community/csm-1b | | Dia | Dialogue-focused TTS | EN | mlx-community/Dia-1.6B-bf16 | | OuteTTS | Efficient TTS model | EN | mlx-community/OuteTTS-0.2-500M | | Spark | SparkTTS model | EN, ZH | mlx-community/SparkTTS-0.5B-bf16 | | Chatterbox | Expressive multilingual TTS | EN, ES, FR, DE, IT, PT, PL, TR, RU, NL, CS, AR, ZH, JA, HU, KO | mlx-community/Chatterbox-bf16 | | Soprano | High-quality TTS | EN | mlx-community/Soprano-bf16 |
Speech-to-Text (STT)
| Model | Description | Languages | Repo | |-------|-------------|-----------|------| | Whisper | OpenAI's robust STT model | 99+ languages | mlx-community/whisper-large-v3-turbo-asr-fp16 | | Parakeet | NVIDIA's accurate STT | EN | mlx-community/parakeet-tdt-0.6b-v2 | | Voxtral | Mistral's speech model | Multiple | mlx-community/Voxtral-Mini-3B-2507-bf16 | | VibeVoice-ASR | Microsoft's 9B ASR with diarization & timestamps | Multiple | mlx-community/VibeVoice-ASR-bf16 |
Speech-to-Speech (STS)
| Model | Description | Use Case | Repo | |-------|-------------|----------|------| | SAM-Audio | Text-guided source separation | Extract specific sounds | mlx-community/sam-audio-large | | Liquid2.5-Audio* | Speech-to-Speech, Text-to-Speech and Speech-to-Text | Speech interactions | mlx-community/LFM2.5-Audio-1.5B-8bit | MossFormer2 SE | Speech enhancement | Noise removal | starkdmi/MossFormer2_SE_48K_MLX |
Model Examples
Kokoro TTS
Kokoro is a fast, multilingual TTS model with 54 voice presets.
from mlx_audio.tts.utils import load_model
model = load_model("mlx-community/Kokoro-82M-bf16")
# Generate with different voices for result in model.generate( text="Welcome to MLX-Audio!", voice="af_heart", # American female speed=1.0, lang_code="a" # American English ): audio = result.audio
Available Voices:
af_heart, af_bella, af_nova, af_sky, am_adam, am_echo, etc.bf_alice, bf_emma, bm_daniel, bm_george, etc.jf_alpha, jm_kumo, etc.zf_xiaobei, zm_yunxi, etc.Language Codes: | Code | Language | Note | |------|----------|------| | a | American English | Default | | b | British English | | | j | Japanese | Requires pip install misaki[ja] | | z | Mandarin Chinese | Requires pip install misaki[zh] | | e | Spanish | | | f | French | |
Qwen3-TTS
Alibaba's state-of-the-art multilingual TTS with voice cloning, emotion control, and voice design capabilities.
from mlx_audio.tts.utils import load_model
model = load_model("mlx-community/Qwen3-TTS-12Hz-0.6B-Base-bf16") results = list(model.generate( text="Hello, welcome to MLX-Audio!", voice="Chelsie", language="English", ))
audio = results[0].audio # mx.array
See the Qwen3-TTS README for voice cloning, CustomVoice, VoiceDesign, and all available models.
CSM (Voice Cloning)
Clone any voice using a reference audio sample:
mlx_audio.tts.generate \
--model mlx-community/csm-1b \ --text "Hello from Sesame." \ --ref_audio ./reference_voice.wav \ --play
Whisper STT
from mlx_audio.stt.utils import load_model, transcribe
model = load_model("mlx-community/whisper-large-v3-turbo-asr-fp16") result = transcribe("audio.wav", model=model) print(result["text"])
VibeVoice-ASR
Microsoft's 9B parameter speech-to-text model with speaker diarization and timestamps. Supports long-form audio (up to 60 minutes) and outputs structured JSON.
from mlx_audio.stt.utils import load
model = load("mlx-community/VibeVoice-ASR-bf16")
# Basic transcription result = model.generate(audio="meeting.wav", max_tokens=8192, temperature=0.0) print(result.text) # [{"Start":0,"End":5.2,"Speaker":0,"Content":"Hello everyone, let's begin."}, # {"Start":5.5,"End":9.8,"Speaker":1,"Content":"Thanks for joining today."}]
# Access parsed segments for seg in result.segments: print(f"[{seg['start_time']:.1f}-{seg['end_time']:.1f}] Speaker {seg['speaker_id']}: {seg['text']}")
Streaming transcription:
# Stream tokens as they are generated
for text in model.stream_transcribe(audio="speech.wav", max_tokens=4096): print(text, end="", flush=True)
With context (hotwords/metadata):
result = model.generate(
audio="technical_talk.wav", context="MLX, Apple Silicon, PyTorch, Transformer", max_tokens=8192, temperature=0.0, )
CLI usage:
# Basic transcription
python -m mlx_audio.stt.generate \ --model mlx-community/VibeVoice-ASR-bf16 \ --audio meeting.wav \ --output-path output \ --format json \ --max-tokens 8192 \ --verbose
# With context/hotwords python -m mlx_audio.stt.generate \ --model mlx-community/VibeVoice-ASR-bf16 \ --audio technical_talk.wav \ --output-path output \ --format json \ --max-tokens 8192 \ --context "MLX, Apple Silicon, PyTorch, Transformer" \ --verbose
SAM-Audio (Source Separation)
Separate specific sounds from audio using text prompts:
from mlx_audio.sts import SAMAudio, SAMAudioProcessor, save_audio
model = SAMAudio.from_pretrained("mlx-community/sam-audio-large") processor = SAMAudioProcessor.from_pretrained("mlx-community/sam-audio-large")
batch = processor( descriptions=["A person speaking"], audios=["mixed_audio.wav"], )
result = model.separate_long( batch.audios, descriptions=batch.descriptions, anchors=batch.anchor_ids, chunk_seconds=10.0, overlap_seconds=3.0, ode_opt={"method": "midpoint", "step_size": 2/32}, )
save_audio(result.target[0], "voice.wav") save_audio(result.residual[0], "background.wav")
MossFormer2 (Speech Enhancement)
Remove noise from speech recordings:
from mlx_audio.sts import MossFormer2SEModel, save_audio
model = MossFormer2SEModel.from_pretrained("starkdmi/MossFormer2_SE_48K_MLX") enhanced = model.enhance("noisy_speech.wav") save_audio(enhanced, "clean.wav", 48000)
Web Interface & API Server
MLX-Audio includes a modern web interface and OpenAI-compatible API.
Starting the Server
# Start API server
mlx_audio.server --host 0.0.0.0 --port 8000
# Start web UI (in another terminal) cd mlx_audio/ui npm install && npm run dev
API Endpoints
Text-to-Speech (OpenAI-compatible):
curl -X POST http://localhost:8000/v1/audio/speech \
-H "Content-Type: application/json" \ -d '{"model": "mlx-community/Kokoro-82M-bf16", "input": "Hello!", "voice": "af_heart"}' \ --output speech.wav
Speech-to-Text:
curl -X POST http://localhost:8000/v1/audio/transcriptions \
-F "[email protected]" \ -F "model=mlx-community/whisper-large-v3-turbo-asr-fp16"
Quantization
Swift
Looking for Swift/iOS support? Check out mlx-audio-swift for on-device TTS using MLX on macOS and iOS. Reduce model size and improve performance with quantization using the convert script:
# Convert and quantize to 4-bit
python -m mlx_audio.convert \ --hf-path prince-canuma/Kokoro-82M \ --mlx-path ./Kokoro-82M-4bit \ --quantize \ --q-bits 4 \ --upload-repo username/Kokoro-82M-4bit (optional: if you want to upload the model to Hugging Face)
# Convert with specific dtype (bfloat16) python -m mlx_audio.convert \ --hf-path prince-canuma/Kokoro-82M \ --mlx-path ./Kokoro-82M-bf16 \ --dtype bfloat16 \ --upload-repo username/Kokoro-82M-bf16 (optional: if you want to upload the model to Hugging Face)
Options: | Flag | Description | |------|-------------| | --hf-path | Source Hugging Face model or local path | | --mlx-path | Output directory for converted model | | -q, --quantize | Enable quantization | | --q-bits | Bits per weight (4, 6, or 8) | | --q-group-size | Group size for quantization (default: 64) | | --dtype | Weight dtype: float16, bfloat16, float32 | | --upload-repo | Upload converted model to HF Hub |
Requirements
Installing ffmpeg
ffmpeg is required for saving audio in MP3 or FLAC format. Install it using:
# macOS (using Homebrew)
brew install ffmpeg
# Ubuntu/Debian sudo apt install ffmpeg
WAV format works without ffmpeg.
License
Citation
@misc{mlx-audio,
author = {Canuma, Prince}, title = {MLX Audio}, year = {2025}, howpublished = {\url{https://github.com/Blaizzy/mlx-audio}}, note = {Audio processing library for Apple Silicon with TTS, STT, and STS capabilities.} }
Acknowledgements
MLX-Audio is an open-source audio AI framework for fast text-to-speech, speech-to-text, and speech-to-speech processing, optimized for Apple Silicon using MLX.