mlx-audio 0.3.1

MLX-Audio is a package for inference of text-to-speech (TTS) and speech-to-speech (STS) models locally on your Mac using MLX

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

• Fast inference optimized for Apple Silicon (M series chips)
• Multiple model architectures for TTS, STT, and STS
• Multilingual support across models
• Voice customization and cloning capabilities
• Adjustable speech speed control
• Interactive web interface with 3D audio visualization
• OpenAI-compatible REST API
• Quantization support (3-bit, 4-bit, 6-bit, 8-bit, and more) for optimized performance
• Swift package for iOS/macOS integration

Installation

Using pip

pip install mlx-audio

Using uv to install only the command line tools

Latest release from pypi:

uv tool install --force mlx-audio --prerelease=allow

Latest code from github:

uv tool install --force git+https://github.com/Blaizzy/mlx-audio.git --prerelease=allow

For 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
Qwen3-ASR	Alibaba's multilingual ASR	ZH, EN, JA, KO, + more	mlx-community/Qwen3-ASR-1.7B-8bit
Qwen3-ForcedAligner	Word-level audio alignment	ZH, EN, JA, KO, + more	mlx-community/Qwen3-ForcedAligner-0.6B-8bit
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:

• American English: af_heart, af_bella, af_nova, af_sky, am_adam, am_echo, etc.
• British English: bf_alice, bf_emma, bm_daniel, bm_george, etc.
• Japanese: jf_alpha, jm_kumo, etc.
• Chinese: 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.generate import generate_transcription

result = generate_transcription(
    model="mlx-community/whisper-large-v3-turbo-asr-fp16",
    audio="audio.wav",
)
print(result.text)

Qwen3-ASR & ForcedAligner

Alibaba's multilingual speech models for transcription and word-level alignment.

from mlx_audio.stt import load

# Speech recognition
model = load("mlx-community/Qwen3-ASR-0.6B-8bit")
result = model.generate("audio.wav", language="English")
print(result.text)

# Word-level forced alignment
aligner = load("mlx-community/Qwen3-ForcedAligner-0.6B-8bit")
result = aligner.generate("audio.wav", text="I have a dream", language="English")
for item in result:
    print(f"[{item.start_time:.2f}s - {item.end_time:.2f}s] {item.text}")

See the Qwen3-ASR README for CLI usage, all models, and more examples.

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 "file=@audio.wav" \
  -F "model=mlx-community/whisper-large-v3-turbo-asr-fp16"

Quantization

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

Swift

Looking for Swift/iOS support? Check out mlx-audio-swift for on-device TTS using MLX on macOS and iOS.

Requirements

• Python 3.10+
• Apple Silicon Mac (M1/M2/M3/M4)
• MLX framework
• ffmpeg (required for MP3/FLAC audio encoding)

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

MIT 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

• Apple MLX Team for the MLX framework