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), speech-to-speech (STS), music generation, and more on Apple Silicon.

Table of Contents

• Features
• Installation
• Quick Start
• Supported Models
• Model Examples
• Web Interface & API Server
• Quantization
• Swift
• Requirements
• License
• Citation
• Acknowledgements

Features

• Fast inference optimized for Apple Silicon (M series chips)
• Multiple model architectures for TTS, STT, STS, and music generation
• 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, server]"

Quick Start

Command Line

# Basic TTS generation
mlx_audio.tts.generate --model mlx-community/Qwen3-TTS-12Hz-0.6B-CustomVoice-8bit --text 'Hello, world!' --voice Vivian

# With a different voice and language hint
mlx_audio.tts.generate --model mlx-community/Qwen3-TTS-12Hz-0.6B-CustomVoice-8bit --text 'Welcome to MLX-Audio!' --voice Ryan --lang_code English

# Play audio immediately
mlx_audio.tts.generate --model mlx-community/Qwen3-TTS-12Hz-0.6B-CustomVoice-8bit --text 'Hello!' --voice Vivian --play

# Save to a specific directory
mlx_audio.tts.generate --model mlx-community/Qwen3-TTS-12Hz-0.6B-CustomVoice-8bit --text 'Hello!' --voice Vivian --output_path ./my_audio

# Stream audio during generation
mlx_audio.tts.generate --model mlx-community/Qwen3-TTS-12Hz-0.6B-CustomVoice-8bit --text 'Hello!' --voice Vivian --stream

# Stream audio during generation and save it to disk
mlx_audio.tts.generate --model mlx-community/Qwen3-TTS-12Hz-0.6B-CustomVoice-8bit --text 'Hello!' --voice Vivian --stream --save

# Join multiple generated segments into one file
mlx_audio.tts.generate --model mlx-community/Qwen3-TTS-12Hz-0.6B-CustomVoice-8bit --text $'Hello!\nHow are you?' --voice Vivian --join_audio

By default, when generation yields multiple segments, mlx-audio saves numbered files such as audio_000.wav and audio_001.wav. Use --join_audio to save one combined file instead. When using --stream, add --save to write the streamed audio to disk.

Python API

from mlx_audio.tts.utils import load_model

# Load model
model = load_model("mlx-community/Qwen3-TTS-12Hz-0.6B-CustomVoice-8bit")

# Generate speech
for result in model.generate(
    "Hello from MLX-Audio!",
    voice="Vivian",
    lang_code="English",
):
    print(f"Generated {result.audio.shape[0]} samples")
    # result.audio contains the waveform as mx.array

Supported Models

Text-to-Speech (TTS)

Speech-to-Text (STT)

Voice Activity Detection / Speaker Diarization (VAD)

See the model READMEs for API details, streaming examples, and conversion steps.

Speech-to-Speech (STS)

Music Generation

Model Examples

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-CustomVoice-bf16")
results = list(model.generate_custom_voice(
    text="Hello, welcome to MLX-Audio!",
    speaker="Vivian",
    language="English",
))

audio = results[0].audio  # mx.array

See the Qwen3-TTS README for voice cloning, CustomVoice, VoiceDesign, and all available models.

OmniVoice

OmniVoice is a zero-shot multilingual TTS model for 646+ languages with voice cloning, batch generation, pronunciation controls, and nonverbal tags such as [laughter] and [sigh]. It uses a bidirectional Qwen3 backbone with iterative masked generation and a HiggsAudioV2 acoustic tokenizer.

from mlx_audio.tts.utils import load_model

model = load_model("mlx-community/OmniVoice-bf16")

# Basic multilingual TTS
for result in model.generate(
    text="Hello from OmniVoice running on Apple Silicon.",
    language="english",
    duration_s=5.0,
    num_steps=32,
):
    audio = result.audio

# Zero-shot voice cloning
for result in model.generate(
    text="This sentence uses the reference speaker.",
    language="english",
    ref_audio="reference.wav",
    ref_text="Transcript of the reference audio.",
    duration_s=5.0,
):
    audio = result.audio

For stable voice cloning, provide ref_text that matches the reference clip. OmniVoice also supports generate_batch() for batched TTS and inline pronunciation controls.

Ming Omni TTS (BailingMM)

mlx_audio.tts.generate \
    --model mlx-community/Ming-omni-tts-16.8B-A3B-bf16 \
    --prompt "Please generate speech based on the following description.\n" \
    --text "This is a quick Ming Omni test." \
    --lang_code en \
    --output_path audio_io \
    --file_prefix ming_basic \
    --verbose

See the Ming Omni TTS README for CLI and Python cookbook examples, and the Ming Dense README for Ming-3.0-tts-0.5B and Ming-omni-tts-0.5B.

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")
# Or use a quantized variant for lower memory usage:
# model = load_model("mlx-community/Kokoro-82M-8bit")
# model = load_model("mlx-community/Kokoro-82M-4bit")

# 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.

Kokoro requires pip install misaki for text processing. Japanese and Mandarin may additionally require pip install misaki[ja] or pip install misaki[zh].

Language Codes:

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.

MiMo-V2.5-ASR

from mlx_audio.stt import load

model = load("mlx-community/MiMo-V2.5-ASR-MLX")
result = model.generate("audio.wav", language="en")
print(result.text)

See the MiMo-V2.5-ASR README for local-path loading.

Phonon-1

Fermion Research's compact English Qwen3-ASR derivatives load directly from their public transport repositories:

from mlx_audio.stt import load

model = load("FermionResearch/Phonon-1")
result = model.generate("audio.wav", language="English")
print(result.text)

Available builds are Phonon-1-Micro (285 MB), Phonon-1 (415 MB), and Phonon-1-Big (581 MB). See the Phonon-1 README.

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

Phonon-2 and Phonon-2.5

Speech recognition with Fermion Research's five-value Parakeet TDT models. Phonon-2.5 supports 25 European languages; Phonon-2.5 English and Phonon-2 support English. Load the published checkpoints directly; no Fermion runtime is needed.

from mlx_audio.stt import load

model = load("FermionResearch/Phonon-2.5")
result = model.generate("audio.wav", chunk_duration=30, overlap_duration=2)
print(result.text)

Use FermionResearch/Phonon-2.5-English for English-only recognition. See the Phonon README for all model IDs, timestamps, and chunk streaming.

Parakeet (Multilingual STT)

NVIDIA's high-accuracy speech-to-text model. Parakeet v3 supports 25 European languages.

from mlx_audio.stt.utils import load

# Load the multilingual v3 model
model = load("mlx-community/parakeet-tdt-0.6b-v3")

# Transcribe audio
result = model.generate("audio.wav")
print(f"Text: {result.text}")

# Access word-level timestamps
for sentence in result.sentences:
    print(f"[{sentence.start:.2f}s - {sentence.end:.2f}s] {sentence.text}")

Streaming transcription:

for chunk in model.generate("long_audio.wav", stream=True):
    print(chunk.text, end="", flush=True)

Supported languages (v3): Bulgarian, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Greek, Hungarian, Italian, Latvian, Lithuanian, Maltese, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish, Russian, Ukrainian

CLI usage:

python -m mlx_audio.stt.generate \
    --model mlx-community/parakeet-tdt-0.6b-v3 \
    --audio speech.wav \
    --output-path output \
    --format json \
    --verbose

KugelAudio

SOTA open-source 7B TTS model for 24 European languages, based on Microsoft VibeVoice. Uses a hybrid AR + Diffusion architecture (Qwen2.5 LM + SDE-DPM-Solver++ diffusion head + VAE decoder).

from mlx_audio.tts.utils import load_model

model = load_model("kugelaudio/kugelaudio-0-open")

for result in model.generate(
    text="Hello, welcome to MLX-Audio!",
    cfg_scale=3.0,       # Classifier-free guidance (1.0=fast, 3.0=quality)
    ddpm_steps=10,       # Diffusion steps (5=fast, 10=balanced, 20=max quality)
):
    audio = result.audio  # mx.array, 24kHz

The model loads directly from HuggingFace (weights are remapped automatically via sanitize()). To quantize or save in a pre-converted format:

python -m mlx_audio.convert \
    --hf-path kugelaudio/kugelaudio-0-open \
    --mlx-path ./kugelaudio-0-open-bf16 \
    --dtype bfloat16

Supported languages (24): English, German, French, Spanish, Italian, Portuguese, Dutch, Polish, Russian, Ukrainian, Czech, Romanian, Hungarian, Swedish, Danish, Finnish, Norwegian, Greek, Bulgarian, Slovak, Croatian, Serbian, Turkish

Note: Requires ~17GB memory (7B params in bfloat16). Pre-encoded voice presets (voice cloning) are not yet available in the upstream model — the model generates speech with a default voice.

LongCat-AudioDiT

SOTA diffusion-based TTS operating in the waveform latent space. Uses Conditional Flow Matching with a DiT backbone and WAV-VAE codec at 24kHz. Supports zero-shot voice cloning.

from mlx_audio.tts.utils import load

model = load("mlx-community/LongCat-AudioDiT-1B-bf16")

# Zero-shot TTS
result = next(model.generate("Hello, this is a test of AudioDiT."))
audio = result.audio  # mx.array, 24kHz

# Voice cloning (use "apg" guidance for best similarity)
result = next(model.generate(
    text="Today is warm turning to rain.",
    ref_audio="reference.wav",
    ref_text="Transcript of the reference audio.",
    guidance_method="apg",
    cfg_strength=4.0,
    steps=16,
))

See the LongCat-AudioDiT README for all parameters and CLI usage.

Voxtral TTS

Mistral's 4B multilingual text-to-speech with 20 voice presets across 9 languages.

from mlx_audio.tts.utils import load

model = load("mlx-community/Voxtral-4B-TTS-2603-mlx-bf16")

for result in model.generate(text="Hello, how are you today?", voice="casual_male"):
    print(result.audio_duration)

Voices: casual_male, casual_female, cheerful_female, neutral_male, neutral_female, fr_male, fr_female, es_male, es_female, de_male, de_female, it_male, it_female, pt_male, pt_female, nl_male, nl_female, ar_male, hi_male, hi_female

Voxtral Realtime

Mistral's 4B parameter streaming speech-to-text model, optimized for low-latency transcription.

Available variants: 4bit (smaller/faster) | fp16 (full precision)

from mlx_audio.stt.utils import load

# Use 4bit for faster inference, fp16 for full precision
model = load("mlx-community/Voxtral-Mini-4B-Realtime-2602-4bit")

# Transcribe audio
result = model.generate("audio.wav")
print(result.text)

# Streaming transcription
for chunk in model.generate("audio.wav", stream=True):
    print(chunk, end="", flush=True)

# Adjust transcription delay (lower = faster but less accurate)
result = model.generate("audio.wav", transcription_delay_ms=240)

MedASR (Medical Transcription)

Specialized model for medical terms and dictation.

from mlx_audio.stt.utils import load, transcribe

model = load("mlx-community/medasr")
result = transcribe("medical_dictation.wav", model=model)
print(result["text"])

Live Transcription Example:

# Continuous live transcription with VAD
python examples/medasr_live.py

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 MXFP4 quantization
python -m mlx_audio.convert \
    --hf-path prince-canuma/Kokoro-82M \
    --mlx-path ./Kokoro-82M-mxfp4 \
    --quantize \
    --q-mode mxfp4

# 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:

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/OGG/Opus/Vorbis audio encoding)

Installing ffmpeg

ffmpeg is required for saving audio in MP3, FLAC, OGG, Opus, or Vorbis 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