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

pip install mlx-audio

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!"

# With voice selection and speed adjustment
mlx_audio.tts.generate --model mlx-community/Kokoro-82M-bf16 --text "Hello!" --voice af_heart --speed 1.2

# Play audio immediately
mlx_audio.tts.generate --model mlx-community/Kokoro-82M-bf16 --text "Hello!" --play

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
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-mlx
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

Speech-to-Speech (STS)

Model	Description	Use Case	Repo
SAM-Audio	Text-guided source separation	Extract specific sounds	mlx-community/sam-audio-large
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

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-mlx")
result = transcribe("audio.wav", model=model)
print(result["text"])

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-mlx"

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

Pre-quantized models available:

Swift Integration

Native Swift package for iOS and macOS apps.

Installation

Add to your Package.swift:

dependencies: [
    .package(url: "https://github.com/Blaizzy/mlx-audio.git", from: "0.2.5")
]

Usage

import MLXAudio

// Create session (auto-downloads model)
let session = try await MarvisSession(voice: .conversationalA)

// Generate speech (auto-plays)
let result = try await session.generate(for: "Hello from Swift!")

// Streaming generation
for try await chunk in session.stream(text: "Streaming audio...") {
    print("Chunk: \(chunk.sampleCount) samples")
}

Supported Platforms: macOS 14.0+, iOS 16.0+

Requirements

Python 3.9+
Apple Silicon Mac (M1/M2/M3/M4)
MLX framework

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

Name		Name	Last commit message	Last commit date
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.swiftpm/xcode/package.xcworkspace		.swiftpm/xcode/package.xcworkspace
examples		examples
mlx_audio		mlx_audio
mlx_audio_swift/tts		mlx_audio_swift/tts
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
CONTRIBUTIONS.md		CONTRIBUTIONS.md
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
Package.resolved		Package.resolved
Package.swift		Package.swift
README.md		README.md
XCODE_BUILD_TROUBLESHOOTING.md		XCODE_BUILD_TROUBLESHOOTING.md
pyproject.toml		pyproject.toml
pytest.ini		pytest.ini
uv.lock		uv.lock

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MLX-Audio

Features

Installation

Quick Start

Command Line

Python API

Supported Models

Text-to-Speech (TTS)

Speech-to-Text (STT)

Speech-to-Speech (STS)

Model Examples

Kokoro TTS

CSM (Voice Cloning)

Whisper STT

SAM-Audio (Source Separation)

MossFormer2 (Speech Enhancement)

Web Interface & API Server

Starting the Server

API Endpoints

Quantization

Swift Integration

Installation

Usage

Requirements

License

Citation

Acknowledgements

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