kokoro-mlx

Kokoro TTS inference on Apple Silicon via MLX.

Pure MLX implementation of the full Kokoro-82M text-to-speech pipeline. No PyTorch, no transformers, no third-party ML frameworks. Three lines to speak.

This package provides inference code only. Model weights are developed by hexgrad under the Apache 2.0 license and downloaded separately from HuggingFace Hub on first use.

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Quick Start

Apple Silicon required. Python 3.10–3.12, MLX 0.22+.

pip install kokoro-mlx

from kokoro_mlx import KokoroTTS

tts = KokoroTTS.from_pretrained()
tts.speak("Hello, world.")

Model weights download automatically from HuggingFace Hub on first use.

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Features

• Fully on-device via MLX, no server, no cloud, no network during inference
• Pure implementation with no PyTorch or transformers dependency
• 48 kHz output from native 24 kHz via FFT upsampling, matching the sample rate modern audio hardware expects
• Mixed-precision vocoder, bf16 through the network, float32 for the final waveform reconstruction
• Gapless streaming over a single persistent audio stream with no inter-chunk silence
• 54 voices across American English, British English, and additional languages
• WAV export with a single method call
• Thread-safe with internal lock for concurrent callers
• Context manager for automatic resource cleanup
• Speed control from any multiplier

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API

KokoroTTS.from_pretrained(model_id_or_path)

Load a model from a local directory or the HuggingFace Hub.

tts = KokoroTTS.from_pretrained()
# or a specific repo
tts = KokoroTTS.from_pretrained("mlx-community/Kokoro-82M-bf16")
# or a local directory
tts = KokoroTTS.from_pretrained("/path/to/model")

tts.generate(text, voice, speed, sample_rate) -> TTSResult

Synthesize text and return a TTSResult.

Parameter	Type	Default	Description
text	str	required	Input text to synthesize
voice	str	"af_heart"	Voice name (see Available Voices)
speed	float	1.0	Speaking rate multiplier (>1 faster, <1 slower)
sample_rate	int	24000	Output sample rate: 24000 (native) or 48000 (2x upsampled)

tts.generate_stream(text, voice, speed, sample_rate) -> Iterator[np.ndarray]

Synthesize text and yield audio chunks sentence by sentence. Lower latency than generate for longer inputs.

tts.speak(text, voice, speed, stream, stop_event, sample_rate)

Synthesize and immediately play text through the speakers.

Parameter	Type	Default	Description
text	str	required	Input text to synthesize
voice	str	"af_heart"	Voice name
speed	float	1.0	Speaking rate multiplier
stream	bool	False	Play chunk-by-chunk for lower latency
stop_event	threading.Event or None	None	Set to interrupt playback
sample_rate	int	24000	Output sample rate: 24000 or 48000

tts.save(text, path, voice, speed, sample_rate) -> TTSResult

Synthesize text and write audio to a WAV file.

result = tts.save("Hello, world.", "output.wav", sample_rate=48000)

tts.list_voices() -> list[str]

Return a sorted list of all available voice names.

voices = tts.list_voices()
# ['af_alloy', 'af_aoede', 'af_bella', ...]

tts.close()

Release held resources. Called automatically when using the context manager.

with KokoroTTS.from_pretrained() as tts:
    tts.save("Hello, world.", "output.wav")

TTSResult

@dataclass
class TTSResult:
    audio: np.ndarray   # float32
    sample_rate: int    # 24000 or 48000
    duration: float     # seconds
    voice: str          # voice name used

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Available Voices

Voice names follow a prefix convention: the first two characters identify the accent and gender.

Prefix	Description
af_	American English, Female
am_	American English, Male
bf_	British English, Female
bm_	British English, Male
ef_	Other English, Female
em_	Other English, Male
ff_	French, Female
hf_	Hindi, Female
hm_	Hindi, Male
if_	Italian, Female
im_	Italian, Male
jf_	Japanese, Female
jm_	Japanese, Male
pf_	Portuguese, Female
pm_	Portuguese, Male
zf_	Chinese Mandarin, Female
zm_	Chinese Mandarin, Male

American English (Female): af_alloy, af_aoede, af_bella, af_heart (default), af_jessica, af_kore, af_nicole, af_nova, af_river, af_sarah, af_sky

American English (Male): am_adam, am_echo, am_eric, am_fenrir, am_liam, am_michael, am_onyx, am_puck, am_santa

British English (Female): bf_alice, bf_emma, bf_isabella, bf_lily

British English (Male): bm_daniel, bm_fable, bm_george, bm_lewis

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Architecture

Text Input
  │
  ▼
G2P / Phonemizer (misaki)
  │
  ▼
Phoneme Sequence
  │
  ▼
TextEncoder (PL-BERT / ALBERT, 12 layers, 768 hidden)
  │
  ▼
ProsodyPredictor (duration + pitch)
  │
  ├── Voice Style Vector (per-voice, 256-dim)
  │
  ▼
Decoder (StyleTTS2-style, AdaIN + residual blocks) [bf16]
  │
  ▼
ISTFTNet Vocoder (80-bin mel → waveform) [float32]
  │
  ▼
Optional 2x FFT upsample (24 kHz → 48 kHz)
  │
  ▼
TTSResult { audio float32, duration, voice }

The network runs in bf16 for throughput. At the vocoder output, the signal is promoted to float32 for waveform reconstruction: magnitude recovery, phase extraction, inverse DFT, and overlap-add synthesis. This keeps inference fast while preserving the precision the iSTFT path needs.

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Requirements

• Apple Silicon Mac (M1 or later)
• macOS 13+
• Python 3.10–3.12
• MLX 0.22+

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Development

git clone https://github.com/gabrimatic/kokoro-mlx.git
cd kokoro-mlx
python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
python -m pytest tests/ -v

Skip model-loading tests with -m "not slow".

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Credits

Kokoro-82M by hexgrad · MLX by Apple · misaki G2P by hexgrad · MLX weights from mlx-community

Legal notices

Model License

This package provides inference code only. It does not include model weights.

The Kokoro-82M model weights are developed by hexgrad and released under the Apache License 2.0. The MLX conversion is hosted by mlx-community under the same license. By downloading and using the model weights, you agree to the terms of the Apache 2.0 license.

Trademarks

"MLX" is a trademark of Apple Inc. "HuggingFace" is a trademark of Hugging Face, Inc.

This project is not affiliated with, endorsed by, or sponsored by Apple, Hugging Face, or any other trademark holder. All trademark names are used solely to describe compatibility with their respective technologies.

Third-Party Licenses

This project depends on:

Package	License
mlx	MIT
numpy	BSD-3-Clause
huggingface-hub	Apache-2.0
soundfile	BSD-3-Clause
misaki	Apache-2.0
sounddevice (optional)	MIT

License

This inference code is released under the MIT License. See LICENSE for details.

The model weights have their own license (Apache 2.0). See Model License above.

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Created by Soroush Yousefpour