Learn-skills.dev qwen3-tts-mlx

Local Qwen3-TTS speech synthesis on Apple Silicon via MLX. Use for offline narration, audiobooks, video voiceovers, and multilingual TTS.

install
source · Clone the upstream repo
git clone https://github.com/NeverSight/learn-skills.dev
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/NeverSight/learn-skills.dev "$T" && mkdir -p ~/.claude/skills && cp -r "$T/data/skills-md/agiseek/agent-skills/qwen3-tts-mlx" ~/.claude/skills/neversight-learn-skills-dev-qwen3-tts-mlx && rm -rf "$T"
manifest: data/skills-md/agiseek/agent-skills/qwen3-tts-mlx/SKILL.md
source content

Qwen3-TTS MLX

Run Qwen3-TTS locally on Apple Silicon (M1/M2/M3/M4) using MLX. Supports 11 languages, 9 built-in voices, voice cloning, and voice design from text descriptions.

When to Use

  • Generate speech fully offline on a Mac
  • Produce narration, audiobooks, podcasts, or video voiceovers
  • Create multilingual TTS with controllable style and emotion
  • Clone any voice from a short audio sample
  • Design custom voices from text descriptions

Quick Start

Install

pip install mlx-audio
brew install ffmpeg

Basic Usage

python scripts/run_tts.py custom-voice \
  --text "Hello, welcome to local text to speech." \
  --voice Ryan \
  --output output.wav

With Style Control

python scripts/run_tts.py custom-voice \
  --text "Breaking news: local AI model achieves human-level speech." \
  --voice Uncle_Fu \
  --instruct "news anchor tone, calm and authoritative" \
  --output news.wav

Model Variants

VariantModelSizeMemoryUse Case
CustomVoice
mlx-community/Qwen3-TTS-12Hz-0.6B-CustomVoice-4bit
~1GB~4GBBuilt-in voices + style control (recommended)
VoiceDesign
mlx-community/Qwen3-TTS-12Hz-1.7B-VoiceDesign-5bit
~2GB~5GBCreate voices from text descriptions
Base
mlx-community/Qwen3-TTS-12Hz-0.6B-Base-4bit
~1GB~4GBVoice cloning from reference audio

Supported Languages

LanguageCodeNotes
Auto-detect
auto
Default, detects from text
Chinese
Chinese
Mandarin
English
English
Japanese
Japanese
Korean
Korean
French
French
German
German
Spanish
Spanish
Portuguese
Portuguese
Italian
Italian
Russian
Russian

Built-in Voices

VoiceLanguageCharacter
VivianChineseFemale, bright, young
SerenaChineseFemale, gentle, soft
Uncle_FuChineseMale, authoritative, news anchor
DylanChineseMale, Beijing dialect
EricChineseMale, Sichuan dialect
RyanEnglishMale, energetic
AidenEnglishMale, clear, neutral
Ono_AnnaJapaneseFemale
SoheeKoreanFemale

Voice Selection Guide:

ScenarioRecommended Voice
Chinese news/narrationUncle_Fu
Chinese casual/livelyEric
Chinese female, professionalVivian
Chinese female, storytellingSerena
English energetic contentRyan
English neutral/educationalAiden
Japanese contentOno_Anna
Korean contentSohee

Modes

1) CustomVoice

Use built-in voices with optional emotion/style control via

--instruct
.

python scripts/run_tts.py custom-voice \
  --text "This is amazing news!" \
  --voice Vivian \
  --instruct "excited and happy" \
  --output excited.wav

Style instruction examples:

  • "calm and warm"
    - Soft, friendly delivery
  • "news anchor, authoritative"
    - Professional broadcast style
  • "excited and energetic"
    - High energy, enthusiastic
  • "sad and melancholic"
    - Emotional, somber tone
  • "whispering, intimate"
    - Quiet, close-mic feel

2) VoiceDesign

Create a completely new voice by describing it in natural language.

python scripts/run_tts.py voice-design \
  --text "Welcome to our podcast." \
  --instruct "warm, mature male narrator with low pitch and gentle tone" \
  --output podcast_intro.wav

Voice description examples:

  • "young cheerful female with high pitch"
  • "elderly wise male with deep resonant voice"
  • "professional female news anchor, clear articulation"
  • "friendly young male, casual and relaxed"

3) VoiceClone

Clone any voice from a reference audio sample (5-10 seconds recommended).

python scripts/run_tts.py voice-clone \
  --text "This is my cloned voice speaking new content." \
  --ref_audio reference.wav \
  --ref_text "The exact transcript of the reference audio" \
  --output cloned.wav

Tips for voice cloning:

  • Use clean audio without background noise
  • 5-10 seconds of speech works best
  • Provide accurate transcript of the reference
  • Reference and output language should match

CLI Parameters

ParameterRequiredDefaultDescription
--text
Yes-Text to synthesize
--voice
NoVivianBuilt-in voice (CustomVoice only)
--lang_code
NoautoLanguage code
--instruct
No-Style control or voice description
--speed
No1.0Speech speed multiplier
--temperature
No0.7Sampling temperature (higher = more variation)
--model
No(per mode)Override default model
--output
No-Output file path
--out-dir
No./outputsOutput directory when --output not set
--ref_audio
VoiceClone-Reference audio file
--ref_text
VoiceClone-Reference audio transcript

Python API

Using generate_audio (recommended)

from mlx_audio.tts.generate import generate_audio

# CustomVoice with style control
generate_audio(
    text="Hello from Qwen3-TTS!",
    model="mlx-community/Qwen3-TTS-12Hz-0.6B-CustomVoice-4bit",
    voice="Ryan",
    lang_code="english",
    instruct="friendly and warm",
    output_path=".",
    file_prefix="hello",
    audio_format="wav",
    join_audio=True,
    verbose=True,
)

Using Model directly

from mlx_audio.tts.utils import load
import soundfile as sf
import numpy as np

# Load model
model = load("mlx-community/Qwen3-TTS-12Hz-0.6B-CustomVoice-4bit")

# Generate audio (returns a generator)
audio_chunks = []
for chunk in model.generate_custom_voice(
    text="Hello from Qwen3-TTS.",
    speaker="Ryan",
    language="english",
    instruct="clear, steady delivery"
):
    if hasattr(chunk, 'audio') and chunk.audio is not None:
        audio_chunks.append(chunk.audio)

# Combine and save
audio = np.concatenate(audio_chunks)
sf.write("output.wav", audio, 24000)

VoiceDesign

from mlx_audio.tts.generate import generate_audio

generate_audio(
    text="Welcome to the show.",
    model="mlx-community/Qwen3-TTS-12Hz-1.7B-VoiceDesign-5bit",
    instruct="warm, friendly female narrator with medium pitch",
    lang_code="english",
    output_path=".",
    file_prefix="voice_design",
    join_audio=True,
)

VoiceClone

from mlx_audio.tts.generate import generate_audio

generate_audio(
    text="New content in the cloned voice.",
    model="mlx-community/Qwen3-TTS-12Hz-0.6B-Base-4bit",
    ref_audio="reference.wav",
    ref_text="Transcript of the reference audio",
    output_path=".",
    file_prefix="cloned",
    join_audio=True,
)

Batch Processing

Use

scripts/batch_dubbing.py
for processing multiple lines:

python scripts/batch_dubbing.py \
  --input dubbing.json \
  --out-dir outputs

See

references/dubbing_format.md
for the JSON format.

Performance

MetricValue
Sample rate24,000 Hz
Real-time factor~0.7x (faster than real-time)
Peak memory~4-6 GB
First runDownloads model (~1-2GB)

Troubleshooting

IssueSolution
Slow generationUse 4-bit CustomVoice model
Unnatural pausesAdd punctuation, keep sentences short
Wrong language detectedSpecify
--lang_code
explicitly
Voice cloning qualityUse cleaner reference audio, accurate transcript
Tokenizer warningsHarmless, can be ignored
Out of memoryClose other apps, use 4-bit model