Skill: h1bomb/qwen3-tts-mlx (version 2.1.0)

--- name: qwen3-tts-mlx description: Local Qwen3-TTS speech synthesis on Apple Silicon via MLX. Use for offline narration, audiobooks, video voiceovers, and multilingual TTS. metadata: author: agiseek version: "1.2.0" --- # 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 ```bash pip install mlx-audio brew install ffmpeg ``` ### Basic Usage ```bash python scripts/run_tts.py custom-voice \ --text "Hello, welcome to local text to speech." \ --voice Ryan \ --output output.wav ``` ### With Style Control ```bash 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 | Variant | Model | Size | Memory | Use Case | |---------|-------|------|--------|----------| | CustomVoice | `mlx-community/Qwen3-TTS-12Hz-0.6B-CustomVoice-4bit` | ~1GB | ~4GB | Built-in voices + style control (recommended) | | VoiceDesign | `mlx-community/Qwen3-TTS-12Hz-1.7B-VoiceDesign-5bit` | ~2GB | ~5GB | Create voices from text descriptions | | Base | `mlx-community/Qwen3-TTS-12Hz-0.6B-Base-4bit` | ~1GB | ~4GB | Voice cloning from reference audio | ## Supported Languages | Language | Code | Notes | |----------|------|-------| | 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 | Voice | Language | Character | |-------|----------|-----------| | Vivian | Chinese | Female, bright, young | | Serena | Chinese | Female, gentle, soft | | Uncle_Fu | Chinese | Male, authoritative, news anchor | | Dylan | Chinese | Male, Beijing dialect | | Eric | Chinese | Male, Sichuan dialect | | Ryan | English | Male, energetic | | Aiden | English | Male, clear, neutral | | Ono_Anna | Japanese | Female | | Sohee | Korean | Female | **Voice Selection Guide:** | Scenario | Recommended Voice | |----------|-------------------| | Chinese news/narration | Uncle_Fu | | Chinese casual/lively | Eric | | Chinese female, professional | Vivian | | Chinese female, storytelling | Serena | | English energetic content | Ryan | | English neutral/educational | Aiden | | Japanese content | Ono_Anna | | Korean content | Sohee | ## Modes ### 1) CustomVoice Use built-in voices with optional emotion/style control via `--instruct`. ```bash 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. ```bash 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). ```bash 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 | Parameter | Required | Default | Description | |-----------|----------|---------|-------------| | `--text` | Yes | - | Text to synthesize | | `--voice` | No | Vivian | Built-in voice (CustomVoice only) | | `--lang_code` | No | auto | Language code | | `--instruct` | No | - | Style control or voice description | | `--speed` | No | 1.0 | Speech speed multiplier | | `--temperature` | No | 0.7 | Sampling temperature (higher = more variation) | | `--model` | No | (per mode) | Override default model | | `--output` | No | - | Output file path | | `--out-dir` | No | ./outputs | Output directory when --output not set | | `--ref_audio` | VoiceClone | - | Reference audio file | | `--ref_text` | VoiceClone | - | Reference audio transcript | ## Python API ### Using generate_audio (recommended) ```python 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 ```python 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 ```python 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 ```python 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: ```bash python scripts/batch_dubbing.py \ --input dubbing.json \ --out-dir outputs ``` See `references/dubbing_format.md` for the JSON format. ## Performance | Metric | Value | |--------|-------| | Sample rate | 24,000 Hz | | Real-time factor | ~0.7x (faster than real-time) | | Peak memory | ~4-6 GB | | First run | Downloads model (~1-2GB) | ## Troubleshooting | Issue | Solution | |-------|----------| | Slow generation | Use 4-bit CustomVoice model | | Unnatural pauses | Add punctuation, keep sentences short | | Wrong language detected | Specify `--lang_code` explicitly | | Voice cloning quality | Use cleaner reference audio, accurate transcript | | Tokenizer warnings | Harmless, can be ignored | | Out of memory | Close other apps, use 4-bit model |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)