qwen3-tts-voice-clone

name	qwen3-tts-voice-clone
description	Offline zero-shot voice cloning. Provide a reference audio sample and target text — outputs a WAV file in the cloned voice. No GPU required.
version	2
language	python

Qwen3-TTS Voice Cloning (0.6B)

Zero-shot offline voice cloning powered by Alibaba's Qwen3-TTS 0.6B model.

When to Use

• User provides a reference voice sample (.wav, .mp3, .flac) + target text
• User asks to "clone my voice", "模仿这个声音说话", "声音克隆", "make X say Y"
• Offline / local-only operation is required — no cloud API calls
• Target languages: Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian (+ Auto detection)

Boundaries

• Does not handle video input — user must extract audio first
• Does not require the reference audio transcript (x_vector_only_mode)
• Reference audio: ideally ~10 seconds, clean, single speaker
• Quality is lower than the 1.7B model, but runs on consumer hardware

Hardware

Platform	Backend	Speed
macOS (Apple Silicon)	MPS	Fast
Linux / Windows (NVIDIA)	CUDA (bfloat16)	Fastest
Any (CPU fallback)	CPU (float32)	Slow

Auto-detected: CUDA → MPS → CPU.

Procedure

1. Install dependencies

pip install qwen-tts soundfile transformers accelerate torch

FlashAttention 2 is optional (CUDA only):

pip install flash-attn --no-build-isolation

2. Run voice clone

python skill/clone_voice.py \
  --reference /path/to/speaker_sample.wav \
  --text "Hello, this is my cloned voice." \
  --output cloned.wav

Flag	Default	Description
-r, --reference	(required)	Path to reference audio
-t, --text	(required)	Text to synthesize
-o, --output	output.wav	Output file path
-l, --language	Auto	Target language (see list above)

First run downloads ~1.2 GB model from HuggingFace Hub — one-time only.

China users: Set HF_ENDPOINT=https://hf-mirror.com before first run.

Agent Integration

This skill can be loaded by any AI coding agent (Pi, Cursor, Copilot, etc.). When loaded, the agent should:

1. Check python3 --version ≥ 3.10
2. Ensure dependencies installed: pip install qwen-tts soundfile transformers accelerate torch
3. Locate the reference audio from user's message
4. Run: python skill/clone_voice.py -r <audio> -t <text> -o <out.wav> [-l <lang>]
5. Report the output file path and duration

Pitfalls

• First-run download: ~1.2 GB from HuggingFace — 5–15 minutes. Warn the user.
• CPU is slow: ~5s of speech takes 30–60s. Set expectations.
• FlashAttention macOS: flash-attn is CUDA-only; skip on macOS.
• Long text (>200 chars): may OOM on 8GB. Split into sentences.
• Poor reference audio: noisy, multi-speaker, or <3s samples produce bad results. Use clean 10s+ single-speaker audio.
• Python version: requires 3.10+. Run python3 --version first.

Verification

• Output .wav file exists and is >1 KB
• Audio has recognizable speaker characteristics from reference
• Sample rate is 24000 Hz
• No Python traceback or CUDA/MPS errors in stderr