qwen3-tts（qwen3 TTS 搜索）

Qwen3-TTS

Build text-to-speech applications using Qwen3-TTS from Alibaba Qwen. Reference the local repository at D:\code\qwen3-tts for source code and examples.

Quick Reference

Environment Setup

# Create fresh environment
conda create -n qwen3-tts python=3.12 -y
conda activate qwen3-tts

# Install package
pip install -U qwen-tts

# Optional: FlashAttention 2 for reduced GPU memory
pip install -U flash-attn --no-build-isolation

Available Models

Task Workflows

1. Custom Voice Generation

Use preset speakers with optional style instructions.

import torch
import soundfile as sf
from qwen_tts import Qwen3TTSModel

model = Qwen3TTSModel.from_pretrained(
    "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
    device_map="cuda:0",
    dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
)

# Single generation
wavs, sr = model.generate_custom_voice(
    text="Hello, how are you today?",
    language="English",  # Or "Auto" for auto-detection
    speaker="Ryan",
    instruct="Speak with enthusiasm",  # Optional style control
)
sf.write("output.wav", wavs[0], sr)

# Batch generation
wavs, sr = model.generate_custom_voice(
    text=["First sentence.", "Second sentence."],
    language=["English", "English"],
    speaker=["Ryan", "Aiden"],
    instruct=["Happy tone", "Calm tone"],
)

Available Speakers:

2. Voice Design

Create new voices from natural language descriptions.

model = Qwen3TTSModel.from_pretrained(
    "Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign",
    device_map="cuda:0",
    dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
)

wavs, sr = model.generate_voice_design(
    text="Welcome to our presentation today.",
    language="English",
    instruct="Professional male voice, warm baritone, confident and clear",
)
sf.write("designed_voice.wav", wavs[0], sr)

3. Voice Cloning

Clone a voice from a reference audio sample (3+ seconds recommended).

model = Qwen3TTSModel.from_pretrained(
    "Qwen/Qwen3-TTS-12Hz-1.7B-Base",
    device_map="cuda:0",
    dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
)

# Direct cloning
wavs, sr = model.generate_voice_clone(
    text="This is the cloned voice speaking.",
    language="English",
    ref_audio="path/to/reference.wav",  # Or URL or (numpy_array, sr) tuple
    ref_text="Transcript of the reference audio.",
)
sf.write("cloned.wav", wavs[0], sr)

# Reusable clone prompt (for multiple generations)
prompt = model.create_voice_clone_prompt(
    ref_audio="path/to/reference.wav",
    ref_text="Transcript of the reference audio.",
)
wavs, sr = model.generate_voice_clone(
    text="Another sentence with the same voice.",
    language="English",
    voice_clone_prompt=prompt,
)

4. Voice Design + Clone Workflow

Design a voice, then reuse it across multiple generations.

# Step 1: Design the voice
design_model = Qwen3TTSModel.from_pretrained(
    "Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign",
    device_map="cuda:0",
    dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
)

ref_text = "Sample text for the reference audio."
ref_wavs, sr = design_model.generate_voice_design(
    text=ref_text,
    language="English",
    instruct="Young energetic male, tenor range",
)

# Step 2: Create reusable clone prompt
clone_model = Qwen3TTSModel.from_pretrained(
    "Qwen/Qwen3-TTS-12Hz-1.7B-Base",
    device_map="cuda:0",
    dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
)

prompt = clone_model.create_voice_clone_prompt(
    ref_audio=(ref_wavs[0], sr),
    ref_text=ref_text,
)

# Step 3: Generate multiple outputs with consistent voice
for sentence in ["First line.", "Second line.", "Third line."]:
    wavs, sr = clone_model.generate_voice_clone(
        text=sentence,
        language="English",
        voice_clone_prompt=prompt,
    )

5. Audio Tokenization

Encode and decode audio for transport or processing.

from qwen_tts import Qwen3TTSTokenizer
import soundfile as sf

tokenizer = Qwen3TTSTokenizer.from_pretrained(
    "Qwen/Qwen3-TTS-Tokenizer-12Hz",
    device_map="cuda:0",
)

# Encode audio (accepts path, URL, numpy array, or base64)
enc = tokenizer.encode("path/to/audio.wav")

# Decode back to waveform
wavs, sr = tokenizer.decode(enc)
sf.write("reconstructed.wav", wavs[0], sr)

Generation Parameters

Common parameters for all generate_* methods:

wavs, sr = model.generate_custom_voice(
    text="...",
    language="Auto",
    speaker="Ryan",
    max_new_tokens=2048,
    do_sample=True,
    top_k=50,
    top_p=1.0,
    temperature=0.9,
    repetition_penalty=1.05,
)

Web UI Demo

Launch local Gradio demo:

# CustomVoice demo
qwen-tts-demo Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --ip 0.0.0.0 --port 8000

# VoiceDesign demo
qwen-tts-demo Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign --ip 0.0.0.0 --port 8000

# Base (voice clone) demo - requires HTTPS for microphone
qwen-tts-demo Qwen/Qwen3-TTS-12Hz-1.7B-Base --ip 0.0.0.0 --port 8000 \
  --ssl-certfile cert.pem --ssl-keyfile key.pem --no-ssl-verify

Supported Languages

Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian

Pass language="Auto" for automatic detection, or specify explicitly for best quality.

References

• Fine-tuning guide: See references/finetuning.md for training custom speakers
• API details: See references/api-reference.md for complete method signatures
• Local repo: D:\code\qwen3-tts contains source code and examples