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# Qwen3-TTS Gradio Demo for HuggingFace Spaces with Zero GPU
# Supports: Voice Design, Voice Clone (Base), TTS (CustomVoice)
#import subprocess
#subprocess.run('pip install flash-attn==2.7.4.post1', shell=True)
import os
import spaces
import gradio as gr
import numpy as np
import torch
from huggingface_hub import snapshot_download, login
from qwen_tts import Qwen3TTSModel
HF_TOKEN = os.environ.get('HF_TOKEN')
login(token=HF_TOKEN)
# Model size options
MODEL_SIZES = ["0.6B", "1.7B"]
# Speaker and language choices for CustomVoice model
SPEAKERS = [
"Aiden", "Dylan", "Eric", "Ono_anna", "Ryan", "Serena", "Sohee", "Uncle_fu", "Vivian"
]
LANGUAGES = ["Auto", "Chinese", "English", "Japanese", "Korean", "French", "German", "Spanish", "Portuguese", "Russian"]
def get_model_path(model_type: str, model_size: str) -> str:
"""Get model path based on type and size."""
return snapshot_download(f"Qwen/Qwen3-TTS-12Hz-{model_size}-{model_type}")
# ============================================================================
# GLOBAL MODEL LOADING - Load all models at startup
# ============================================================================
print("Loading all models to CUDA...")
# Voice Design model (1.7B only)
print("Loading VoiceDesign 1.7B model...")
voice_design_model = Qwen3TTSModel.from_pretrained(
get_model_path("VoiceDesign", "1.7B"),
device_map="cuda",
dtype=torch.bfloat16,
token=HF_TOKEN,
attn_implementation="kernels-community/flash-attn3",
)
# Base (Voice Clone) models - both sizes
print("Loading Base 0.6B model...")
base_model_0_6b = Qwen3TTSModel.from_pretrained(
get_model_path("Base", "0.6B"),
device_map="cuda",
dtype=torch.bfloat16,
token=HF_TOKEN,
attn_implementation="kernels-community/flash-attn3",
)
print("Loading Base 1.7B model...")
base_model_1_7b = Qwen3TTSModel.from_pretrained(
get_model_path("Base", "1.7B"),
device_map="cuda",
dtype=torch.bfloat16,
token=HF_TOKEN,
attn_implementation="kernels-community/flash-attn3",
)
# CustomVoice models - both sizes
print("Loading CustomVoice 0.6B model...")
custom_voice_model_0_6b = Qwen3TTSModel.from_pretrained(
get_model_path("CustomVoice", "0.6B"),
device_map="cuda",
dtype=torch.bfloat16,
token=HF_TOKEN,
attn_implementation="kernels-community/flash-attn3",
)
print("Loading CustomVoice 1.7B model...")
custom_voice_model_1_7b = Qwen3TTSModel.from_pretrained(
get_model_path("CustomVoice", "1.7B"),
device_map="cuda",
dtype=torch.bfloat16,
token=HF_TOKEN,
attn_implementation="kernels-community/flash-attn3",
)
print("All models loaded successfully!")
# Model lookup dictionaries for easy access
BASE_MODELS = {
"0.6B": base_model_0_6b,
"1.7B": base_model_1_7b,
}
CUSTOM_VOICE_MODELS = {
"0.6B": custom_voice_model_0_6b,
"1.7B": custom_voice_model_1_7b,
}
# ============================================================================
def _normalize_audio(wav, eps=1e-12, clip=True):
"""Normalize audio to float32 in [-1, 1] range."""
x = np.asarray(wav)
if np.issubdtype(x.dtype, np.integer):
info = np.iinfo(x.dtype)
if info.min < 0:
y = x.astype(np.float32) / max(abs(info.min), info.max)
else:
mid = (info.max + 1) / 2.0
y = (x.astype(np.float32) - mid) / mid
elif np.issubdtype(x.dtype, np.floating):
y = x.astype(np.float32)
m = np.max(np.abs(y)) if y.size else 0.0
if m > 1.0 + 1e-6:
y = y / (m + eps)
else:
raise TypeError(f"Unsupported dtype: {x.dtype}")
if clip:
y = np.clip(y, -1.0, 1.0)
if y.ndim > 1:
y = np.mean(y, axis=-1).astype(np.float32)
return y
def _audio_to_tuple(audio):
"""Convert Gradio audio input to (wav, sr) tuple."""
if audio is None:
return None
if isinstance(audio, tuple) and len(audio) == 2 and isinstance(audio[0], int):
sr, wav = audio
wav = _normalize_audio(wav)
return wav, int(sr)
if isinstance(audio, dict) and "sampling_rate" in audio and "data" in audio:
sr = int(audio["sampling_rate"])
wav = _normalize_audio(audio["data"])
return wav, sr
return None
@spaces.GPU(duration=60)
def generate_voice_design(text, language, voice_description, progress=gr.Progress(track_tqdm=True)):
"""Generate speech using Voice Design model (1.7B only)."""
if not text or not text.strip():
return None, "Error: Text is required."
if not voice_description or not voice_description.strip():
return None, "Error: Voice description is required."
try:
wavs, sr = voice_design_model.generate_voice_design(
text=text.strip(),
language=language,
instruct=voice_description.strip(),
non_streaming_mode=True,
max_new_tokens=2048,
)
return (sr, wavs[0]), "Voice design generation completed successfully!"
except Exception as e:
return None, f"Error: {type(e).__name__}: {e}"
@spaces.GPU(duration=60)
def generate_voice_clone(ref_audio, ref_text, target_text, language, use_xvector_only, model_size, progress=gr.Progress(track_tqdm=True)):
"""Generate speech using Base (Voice Clone) model."""
if not target_text or not target_text.strip():
return None, "Error: Target text is required."
audio_tuple = _audio_to_tuple(ref_audio)
if audio_tuple is None:
return None, "Error: Reference audio is required."
if not use_xvector_only and (not ref_text or not ref_text.strip()):
return None, "Error: Reference text is required when 'Use x-vector only' is not enabled."
try:
tts = BASE_MODELS[model_size]
wavs, sr = tts.generate_voice_clone(
text=target_text.strip(),
language=language,
ref_audio=audio_tuple,
ref_text=ref_text.strip() if ref_text else None,
x_vector_only_mode=use_xvector_only,
max_new_tokens=2048,
)
return (sr, wavs[0]), "Voice clone generation completed successfully!"
except Exception as e:
return None, f"Error: {type(e).__name__}: {e}"
@spaces.GPU(duration=60)
def generate_custom_voice(text, language, speaker, instruct, model_size, progress=gr.Progress(track_tqdm=True)):
"""Generate speech using CustomVoice model."""
if not text or not text.strip():
return None, "Error: Text is required."
if not speaker:
return None, "Error: Speaker is required."
try:
tts = CUSTOM_VOICE_MODELS[model_size]
wavs, sr = tts.generate_custom_voice(
text=text.strip(),
language=language,
speaker=speaker.lower().replace(" ", "_"),
instruct=instruct.strip() if instruct else None,
non_streaming_mode=True,
max_new_tokens=2048,
)
return (sr, wavs[0]), "Generation completed successfully!"
except Exception as e:
return None, f"Error: {type(e).__name__}: {e}"
# Build Gradio UI
def build_ui():
theme = gr.themes.Soft(
font=[gr.themes.GoogleFont("Source Sans Pro"), "Arial", "sans-serif"],
)
css = """
.gradio-container {max-width: none !important;}
.tab-content {padding: 20px;}
"""
with gr.Blocks(theme=theme, css=css, title="Qwen3-TTS Demo") as demo:
gr.Markdown(
"""
# Qwen3-TTS Demo
A unified Text-to-Speech demo featuring three powerful modes:
- **Voice Design**: Create custom voices using natural language descriptions
- **Voice Clone (Base)**: Clone any voice from a reference audio
- **TTS (CustomVoice)**: Generate speech with predefined speakers and optional style instructions
Built with [Qwen3-TTS](https://github.com/QwenLM/Qwen3-TTS) by Alibaba Qwen Team.
"""
)
with gr.Tabs():
# Tab 1: Voice Design (Default, 1.7B only)
with gr.Tab("Voice Design"):
gr.Markdown("### Create Custom Voice with Natural Language")
with gr.Row():
with gr.Column(scale=2):
design_text = gr.Textbox(
label="Text to Synthesize",
lines=4,
placeholder="Enter the text you want to convert to speech...",
value="It's in the top drawer... wait, it's empty? No way, that's impossible! I'm sure I put it there!"
)
design_language = gr.Dropdown(
label="Language",
choices=LANGUAGES,
value="Auto",
interactive=True,
)
design_instruct = gr.Textbox(
label="Voice Description",
lines=3,
placeholder="Describe the voice characteristics you want...",
value="Speak in an incredulous tone, but with a hint of panic beginning to creep into your voice."
)
design_btn = gr.Button("Generate with Custom Voice", variant="primary")
with gr.Column(scale=2):
design_audio_out = gr.Audio(label="Generated Audio", type="numpy")
design_status = gr.Textbox(label="Status", lines=2, interactive=False)
design_btn.click(
generate_voice_design,
inputs=[design_text, design_language, design_instruct],
outputs=[design_audio_out, design_status],
)
# Tab 2: Voice Clone (Base)
with gr.Tab("Voice Clone (Base)"):
gr.Markdown("### Clone Voice from Reference Audio")
with gr.Row():
with gr.Column(scale=2):
clone_ref_audio = gr.Audio(
label="Reference Audio (Upload a voice sample to clone)",
type="numpy",
)
clone_ref_text = gr.Textbox(
label="Reference Text (Transcript of the reference audio)",
lines=2,
placeholder="Enter the exact text spoken in the reference audio...",
)
clone_xvector = gr.Checkbox(
label="Use x-vector only (No reference text needed, but lower quality)",
value=False,
)
with gr.Column(scale=2):
clone_target_text = gr.Textbox(
label="Target Text (Text to synthesize with cloned voice)",
lines=4,
placeholder="Enter the text you want the cloned voice to speak...",
)
with gr.Row():
clone_language = gr.Dropdown(
label="Language",
choices=LANGUAGES,
value="Auto",
interactive=True,
)
clone_model_size = gr.Dropdown(
label="Model Size",
choices=MODEL_SIZES,
value="1.7B",
interactive=True,
)
clone_btn = gr.Button("Clone & Generate", variant="primary")
with gr.Row():
clone_audio_out = gr.Audio(label="Generated Audio", type="numpy")
clone_status = gr.Textbox(label="Status", lines=2, interactive=False)
clone_btn.click(
generate_voice_clone,
inputs=[clone_ref_audio, clone_ref_text, clone_target_text, clone_language, clone_xvector, clone_model_size],
outputs=[clone_audio_out, clone_status],
)
# Tab 3: TTS (CustomVoice)
with gr.Tab("TTS (CustomVoice)"):
gr.Markdown("### Text-to-Speech with Predefined Speakers")
with gr.Row():
with gr.Column(scale=2):
tts_text = gr.Textbox(
label="Text to Synthesize",
lines=4,
placeholder="Enter the text you want to convert to speech...",
value="Hello! Welcome to Text-to-Speech system. This is a demo of our TTS capabilities."
)
with gr.Row():
tts_language = gr.Dropdown(
label="Language",
choices=LANGUAGES,
value="English",
interactive=True,
)
tts_speaker = gr.Dropdown(
label="Speaker",
choices=SPEAKERS,
value="Ryan",
interactive=True,
)
with gr.Row():
tts_instruct = gr.Textbox(
label="Style Instruction (Optional)",
lines=2,
placeholder="e.g., Speak in a cheerful and energetic tone",
)
tts_model_size = gr.Dropdown(
label="Model Size",
choices=MODEL_SIZES,
value="1.7B",
interactive=True,
)
tts_btn = gr.Button("Generate Speech", variant="primary")
with gr.Column(scale=2):
tts_audio_out = gr.Audio(label="Generated Audio", type="numpy")
tts_status = gr.Textbox(label="Status", lines=2, interactive=False)
tts_btn.click(
generate_custom_voice,
inputs=[tts_text, tts_language, tts_speaker, tts_instruct, tts_model_size],
outputs=[tts_audio_out, tts_status],
)
gr.Markdown(
"""
---
**Note**: This demo uses HuggingFace Spaces Zero GPU. Each generation has a time limit.
For longer texts, please split them into smaller segments.
"""
)
return demo
if __name__ == "__main__":
demo = build_ui()
demo.launch() |