import os
import numpy as np
import torch
import gradio as gr
import spaces
from typing import Optional, Tuple
from funasr import AutoModel
from pathlib import Path
os.environ["TOKENIZERS_PARALLELISM"]="false"if os.environ.get("HF_REPO_ID","").strip()=="":os.environ["HF_REPO_ID"]="openbmb/VoxCPM-0.5B"import voxcpmclassVoxCPMDemo:def__init__(self)->None:# 设备检测优先级: CUDA > MPS > CPUif torch.cuda.is_available():self.device ="cuda"elifhasattr(torch.backends,'mps')and torch.backends.mps.is_available():self.device ="mps"else:self.device ="cpu"# 添加设备类型信息显示device_info ={"cuda":"NVIDIA GPU (CUDA)","mps":"Apple Silicon GPU (MPS)","cpu":"CPU"}print(f"🚀 Running on device: {self.device} ({device_info.get(self.device,'Unknown')})")# 显示额外设备信息if self.device =="cuda":print(f"📊 GPU Count: {torch.cuda.device_count()}")if torch.cuda.is_available():print(f"🎯 GPU Name: {torch.cuda.get_device_name(0)}")print(f"💾 GPU Memory: {torch.cuda.get_device_properties(0).total_memory /1024**3:.1f} GB")elif self.device =="mps":print("🍎 Apple Silicon GPU detected - Using Metal Performance Shaders")print("💡 Note: MPS provides efficient GPU acceleration on Apple Silicon devices")# ASR model for prompt text recognitionself.asr_model_id ="iic/SenseVoiceSmall"# 根据 self.device 设置 ASR 模型设备if self.device =="cuda":asr_device ="cuda:0"elif self.device =="mps":asr_device ="mps"else:asr_device ="cpu"self.asr_model: Optional[AutoModel]= AutoModel(model=self.asr_model_id,disable_update=True,log_level='DEBUG',device=asr_device,)# TTS model (lazy init)self.voxcpm_model: Optional[voxcpm.VoxCPM]=Noneself.default_local_model_dir ="./models/VoxCPM-0.5B"# ---------- Model helpers ----------def_resolve_model_dir(self)->str:"""Resolve model directory:1) Use local checkpoint directory if exists2) If HF_REPO_ID env is set, download into models/{repo}3) Fallback to 'models'"""if os.path.isdir(self.default_local_model_dir):return self.default_local_model_dirrepo_id = os.environ.get("HF_REPO_ID","").strip()iflen(repo_id)>0:target_dir = os.path.join("models", repo_id.replace("/","__"))ifnot os.path.isdir(target_dir):try:from huggingface_hub import snapshot_download # type: ignoreos.makedirs(target_dir, exist_ok=True)print(f"Downloading model from HF repo '{repo_id}' to '{target_dir}' ...")snapshot_download(repo_id=repo_id, local_dir=target_dir, local_dir_use_symlinks=False)except Exception as e:print(f"Warning: HF download failed: {e}. Falling back to 'data'.")return"models"return target_dirreturn"models"defget_or_load_voxcpm(self)-> voxcpm.VoxCPM:if self.voxcpm_model isnotNone:return self.voxcpm_modelprint("Model not loaded, initializing...")model_dir = self._resolve_model_dir()print(f"Using model dir: {model_dir}")try:# 官方推荐方案：不传递 device 参数，让官方代码自动检测# 仅禁用 denoiser 以避免 transformers 兼容性问题# 仅在 CUDA 上启用 torch.compile 优化optimize =(self.device =="cuda")self.voxcpm_model = voxcpm.VoxCPM(voxcpm_model_path=model_dir,enable_denoiser=False,optimize=optimize)except Exception as e:print(f"Error initializing VoxCPM: {e}")raiseprint("✅ Model loaded successfully.")return self.voxcpm_model# ---------- Functional endpoints ----------defprompt_wav_recognition(self, prompt_wav: Optional[str])->str:if prompt_wav isNone:return""res = self.asr_model.generate(input=prompt_wav, language="auto", use_itn=True)text = res[0]["text"].split('|>')[-1]return textdefgenerate_tts_audio(self,text_input:str,prompt_wav_path_input: Optional[str]=None,prompt_text_input: Optional[str]=None,cfg_value_input:float=2.0,inference_timesteps_input:int=10,do_normalize:bool=True,denoise:bool=True,)-> Tuple[int, np.ndarray]:"""Generate speech from text using VoxCPM; optional reference audio for voice style guidance.Returns (sample_rate, waveform_numpy)"""current_model = self.get_or_load_voxcpm()text =(text_input or"").strip()iflen(text)==0:raise ValueError("Please input text to synthesize.")prompt_wav_path = prompt_wav_path_input if prompt_wav_path_input elseNoneprompt_text = prompt_text_input if prompt_text_input elseNoneprint(f"Generating audio for text: '{text[:60]}...'")# 在 MPS 设备上禁用 denoise 以避免兼容性问题if self.device =="mps":denoise =Falseprint("💡 Note: Denoise disabled on MPS device for compatibility")wav = current_model.generate(text=text,prompt_text=prompt_text,prompt_wav_path=prompt_wav_path,cfg_value=float(cfg_value_input),inference_timesteps=int(inference_timesteps_input),normalize=do_normalize,denoise=denoise,)return(16000, wav)# ---------- UI Builders ----------defcreate_demo_interface(demo: VoxCPMDemo):"""Build the Gradio UI for VoxCPM demo."""# static assets (logo path)gr.set_static_paths(paths=[Path.cwd().absolute()/"assets"])with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue",secondary_hue="gray",neutral_hue="slate",font=[gr.themes.GoogleFont("Inter"),"Arial","sans-serif"]),css=""".logo-container {text-align: center;margin: 0.5rem 0 1rem 0;}.logo-container img {height: 80px;width: auto;max-width: 200px;display: inline-block;}/* Bold accordion labels */#acc_quick details > summary,#acc_tips details > summary {font-weight: 600 !important;font-size: 1.1em !important;}/* Bold labels for specific checkboxes */#chk_denoise label,#chk_denoise span,#chk_normalize label,#chk_normalize span {font-weight: 600;}""")as interface:# Header logogr.HTML('<div class="logo-container"><img src="/gradio_api/file=assets/voxcpm_logo.png" alt="VoxCPM Logo"></div>')# Quick Startwith gr.Accordion("📋 Quick Start Guide ｜快速入门",open=False, elem_id="acc_quick"):gr.Markdown("""### How to Use ｜使用说明1. **(Optional) Provide a Voice Prompt** - Upload or record an audio clip to provide the desired voice characteristics for synthesis. **（可选）提供参考声音** - 上传或录制一段音频，为声音合成提供音色、语调和情感等个性化特征2. **(Optional) Enter prompt text** - If you provided a voice prompt, enter the corresponding transcript here (auto-recognition available). **（可选项）输入参考文本** - 如果提供了参考语音，请输入其对应的文本内容（支持自动识别）。3. **Enter target text** - Type the text you want the model to speak. **输入目标文本** - 输入您希望模型朗读的文字内容。4. **Generate Speech** - Click the "Generate" button to create your audio. **生成语音** - 点击"生成"按钮，即可为您创造出音频。""")# Pro Tipswith gr.Accordion("💡 Pro Tips ｜使用建议",open=False, elem_id="acc_tips"):gr.Markdown("""### Prompt Speech Enhancement｜参考语音降噪- **Enable** to remove background noise for a clean, studio-like voice, with an external ZipEnhancer component. **启用**：通过 ZipEnhancer 组件消除背景噪音，获得更好的音质。- **Disable** to preserve the original audio's background atmosphere. **禁用**：保留原始音频的背景环境声，如果想复刻相应声学环境。### Text Normalization｜文本正则化- **Enable** to process general text with an external WeTextProcessing component. **启用**：使用 WeTextProcessing 组件，可处理常见文本。- **Disable** to use VoxCPM's native text understanding ability. For example, it supports phonemes input ({HH AH0 L OW1}), try it! **禁用**：将使用 VoxCPM 内置的文本理解能力。如，支持音素输入（如 {da4}{jia1}好）和公式符号合成，尝试一下！### CFG Value｜CFG 值- **Lower CFG** if the voice prompt sounds strained or expressive. **调低**：如果提示语音听起来不自然或过于夸张。- **Higher CFG** for better adherence to the prompt speech style or input text. **调高**：为更好地贴合提示音频的风格或输入文本。### Inference Timesteps｜推理时间步- **Lower** for faster synthesis speed. **调低**：合成速度更快。- **Higher** for better synthesis quality. **调高**：合成质量更佳。""")# Main controlswith gr.Row():with gr.Column():prompt_wav = gr.Audio(sources=["upload",'microphone'],type="filepath",label="Prompt Speech (Optional, or let VoxCPM improvise)",value="./examples/example.wav",)DoDenoisePromptAudio = gr.Checkbox(value=False,label="Prompt Speech Enhancement",elem_id="chk_denoise",info="We use ZipEnhancer model to denoise the prompt audio.")with gr.Row():prompt_text = gr.Textbox(value="Just by listening a few minutes a day, you'll be able to eliminate negative thoughts by conditioning your mind to be more positive.",label="Prompt Text",placeholder="Please enter the prompt text. Automatic recognition is supported, and you can correct the results yourself...")run_btn = gr.Button("Generate Speech", variant="primary")with gr.Column():cfg_value = gr.Slider(minimum=1.0,maximum=3.0,value=2.0,step=0.1,label="CFG Value (Guidance Scale)",info="Higher values increase adherence to prompt, lower values allow more creativity")inference_timesteps = gr.Slider(minimum=4,maximum=30,value=10,step=1,label="Inference Timesteps",info="Number of inference timesteps for generation (higher values may improve quality but slower)")with gr.Row():text = gr.Textbox(value="VoxCPM is an innovative end-to-end TTS model from ModelBest, designed to generate highly realistic speech.",label="Target Text",)with gr.Row():DoNormalizeText = gr.Checkbox(value=False,label="Text Normalization",elem_id="chk_normalize",info="We use wetext library to normalize the input text.")audio_output = gr.Audio(label="Output Audio")# Wiringrun_btn.click(fn=demo.generate_tts_audio,inputs=[text, prompt_wav, prompt_text, cfg_value, inference_timesteps, DoNormalizeText, DoDenoisePromptAudio],outputs=[audio_output],show_progress=True,api_name="generate",)prompt_wav.change(fn=demo.prompt_wav_recognition, inputs=[prompt_wav], outputs=[prompt_text])return interfacedefrun_demo(server_name:str="localhost", server_port:int=7860, show_error:bool=True):demo = VoxCPMDemo()interface = create_demo_interface(demo)# Recommended to enable queue on Spaces for better throughputinterface.queue(max_size=10).launch(server_name=server_name, server_port=server_port, show_error=show_error)if __name__ =="__main__":run_demo()
用于加载音频文件
pip install torchcodec
运行
python app.py
➜ VoxCPM git:(main) ✗ python app.py
🚀 Running on device:mps(Apple Silicon GPU(MPS))
🍎 Apple Silicon GPU detected - Using Metal Performance Shaders
💡 Note:MPS provides efficient GPU acceleration on Apple Silicon devices
funasr version:1.2.7.
