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/VoxCPM1.5" import voxcpm class VoxCPMDemo: def __init__(self) -> None: self.device = "cuda" if torch.cuda.is_available() else "cpu" print(f"🚀 Running on device: {self.device}") # ASR model for prompt text recognition self.asr_model_id = "iic/SenseVoiceSmall" self.asr_model: Optional[AutoModel] = AutoModel( model=self.asr_model_id, disable_update=True, log_level='DEBUG', device="cuda:0" if self.device == "cuda" else "cpu", ) # TTS model (lazy init) self.voxcpm_model: Optional[voxcpm.VoxCPM] = None self.default_local_model_dir = "./models/VoxCPM1.5" # ---------- Model helpers ---------- def _resolve_model_dir(self) -> str: """ Resolve model directory: 1) Use local checkpoint directory if exists 2) 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_dir repo_id = os.environ.get("HF_REPO_ID", "").strip() if len(repo_id) > 0: target_dir = os.path.join("models", repo_id.replace("/", "__")) # Check if directory exists AND contains config.json if not os.path.isdir(target_dir) or not os.path.exists(os.path.join(target_dir, "config.json")): try: from huggingface_hub import snapshot_download # type: ignore os.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_dir return "models" def get_or_load_voxcpm(self) -> voxcpm.VoxCPM: if self.voxcpm_model is not None: return self.voxcpm_model print("Model not loaded, initializing...") model_dir = self._resolve_model_dir() print(f"Using model dir: {model_dir}") self.voxcpm_model = voxcpm.VoxCPM(voxcpm_model_path=model_dir) print("Model loaded successfully.") return self.voxcpm_model # ---------- Functional endpoints ---------- def prompt_wav_recognition(self, prompt_wav: Optional[str]) -> str: if prompt_wav is None: return "" res = self.asr_model.generate(input=prompt_wav, language="auto", use_itn=True) text = res[0]["text"].split('|>')[-1] return text def generate_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() if len(text) == 0: raise ValueError("Please input text to synthesize.") prompt_wav_path = prompt_wav_path_input if prompt_wav_path_input else None prompt_text = prompt_text_input if prompt_text_input else None print(f"Generating audio for text: '{text[:60]}...'") 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 (current_model.tts_model.sample_rate, wav) # ---------- UI Builders ---------- def create_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 logo gr.HTML('
VoxCPM Logo
') # Quick Start with gr.Accordion("📋 快速入门", open=False, elem_id="acc_quick"): gr.Markdown(""" ### 使用说明 1. **（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. **（可选）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. **输入目标文本** - 输入您希望模型朗读的文字内容。 4. **生成语音** - 点击"生成语音"按钮，即可为您创造出音频。 """) # Pro Tips with gr.Accordion("💡 使用建议", open=False, elem_id="acc_tips"): gr.Markdown(""" ### 参考语音降噪 - **启用**：通过 ZipEnhancer 组件消除背景噪音，但会将音频采样率限制在16kHz，限制克隆上限。 - **禁用**：保留原始音频的全部信息，包括背景环境声，最高支持44.1kHz的音频复刻。 ### 文本正则化 - **启用**：使用 WeTextProcessing 组件，可支持常见文本的正则化处理。 - **禁用**：将使用 VoxCPM 内置的文本理解能力。如，支持音素输入（如中文转拼音：{ni3}{hao3}；英文转CMUDict：{HH AH0 L OW1}）和公式符号合成，尝试一下！ ### CFG 值 - **调低**：如果提示语音听起来不自然或过于夸张，或者长文本输入出现稳定性问题。 - **调高**：为更好地贴合提示音频的风格或输入文本， 或者极短文本输入出现稳定性问题。 ### 推理时间步 - **调低**：合成速度更快。 - **调高**：合成质量更佳。 """) # Main controls with gr.Row(): with gr.Column(): prompt_wav = gr.Audio( sources=["upload", 'microphone'], type="filepath", label="参考语音（可选，或让 VoxCPM 自由发挥）", value="./examples/example.wav", ) DoDenoisePromptAudio = gr.Checkbox( value=False, label="参考语音增强", elem_id="chk_denoise", info="使用 ZipEnhancer 模型对参考音频进行降噪。" ) 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="参考文本", placeholder="请输入参考文本。支持自动识别，您也可以自行修改结果..." ) run_btn = gr.Button("生成语音", variant="primary") with gr.Column(): cfg_value = gr.Slider( minimum=1.0, maximum=3.0, value=2.0, step=0.1, label="CFG 值 (引导比例)", info="值越高越贴合提示，值越低允许更多的创造性" ) inference_timesteps = gr.Slider( minimum=4, maximum=30, value=10, step=1, label="推理时间步", info="生成的推理时间步数（值越高可能质量越好，但速度更慢）" ) with gr.Row(): text = gr.Textbox( value="VoxCPM 是 ModelBest 推出的一款创新型端到端 TTS 模型，旨在生成极具表现力的语音。", label="目标文本", ) with gr.Row(): DoNormalizeText = gr.Checkbox( value=False, label="文本正则化", elem_id="chk_normalize", info="使用 wetext 库对输入文本进行标准化。" ) audio_output = gr.Audio(label="输出音频") # Wiring run_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 interface def run_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 throughput interface.queue(max_size=10, default_concurrency_limit=1).launch(server_name=server_name, server_port=server_port, show_error=show_error) if __name__ == "__main__": run_demo()