- 自部署单独用支持：给添哥

.gitignore

@@ -4,3 +4,4 @@ voxcpm.egg-info
 .DS_Store
 ./pretrained_models/
 app_local.py
+models/

app.py

@@ -54,7 +54,7 @@ _EXAMPLES_FOOTER_EN = (
 )
 
 _USAGE_INSTRUCTIONS_ZH = (
-    "**VoxCPM2 — 三种语音生成方式：**\n\n"
+    "**三种语音生成方式：**\n\n"
     "🎨 **声音设计（Voice Design）**  \n"
     "无需参考音频。在 **Control Instruction** 中描述目标音色特征"
     "（性别、年龄、语气、情绪、语速等），VoxCPM2 即可为你从零创造独一无二的声音。\n\n"
@@ -65,6 +65,8 @@ _USAGE_INSTRUCTIONS_ZH = (
     "开启 **极致克隆模式** 并提供参考音频的文字内容（可自动识别）。"
     "模型会将参考音频视为已说出的前文，以**音频续写**的方式完整还原参考音频中的所有声音细节。"
     "注意：该模式与可控克隆模式互斥，将禁用Control Instruction。\n\n"
+    "目前支持的方言包括：\n"
+    "「四川话、粤语、吴语、东北话、河南话、陕西话、山东话、天津话、闽南话」"
 )
 
 _EXAMPLES_FOOTER_ZH = (
@@ -222,9 +224,9 @@ _APP_THEME = gr.themes.Soft(
 class VoxCPMDemo:
     def __init__(self, model_id: str = "openbmb/VoxCPM2") -> None:
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        logger.info(f"Running on device: {self.device}")
+        logger.info(f"运行在设备上: {self.device}")
 
-        self.asr_model_id = "iic/SenseVoiceSmall"
+        self.asr_model_id = "./models/iic/SenseVoiceSmall"
         self.asr_model: Optional[AutoModel] = AutoModel(
             model=self.asr_model_id,
             disable_update=True,
@@ -486,7 +488,7 @@ def run_demo(
     server_name: str = "0.0.0.0",
     server_port: int = 8808,
     show_error: bool = True,
-    model_id: str = "openbmb/VoxCPM2",
+    model_id: str = "./models/openbmb/VoxCPM2",
 ):
     demo = VoxCPMDemo(model_id=model_id)
     interface = create_demo_interface(demo)
@@ -504,9 +506,9 @@ if __name__ == "__main__":
     import argparse
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--model-id", type=str, default="openbmb/VoxCPM2",
+        "--model-id", type=str, default="./models/openbmb/VoxCPM2",
-        help="Local path or HuggingFace repo ID (default: openbmb/VoxCPM2)",
+        help="本地路径或HuggingFace仓库ID（默认：./models/openbmb/VoxCPM2）",
     )
-    parser.add_argument("--port", type=int, default=8808, help="Server port")
+    parser.add_argument("--port", type=int, default=8808, help="服务端口")
     args = parser.parse_args()
     run_demo(model_id=args.model_id, server_port=args.port)

app_old.py

@@ -1,280 +0,0 @@
-import os
-import sys
-import numpy as np
-import torch
-import gradio as gr
-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}", file=sys.stderr)
-
-        # 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("/", "__"))
-            if not os.path.isdir(target_dir):
-                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}' ...", file=sys.stderr)
-                    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'.", file=sys.stderr)
-                    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...", file=sys.stderr)
-        model_dir = self._resolve_model_dir()
-        print(f"Using model dir: {model_dir}", file=sys.stderr)
-        self.voxcpm_model = voxcpm.VoxCPM(voxcpm_model_path=model_dir)
-        print("Model loaded successfully.", file=sys.stderr)
-        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]}...'", file=sys.stderr)
-        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 ----------
-
-_APP_THEME = gr.themes.Soft(
-    primary_hue="blue",
-    secondary_hue="gray",
-    neutral_hue="slate",
-    font=[gr.themes.GoogleFont("Inter"), "Arial", "sans-serif"],
-)
-
-_CUSTOM_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;
-}
-"""
-
-
-def create_demo_interface(demo: VoxCPMDemo):
-    """Build the Gradio UI for VoxCPM demo."""
-    gr.set_static_paths(paths=[Path.cwd().absolute()/"assets"])
-
-    with gr.Blocks() as interface:
-        # Header logo
-        gr.HTML('<div class="logo-container"><img src="/gradio_api/file=assets/voxcpm_logo.png" alt="VoxCPM Logo"></div>')
-
-        # Quick Start
-        with 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 Tips
-        with 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 voice, with an external ZipEnhancer component. However, this will limit the audio sampling rate to 16kHz, restricting the cloning quality ceiling.  
-              **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。
-            - **Disable** to preserve the original audio's all information, including background atmosphere, and support audio cloning up to 44.1kHz sampling rate.  
-              **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。
-
-            ### Text Normalization｜文本正则化
-            - **Enable** to process general text with an external WeTextProcessing component.  
-              **启用**：使用 WeTextProcessing 组件，可支持常见文本的正则化处理。
-            - **Disable** to use VoxCPM's native text understanding ability. For example, it supports phonemes input (For Chinese, phonemes are converted using pinyin, {ni3}{hao3}; For English, phonemes are converted using CMUDict, {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 Value｜CFG 值
-            - **Lower CFG** if the voice prompt sounds strained or expressive, or instability occurs with long text input.  
-              **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。
-            - **Higher CFG** for better adherence to the prompt speech style or input text, or instability occurs with too short text input.
-              **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。
-
-            ### 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 controls
-        with 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")
-
-        # 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)
-    interface.queue(max_size=10, default_concurrency_limit=1).launch(
-        server_name=server_name,
-        server_port=server_port,
-        show_error=show_error,
-        theme=_APP_THEME,
-        css=_CUSTOM_CSS,
-    )
-
-
-if __name__ == "__main__":
-    run_demo()

modeldown.py

@@ -0,0 +1,33 @@
+"""
+模型下载脚本
+
+"""
+from modelscope import snapshot_download
+
+
+def download(repo_id:str, local_dir:str):
+    """
+   下载模型仓库或单个文件
+
+
+    Args:
+        repo_id (str): 用户名/仓库名，例如 'stabilityai/sdxl-turbo'
+        local_dir (str or Path): 下载文件放置的本地目录路径
+
+    Returns:
+        str: 下载文件的本地路径
+
+    Raises:
+        ValueError: 当 repo_id 格式不正确时
+    """
+    model_dir = snapshot_download(
+        repo_id,
+        repo_type='model',
+        local_dir=f"{local_dir}/{repo_id}",
+    )
+    
+
+
+if __name__ == "__main__":
+    download("OpenBMB/VoxCPM2", "./models")
+    download("iic/SenseVoiceSmall", "./models")

pyproject.toml

@@ -47,8 +47,7 @@ dependencies = [
     "funasr",
     "spaces",
     "argbind",
-    "safetensors"
+    "safetensors",
-
 ]
 
 [project.optional-dependencies]