Upload app.py

app.py

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+import os
+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["TORCHDYNAMO_DISABLE"] = "1"
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+if os.environ.get("HF_REPO_ID", "").strip() == "":
+    os.environ["HF_REPO_ID"] = "openbmb/VoxCPM-0.5B"
+
+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/VoxCPM-0.5B"
+
+    # ---------- 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}' ...")
+                    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 'models'.")
+                    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 (16000, 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('<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, 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 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
+
+
+# ---------- Launch ----------
+if __name__ == "__main__":
+    demo = VoxCPMDemo()
+    interface = create_demo_interface(demo)
+    interface.queue(max_size=10).launch(
+        server_name="0.0.0.0",
+        server_port=int(os.environ.get("PORT", 7860))
+    )