init

.gitattributes

@@ -33,3 +33,12 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.avi filter=lfs diff=lfs merge=lfs -text
+*.mov filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,557 @@
+import os
+import sys
+import logging
+import traceback
+import numpy as np
+import gradio as gr  
+from typing import Optional, Tuple
+import soundfile as sf
+from pathlib import Path
+import requests
+import json
+import base64
+import io
+import tempfile
+import uuid
+import time
+
+# 配置日志
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.StreamHandler(sys.stdout),
+        logging.FileHandler('app.log', mode='a', encoding='utf-8')
+    ]
+)
+logger = logging.getLogger(__name__)
+
+# 启动日志
+logger.info("="*50)
+logger.info("🚀 VoxCPM应用启动中...")
+logger.info(f"Python版本: {sys.version}")
+logger.info(f"工作目录: {os.getcwd()}")
+logger.info(f"环境变量PORT: {os.environ.get('PORT', '未设置')}")
+logger.info(f"环境变量RAY_SERVE_URL: {os.environ.get('RAY_SERVE_URL', '未设置')}")
+logger.info("="*50)
+
+
+class RayServeVoxCPMClient:
+    """Client wrapper that talks to Ray Serve TTS API."""
+
+    def __init__(self) -> None:
+        logger.info("📡 初始化RayServeVoxCPMClient...")
+        
+        try:
+            # Ray Serve API URL (can be overridden via env)
+            self.RAY_SERVE_DEFAULT_URL = "https://d09162224-pytorch251-cuda124-u-5512-iyr4lse3-8970.550c.cloud"
+            self.api_url = self._resolve_server_url()
+            logger.info(f"🔗 准备连接到Ray Serve API: {self.api_url}")
+            
+            # Test connection
+            logger.info("⏳ 测试Ray Serve连接...")
+            health_start = time.time()
+            health_response = requests.get(f"{self.api_url}/health", timeout=10)
+            health_response.raise_for_status()
+            health_time = time.time() - health_start
+            logger.info(f"✅ 成功连接到Ray Serve API: {self.api_url} (耗时: {health_time:.3f}秒)")
+            
+        except Exception as e:
+            logger.error(f"❌ 初始化RayServeVoxCPMClient失败: {e}")
+            logger.error(f"错误详情: {traceback.format_exc()}")
+            raise
+
+    # ----------- Helpers -----------
+    def _resolve_server_url(self) -> str:
+        """Resolve Ray Serve API base URL, prefer env RAY_SERVE_URL."""
+        return os.environ.get("RAY_SERVE_URL", self.RAY_SERVE_DEFAULT_URL).rstrip("/")
+
+    def _audio_file_to_base64(self, audio_file_path: str) -> str:
+        """
+        将音频文件转换为base64编码
+        
+        Args:
+            audio_file_path: 音频文件路径
+            
+        Returns:
+            base64编码的音频数据
+        """
+        try:
+            with open(audio_file_path, 'rb') as f:
+                audio_bytes = f.read()
+            return base64.b64encode(audio_bytes).decode('utf-8')
+        except Exception as e:
+            logger.error(f"音频文件转base64失败: {e}")
+            raise
+    
+    def _base64_to_audio_array(self, base64_audio: str, sample_rate: int = 16000) -> Tuple[int, np.ndarray]:
+        """
+        将base64编码的音频转换为numpy数组
+        
+        Args:
+            base64_audio: base64编码的音频数据
+            sample_rate: 期望的采样率
+            
+        Returns:
+            (sample_rate, audio_array) tuple
+        """
+        try:
+            # 解码base64
+            audio_bytes = base64.b64decode(base64_audio)
+            
+            # 创建临时文件
+            with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_file:
+                tmp_file.write(audio_bytes)
+                tmp_file_path = tmp_file.name
+            
+            # 读取音频文件
+            try:
+                audio_data, sr = sf.read(tmp_file_path, dtype='float32')
+                
+                # 转换为单声道
+                if audio_data.ndim == 2:
+                    audio_data = audio_data[:, 0]
+                
+                # 转换为int16格式（Gradio期望的格式）
+                audio_int16 = (audio_data * 32767).astype(np.int16)
+                
+                return sr, audio_int16
+            finally:
+                # 清理临时文件
+                try:
+                    os.unlink(tmp_file_path)
+                except:
+                    pass
+                    
+        except Exception as e:
+            logger.error(f"base64转音频数组失败: {e}")
+            raise
+    
+    # ----------- Functional endpoints -----------
+    def prompt_wav_recognition(self, prompt_wav: Optional[str]) -> str:
+        """Use Ray Serve ASR API for speech recognition."""
+        logger.info(f"🎵 开始语音识别，输入文件: {prompt_wav}")
+        
+        if prompt_wav is None or not prompt_wav.strip():
+            logger.info("⚠️  没有提供音频文件，跳过语音识别")
+            return ""
+        
+        try:
+            start_time = time.time()
+            logger.info(f"📁 处理音频文件: {prompt_wav}")
+            
+            # 将音频文件转换为base64
+            convert_start = time.time()
+            audio_base64 = self._audio_file_to_base64(prompt_wav)
+            convert_time = time.time() - convert_start
+            logger.info(f"🔄 音频转base64耗时: {convert_time:.3f}秒")
+            
+            logger.info("📡 调用Ray Serve ASR API...")
+            
+            # 构建ASR请求
+            asr_request = {
+                "reqid": str(uuid.uuid4()),
+                "audio_data": audio_base64,
+                "language": "auto",
+                "use_itn": True
+            }
+            
+            # 调用ASR接口
+            api_start = time.time()
+            response = requests.post(
+                f"{self.api_url}/asr",
+                json=asr_request,
+                headers={"Content-Type": "application/json"},
+                timeout=30
+            )
+            response.raise_for_status()
+            api_time = time.time() - api_start
+            
+            result_data = response.json()
+            total_time = time.time() - start_time
+            
+            logger.info(f"⏱️  ASR API请求耗时: {api_time:.3f}秒")
+            logger.info(f"⏱️  ASR总耗时: {total_time:.3f}秒")
+            logger.info(f"✅ 语音识别完成，响应: {result_data}")
+            
+            # 检查响应状态
+            if result_data.get("code") == 3000:
+                recognized_text = result_data.get("text", "")
+                logger.info(f"🎯 识别结果: '{recognized_text}'")
+                return recognized_text
+            else:
+                logger.warning(f"⚠️  ASR识别失败: {result_data.get('message', 'Unknown error')}")
+                return ""
+                
+        except Exception as e:
+            logger.error(f"❌ 语音识别失败: {e}")
+            logger.error(f"错误详情: {traceback.format_exc()}")
+            return ""
+
+    def _call_ray_serve_generate(
+        self,
+        text: str,
+        prompt_wav_path: Optional[str] = None,
+        prompt_text: Optional[str] = None,
+        cfg_value: float = 2.0,
+        inference_timesteps: int = 10,
+        do_normalize: bool = True,
+        denoise: bool = True,
+    ) -> Tuple[int, np.ndarray]:
+        """
+        Call Ray Serve /generate API and return (sample_rate, waveform).
+        """
+        logger.info(f"🔥 调用Ray Serve生成API，文本: '{text[:60]}...'")
+        
+        try:
+            start_time = time.time()
+            
+            # 构建请求数据
+            prepare_start = time.time()
+            audio_config = {
+                "voice_type": "default",  # 使用默认模式，或者可以根据需要调整
+                "encoding": "wav",
+                "speed_ratio": 1.0,
+                "cfg_value": cfg_value,
+                "inference_timesteps": inference_timesteps
+            }
+            
+            # 如果有参考音频和文本，使用voice-clone模式
+            if prompt_wav_path and prompt_text:
+                logger.info("🎭 使用语音克隆模式")
+                convert_start = time.time()
+                audio_base64 = self._audio_file_to_base64(prompt_wav_path)
+                convert_time = time.time() - convert_start
+                logger.info(f"🔄 参考音频转base64耗时: {convert_time:.3f}秒")
+                
+                audio_config.update({
+                    "voice_type": None,  # 清除voice_type，使用克隆模式
+                    "prompt_wav": audio_base64,
+                    "prompt_text": prompt_text
+                })
+            else:
+                logger.info("🎤 使用默认语音模式")
+            
+            request_data = {
+                "audio": audio_config,
+                "request": {
+                    "reqid": str(uuid.uuid4()),
+                    "text": text,
+                    "operation": "query",
+                    "do_normalize": do_normalize,
+                    "denoise": denoise
+                }
+            }
+            prepare_time = time.time() - prepare_start
+            logger.info(f"⏱️  请求数据准备耗时: {prepare_time:.3f}秒")
+            
+            logger.info(f"📡 发送请求到Ray Serve: {self.api_url}/generate")
+            logger.info(f"📊 请求参数: CFG={cfg_value}, 推理步数={inference_timesteps}, 文本长度={len(text)}")
+            
+            # 调用生成接口
+            api_start = time.time()
+            response = requests.post(
+                f"{self.api_url}/generate",
+                json=request_data,
+                headers={"Content-Type": "application/json"},
+                timeout=120  # TTS可能需要较长时间
+            )
+            response.raise_for_status()
+            api_time = time.time() - api_start
+            
+            result_data = response.json()
+            logger.info(f"⏱️  TTS API请求耗时: {api_time:.3f}秒")
+            logger.info(f"✅ Ray Serve响应: code={result_data.get('code')}, message={result_data.get('message')}")
+            
+            # 检查响应状态
+            if result_data.get("code") == 3000:
+                # 成功生成音频
+                audio_base64 = result_data.get("data", "")
+                if not audio_base64:
+                    raise RuntimeError("Ray Serve返回的音频数据为空")
+                
+                # 将base64音频转换为numpy数组
+                decode_start = time.time()
+                sample_rate, audio_array = self._base64_to_audio_array(audio_base64)
+                decode_time = time.time() - decode_start
+                total_time = time.time() - start_time
+                
+                duration_ms = result_data.get('addition', {}).get('duration', 'unknown')
+                logger.info(f"🔄 音频解码耗时: {decode_time:.3f}秒")
+                logger.info(f"⏱️  TTS总耗时: {total_time:.3f}秒")
+                logger.info(f"🎵 音频生成成功，采样率: {sample_rate}, 时长: {duration_ms}ms")
+                logger.info(f"📈 性能指标: API={api_time:.3f}s, 解码={decode_time:.3f}s, 总计={total_time:.3f}s")
+                
+                return sample_rate, audio_array
+            else:
+                error_msg = result_data.get("message", "Unknown error")
+                raise RuntimeError(f"Ray Serve生成失败: {error_msg}")
+                
+        except requests.exceptions.RequestException as e:
+            logger.error(f"❌ Ray Serve请求失败: {e}")
+            raise RuntimeError(f"Failed to connect Ray Serve TTS service: {e}. Check RAY_SERVE_URL='{self.api_url}' and service status")
+        except Exception as e:
+            logger.error(f"❌ Ray Serve调用异常: {e}")
+            raise
+
+    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]:
+        logger.info("🎤 开始TTS音频生成...")
+        logger.info(f"📝 输入文本: '{text_input[:60]}{'...' if len(text_input) > 60 else ''}'")
+        logger.info(f"🎵 参考音频: {prompt_wav_path_input or '无'}")
+        logger.info(f"📄 参考文本: '{prompt_text_input[:30]}{'...' if prompt_text_input and len(prompt_text_input) > 30 else ''}' " if prompt_text_input else "无")
+        logger.info(f"⚙️  CFG值: {cfg_value_input}, 推理步数: {inference_timesteps_input}")
+        logger.info(f"🔧 文本正规化: {do_normalize}, 音频降噪: {denoise}")
+        
+        try:
+            full_start_time = time.time()
+            
+            text = (text_input or "").strip()
+            if len(text) == 0:
+                logger.error("❌ 输入文本为空")
+                raise ValueError("Please input text to synthesize.")
+            
+            prompt_wav_path = prompt_wav_path_input or ""
+            prompt_text = prompt_text_input or ""
+            cfg_value = cfg_value_input if cfg_value_input is not None else 2.0
+            inference_timesteps = inference_timesteps_input if inference_timesteps_input is not None else 10
+            
+            logger.info("🚀 调用Ray Serve TTS生成引擎...")
+            generate_start = time.time()
+            sr, wav_np = self._call_ray_serve_generate(
+                text=text,
+                prompt_wav_path=prompt_wav_path,
+                prompt_text=prompt_text,
+                cfg_value=cfg_value,
+                inference_timesteps=inference_timesteps,
+                do_normalize=do_normalize,
+                denoise=denoise,
+            )
+            generate_time = time.time() - generate_start
+            full_time = time.time() - full_start_time
+            
+            logger.info(f"✅ TTS生成完成，采样率: {sr}, 音频长度: {len(wav_np) if hasattr(wav_np, '__len__') else 'unknown'}")
+            logger.info(f"🏁 完整TTS流程耗时: {full_time:.3f}秒 (生成={generate_time:.3f}s)")
+            return (sr, wav_np)
+            
+        except Exception as e:
+            logger.error(f"❌ TTS音频生成失败: {e}")
+            logger.error(f"错误详情: {traceback.format_exc()}")
+            raise
+
+
+# ---------- UI Builders ----------
+
+def create_demo_interface(client: RayServeVoxCPMClient):
+    """Build the Gradio UI for Gradio API VoxCPM client."""
+    logger.info("🎨 开始创建Gradio界面...")
+    
+    try:
+        assets_path = Path.cwd().absolute()/"assets"
+        logger.info(f"📁 设置静态资源路径: {assets_path}")
+        gr.set_static_paths(paths=[assets_path])
+        logger.info("✅ 静态资源路径设置完成")
+    except Exception as e:
+        logger.warning(f"⚠️  静态资源路径设置失败: {e}")
+        logger.warning("继续创建界面...")
+
+    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 labels for specific checkboxes */
+        #chk_denoise label,
+        #chk_denoise span,
+        #chk_normalize label,
+        #chk_normalize span {
+            font-weight: 600;
+        }
+        """
+    ) as interface:
+        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):
+            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):
+            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。
+
+            ### Long Text (e.g., >5 min speech)｜长文本 (如 >5分钟的合成语音)
+            While VoxCPM can handle long texts directly, we recommend using empty lines to break very long content into paragraphs; the model will then synthesize each paragraph individually.  
+            虽然 VoxCPM 支持直接生成长文本，但如果目标文本过长，我们建议使用换行符将内容分段；模型将对每个段落分别合成。
+            """)
+
+        with gr.Row():
+            with gr.Column():
+                prompt_wav = gr.Audio(
+                    sources=["upload", 'microphone'],
+                    type="filepath",
+                    label="Prompt Speech",
+                    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 expressive speech.",
+                        label="Target Text",
+                        info="Default processing splits text on \\n into paragraphs; each is synthesized as a chunk and then concatenated into the final audio."
+                    )
+                with gr.Row():
+                    DoNormalizeText = gr.Checkbox(
+                        value=False,
+                        label="Text Normalization",
+                        elem_id="chk_normalize",
+                        info="We use WeTextPorcessing library to normalize the input text."
+                    )
+                audio_output = gr.Audio(label="Output Audio")
+
+        # Wire events
+        run_btn.click(
+            fn=client.generate_tts_audio,
+            inputs=[text, prompt_wav, prompt_text, cfg_value, inference_timesteps, DoNormalizeText, DoDenoisePromptAudio],
+            outputs=[audio_output],
+            show_progress=True,
+            api_name="generate",
+            concurrency_limit=None,
+        )
+        prompt_wav.change(fn=client.prompt_wav_recognition, inputs=[prompt_wav], outputs=[prompt_text])
+        
+        logger.info("🔗 事件绑定完成")
+
+    logger.info("✅ Gradio界面构建完成")
+    return interface
+
+
+def run_demo():
+    """启动演示应用"""
+    logger.info("🚀 开始启动VoxCPM演示应用...")
+    
+    try:
+        # 创建客户端
+        logger.info("📡 创建Ray Serve API客户端...")
+        client = RayServeVoxCPMClient()
+        logger.info("✅ Ray Serve API客户端创建成功")
+        
+        # 创建界面
+        logger.info("🎨 创建Gradio界面...")
+        interface = create_demo_interface(client)
+        logger.info("✅ Gradio界面创建成功")
+        
+        # 获取端口配置
+        port = int(os.environ.get('PORT', 7860))
+        logger.info(f"🌐 准备在端口 {port} 启动服务...")
+        
+        # 启动应用
+        logger.info("🚀 启动Gradio应用...")
+        interface.launch(
+            server_port=port, 
+            server_name="0.0.0.0",
+            show_error=True,
+        )
+        logger.info("✅ 应用启动成功！")
+        
+    except Exception as e:
+        logger.error(f"❌ 应用启动失败: {e}")
+        logger.error(f"错误详情: {traceback.format_exc()}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    try:
+        logger.info("🎬 开始执行主程序...")
+        run_demo()
+    except KeyboardInterrupt:
+        logger.info("⏹️  收到中断信号，正在退出...")
+    except Exception as e:
+        logger.error(f"💥 主程序异常退出: {e}")
+        logger.error(f"错误详情: {traceback.format_exc()}")
+        sys.exit(1)
+    finally:
+        logger.info("🔚 程序结束")

examples/example.wav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:009638e7474ac4eb2ca5b23d28d9114c33377eb5c91e8d6f7000a0c36d6eaa8e
+size 1439096

requirements.txt

@@ -0,0 +1,5 @@
+# Core dependencies
+gradio>=4.0.0
+requests>=2.25.0
+numpy>=1.21.0
+soundfile>=0.12.1