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README (1).md

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+---
+title: Vevo for Zero-shot VC, TTS, and More
+emoji: 🐠
+colorFrom: indigo
+colorTo: yellow
+sdk: gradio
+sdk_version: 5.25.2
+app_file: app.py
+pinned: false
+license: mit
+short_description: Controllable Zero-Shot Voice Imitation
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app (1) (1).py

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+import os
+import sys
+import importlib.util
+import site
+import json
+import torch
+import gradio as gr
+import torchaudio
+import numpy as np
+from huggingface_hub import snapshot_download, hf_hub_download
+import subprocess
+import re
+import spaces
+
+# 创建一个全局变量来跟踪已下载的资源
+# Create a global variable to track downloaded resources
+downloaded_resources = {
+    "configs": False,
+    "tokenizer_vq32": False,
+    "tokenizer_vq8192": False,
+    "ar_Vq32ToVq8192": False,
+    "ar_PhoneToVq8192": False,
+    "fmt_Vq8192ToMels": False,
+    "vocoder": False
+}
+
+def install_espeak():
+    """Detect and install espeak-ng dependency"""
+    try:
+        # Check if espeak-ng is already installed
+        result = subprocess.run(["which", "espeak-ng"], capture_output=True, text=True)
+        if result.returncode != 0:
+            print("Detected espeak-ng not installed in the system, attempting to install...")
+            # Try to install espeak-ng and its data using apt-get
+            subprocess.run(["apt-get", "update"], check=True)
+            # Install espeak-ng and the corresponding language data package
+            subprocess.run(["apt-get", "install", "-y", "espeak-ng", "espeak-ng-data"], check=True)
+            print("espeak-ng and its data packages installed successfully!")
+        else:
+            print("espeak-ng is already installed in the system.")
+            # Even if already installed, try to update data to ensure integrity (optional but sometimes helpful)
+            # print("Attempting to update espeak-ng data...")
+            # subprocess.run(["apt-get", "update"], check=True)
+            # subprocess.run(["apt-get", "install", "--only-upgrade", "-y", "espeak-ng-data"], check=True)
+
+        # Verify Chinese support (optional)
+        try:
+            voices_result = subprocess.run(["espeak-ng", "--voices=cmn"], capture_output=True, text=True, check=True)
+            if "cmn" in voices_result.stdout:
+                print("espeak-ng supports 'cmn' language.")
+            else:
+                print("Warning: espeak-ng is installed, but 'cmn' language still seems unavailable.")
+        except Exception as e:
+             print(f"Error verifying espeak-ng Chinese support (may not affect functionality): {e}")
+
+    except Exception as e:
+        print(f"Error installing espeak-ng: {e}")
+        print("Please try to run manually: apt-get update && apt-get install -y espeak-ng espeak-ng-data")
+
+# Install espeak before all other operations
+install_espeak()
+
+def patch_langsegment_init():
+    try:
+        # Try to find the location of the LangSegment package
+        spec = importlib.util.find_spec("LangSegment")
+        if spec is None or spec.origin is None:
+            print("Unable to locate LangSegment package.")
+            return
+
+        # Build the path to __init__.py
+        init_path = os.path.join(os.path.dirname(spec.origin), '__init__.py')
+        
+        if not os.path.exists(init_path):
+            print(f"LangSegment __init__.py file not found at: {init_path}")
+            # Try to find in site-packages, applicable in some environments
+            for site_pkg_path in site.getsitepackages():
+                potential_path = os.path.join(site_pkg_path, 'LangSegment', '__init__.py')
+                if os.path.exists(potential_path):
+                    init_path = potential_path
+                    print(f"Found __init__.py in site-packages: {init_path}")
+                    break
+            else: # If the loop ends normally (no break)
+                 print(f"Also unable to find __init__.py in site-packages")
+                 return
+
+
+        print(f"Attempting to read LangSegment __init__.py: {init_path}")
+        with open(init_path, 'r') as f:
+            lines = f.readlines()
+
+        modified = False
+        new_lines = []
+        target_line_prefix = "from .LangSegment import"
+
+        for line in lines:
+            stripped_line = line.strip()
+            if stripped_line.startswith(target_line_prefix):
+                if 'setLangfilters' in stripped_line or 'getLangfilters' in stripped_line:
+                    print(f"Found line that needs modification: {stripped_line}")
+                    # Remove setLangfilters and getLangfilters
+                    modified_line = stripped_line.replace(',setLangfilters', '')
+                    modified_line = modified_line.replace(',getLangfilters', '')
+                    # Ensure comma handling is correct (e.g., if they are the last items)
+                    modified_line = modified_line.replace('setLangfilters,', '')
+                    modified_line = modified_line.replace('getLangfilters,', '')
+                    # If they are the only extra imports, remove any redundant commas
+                    modified_line = modified_line.rstrip(',') 
+                    new_lines.append(modified_line + '\n')
+                    modified = True
+                    print(f"Modified line: {modified_line.strip()}")
+                else:
+                    new_lines.append(line) # Line is fine, keep as is
+            else:
+                new_lines.append(line) # Non-target line, keep as is
+
+        if modified:
+            print(f"Attempting to write back modified LangSegment __init__.py to: {init_path}")
+            try:
+                with open(init_path, 'w') as f:
+                    f.writelines(new_lines)
+                print("LangSegment __init__.py modified successfully.")
+                # Try to reload the module to make changes effective (may not work, depending on import chain)
+                try:
+                    import LangSegment
+                    importlib.reload(LangSegment)
+                    print("LangSegment module has been attempted to reload.")
+                except Exception as reload_e:
+                     print(f"Error reloading LangSegment (may have no impact): {reload_e}")
+            except PermissionError:
+                print(f"Error: Insufficient permissions to modify {init_path}. Consider modifying requirements.txt.")
+            except Exception as write_e:
+                print(f"Other error occurred when writing LangSegment __init__.py: {write_e}")
+        else:
+            print("LangSegment __init__.py doesn't need modification.")
+
+    except ImportError:
+         print("LangSegment package not found, unable to fix.")
+    except Exception as e:
+        print(f"Unexpected error occurred when fixing LangSegment package: {e}")
+
+# Execute the fix before all other imports (especially Amphion) that might trigger LangSegment
+patch_langsegment_init()
+
+# Clone Amphion repository
+if not os.path.exists("Amphion"):
+    subprocess.run(["git", "clone", "https://github.com/open-mmlab/Amphion.git"])
+    os.chdir("Amphion")
+else:
+    if not os.getcwd().endswith("Amphion"):
+        os.chdir("Amphion")
+
+# Add Amphion to the path
+if os.path.dirname(os.path.abspath("Amphion")) not in sys.path:
+    sys.path.append(os.path.dirname(os.path.abspath("Amphion")))
+
+# Ensure needed directories exist
+os.makedirs("wav", exist_ok=True)
+os.makedirs("ckpts/Vevo", exist_ok=True)
+
+from models.vc.vevo.vevo_utils import VevoInferencePipeline, save_audio, load_wav
+
+# Download and setup config files
+def setup_configs():
+    if downloaded_resources["configs"]:
+        print("Config files already downloaded, skipping...")
+        return
+        
+    config_path = "models/vc/vevo/config"
+    os.makedirs(config_path, exist_ok=True)
+    
+    config_files = [
+        "PhoneToVq8192.json",
+        "Vocoder.json",
+        "Vq32ToVq8192.json",
+        "Vq8192ToMels.json",
+        "hubert_large_l18_c32.yaml",
+    ]
+    
+    for file in config_files:
+        file_path = f"{config_path}/{file}"
+        if not os.path.exists(file_path):
+            try:
+                file_data = hf_hub_download(
+                    repo_id="amphion/Vevo", 
+                    filename=f"config/{file}", 
+                    repo_type="model",
+                )
+                os.makedirs(os.path.dirname(file_path), exist_ok=True)
+                # Copy file to target location
+                subprocess.run(["cp", file_data, file_path])
+            except Exception as e:
+                print(f"Error downloading config file {file}: {e}")
+    
+    downloaded_resources["configs"] = True
+
+setup_configs()
+
+# Device configuration
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+print(f"Using device: {device}")
+
+# Initialize pipeline dictionary
+inference_pipelines = {}
+
+# Download all necessary model resources at startup
+def preload_all_resources():
+    print("Preloading all model resources...")
+    # Download configuration files
+    setup_configs()
+    
+    # Store the downloaded model paths
+    global downloaded_content_tokenizer_path
+    global downloaded_content_style_tokenizer_path
+    global downloaded_ar_vq32_path
+    global downloaded_ar_phone_path
+    global downloaded_fmt_path
+    global downloaded_vocoder_path
+    
+    # Download Content Tokenizer (vq32)
+    if not downloaded_resources["tokenizer_vq32"]:
+        print("Preloading Content Tokenizer (vq32)...")
+        local_dir = snapshot_download(
+            repo_id="amphion/Vevo",
+            repo_type="model",
+            cache_dir="./ckpts/Vevo",
+            allow_patterns=["tokenizer/vq32/*"],
+        )
+        downloaded_content_tokenizer_path = local_dir
+        downloaded_resources["tokenizer_vq32"] = True
+        print("Content Tokenizer (vq32) download completed")
+    
+    # Download Content-Style Tokenizer (vq8192)
+    if not downloaded_resources["tokenizer_vq8192"]:
+        print("Preloading Content-Style Tokenizer (vq8192)...")
+        local_dir = snapshot_download(
+            repo_id="amphion/Vevo",
+            repo_type="model",
+            cache_dir="./ckpts/Vevo",
+            allow_patterns=["tokenizer/vq8192/*"],
+        )
+        downloaded_content_style_tokenizer_path = local_dir
+        downloaded_resources["tokenizer_vq8192"] = True
+        print("Content-Style Tokenizer (vq8192) download completed")
+    
+    # Download Autoregressive Transformer (Vq32ToVq8192)
+    if not downloaded_resources["ar_Vq32ToVq8192"]:
+        print("Preloading Autoregressive Transformer (Vq32ToVq8192)...")
+        local_dir = snapshot_download(
+            repo_id="amphion/Vevo",
+            repo_type="model",
+            cache_dir="./ckpts/Vevo",
+            allow_patterns=["contentstyle_modeling/Vq32ToVq8192/*"],
+        )
+        downloaded_ar_vq32_path = local_dir
+        downloaded_resources["ar_Vq32ToVq8192"] = True
+        print("Autoregressive Transformer (Vq32ToVq8192) download completed")
+    
+    # Download Autoregressive Transformer (PhoneToVq8192)
+    if not downloaded_resources["ar_PhoneToVq8192"]:
+        print("Preloading Autoregressive Transformer (PhoneToVq8192)...")
+        local_dir = snapshot_download(
+            repo_id="amphion/Vevo",
+            repo_type="model",
+            cache_dir="./ckpts/Vevo",
+            allow_patterns=["contentstyle_modeling/PhoneToVq8192/*"],
+        )
+        downloaded_ar_phone_path = local_dir
+        downloaded_resources["ar_PhoneToVq8192"] = True
+        print("Autoregressive Transformer (PhoneToVq8192) download completed")
+    
+    # Download Flow Matching Transformer
+    if not downloaded_resources["fmt_Vq8192ToMels"]:
+        print("Preloading Flow Matching Transformer (Vq8192ToMels)...")
+        local_dir = snapshot_download(
+            repo_id="amphion/Vevo",
+            repo_type="model",
+            cache_dir="./ckpts/Vevo",
+            allow_patterns=["acoustic_modeling/Vq8192ToMels/*"],
+        )
+        downloaded_fmt_path = local_dir
+        downloaded_resources["fmt_Vq8192ToMels"] = True
+        print("Flow Matching Transformer (Vq8192ToMels) download completed")
+    
+    # Download Vocoder
+    if not downloaded_resources["vocoder"]:
+        print("Preloading Vocoder...")
+        local_dir = snapshot_download(
+            repo_id="amphion/Vevo",
+            repo_type="model",
+            cache_dir="./ckpts/Vevo",
+            allow_patterns=["acoustic_modeling/Vocoder/*"],
+        )
+        downloaded_vocoder_path = local_dir
+        downloaded_resources["vocoder"] = True
+        print("Vocoder download completed")
+    
+    print("All model resources preloading completed!")
+
+# Initialize path variables to store downloaded model paths
+downloaded_content_tokenizer_path = None
+downloaded_content_style_tokenizer_path = None
+downloaded_ar_vq32_path = None
+downloaded_ar_phone_path = None
+downloaded_fmt_path = None
+downloaded_vocoder_path = None
+
+# Preload all resources before creating the Gradio interface
+preload_all_resources()
+
+def get_pipeline(pipeline_type):
+    if pipeline_type in inference_pipelines:
+        return inference_pipelines[pipeline_type]
+    
+    # Initialize pipeline based on the required pipeline type
+    if pipeline_type == "style" or pipeline_type == "voice":
+        # Use already downloaded Content Tokenizer
+        if downloaded_resources["tokenizer_vq32"]:
+            content_tokenizer_ckpt_path = os.path.join(
+                downloaded_content_tokenizer_path, "tokenizer/vq32/hubert_large_l18_c32.pkl"
+            )
+        else:
+            # Fallback to direct download
+            local_dir = snapshot_download(
+                repo_id="amphion/Vevo",
+                repo_type="model",
+                cache_dir="./ckpts/Vevo",
+                allow_patterns=["tokenizer/vq32/*"],
+            )
+            content_tokenizer_ckpt_path = os.path.join(
+                local_dir, "tokenizer/vq32/hubert_large_l18_c32.pkl"
+            )
+        
+        # Use already downloaded Content-Style Tokenizer
+        if downloaded_resources["tokenizer_vq8192"]:
+            content_style_tokenizer_ckpt_path = os.path.join(
+                downloaded_content_style_tokenizer_path, "tokenizer/vq8192"
+            )
+        else:
+            # Fallback to direct download
+            local_dir = snapshot_download(
+                repo_id="amphion/Vevo",
+                repo_type="model",
+                cache_dir="./ckpts/Vevo",
+                allow_patterns=["tokenizer/vq8192/*"],
+            )
+            content_style_tokenizer_ckpt_path = os.path.join(local_dir, "tokenizer/vq8192")
+        
+        # Use already downloaded Autoregressive Transformer
+        ar_cfg_path = "./models/vc/vevo/config/Vq32ToVq8192.json"
+        if downloaded_resources["ar_Vq32ToVq8192"]:
+            ar_ckpt_path = os.path.join(
+                downloaded_ar_vq32_path, "contentstyle_modeling/Vq32ToVq8192"
+            )
+        else:
+            # Fallback to direct download
+            local_dir = snapshot_download(
+                repo_id="amphion/Vevo",
+                repo_type="model",
+                cache_dir="./ckpts/Vevo",
+                allow_patterns=["contentstyle_modeling/Vq32ToVq8192/*"],
+            )
+            ar_ckpt_path = os.path.join(local_dir, "contentstyle_modeling/Vq32ToVq8192")
+        
+        # Use already downloaded Flow Matching Transformer
+        fmt_cfg_path = "./models/vc/vevo/config/Vq8192ToMels.json"
+        if downloaded_resources["fmt_Vq8192ToMels"]:
+            fmt_ckpt_path = os.path.join(
+                downloaded_fmt_path, "acoustic_modeling/Vq8192ToMels"
+            )
+        else:
+            # Fallback to direct download
+            local_dir = snapshot_download(
+                repo_id="amphion/Vevo",
+                repo_type="model",
+                cache_dir="./ckpts/Vevo",
+                allow_patterns=["acoustic_modeling/Vq8192ToMels/*"],
+            )
+            fmt_ckpt_path = os.path.join(local_dir, "acoustic_modeling/Vq8192ToMels")
+        
+        # Use already downloaded Vocoder
+        vocoder_cfg_path = "./models/vc/vevo/config/Vocoder.json"
+        if downloaded_resources["vocoder"]:
+            vocoder_ckpt_path = os.path.join(
+                downloaded_vocoder_path, "acoustic_modeling/Vocoder"
+            )
+        else:
+            # Fallback to direct download
+            local_dir = snapshot_download(
+                repo_id="amphion/Vevo",
+                repo_type="model",
+                cache_dir="./ckpts/Vevo",
+                allow_patterns=["acoustic_modeling/Vocoder/*"],
+            )
+            vocoder_ckpt_path = os.path.join(local_dir, "acoustic_modeling/Vocoder")
+        
+        # Initialize pipeline
+        inference_pipeline = VevoInferencePipeline(
+            content_tokenizer_ckpt_path=content_tokenizer_ckpt_path,
+            content_style_tokenizer_ckpt_path=content_style_tokenizer_ckpt_path,
+            ar_cfg_path=ar_cfg_path,
+            ar_ckpt_path=ar_ckpt_path,
+            fmt_cfg_path=fmt_cfg_path,
+            fmt_ckpt_path=fmt_ckpt_path,
+            vocoder_cfg_path=vocoder_cfg_path,
+            vocoder_ckpt_path=vocoder_ckpt_path,
+            device=device,
+        )
+        
+    elif pipeline_type == "timbre":
+        # Use already downloaded Content-Style Tokenizer
+        if downloaded_resources["tokenizer_vq8192"]:
+            content_style_tokenizer_ckpt_path = os.path.join(
+                downloaded_content_style_tokenizer_path, "tokenizer/vq8192"
+            )
+        else:
+            # Fallback to direct download
+            local_dir = snapshot_download(
+                repo_id="amphion/Vevo",
+                repo_type="model",
+                cache_dir="./ckpts/Vevo",
+                allow_patterns=["tokenizer/vq8192/*"],
+            )
+            content_style_tokenizer_ckpt_path = os.path.join(local_dir, "tokenizer/vq8192")
+        
+        # Use already downloaded Flow Matching Transformer
+        fmt_cfg_path = "./models/vc/vevo/config/Vq8192ToMels.json"
+        if downloaded_resources["fmt_Vq8192ToMels"]:
+            fmt_ckpt_path = os.path.join(
+                downloaded_fmt_path, "acoustic_modeling/Vq8192ToMels"
+            )
+        else:
+            # Fallback to direct download
+            local_dir = snapshot_download(
+                repo_id="amphion/Vevo",
+                repo_type="model",
+                cache_dir="./ckpts/Vevo",
+                allow_patterns=["acoustic_modeling/Vq8192ToMels/*"],
+            )
+            fmt_ckpt_path = os.path.join(local_dir, "acoustic_modeling/Vq8192ToMels")
+        
+        # Use already downloaded Vocoder
+        vocoder_cfg_path = "./models/vc/vevo/config/Vocoder.json"
+        if downloaded_resources["vocoder"]:
+            vocoder_ckpt_path = os.path.join(
+                downloaded_vocoder_path, "acoustic_modeling/Vocoder"
+            )
+        else:
+            # Fallback to direct download
+            local_dir = snapshot_download(
+                repo_id="amphion/Vevo",
+                repo_type="model",
+                cache_dir="./ckpts/Vevo",
+                allow_patterns=["acoustic_modeling/Vocoder/*"],
+            )
+            vocoder_ckpt_path = os.path.join(local_dir, "acoustic_modeling/Vocoder")
+        
+        # Initialize pipeline
+        inference_pipeline = VevoInferencePipeline(
+            content_style_tokenizer_ckpt_path=content_style_tokenizer_ckpt_path,
+            fmt_cfg_path=fmt_cfg_path,
+            fmt_ckpt_path=fmt_ckpt_path,
+            vocoder_cfg_path=vocoder_cfg_path,
+            vocoder_ckpt_path=vocoder_ckpt_path,
+            device=device,
+        )
+        
+    elif pipeline_type == "tts":
+        # Use already downloaded Content-Style Tokenizer
+        if downloaded_resources["tokenizer_vq8192"]:
+            content_style_tokenizer_ckpt_path = os.path.join(
+                downloaded_content_style_tokenizer_path, "tokenizer/vq8192"
+            )
+        else:
+            # Fallback to direct download
+            local_dir = snapshot_download(
+                repo_id="amphion/Vevo",
+                repo_type="model",
+                cache_dir="./ckpts/Vevo",
+                allow_patterns=["tokenizer/vq8192/*"],
+            )
+            content_style_tokenizer_ckpt_path = os.path.join(local_dir, "tokenizer/vq8192")
+        
+        # Use already downloaded Autoregressive Transformer (TTS specific)
+        ar_cfg_path = "./models/vc/vevo/config/PhoneToVq8192.json"
+        if downloaded_resources["ar_PhoneToVq8192"]:
+            ar_ckpt_path = os.path.join(
+                downloaded_ar_phone_path, "contentstyle_modeling/PhoneToVq8192"
+            )
+        else:
+            # Fallback to direct download
+            local_dir = snapshot_download(
+                repo_id="amphion/Vevo",
+                repo_type="model",
+                cache_dir="./ckpts/Vevo",
+                allow_patterns=["contentstyle_modeling/PhoneToVq8192/*"],
+            )
+            ar_ckpt_path = os.path.join(local_dir, "contentstyle_modeling/PhoneToVq8192")
+        
+        # Use already downloaded Flow Matching Transformer
+        fmt_cfg_path = "./models/vc/vevo/config/Vq8192ToMels.json"
+        if downloaded_resources["fmt_Vq8192ToMels"]:
+            fmt_ckpt_path = os.path.join(
+                downloaded_fmt_path, "acoustic_modeling/Vq8192ToMels"
+            )
+        else:
+            # Fallback to direct download
+            local_dir = snapshot_download(
+                repo_id="amphion/Vevo",
+                repo_type="model",
+                cache_dir="./ckpts/Vevo",
+                allow_patterns=["acoustic_modeling/Vq8192ToMels/*"],
+            )
+            fmt_ckpt_path = os.path.join(local_dir, "acoustic_modeling/Vq8192ToMels")
+        
+        # Use already downloaded Vocoder
+        vocoder_cfg_path = "./models/vc/vevo/config/Vocoder.json"
+        if downloaded_resources["vocoder"]:
+            vocoder_ckpt_path = os.path.join(
+                downloaded_vocoder_path, "acoustic_modeling/Vocoder"
+            )
+        else:
+            # Fallback to direct download
+            local_dir = snapshot_download(
+                repo_id="amphion/Vevo",
+                repo_type="model",
+                cache_dir="./ckpts/Vevo",
+                allow_patterns=["acoustic_modeling/Vocoder/*"],
+            )
+            vocoder_ckpt_path = os.path.join(local_dir, "acoustic_modeling/Vocoder")
+        
+        # Initialize pipeline
+        inference_pipeline = VevoInferencePipeline(
+            content_style_tokenizer_ckpt_path=content_style_tokenizer_ckpt_path,
+            ar_cfg_path=ar_cfg_path,
+            ar_ckpt_path=ar_ckpt_path,
+            fmt_cfg_path=fmt_cfg_path,
+            fmt_ckpt_path=fmt_ckpt_path,
+            vocoder_cfg_path=vocoder_cfg_path,
+            vocoder_ckpt_path=vocoder_ckpt_path,
+            device=device,
+        )
+    
+    # Cache pipeline instance
+    inference_pipelines[pipeline_type] = inference_pipeline
+    return inference_pipeline
+
+# Implement VEVO functionality functions
+@spaces.GPU()
+def vevo_style(content_wav, style_wav):
+    temp_content_path = "wav/temp_content.wav"
+    temp_style_path = "wav/temp_style.wav"
+    output_path = "wav/output_vevostyle.wav"
+    
+    # Check and process audio data
+    if content_wav is None or style_wav is None:
+        raise ValueError("Please upload audio files")
+    
+    # Process audio format
+    if isinstance(content_wav, tuple) and len(content_wav) == 2:
+        if isinstance(content_wav[0], np.ndarray):
+            content_data, content_sr = content_wav
+        else:
+            content_sr, content_data = content_wav
+        
+        # Ensure single channel
+        if len(content_data.shape) > 1 and content_data.shape[1] > 1:
+            content_data = np.mean(content_data, axis=1)
+        
+        # Resample to 24kHz
+        if content_sr != 24000:
+            content_tensor = torch.FloatTensor(content_data).unsqueeze(0)
+            content_tensor = torchaudio.functional.resample(content_tensor, content_sr, 24000)
+            content_sr = 24000
+        else:
+            content_tensor = torch.FloatTensor(content_data).unsqueeze(0)
+        
+        # Normalize volume
+        content_tensor = content_tensor / (torch.max(torch.abs(content_tensor)) + 1e-6) * 0.95
+    else:
+        raise ValueError("Invalid content audio format")
+    
+    if isinstance(style_wav[0], np.ndarray):
+        style_data, style_sr = style_wav
+    else:
+        style_sr, style_data = style_wav
+
+    # Ensure single channel
+    if len(style_data.shape) > 1 and style_data.shape[1] > 1:
+        style_data = np.mean(style_data, axis=1)
+
+    # Resample to 24kHz
+    if style_sr != 24000:
+        style_tensor = torch.FloatTensor(style_data).unsqueeze(0)
+        style_tensor = torchaudio.functional.resample(style_tensor, style_sr, 24000)
+        style_sr = 24000
+    else:
+        style_tensor = torch.FloatTensor(style_data).unsqueeze(0)
+
+    # Normalize volume
+    style_tensor = style_tensor / (torch.max(torch.abs(style_tensor)) + 1e-6) * 0.95
+    
+    # Print debug information
+    print(f"Content audio shape: {content_tensor.shape}, sample rate: {content_sr}")
+    print(f"Style audio shape: {style_tensor.shape}, sample rate: {style_sr}")
+    
+    # Save audio
+    torchaudio.save(temp_content_path, content_tensor, content_sr)
+    torchaudio.save(temp_style_path, style_tensor, style_sr)
+    
+    try:
+        # Get pipeline
+        pipeline = get_pipeline("style")
+        
+        # Inference
+        gen_audio = pipeline.inference_ar_and_fm(
+            src_wav_path=temp_content_path,
+            src_text=None,
+            style_ref_wav_path=temp_style_path,
+            timbre_ref_wav_path=temp_content_path,
+        )
+        
+        # Check if generated audio is numerical anomaly
+        if torch.isnan(gen_audio).any() or torch.isinf(gen_audio).any():
+            print("Warning: Generated audio contains NaN or Inf values")
+            gen_audio = torch.nan_to_num(gen_audio, nan=0.0, posinf=0.95, neginf=-0.95)
+        
+        print(f"Generated audio shape: {gen_audio.shape}, max: {torch.max(gen_audio)}, min: {torch.min(gen_audio)}")
+        
+        # Save generated audio
+        save_audio(gen_audio, output_path=output_path)
+        
+        return output_path
+    except Exception as e:
+        print(f"Error during processing: {e}")
+        import traceback
+        traceback.print_exc()
+        raise e
+
+@spaces.GPU()
+def vevo_timbre(content_wav, reference_wav):
+    temp_content_path = "wav/temp_content.wav"
+    temp_reference_path = "wav/temp_reference.wav"
+    output_path = "wav/output_vevotimbre.wav"
+    
+    # Check and process audio data
+    if content_wav is None or reference_wav is None:
+        raise ValueError("Please upload audio files")
+    
+    # Process content audio format
+    if isinstance(content_wav, tuple) and len(content_wav) == 2:
+        if isinstance(content_wav[0], np.ndarray):
+            content_data, content_sr = content_wav
+        else:
+            content_sr, content_data = content_wav
+        
+        # Ensure single channel
+        if len(content_data.shape) > 1 and content_data.shape[1] > 1:
+            content_data = np.mean(content_data, axis=1)
+        
+        # Resample to 24kHz
+        if content_sr != 24000:
+            content_tensor = torch.FloatTensor(content_data).unsqueeze(0)
+            content_tensor = torchaudio.functional.resample(content_tensor, content_sr, 24000)
+            content_sr = 24000
+        else:
+            content_tensor = torch.FloatTensor(content_data).unsqueeze(0)
+        
+        # Normalize volume
+        content_tensor = content_tensor / (torch.max(torch.abs(content_tensor)) + 1e-6) * 0.95
+    else:
+        raise ValueError("Invalid content audio format")
+    
+    # Process reference audio format
+    if isinstance(reference_wav, tuple) and len(reference_wav) == 2:
+        if isinstance(reference_wav[0], np.ndarray):
+            reference_data, reference_sr = reference_wav
+        else:
+            reference_sr, reference_data = reference_wav
+        
+        # Ensure single channel
+        if len(reference_data.shape) > 1 and reference_data.shape[1] > 1:
+            reference_data = np.mean(reference_data, axis=1)
+        
+        # Resample to 24kHz
+        if reference_sr != 24000:
+            reference_tensor = torch.FloatTensor(reference_data).unsqueeze(0)
+            reference_tensor = torchaudio.functional.resample(reference_tensor, reference_sr, 24000)
+            reference_sr = 24000
+        else:
+            reference_tensor = torch.FloatTensor(reference_data).unsqueeze(0)
+        
+        # Normalize volume
+        reference_tensor = reference_tensor / (torch.max(torch.abs(reference_tensor)) + 1e-6) * 0.95
+    else:
+        raise ValueError("Invalid reference audio format")
+    
+    # Print debug information
+    print(f"Content audio shape: {content_tensor.shape}, sample rate: {content_sr}")
+    print(f"Reference audio shape: {reference_tensor.shape}, sample rate: {reference_sr}")
+    
+    # Save uploaded audio
+    torchaudio.save(temp_content_path, content_tensor, content_sr)
+    torchaudio.save(temp_reference_path, reference_tensor, reference_sr)
+    
+    try:
+        # Get pipeline
+        pipeline = get_pipeline("timbre")
+        
+        # Inference
+        gen_audio = pipeline.inference_fm(
+            src_wav_path=temp_content_path,
+            timbre_ref_wav_path=temp_reference_path,
+            flow_matching_steps=32,
+        )
+        
+        # Check if generated audio is numerical anomaly
+        if torch.isnan(gen_audio).any() or torch.isinf(gen_audio).any():
+            print("Warning: Generated audio contains NaN or Inf values")
+            gen_audio = torch.nan_to_num(gen_audio, nan=0.0, posinf=0.95, neginf=-0.95)
+        
+        print(f"Generated audio shape: {gen_audio.shape}, max: {torch.max(gen_audio)}, min: {torch.min(gen_audio)}")
+        
+        # Save generated audio
+        save_audio(gen_audio, output_path=output_path)
+        
+        return output_path
+    except Exception as e:
+        print(f"Error during processing: {e}")
+        import traceback
+        traceback.print_exc()
+        raise e
+
+@spaces.GPU()
+def vevo_voice(content_wav, style_reference_wav, timbre_reference_wav):
+    temp_content_path = "wav/temp_content.wav"
+    temp_style_path = "wav/temp_style.wav"
+    temp_timbre_path = "wav/temp_timbre.wav"
+    output_path = "wav/output_vevovoice.wav"
+    
+    # Check and process audio data
+    if content_wav is None or style_reference_wav is None or timbre_reference_wav is None:
+        raise ValueError("Please upload all required audio files")
+    
+    # Process content audio format
+    if isinstance(content_wav, tuple) and len(content_wav) == 2:
+        if isinstance(content_wav[0], np.ndarray):
+            content_data, content_sr = content_wav
+        else:
+            content_sr, content_data = content_wav
+        
+        # Ensure single channel
+        if len(content_data.shape) > 1 and content_data.shape[1] > 1:
+            content_data = np.mean(content_data, axis=1)
+        
+        # Resample to 24kHz
+        if content_sr != 24000:
+            content_tensor = torch.FloatTensor(content_data).unsqueeze(0)
+            content_tensor = torchaudio.functional.resample(content_tensor, content_sr, 24000)
+            content_sr = 24000
+        else:
+            content_tensor = torch.FloatTensor(content_data).unsqueeze(0)
+        
+        # Normalize volume
+        content_tensor = content_tensor / (torch.max(torch.abs(content_tensor)) + 1e-6) * 0.95
+    else:
+        raise ValueError("Invalid content audio format")
+    
+    # Process style reference audio format
+    if isinstance(style_reference_wav, tuple) and len(style_reference_wav) == 2:
+        if isinstance(style_reference_wav[0], np.ndarray):
+            style_data, style_sr = style_reference_wav
+        else:
+            style_sr, style_data = style_reference_wav
+        
+        # Ensure single channel
+        if len(style_data.shape) > 1 and style_data.shape[1] > 1:
+            style_data = np.mean(style_data, axis=1)
+        
+        # Resample to 24kHz
+        if style_sr != 24000:
+            style_tensor = torch.FloatTensor(style_data).unsqueeze(0)
+            style_tensor = torchaudio.functional.resample(style_tensor, style_sr, 24000)
+            style_sr = 24000
+        else:
+            style_tensor = torch.FloatTensor(style_data).unsqueeze(0)
+        
+        # Normalize volume
+        style_tensor = style_tensor / (torch.max(torch.abs(style_tensor)) + 1e-6) * 0.95
+    else:
+        raise ValueError("Invalid style reference audio format")
+    
+    # Process timbre reference audio format
+    if isinstance(timbre_reference_wav, tuple) and len(timbre_reference_wav) == 2:
+        if isinstance(timbre_reference_wav[0], np.ndarray):
+            timbre_data, timbre_sr = timbre_reference_wav
+        else:
+            timbre_sr, timbre_data = timbre_reference_wav
+        
+        # Ensure single channel
+        if len(timbre_data.shape) > 1 and timbre_data.shape[1] > 1:
+            timbre_data = np.mean(timbre_data, axis=1)
+        
+        # Resample to 24kHz
+        if timbre_sr != 24000:
+            timbre_tensor = torch.FloatTensor(timbre_data).unsqueeze(0)
+            timbre_tensor = torchaudio.functional.resample(timbre_tensor, timbre_sr, 24000)
+            timbre_sr = 24000
+        else:
+            timbre_tensor = torch.FloatTensor(timbre_data).unsqueeze(0)
+        
+        # Normalize volume
+        timbre_tensor = timbre_tensor / (torch.max(torch.abs(timbre_tensor)) + 1e-6) * 0.95
+    else:
+        raise ValueError("Invalid timbre reference audio format")
+    
+    # Print debug information
+    print(f"Content audio shape: {content_tensor.shape}, sample rate: {content_sr}")
+    print(f"Style reference audio shape: {style_tensor.shape}, sample rate: {style_sr}")
+    print(f"Timbre reference audio shape: {timbre_tensor.shape}, sample rate: {timbre_sr}")
+    
+    # Save uploaded audio
+    torchaudio.save(temp_content_path, content_tensor, content_sr)
+    torchaudio.save(temp_style_path, style_tensor, style_sr)
+    torchaudio.save(temp_timbre_path, timbre_tensor, timbre_sr)
+    
+    try:
+        # Get pipeline
+        pipeline = get_pipeline("voice")
+        
+        # Inference
+        gen_audio = pipeline.inference_ar_and_fm(
+            src_wav_path=temp_content_path,
+            src_text=None,
+            style_ref_wav_path=temp_style_path,
+            timbre_ref_wav_path=temp_timbre_path,
+        )
+        
+        # Check if generated audio is numerical anomaly
+        if torch.isnan(gen_audio).any() or torch.isinf(gen_audio).any():
+            print("Warning: Generated audio contains NaN or Inf values")
+            gen_audio = torch.nan_to_num(gen_audio, nan=0.0, posinf=0.95, neginf=-0.95)
+        
+        print(f"Generated audio shape: {gen_audio.shape}, max: {torch.max(gen_audio)}, min: {torch.min(gen_audio)}")
+        
+        # Save generated audio
+        save_audio(gen_audio, output_path=output_path)
+        
+        return output_path
+    except Exception as e:
+        print(f"Error during processing: {e}")
+        import traceback
+        traceback.print_exc()
+        raise e
+
+@spaces.GPU()
+def vevo_tts(text, ref_wav, timbre_ref_wav=None, style_ref_text=None, src_language="en", ref_language="en", style_ref_text_language="en"):
+    temp_ref_path = "wav/temp_ref.wav"
+    temp_timbre_path = "wav/temp_timbre.wav"
+    output_path = "wav/output_vevotts.wav"
+     
+    # Check and process audio data
+    if ref_wav is None:
+        raise ValueError("Please upload a reference audio file")
+    
+    # Process reference audio format
+    if isinstance(ref_wav, tuple) and len(ref_wav) == 2:
+        if isinstance(ref_wav[0], np.ndarray):
+            ref_data, ref_sr = ref_wav
+        else:
+            ref_sr, ref_data = ref_wav
+        
+        # Ensure single channel
+        if len(ref_data.shape) > 1 and ref_data.shape[1] > 1:
+            ref_data = np.mean(ref_data, axis=1)
+        
+        # Resample to 24kHz
+        if ref_sr != 24000:
+            ref_tensor = torch.FloatTensor(ref_data).unsqueeze(0)
+            ref_tensor = torchaudio.functional.resample(ref_tensor, ref_sr, 24000)
+            ref_sr = 24000
+        else:
+            ref_tensor = torch.FloatTensor(ref_data).unsqueeze(0)
+        
+        # Normalize volume
+        ref_tensor = ref_tensor / (torch.max(torch.abs(ref_tensor)) + 1e-6) * 0.95
+    else:
+        raise ValueError("Invalid reference audio format")
+    
+    # Print debug information
+    print(f"Reference audio shape: {ref_tensor.shape}, sample rate: {ref_sr}")
+    if style_ref_text:
+        print(f"Style reference text: {style_ref_text}, language: {style_ref_text_language}")
+    
+    # Save uploaded audio
+    torchaudio.save(temp_ref_path, ref_tensor, ref_sr)
+    
+    if timbre_ref_wav is not None:
+        if isinstance(timbre_ref_wav, tuple) and len(timbre_ref_wav) == 2:
+            if isinstance(timbre_ref_wav[0], np.ndarray):
+                timbre_data, timbre_sr = timbre_ref_wav
+            else:
+                timbre_sr, timbre_data = timbre_ref_wav
+            
+            # Ensure single channel
+            if len(timbre_data.shape) > 1 and timbre_data.shape[1] > 1:
+                timbre_data = np.mean(timbre_data, axis=1)
+            
+            # Resample to 24kHz
+            if timbre_sr != 24000:
+                timbre_tensor = torch.FloatTensor(timbre_data).unsqueeze(0)
+                timbre_tensor = torchaudio.functional.resample(timbre_tensor, timbre_sr, 24000)
+                timbre_sr = 24000
+            else:
+                timbre_tensor = torch.FloatTensor(timbre_data).unsqueeze(0)
+            
+            # Normalize volume
+            timbre_tensor = timbre_tensor / (torch.max(torch.abs(timbre_tensor)) + 1e-6) * 0.95
+            
+            print(f"Timbre reference audio shape: {timbre_tensor.shape}, sample rate: {timbre_sr}")
+            torchaudio.save(temp_timbre_path, timbre_tensor, timbre_sr)
+        else:
+            raise ValueError("Invalid timbre reference audio format")
+    else:
+        temp_timbre_path = temp_ref_path
+    
+    try:
+        # Get pipeline
+        pipeline = get_pipeline("tts")
+        
+        # Inference
+        gen_audio = pipeline.inference_ar_and_fm(
+            src_wav_path=None,
+            src_text=text,
+            style_ref_wav_path=temp_ref_path,
+            timbre_ref_wav_path=temp_timbre_path,
+            style_ref_wav_text=style_ref_text,
+            src_text_language=src_language,
+            style_ref_wav_text_language=style_ref_text_language,
+        )
+        
+        # Check if generated audio is numerical anomaly
+        if torch.isnan(gen_audio).any() or torch.isinf(gen_audio).any():
+            print("Warning: Generated audio contains NaN or Inf values")
+            gen_audio = torch.nan_to_num(gen_audio, nan=0.0, posinf=0.95, neginf=-0.95)
+        
+        print(f"Generated audio shape: {gen_audio.shape}, max: {torch.max(gen_audio)}, min: {torch.min(gen_audio)}")
+        
+        # Save generated audio
+        save_audio(gen_audio, output_path=output_path)
+        
+        return output_path
+    except Exception as e:
+        print(f"Error during processing: {e}")
+        import traceback
+        traceback.print_exc()
+        raise e
+
+# Create Gradio interface
+with gr.Blocks(title="Vevo: Controllable Zero-Shot Voice Imitation with Self-Supervised Disentanglement") as demo:
+    gr.Markdown("# Vevo: Controllable Zero-Shot Voice Imitation with Self-Supervised Disentanglement")        
+    # Add link tag line
+    with gr.Row(elem_id="links_row"):
+        gr.HTML("""
+        <div style="display: flex; justify-content: flex-start; gap: 8px; margin: 0 0; padding-left: 0px;">
+            <a href="https://arxiv.org/abs/2502.07243" target="_blank" style="text-decoration: none;">
+                <img alt="arXiv Paper" src="https://img.shields.io/badge/arXiv-Paper-red">
+            </a>
+            <a href="https://openreview.net/pdf?id=anQDiQZhDP" target="_blank" style="text-decoration: none;">
+                <img alt="ICLR Paper" src="https://img.shields.io/badge/ICLR-Paper-64b63a">
+            </a>
+            <a href="https://huggingface.co/amphion/Vevo" target="_blank" style="text-decoration: none;">
+                <img alt="HuggingFace Model" src="https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Model-yellow">
+            </a>
+            <a href="https://github.com/open-mmlab/Amphion/tree/main/models/vc/vevo" target="_blank" style="text-decoration: none;">
+                <img alt="GitHub Repo" src="https://img.shields.io/badge/GitHub-Repo-blue">
+            </a>
+        </div>
+        """)
+
+    with gr.Tab("Vevo-Timbre"):
+        gr.Markdown("### Vevo-Timbre: Maintain style but transfer timbre")
+        with gr.Row():
+            with gr.Column():
+                timbre_content = gr.Audio(label="Source Audio", type="numpy")
+                timbre_reference = gr.Audio(label="Timbre Reference", type="numpy")
+                timbre_button = gr.Button("Generate")
+            with gr.Column():
+                timbre_output = gr.Audio(label="Result")
+        timbre_button.click(vevo_timbre, inputs=[timbre_content, timbre_reference], outputs=timbre_output)
+
+    with gr.Tab("Vevo-Style"):
+        gr.Markdown("### Vevo-Style: Maintain timbre but transfer style (accent, emotion, etc.)")
+        with gr.Row():
+            with gr.Column():
+                style_content = gr.Audio(label="Source Audio", type="numpy")
+                style_reference = gr.Audio(label="Style Reference", type="numpy")
+                style_button = gr.Button("Generate")
+            with gr.Column():
+                style_output = gr.Audio(label="Result")
+        style_button.click(vevo_style, inputs=[style_content, style_reference], outputs=style_output)
+
+    with gr.Tab("Vevo-Voice"):
+        gr.Markdown("### Vevo-Voice: Transfers both style and timbre with separate references")
+        with gr.Row():
+            with gr.Column():
+                voice_content = gr.Audio(label="Source Audio", type="numpy")
+                voice_style_reference = gr.Audio(label="Style Reference", type="numpy")
+                voice_timbre_reference = gr.Audio(label="Timbre Reference", type="numpy")
+                voice_button = gr.Button("Generate")
+            with gr.Column():
+                voice_output = gr.Audio(label="Result")
+        voice_button.click(vevo_voice, inputs=[voice_content, voice_style_reference, voice_timbre_reference], outputs=voice_output)
+    
+    
+    
+    with gr.Tab("Vevo-TTS"):
+        gr.Markdown("### Vevo-TTS: Text-to-speech with separate style and timbre references")
+        with gr.Row():
+            with gr.Column():
+                tts_text = gr.Textbox(label="Target Text", placeholder="Enter text to synthesize...", lines=3)
+                tts_src_language = gr.Dropdown(["en", "zh", "de", "fr", "ja", "ko"], label="Text Language", value="en")
+                tts_reference = gr.Audio(label="Style Reference", type="numpy")                
+                tts_style_ref_text = gr.Textbox(label="Style Reference Text", placeholder="Enter style reference text...", lines=3)
+                tts_style_ref_text_language = gr.Dropdown(["en", "zh", "de", "fr", "ja", "ko"], label="Style Reference Text Language", value="en")
+                tts_timbre_reference = gr.Audio(label="Timbre Reference", type="numpy")
+                tts_button = gr.Button("Generate")
+            with gr.Column():
+                tts_output = gr.Audio(label="Result")
+        
+        tts_button.click(
+            vevo_tts, 
+            inputs=[tts_text, tts_reference, tts_timbre_reference, tts_style_ref_text, tts_src_language, tts_style_ref_text_language], 
+            outputs=tts_output
+        )
+    
+    gr.Markdown("""
+    ## About VEVO
+    VEVO is a versatile voice synthesis and conversion model that offers four main functionalities:
+    1. **Vevo-Style**: Maintains timbre but transfers style (accent, emotion, etc.)
+    2. **Vevo-Timbre**: Maintains style but transfers timbre
+    3. **Vevo-Voice**: Transfers both style and timbre with separate references
+    4. **Vevo-TTS**: Text-to-speech with separate style and timbre references
+    
+    For more information, visit the [Amphion project](https://github.com/open-mmlab/Amphion)
+    """)
+
+# Launch application
+demo.launch() 

requirements (2).txt

@@ -0,0 +1,30 @@
+gradio>=3.50.2
+torch>=2.0.0
+torchaudio>=2.0.0
+numpy>=1.20.0
+huggingface_hub>=0.14.1
+librosa>=0.9.2
+PyYAML>=6.0
+accelerate>=0.20.3
+safetensors>=0.3.1
+phonemizer>=3.2.0
+setuptools
+onnxruntime
+transformers==4.41.2
+unidecode
+scipy>=1.12.0
+encodec
+g2p_en
+jieba
+cn2an
+pypinyin
+langsegment==0.2.0
+pyopenjtalk
+pykakasi
+json5
+black>=24.1.1
+ruamel.yaml
+tqdm
+openai-whisper
+ipython
+pyworld