Update requirements.txt

requirements.txt

@@ -1,258 +1,31 @@
-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
-import uuid
-import soundfile as sf
-
-# منابع ضروری
-downloaded_resources = {
-    "configs": False,
-    "tokenizer_vq8192": False,
-    "fmt_Vq8192ToMels": False,
-    "vocoder": False
-}
-
-def install_espeak():
-    try:
-        result = subprocess.run(["which", "espeak-ng"], capture_output=True, text=True)
-        if result.returncode != 0:
-            print("Installing espeak-ng...")
-            subprocess.run(["apt-get", "update"], check=True)
-            subprocess.run(["apt-get", "install", "-y", "espeak-ng", "espeak-ng-data"], check=True)
-    except Exception as e:
-        print(f"Error installing espeak-ng: {e}")
-
-install_espeak()
-
-def patch_langsegment_init():
-    try:
-        spec = importlib.util.find_spec("LangSegment")
-        if spec is None or spec.origin is None: return
-        init_path = os.path.join(os.path.dirname(spec.origin), '__init__.py')
-        if not os.path.exists(init_path):
-            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
-                    break
-            else: return
-
-        with open(init_path, 'r') as f: lines = f.readlines()
-        modified = False
-        new_lines = []
-        target_line_prefix = "from .LangSegment import"
-
-        for line in lines:
-            if line.strip().startswith(target_line_prefix) and ('setLangfilters' in line or 'getLangfilters' in line):
-                mod_line = line.replace(',setLangfilters', '').replace(',getLangfilters', '')
-                mod_line = mod_line.replace('setLangfilters,', '').replace('getLangfilters,', '').rstrip(',') 
-                new_lines.append(mod_line + '\n')
-                modified = True
-            else:
-                new_lines.append(line)
-
-        if modified:
-            with open(init_path, 'w') as f: f.writelines(new_lines)
-            try:
-                import LangSegment
-                importlib.reload(LangSegment)
-            except: pass
-    except: pass
-
-patch_langsegment_init()
-
-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")
-
-if os.path.dirname(os.path.abspath("Amphion")) not in sys.path:
-    sys.path.append(os.path.dirname(os.path.abspath("Amphion")))
-
-os.makedirs("wav", exist_ok=True)
-os.makedirs("ckpts/Vevo", exist_ok=True)
-
-from models.vc.vevo.vevo_utils import VevoInferencePipeline
-
-# --- تابع ذخیره سازی دقیق (16-bit PCM) ---
-# این تابع کلید حل مشکل نویز صداست. فایل را دقیقاً مثل WAV استاندارد ذخیره می‌کند.
-def save_audio_pcm16(waveform, output_path, sample_rate=24000):
-    try:
-        if isinstance(waveform, torch.Tensor):
-            waveform = waveform.detach().cpu()
-            if waveform.dim() == 2 and waveform.shape[0] == 1:
-                waveform = waveform.squeeze(0)
-            waveform = waveform.numpy()
-        
-        # تبدیل به فرمت 16 بیتی برای جلوگیری از نویز
-        # (مدل‌های Vevo با فرمت Float گاهی مشکل دارند)
-        sf.write(output_path, waveform, sample_rate, subtype='PCM_16')
-        
-    except Exception as e:
-        print(f"Save error: {e}")
-        raise e
-
-def setup_configs():
-    if downloaded_resources["configs"]: return
-    config_path = "models/vc/vevo/config"
-    os.makedirs(config_path, exist_ok=True)
-    config_files = ["Vq8192ToMels.json", "Vocoder.json"]
-    
-    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")
-                subprocess.run(["cp", file_data, file_path])
-            except Exception as e: print(f"Error downloading config {file}: {e}")
-    downloaded_resources["configs"] = True
-
-setup_configs()
-
-device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-print(f"Using device: {device}")
-
-inference_pipelines = {}
-
-def preload_all_resources():
-    print("Preloading resources...")
-    setup_configs()
-    
-    global downloaded_content_style_tokenizer_path
-    global downloaded_fmt_path
-    global downloaded_vocoder_path
-    
-    if not downloaded_resources["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
-    
-    if not downloaded_resources["fmt_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
-    
-    if not downloaded_resources["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("Resources ready.")
-
-downloaded_content_style_tokenizer_path = None
-downloaded_fmt_path = None
-downloaded_vocoder_path = None
-
-preload_all_resources()
-
-def get_pipeline():
-    if "timbre" in inference_pipelines:
-        return inference_pipelines["timbre"]
-    
-    pipeline = VevoInferencePipeline(
-        content_style_tokenizer_ckpt_path=os.path.join(downloaded_content_style_tokenizer_path, "tokenizer/vq8192"),
-        fmt_cfg_path="./models/vc/vevo/config/Vq8192ToMels.json",
-        fmt_ckpt_path=os.path.join(downloaded_fmt_path, "acoustic_modeling/Vq8192ToMels"),
-        vocoder_cfg_path="./models/vc/vevo/config/Vocoder.json",
-        vocoder_ckpt_path=os.path.join(downloaded_vocoder_path, "acoustic_modeling/Vocoder"),
-        device=device,
-    )
-    
-    inference_pipelines["timbre"] = pipeline
-    return pipeline
-
-@spaces.GPU()
-def vevo_timbre(content_wav, reference_wav):
-    session_id = str(uuid.uuid4())[:8]
-    temp_content_path = f"wav/c_{session_id}.wav"
-    temp_reference_path = f"wav/r_{session_id}.wav"
-    output_path = f"wav/out_{session_id}.wav"
-    
-    if content_wav is None or reference_wav is None:
-        raise ValueError("Please upload audio files")
-    
-    try:
-        # --- پردازش صدای اصلی ---
-        if isinstance(content_wav, tuple):
-            content_sr, content_data = content_wav if isinstance(content_wav[0], int) else (content_wav[1], content_wav[0])
-        else:
-            content_sr, content_data = content_wav
-
-        if len(content_data.shape) > 1 and content_data.shape[1] > 1:
-            content_data = np.mean(content_data, axis=1)
-        
-        content_tensor = torch.FloatTensor(content_data).unsqueeze(0)
-        
-        if content_sr != 24000:
-            content_tensor = torchaudio.functional.resample(content_tensor, content_sr, 24000)
-            content_sr = 24000
-        
-        content_tensor = content_tensor / (torch.max(torch.abs(content_tensor)) + 1e-6) * 0.95
-
-        # --- پردازش صدای رفرنس ---
-        if isinstance(reference_wav, tuple):
-            ref_sr, ref_data = reference_wav if isinstance(reference_wav[0], int) else (reference_wav[1], reference_wav[0])
-        else:
-            ref_sr, ref_data = reference_wav
-
-        if len(ref_data.shape) > 1 and ref_data.shape[1] > 1:
-            ref_data = np.mean(ref_data, axis=1)
-
-        ref_tensor = torch.FloatTensor(ref_data).unsqueeze(0)
-        if ref_sr != 24000:
-            ref_tensor = torchaudio.functional.resample(ref_tensor, ref_sr, 24000)
-            ref_sr = 24000
-        
-        ref_tensor = ref_tensor / (torch.max(torch.abs(ref_tensor)) + 1e-6) * 0.95
-        
-        # *** ذخیره با فرمت PCM_16 (کلید حل مشکل نویز) ***
-        save_audio_pcm16(content_tensor, temp_content_path, content_sr)
-        save_audio_pcm16(ref_tensor, temp_reference_path, ref_sr)
-        
-        print(f"[{session_id}] Processing...")
-        
-        pipeline = get_pipeline()
-        
-        # اجرای مدل
-        gen_audio = pipeline.inference_fm(
-            src_wav_path=temp_content_path,
-            timbre_ref_wav_path=temp_reference_path,
-            flow_matching_steps=32,
-        )
-        
-        if torch.isnan(gen_audio).any() or torch.isinf(gen_audio).any():
-            gen_audio = torch.nan_to_num(gen_audio, nan=0.0, posinf=0.95, neginf=-0.95)
-        
-        # ذخیره خروجی نهایی
-        save_audio_pcm16(gen_audio, output_path, 24000)
-        return output_path
-
-    finally:
-        if os.path.exists(temp_content_path): os.remove(temp_content_path)
-        if os.path.exists(temp_reference_path): os.remove(temp_reference_path)
-
-with gr.Blocks(title="Vevo-Timbre (High Quality)") as demo:
-    gr.Markdown("## Vevo-Timbre: Zero-Shot Voice Conversion")
-    
-    with gr.Row():
-        with gr.Column():
-            timbre_content = gr.Audio(label="Source Audio", type="numpy")
-            timbre_reference = gr.Audio(label="Target Timbre", type="numpy")
-            timbre_button = gr.Button("Generate", variant="primary")
-        with gr.Column():
-            timbre_output = gr.Audio(label="Result")
-            
-    timbre_button.click(vevo_timbre, inputs=[timbre_content, timbre_reference], outputs=timbre_output)
-
-demo.launch()
+gradio>=3.50.2
+torch
+torchaudio
+numpy<2.0.0
+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
+soundfile