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 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() 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, downloaded_fmt_path, 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: # --- آماده سازی Reference (اول رفرنس را پردازش می‌کنیم تا سطح صدا را بگیریم) --- 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 # محاسبه انرژی رفرنس ref_max_vol = torch.max(torch.abs(ref_tensor)) + 1e-6 ref_tensor = ref_tensor / ref_max_vol * 0.95 # نرمال سازی رفرنس # برش رفرنس به 20 ثانیه if ref_tensor.shape[1] > 24000 * 20: ref_tensor = ref_tensor[:, :24000 * 20] save_audio_pcm16(ref_tensor, temp_reference_path, ref_sr) # --- آماده سازی Content --- 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 # نرمال سازی هوشمند: صدای ورودی را هم‌سطح صدای رفرنس می‌کنیم content_tensor = content_tensor / (torch.max(torch.abs(content_tensor)) + 1e-6) * 0.95 # --- منطق Chunking --- pipeline = get_pipeline() SR = 24000 CHUNK_LEN = 10 * SR OVERLAP = 1 * SR INPUT_SIZE = CHUNK_LEN + OVERLAP total_samples = content_tensor.shape[1] print(f"[{session_id}] High Quality Processing (64 Steps)... Duration: {total_samples/SR:.2f}s") final_parts = [] overlap_buffer = None for start in range(0, total_samples, CHUNK_LEN): end_input = min(start + INPUT_SIZE, total_samples) current_input_chunk = content_tensor[:, start:end_input] save_audio_pcm16(current_input_chunk, temp_content_path, SR) try: gen = pipeline.inference_fm( src_wav_path=temp_content_path, timbre_ref_wav_path=temp_reference_path, flow_matching_steps=64, # <--- کیفیت بالا (قبلاً 32 بود) ) if torch.isnan(gen).any(): gen = torch.nan_to_num(gen, nan=0.0) if gen.dim() == 1: gen = gen.unsqueeze(0) gen = gen.cpu().squeeze(0).numpy() current_len = len(gen) if overlap_buffer is not None: mix_len = len(overlap_buffer) if current_len < mix_len: mix_len = current_len overlap_buffer = overlap_buffer[:mix_len] head_to_mix = gen[:mix_len] body_rest = gen[mix_len:] alpha = np.linspace(0, 1, mix_len) blended_segment = (overlap_buffer * (1 - alpha)) + (head_to_mix * alpha) final_parts.append(blended_segment) if len(body_rest) > OVERLAP: pure_body = body_rest[:-OVERLAP] final_parts.append(pure_body) overlap_buffer = body_rest[-OVERLAP:] else: final_parts.append(body_rest) overlap_buffer = None else: if current_len > OVERLAP: final_parts.append(gen[:-OVERLAP]) overlap_buffer = gen[-OVERLAP:] else: final_parts.append(gen) overlap_buffer = None except Exception as e: print(f"Error in chunk: {e}") missing_len = end_input - start if overlap_buffer is not None: missing_len -= len(overlap_buffer) final_parts.append(overlap_buffer) overlap_buffer = None final_parts.append(np.zeros(max(0, missing_len))) if overlap_buffer is not None: final_parts.append(overlap_buffer) if len(final_parts) > 0: full_audio = np.concatenate(final_parts) else: full_audio = np.zeros(24000) save_audio_pcm16(full_audio, output_path, SR) 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 (Ultra Quality)") as demo: gr.Markdown("## Vevo-Timbre: Zero-Shot Voice Conversion (Ultra Quality)") gr.Markdown(""" **ویژگی‌ها:** - **Steps 64:** کیفیت و دقت بافت صدا دو برابر شده است. - **Auto-Leveling:** سطح صدای شما با مدل تنظیم می‌شود. - **Seamless Stitching:** بدون پرش و بدون اضافه شدن زمان. **نکته مهم:** برای بهترین نتیجه، سعی کنید **لحن، سرعت و احساس** صدای خودتان را شبیه فایل هدف کنید. مدل فقط جنس صدا را تغییر می‌دهد، نه بازیگری شما را! """) with gr.Row(): with gr.Column(): timbre_content = gr.Audio(label="Source Audio (صدای شما)", type="numpy") timbre_reference = gr.Audio(label="Target Timbre (صدای هدف)", type="numpy") timbre_button = gr.Button("Generate (Ultra Quality)", 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()