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import os |
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import sys |
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import importlib.util |
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import site |
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import json |
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import torch |
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import gradio as gr |
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import torchaudio |
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import numpy as np |
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from huggingface_hub import snapshot_download, hf_hub_download |
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import subprocess |
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import uuid |
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import soundfile as sf |
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import spaces |
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downloaded_resources = { |
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"configs": False, |
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"tokenizer_vq8192": False, |
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"fmt_Vq8192ToMels": False, |
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"vocoder": False |
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} |
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def install_espeak(): |
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try: |
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result = subprocess.run(["which", "espeak-ng"], capture_output=True, text=True) |
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if result.returncode != 0: |
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print("Installing espeak-ng...") |
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subprocess.run(["apt-get", "update"], check=True) |
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subprocess.run(["apt-get", "install", "-y", "espeak-ng", "espeak-ng-data"], check=True) |
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except Exception as e: |
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print(f"Error installing espeak-ng: {e}") |
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install_espeak() |
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def patch_langsegment_init(): |
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try: |
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spec = importlib.util.find_spec("LangSegment") |
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if spec is None or spec.origin is None: return |
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init_path = os.path.join(os.path.dirname(spec.origin), '__init__.py') |
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with open(init_path, 'r') as f: lines = f.readlines() |
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modified = False |
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new_lines = [] |
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target_line_prefix = "from .LangSegment import" |
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for line in lines: |
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if line.strip().startswith(target_line_prefix) and ('setLangfilters' in line or 'getLangfilters' in line): |
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mod_line = line.replace(',setLangfilters', '').replace(',getLangfilters', '') |
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mod_line = mod_line.replace('setLangfilters,', '').replace('getLangfilters,', '').rstrip(',') |
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new_lines.append(mod_line + '\n') |
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modified = True |
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else: |
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new_lines.append(line) |
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if modified: |
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with open(init_path, 'w') as f: f.writelines(new_lines) |
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try: |
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import LangSegment |
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importlib.reload(LangSegment) |
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except: pass |
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except: pass |
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patch_langsegment_init() |
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if not os.path.exists("Amphion"): |
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subprocess.run(["git", "clone", "https://github.com/open-mmlab/Amphion.git"]) |
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os.chdir("Amphion") |
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if os.path.dirname(os.path.abspath("Amphion")) not in sys.path: |
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sys.path.append(os.path.dirname(os.path.abspath("Amphion"))) |
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os.makedirs("wav", exist_ok=True) |
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os.makedirs("ckpts/Vevo", exist_ok=True) |
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from models.vc.vevo.vevo_utils import VevoInferencePipeline |
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def save_audio_pcm16(waveform, output_path, sample_rate=24000): |
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try: |
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if isinstance(waveform, torch.Tensor): |
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waveform = waveform.detach().cpu() |
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if waveform.dim() == 2 and waveform.shape[0] == 1: |
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waveform = waveform.squeeze(0) |
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waveform = waveform.numpy() |
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sf.write(output_path, waveform, sample_rate, subtype='PCM_16') |
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except Exception as e: |
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print(f"Save error: {e}") |
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def setup_configs(): |
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if downloaded_resources["configs"]: return |
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config_path = "models/vc/vevo/config" |
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os.makedirs(config_path, exist_ok=True) |
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config_files = ["Vq8192ToMels.json", "Vocoder.json"] |
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for file in config_files: |
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file_path = f"{config_path}/{file}" |
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if not os.path.exists(file_path): |
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try: |
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file_data = hf_hub_download(repo_id="amphion/Vevo", filename=f"config/{file}", repo_type="model") |
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subprocess.run(["cp", file_data, file_path]) |
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except Exception as e: print(f"Error downloading config {file}: {e}") |
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downloaded_resources["configs"] = True |
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setup_configs() |
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") |
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inference_pipelines = {} |
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def preload_all_resources(): |
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setup_configs() |
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global downloaded_content_style_tokenizer_path, downloaded_fmt_path, downloaded_vocoder_path |
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if not downloaded_resources["tokenizer_vq8192"]: |
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downloaded_content_style_tokenizer_path = snapshot_download(repo_id="amphion/Vevo", repo_type="model", cache_dir="./ckpts/Vevo", allow_patterns=["tokenizer/vq8192/*"]) |
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downloaded_resources["tokenizer_vq8192"] = True |
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if not downloaded_resources["fmt_Vq8192ToMels"]: |
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downloaded_fmt_path = snapshot_download(repo_id="amphion/Vevo", repo_type="model", cache_dir="./ckpts/Vevo", allow_patterns=["acoustic_modeling/Vq8192ToMels/*"]) |
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downloaded_resources["fmt_Vq8192ToMels"] = True |
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if not downloaded_resources["vocoder"]: |
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downloaded_vocoder_path = snapshot_download(repo_id="amphion/Vevo", repo_type="model", cache_dir="./ckpts/Vevo", allow_patterns=["acoustic_modeling/Vocoder/*"]) |
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downloaded_resources["vocoder"] = True |
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downloaded_content_style_tokenizer_path = None |
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downloaded_fmt_path = None |
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downloaded_vocoder_path = None |
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preload_all_resources() |
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def get_pipeline(): |
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if "timbre" in inference_pipelines: return inference_pipelines["timbre"] |
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pipeline = VevoInferencePipeline( |
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content_style_tokenizer_ckpt_path=os.path.join(downloaded_content_style_tokenizer_path, "tokenizer/vq8192"), |
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fmt_cfg_path="./models/vc/vevo/config/Vq8192ToMels.json", |
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fmt_ckpt_path=os.path.join(downloaded_fmt_path, "acoustic_modeling/Vq8192ToMels"), |
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vocoder_cfg_path="./models/vc/vevo/config/Vocoder.json", |
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vocoder_ckpt_path=os.path.join(downloaded_vocoder_path, "acoustic_modeling/Vocoder"), |
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device=device, |
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) |
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inference_pipelines["timbre"] = pipeline |
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return pipeline |
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@spaces.GPU() |
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def vevo_timbre(content_wav, reference_wav): |
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session_id = str(uuid.uuid4())[:8] |
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temp_content_path = f"wav/c_{session_id}.wav" |
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temp_reference_path = f"wav/r_{session_id}.wav" |
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output_path = f"wav/out_{session_id}.wav" |
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if content_wav is None or reference_wav is None: |
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raise ValueError("Please upload audio files") |
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try: |
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SR = 24000 |
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if isinstance(content_wav, tuple): |
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content_sr, content_data = content_wav if isinstance(content_wav[0], int) else (content_wav[1], content_wav[0]) |
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else: |
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content_sr, content_data = content_wav |
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if len(content_data.shape) > 1: content_data = np.mean(content_data, axis=1) |
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content_tensor = torch.FloatTensor(content_data).unsqueeze(0) |
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if content_sr != SR: |
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content_tensor = torchaudio.functional.resample(content_tensor, content_sr, SR) |
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content_tensor = content_tensor / (torch.max(torch.abs(content_tensor)) + 1e-6) * 0.95 |
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content_full_np = content_tensor.squeeze().numpy() |
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if isinstance(reference_wav, tuple): |
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ref_sr, ref_data = reference_wav if isinstance(reference_wav[0], int) else (reference_wav[1], reference_wav[0]) |
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else: |
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ref_sr, ref_data = reference_wav |
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if len(ref_data.shape) > 1: ref_data = np.mean(ref_data, axis=1) |
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ref_tensor = torch.FloatTensor(ref_data).unsqueeze(0) |
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if ref_sr != SR: |
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ref_tensor = torchaudio.functional.resample(ref_tensor, ref_sr, SR) |
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ref_tensor = ref_tensor / (torch.max(torch.abs(ref_tensor)) + 1e-6) * 0.95 |
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if ref_tensor.shape[1] > SR * 20: ref_tensor = ref_tensor[:, :SR * 20] |
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save_audio_pcm16(ref_tensor, temp_reference_path, SR) |
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pipeline = get_pipeline() |
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CORE_CHUNK_SEC = 10.0 |
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PADDING_SEC = 2.0 |
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core_samples = int(CORE_CHUNK_SEC * SR) |
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padding_samples = int(PADDING_SEC * SR) |
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total_samples = len(content_full_np) |
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final_output = [] |
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cursor = 0 |
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print(f"[{session_id}] Starting Center-Only processing...") |
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while cursor < total_samples: |
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read_start = max(0, cursor - padding_samples) |
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read_end = min(total_samples, cursor + core_samples + padding_samples) |
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if cursor >= total_samples: |
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break |
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chunk_input = content_full_np[read_start:read_end] |
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if len(chunk_input) < SR * 0.5: |
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break |
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save_audio_pcm16(torch.FloatTensor(chunk_input).unsqueeze(0), temp_content_path, SR) |
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try: |
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gen = pipeline.inference_fm( |
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src_wav_path=temp_content_path, |
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timbre_ref_wav_path=temp_reference_path, |
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flow_matching_steps=64, |
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) |
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if torch.isnan(gen).any(): gen = torch.nan_to_num(gen, nan=0.0) |
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gen_np = gen.detach().cpu().squeeze().numpy() |
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if cursor == 0: |
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trim_front = 0 |
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else: |
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trim_front = padding_samples |
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valid_length = min(core_samples, total_samples - cursor) |
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if len(gen_np) > trim_front: |
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core_audio = gen_np[trim_front : trim_front + valid_length] |
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fade_len = int(0.05 * SR) |
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if len(final_output) > 0 and len(core_audio) > fade_len: |
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fade_out = np.linspace(1, 0, fade_len) |
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fade_in = np.linspace(0, 1, fade_len) |
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prev_tail = final_output[-1][-fade_len:] |
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curr_head = core_audio[:fade_len] |
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if len(prev_tail) == fade_len: |
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mixed = (prev_tail * fade_out) + (curr_head * fade_in) |
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final_output[-1][-fade_len:] = mixed |
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core_audio = core_audio[fade_len:] |
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final_output.append(core_audio) |
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except Exception as e: |
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print(f"Error processing chunk at {cursor}: {e}") |
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missing = min(core_samples, total_samples - cursor) |
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final_output.append(np.zeros(missing)) |
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cursor += core_samples |
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if len(final_output) > 0: |
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full_audio = np.concatenate(final_output) |
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else: |
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full_audio = np.zeros(SR) |
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save_audio_pcm16(full_audio, output_path, SR) |
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return output_path |
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finally: |
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if os.path.exists(temp_content_path): os.remove(temp_content_path) |
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if os.path.exists(temp_reference_path): os.remove(temp_reference_path) |
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with gr.Blocks(title="Vevo-Timbre (Stable Core)") as demo: |
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gr.Markdown("## Vevo-Timbre: Zero-Shot Voice Conversion") |
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gr.Markdown("Stable Core Logic: Removes generated artifacts at boundaries.") |
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with gr.Row(): |
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with gr.Column(): |
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timbre_content = gr.Audio(label="Source Audio", type="numpy") |
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timbre_reference = gr.Audio(label="Target Timbre", type="numpy") |
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timbre_button = gr.Button("Generate", variant="primary") |
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with gr.Column(): |
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timbre_output = gr.Audio(label="Result") |
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timbre_button.click(vevo_timbre, inputs=[timbre_content, timbre_reference], outputs=timbre_output) |
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demo.launch() |
