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Running
on
Zero
| import os, sys | |
| import librosa | |
| import soundfile as sf | |
| import numpy as np | |
| import re | |
| import unicodedata | |
| from fairseq import checkpoint_utils | |
| import wget | |
| import logging | |
| logging.getLogger("fairseq").setLevel(logging.WARNING) | |
| now_dir = os.getcwd() | |
| sys.path.append(now_dir) | |
| def load_audio(file, sample_rate): | |
| try: | |
| file = file.strip(" ").strip('"').strip("\n").strip('"').strip(" ") | |
| audio, sr = sf.read(file) | |
| if len(audio.shape) > 1: | |
| audio = librosa.to_mono(audio.T) | |
| if sr != sample_rate: | |
| audio = librosa.resample(audio, orig_sr=sr, target_sr=sample_rate) | |
| except Exception as error: | |
| raise RuntimeError(f"An error occurred loading the audio: {error}") | |
| return audio.flatten() | |
| def format_title(title): | |
| formatted_title = ( | |
| unicodedata.normalize("NFKD", title).encode("ascii", "ignore").decode("utf-8") | |
| ) | |
| formatted_title = re.sub(r"[\u2500-\u257F]+", "", formatted_title) | |
| formatted_title = re.sub(r"[^\w\s.-]", "", formatted_title) | |
| formatted_title = re.sub(r"\s+", "_", formatted_title) | |
| return formatted_title | |
| def load_embedding(embedder_model, custom_embedder=None): | |
| embedder_root = os.path.join(now_dir, "rvc", "models", "embedders") | |
| embedding_list = { | |
| "contentvec": os.path.join(embedder_root, "contentvec_base.pt"), | |
| "japanese-hubert-base": os.path.join(embedder_root, "japanese-hubert-base.pt"), | |
| "chinese-hubert-large": os.path.join(embedder_root, "chinese-hubert-large.pt"), | |
| } | |
| online_embedders = { | |
| "japanese-hubert-base": "https://huggingface.co./rinna/japanese-hubert-base/resolve/main/fairseq/model.pt", | |
| "chinese-hubert-large": "https://huggingface.co./TencentGameMate/chinese-hubert-large/resolve/main/chinese-hubert-large-fairseq-ckpt.pt", | |
| } | |
| if embedder_model == "custom": | |
| model_path = custom_embedder | |
| if not custom_embedder and os.path.exists(custom_embedder): | |
| model_path = embedding_list["contentvec"] | |
| else: | |
| model_path = embedding_list[embedder_model] | |
| if embedder_model in online_embedders: | |
| model_path = embedding_list[embedder_model] | |
| url = online_embedders[embedder_model] | |
| print(f"\nDownloading {url} to {model_path}...") | |
| wget.download(url, out=model_path) | |
| else: | |
| model_path = embedding_list["contentvec"] | |
| models = checkpoint_utils.load_model_ensemble_and_task( | |
| [model_path], | |
| suffix="", | |
| ) | |
| # print(f"Embedding model {embedder_model} loaded successfully.") | |
| return models | |