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