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| import os, sys | |
| import librosa | |
| import soundfile as sf | |
| import numpy as np | |
| import re | |
| import unicodedata | |
| import wget | |
| import subprocess | |
| from pydub import AudioSegment | |
| import tempfile | |
| from torch import nn | |
| import logging | |
| from transformers import HubertModel | |
| import warnings | |
| # Remove this to see warnings about transformers models | |
| warnings.filterwarnings("ignore") | |
| logging.getLogger("fairseq").setLevel(logging.ERROR) | |
| logging.getLogger("faiss.loader").setLevel(logging.ERROR) | |
| logging.getLogger("transformers").setLevel(logging.ERROR) | |
| logging.getLogger("torch").setLevel(logging.ERROR) | |
| now_dir = os.getcwd() | |
| sys.path.append(now_dir) | |
| base_path = os.path.join(now_dir, "rvc", "models", "formant", "stftpitchshift") | |
| stft = base_path + ".exe" if sys.platform == "win32" else base_path | |
| class HubertModelWithFinalProj(HubertModel): | |
| def __init__(self, config): | |
| super().__init__(config) | |
| self.final_proj = nn.Linear(config.hidden_size, config.classifier_proj_size) | |
| 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 load_audio_infer( | |
| file, | |
| sample_rate, | |
| **kwargs, | |
| ): | |
| formant_shifting = kwargs.get("formant_shifting", False) | |
| try: | |
| file = file.strip(" ").strip('"').strip("\n").strip('"').strip(" ") | |
| if not os.path.isfile(file): | |
| raise FileNotFoundError(f"File not found: {file}") | |
| 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) | |
| if formant_shifting: | |
| formant_qfrency = kwargs.get("formant_qfrency", 0.8) | |
| formant_timbre = kwargs.get("formant_timbre", 0.8) | |
| from stftpitchshift import StftPitchShift | |
| pitchshifter = StftPitchShift(1024, 32, sample_rate) | |
| audio = pitchshifter.shiftpitch( | |
| audio, | |
| factors=1, | |
| quefrency=formant_qfrency * 1e-3, | |
| distortion=formant_timbre, | |
| ) | |
| except Exception as error: | |
| raise RuntimeError(f"An error occurred loading the audio: {error}") | |
| return np.array(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"), | |
| "chinese-hubert-base": os.path.join(embedder_root, "chinese_hubert_base"), | |
| "japanese-hubert-base": os.path.join(embedder_root, "japanese_hubert_base"), | |
| "korean-hubert-base": os.path.join(embedder_root, "korean_hubert_base"), | |
| } | |
| online_embedders = { | |
| "contentvec": "https://huggingface.co/IAHispano/Applio/resolve/main/Resources/embedders/contentvec/pytorch_model.bin", | |
| "chinese-hubert-base": "https://huggingface.co/IAHispano/Applio/resolve/main/Resources/embedders/chinese_hubert_base/pytorch_model.bin", | |
| "japanese-hubert-base": "https://huggingface.co/IAHispano/Applio/resolve/main/Resources/embedders/japanese_hubert_base/pytorch_model.bin", | |
| "korean-hubert-base": "https://huggingface.co/IAHispano/Applio/resolve/main/Resources/embedders/korean_hubert_base/pytorch_model.bin", | |
| } | |
| config_files = { | |
| "contentvec": "https://huggingface.co/IAHispano/Applio/resolve/main/Resources/embedders/contentvec/config.json", | |
| "chinese-hubert-base": "https://huggingface.co/IAHispano/Applio/resolve/main/Resources/embedders/chinese_hubert_base/config.json", | |
| "japanese-hubert-base": "https://huggingface.co/IAHispano/Applio/resolve/main/Resources/embedders/japanese_hubert_base/config.json", | |
| "korean-hubert-base": "https://huggingface.co/IAHispano/Applio/resolve/main/Resources/embedders/korean_hubert_base/config.json", | |
| } | |
| if embedder_model == "custom": | |
| if os.path.exists(custom_embedder): | |
| model_path = custom_embedder | |
| else: | |
| print(f"Custom embedder not found: {custom_embedder}, using contentvec") | |
| model_path = embedding_list["contentvec"] | |
| else: | |
| model_path = embedding_list[embedder_model] | |
| bin_file = os.path.join(model_path, "pytorch_model.bin") | |
| json_file = os.path.join(model_path, "config.json") | |
| os.makedirs(model_path, exist_ok=True) | |
| if not os.path.exists(bin_file): | |
| url = online_embedders[embedder_model] | |
| print(f"Downloading {url} to {model_path}...") | |
| wget.download(url, out=bin_file) | |
| if not os.path.exists(json_file): | |
| url = config_files[embedder_model] | |
| print(f"Downloading {url} to {model_path}...") | |
| wget.download(url, out=json_file) | |
| models = HubertModelWithFinalProj.from_pretrained(model_path) | |
| return models | |