rvc/lib/utils.py

import os, sys
import librosa
import soundfile as sf
import numpy as np
import re
import unicodedata
import wget
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_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