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import logging
import os
import sys
import warnings
import librosa
import numpy as np
import soundfile as sf
import wget
from torch import nn
from transformers import HubertModel
# 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 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
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