File size: 4,827 Bytes
ecfa0da | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | import os, sys
import librosa
import soundfile as sf
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(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):
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)
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, "programs", "applio_code", "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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