mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-06-24 05:09:18 +08:00
110 lines
3.6 KiB
Python
110 lines
3.6 KiB
Python
# -*- coding: utf-8 -*-
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import sys
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import os
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inp_text = os.environ.get("inp_text")
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inp_wav_dir = os.environ.get("inp_wav_dir")
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exp_name = os.environ.get("exp_name")
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i_part = os.environ.get("i_part")
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all_parts = os.environ.get("all_parts")
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if "_CUDA_VISIBLE_DEVICES" in os.environ:
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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
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from feature_extractor import cnhubert
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opt_dir = os.environ.get("opt_dir")
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sv_path = os.environ.get("sv_path")
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import torch
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is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
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import traceback
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import numpy as np
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from scipy.io import wavfile
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import torchaudio
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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sys.path.append(f"{now_dir}/GPT_SoVITS/eres2net")
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from tools.my_utils import load_audio, clean_path
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from time import time as ttime
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import shutil
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from ERes2NetV2 import ERes2NetV2
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import kaldi as Kaldi
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def my_save(fea, path): #####fix issue: torch.save doesn't support chinese path
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dir = os.path.dirname(path)
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name = os.path.basename(path)
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# tmp_path="%s/%s%s.pth"%(dir,ttime(),i_part)
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tmp_path = "%s%s.pth" % (ttime(), i_part)
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torch.save(fea, tmp_path)
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shutil.move(tmp_path, "%s/%s" % (dir, name))
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sv_cn_dir = "%s/7-sv_cn" % (opt_dir)
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wav32dir = "%s/5-wav32k" % (opt_dir)
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os.makedirs(opt_dir, exist_ok=True)
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os.makedirs(sv_cn_dir, exist_ok=True)
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os.makedirs(wav32dir, exist_ok=True)
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maxx = 0.95
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alpha = 0.5
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if torch.cuda.is_available():
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device = "cuda:0"
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# elif torch.backends.mps.is_available():
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# device = "mps"
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else:
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device = "cpu"
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class SV:
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def __init__(self,device,is_half):
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pretrained_state = torch.load(sv_path, map_location='cpu')
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embedding_model = ERes2NetV2(baseWidth=24,scale=4,expansion=4)
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embedding_model.load_state_dict(pretrained_state)
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embedding_model.eval()
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self.embedding_model=embedding_model
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self.res=torchaudio.transforms.Resample(32000, 16000).to(device)
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if is_half == False:
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self.embedding_model=self.embedding_model.to(device)
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else:
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self.embedding_model=self.embedding_model.half().to(device)
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self.is_half=is_half
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def compute_embedding3(self,wav):#(1,x)#-1~1
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with torch.no_grad():
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wav=self.res(wav)
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if self.is_half==True:wav=wav.half()
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feat = torch.stack([Kaldi.fbank(wav0.unsqueeze(0), num_mel_bins=80, sample_frequency=16000, dither=0) for wav0 in wav])
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sv_emb = self.embedding_model.forward3(feat)
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return sv_emb
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sv=SV(device,is_half)
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def name2go(wav_name, wav_path):
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sv_cn_path = "%s/%s.pt" % (sv_cn_dir, wav_name)
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if os.path.exists(sv_cn_path):return
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wav_path="%s/%s" % (wav32dir, wav_name)
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wav32k,sr0 = torchaudio.load(wav_path)
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assert sr0==32000
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wav32k = wav32k.to(device)
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emb=sv.compute_embedding3(wav32k).cpu() # torch.Size([1, 20480])
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my_save(emb, sv_cn_path)
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with open(inp_text, "r", encoding="utf8") as f:
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lines = f.read().strip("\n").split("\n")
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for line in lines[int(i_part) :: int(all_parts)]:
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try:
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wav_name, spk_name, language, text = line.split("|")
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wav_name = clean_path(wav_name)
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if inp_wav_dir != "" and inp_wav_dir != None:
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wav_name = os.path.basename(wav_name)
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wav_path = "%s/%s" % (inp_wav_dir, wav_name)
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else:
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wav_path = wav_name
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wav_name = os.path.basename(wav_name)
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name2go(wav_name, wav_path)
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except:
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print(line, traceback.format_exc())
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