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https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-05 19:41:56 +08:00
110 lines
3.3 KiB
Python
110 lines
3.3 KiB
Python
# -*- coding: utf-8 -*-
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import sys, 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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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ.get("_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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cnhubert.cnhubert_base_path = os.environ.get("cnhubert_base_dir")
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is_half = eval(os.environ.get("is_half", "True"))
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import pdb, traceback, numpy as np, logging
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from scipy.io import wavfile
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import librosa, torch
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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from my_utils import load_audio
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# from config import cnhubert_base_path
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# cnhubert.cnhubert_base_path=cnhubert_base_path
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# inp_text=sys.argv[1]
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# inp_wav_dir=sys.argv[2]
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# exp_name=sys.argv[3]
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# i_part=sys.argv[4]
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# all_parts=sys.argv[5]
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# os.environ["CUDA_VISIBLE_DEVICES"]=sys.argv[6]
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# cnhubert.cnhubert_base_path=sys.argv[7]
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# opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name
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from time import time as ttime
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import shutil
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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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torch.save(fea, tmp_path)
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shutil.move(tmp_path, "%s/%s" % (dir, name))
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hubert_dir = "%s/4-cnhubert" % (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(hubert_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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device = "cuda:0"
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model = cnhubert.get_model()
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if is_half == True:
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model = model.half().to(device)
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else:
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model = model.to(device)
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def name2go(wav_name):
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hubert_path = "%s/%s.pt" % (hubert_dir, wav_name)
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if os.path.exists(hubert_path):
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return
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wav_path = "%s/%s" % (inp_wav_dir, wav_name)
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tmp_audio = load_audio(wav_path, 32000)
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tmp_max = np.abs(tmp_audio).max()
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if tmp_max > 2.2:
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print("%s-%s-%s-filtered" % (idx0, idx1, tmp_max))
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return
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tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha * 32768)) + (
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(1 - alpha) * 32768
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) * tmp_audio
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tmp_audio = librosa.resample(tmp_audio32, orig_sr=32000, target_sr=16000)
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tensor_wav16 = torch.from_numpy(tmp_audio)
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if is_half == True:
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tensor_wav16 = tensor_wav16.half().to(device)
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else:
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tensor_wav16 = tensor_wav16.to(device)
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ssl = (
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model.model(tensor_wav16.unsqueeze(0))["last_hidden_state"]
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.transpose(1, 2)
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.cpu()
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) # torch.Size([1, 768, 215])
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if np.isnan(ssl.detach().numpy()).sum() != 0:
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return
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wavfile.write(
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"%s/%s" % (wav32dir, wav_name),
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32000,
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tmp_audio32.astype("int16"),
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)
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# torch.save(ssl,hubert_path )
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my_save(ssl, hubert_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,text=line.split("\t")
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wav_name, spk_name, language, text = line.split("|")
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wav_name = os.path.basename(wav_name)
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name2go(wav_name)
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except:
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print(line, traceback.format_exc())
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