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Runtime error
| # -*- coding: utf-8 -*- | |
| import sys | |
| import os | |
| inp_text = os.environ.get("inp_text") | |
| inp_wav_dir = os.environ.get("inp_wav_dir") | |
| exp_name = os.environ.get("exp_name") | |
| i_part = os.environ.get("i_part") | |
| all_parts = os.environ.get("all_parts") | |
| if "_CUDA_VISIBLE_DEVICES" in os.environ: | |
| os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"] | |
| from feature_extractor import cnhubert | |
| opt_dir = os.environ.get("opt_dir") | |
| cnhubert.cnhubert_base_path = os.environ.get("cnhubert_base_dir") | |
| import torch | |
| is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available() | |
| import traceback | |
| import numpy as np | |
| from scipy.io import wavfile | |
| import librosa | |
| now_dir = os.getcwd() | |
| sys.path.append(now_dir) | |
| from tools.my_utils import load_audio, clean_path | |
| # from config import cnhubert_base_path | |
| # cnhubert.cnhubert_base_path=cnhubert_base_path | |
| # inp_text=sys.argv[1] | |
| # inp_wav_dir=sys.argv[2] | |
| # exp_name=sys.argv[3] | |
| # i_part=sys.argv[4] | |
| # all_parts=sys.argv[5] | |
| # os.environ["CUDA_VISIBLE_DEVICES"]=sys.argv[6] | |
| # cnhubert.cnhubert_base_path=sys.argv[7] | |
| # opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name | |
| from time import time as ttime | |
| import shutil | |
| def my_save(fea, path): #####fix issue: torch.save doesn't support chinese path | |
| dir = os.path.dirname(path) | |
| name = os.path.basename(path) | |
| # tmp_path="%s/%s%s.pth"%(dir,ttime(),i_part) | |
| tmp_path = "%s%s.pth" % (ttime(), i_part) | |
| torch.save(fea, tmp_path) | |
| shutil.move(tmp_path, "%s/%s" % (dir, name)) | |
| hubert_dir = "%s/4-cnhubert" % (opt_dir) | |
| wav32dir = "%s/5-wav32k" % (opt_dir) | |
| os.makedirs(opt_dir, exist_ok=True) | |
| os.makedirs(hubert_dir, exist_ok=True) | |
| os.makedirs(wav32dir, exist_ok=True) | |
| maxx = 0.95 | |
| alpha = 0.5 | |
| if torch.cuda.is_available(): | |
| device = "cuda:0" | |
| # elif torch.backends.mps.is_available(): | |
| # device = "mps" | |
| else: | |
| device = "cpu" | |
| model = cnhubert.get_model() | |
| # is_half=False | |
| if is_half == True: | |
| model = model.half().to(device) | |
| else: | |
| model = model.to(device) | |
| nan_fails = [] | |
| def name2go(wav_name, wav_path): | |
| hubert_path = "%s/%s.pt" % (hubert_dir, wav_name) | |
| if os.path.exists(hubert_path): | |
| return | |
| tmp_audio = load_audio(wav_path, 32000) | |
| tmp_max = np.abs(tmp_audio).max() | |
| if tmp_max > 2.2: | |
| print("%s-filtered,%s" % (wav_name, tmp_max)) | |
| return | |
| tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha * 32768)) + ((1 - alpha) * 32768) * tmp_audio | |
| tmp_audio32b = (tmp_audio / tmp_max * (maxx * alpha * 1145.14)) + ((1 - alpha) * 1145.14) * tmp_audio | |
| tmp_audio = librosa.resample(tmp_audio32b, orig_sr=32000, target_sr=16000) # 不是重采样问题 | |
| tensor_wav16 = torch.from_numpy(tmp_audio) | |
| if is_half == True: | |
| tensor_wav16 = tensor_wav16.half().to(device) | |
| else: | |
| tensor_wav16 = tensor_wav16.to(device) | |
| ssl = model.model(tensor_wav16.unsqueeze(0))["last_hidden_state"].transpose(1, 2).cpu() # torch.Size([1, 768, 215]) | |
| if np.isnan(ssl.detach().numpy()).sum() != 0: | |
| nan_fails.append((wav_name, wav_path)) | |
| print("nan filtered:%s" % wav_name) | |
| return | |
| wavfile.write( | |
| "%s/%s" % (wav32dir, wav_name), | |
| 32000, | |
| tmp_audio32.astype("int16"), | |
| ) | |
| my_save(ssl, hubert_path) | |
| with open(inp_text, "r", encoding="utf8") as f: | |
| lines = f.read().strip("\n").split("\n") | |
| for line in lines[int(i_part) :: int(all_parts)]: | |
| try: | |
| # wav_name,text=line.split("\t") | |
| wav_name, spk_name, language, text = line.split("|") | |
| wav_name = clean_path(wav_name) | |
| if inp_wav_dir != "" and inp_wav_dir != None: | |
| wav_name = os.path.basename(wav_name) | |
| wav_path = "%s/%s" % (inp_wav_dir, wav_name) | |
| else: | |
| wav_path = wav_name | |
| wav_name = os.path.basename(wav_name) | |
| name2go(wav_name, wav_path) | |
| except: | |
| print(line, traceback.format_exc()) | |
| if len(nan_fails) > 0 and is_half == True: | |
| is_half = False | |
| model = model.float() | |
| for wav in nan_fails: | |
| try: | |
| name2go(wav[0], wav[1]) | |
| except: | |
| print(wav_name, traceback.format_exc()) | |