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import platform,os,traceback
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import ffmpeg
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import numpy as np
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import gradio as gr
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from tools.i18n.i18n import I18nAuto
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import pandas as pd
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i18n = I18nAuto(language=os.environ.get('language','Auto'))
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def load_audio(file, sr):
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try:
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file = clean_path(file)
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if os.path.exists(file) == False:
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raise RuntimeError(
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"You input a wrong audio path that does not exists, please fix it!"
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)
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out, _ = (
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ffmpeg.input(file, threads=0)
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.output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
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.run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
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)
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except Exception as e:
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traceback.print_exc()
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raise RuntimeError(i18n("音频加载失败"))
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return np.frombuffer(out, np.float32).flatten()
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def clean_path(path_str:str):
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if path_str.endswith(('\\','/')):
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return clean_path(path_str[0:-1])
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path_str = path_str.replace('/', os.sep).replace('\\', os.sep)
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return path_str.strip(" ").strip('\'').strip("\n").strip('"').strip(" ").strip("\u202a")
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def check_for_existance(file_list:list=None,is_train=False,is_dataset_processing=False):
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files_status=[]
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if is_train == True and file_list:
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file_list.append(os.path.join(file_list[0],'2-name2text.txt'))
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file_list.append(os.path.join(file_list[0],'3-bert'))
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file_list.append(os.path.join(file_list[0],'4-cnhubert'))
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file_list.append(os.path.join(file_list[0],'5-wav32k'))
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file_list.append(os.path.join(file_list[0],'6-name2semantic.tsv'))
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for file in file_list:
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if os.path.exists(file):files_status.append(True)
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else:files_status.append(False)
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if sum(files_status)!=len(files_status):
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if is_train:
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for file,status in zip(file_list,files_status):
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if status:pass
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else:gr.Warning(file)
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gr.Warning(i18n('以下文件或文件夹不存在'))
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return False
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elif is_dataset_processing:
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if files_status[0]:
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return True
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elif not files_status[0]:
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gr.Warning(file_list[0])
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elif not files_status[1] and file_list[1]:
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gr.Warning(file_list[1])
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gr.Warning(i18n('以下文件或文件夹不存在'))
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return False
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else:
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if file_list[0]:
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gr.Warning(file_list[0])
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gr.Warning(i18n('以下文件或文件夹不存在'))
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else:
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gr.Warning(i18n('路径不能为空'))
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return False
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return True
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def check_details(path_list=None,is_train=False,is_dataset_processing=False):
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if is_dataset_processing:
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list_path, audio_path = path_list
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if (not list_path.endswith('.list')):
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gr.Warning(i18n('请填入正确的List路径'))
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return
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if audio_path:
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if not os.path.isdir(audio_path):
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gr.Warning(i18n('请填入正确的音频文件夹路径'))
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return
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with open(list_path,"r",encoding="utf8")as f:
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line=f.readline().strip("\n").split("\n")
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wav_name, _, __, ___ = line[0].split("|")
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wav_name=clean_path(wav_name)
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if (audio_path != "" and audio_path != None):
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wav_name = os.path.basename(wav_name)
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wav_path = "%s/%s"%(audio_path, wav_name)
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else:
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wav_path=wav_name
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if os.path.exists(wav_path):
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...
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else:
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gr.Warning(i18n('路径错误'))
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return
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if is_train:
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path_list.append(os.path.join(path_list[0],'2-name2text.txt'))
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path_list.append(os.path.join(path_list[0],'4-cnhubert'))
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path_list.append(os.path.join(path_list[0],'5-wav32k'))
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path_list.append(os.path.join(path_list[0],'6-name2semantic.tsv'))
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phone_path, hubert_path, wav_path, semantic_path = path_list[1:]
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with open(phone_path,'r',encoding='utf-8') as f:
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if f.read(1):...
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else:gr.Warning(i18n('缺少音素数据集'))
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if os.listdir(hubert_path):...
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else:gr.Warning(i18n('缺少Hubert数据集'))
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if os.listdir(wav_path):...
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else:gr.Warning(i18n('缺少音频数据集'))
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df = pd.read_csv(
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semantic_path, delimiter="\t", encoding="utf-8"
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)
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if len(df) >= 1:...
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else:gr.Warning(i18n('缺少语义数据集'))
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