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Create app.py
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app.py
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import os
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import torch
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import gradio as gr
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from openvoice import se_extractor
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from openvoice.api import ToneColorConverter
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from elevenlabs import voices, generate, set_api_key, UnauthenticatedRateLimitError
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ckpt_converter = 'checkpoints_v2/converter'
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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tone_color_converter = ToneColorConverter(f'{ckpt_converter}/config.json', device=device)
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tone_color_converter.load_ckpt(f'{ckpt_converter}/checkpoint.pth')
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base_speaker = f"11labs.mp3"
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source_se, audio_name = se_extractor.get_se(base_speaker, tone_color_converter, vad=True)
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def generate_voice(text, voice_name):
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try:
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audio = generate(
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text[:1000], # Limit to 1000 characters
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voice=voice_name,
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model="eleven_multilingual_v2"
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)
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with open("output" + ".mp3", mode='wb') as f:
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f.write(audio)
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return "output.mp3"
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except UnauthenticatedRateLimitError as e:
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raise Exception("Thanks for trying out ElevenLabs TTS! You've reached the free tier limit. Please provide an API key to continue.")
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except Exception as e:
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raise Exception(e)
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def convert(api_key, text, tgt, voice, save_path):
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os.environ["ELEVEN_API_KEY"] = api_key
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src_path = generate_voice(text, voice)
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reference_speaker = tgt
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target_se, audio_name = se_extractor.get_se(reference_speaker, tone_color_converter, vad=True)
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encode_message = "@MyShell"
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tone_color_converter.convert(
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audio_src_path=src_path,
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src_se=source_se,
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tgt_se=target_se,
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output_path=f"output/{save_path}.wav",
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message=encode_message)
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return f"output/{save_path}.wav"
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class subtitle:
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def __init__(self,index:int, start_time, end_time, text:str):
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self.index = int(index)
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self.start_time = start_time
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self.end_time = end_time
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self.text = text.strip()
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def normalize(self,ntype:str,fps=30):
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if ntype=="prcsv":
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h,m,s,fs=(self.start_time.replace(';',':')).split(":")#seconds
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self.start_time=int(h)*3600+int(m)*60+int(s)+round(int(fs)/fps,2)
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h,m,s,fs=(self.end_time.replace(';',':')).split(":")
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self.end_time=int(h)*3600+int(m)*60+int(s)+round(int(fs)/fps,2)
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elif ntype=="srt":
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h,m,s=self.start_time.split(":")
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s=s.replace(",",".")
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self.start_time=int(h)*3600+int(m)*60+round(float(s),2)
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h,m,s=self.end_time.split(":")
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s=s.replace(",",".")
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self.end_time=int(h)*3600+int(m)*60+round(float(s),2)
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else:
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raise ValueError
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def add_offset(self,offset=0):
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self.start_time+=offset
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if self.start_time<0:
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self.start_time=0
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self.end_time+=offset
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if self.end_time<0:
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self.end_time=0
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def __str__(self) -> str:
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return f'id:{self.index},start:{self.start_time},end:{self.end_time},text:{self.text}'
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def read_srt(uploaded_file):
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offset=0
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with open(uploaded_file.name,"r",encoding="utf-8") as f:
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file=f.readlines()
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subtitle_list=[]
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indexlist=[]
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filelength=len(file)
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for i in range(0,filelength):
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if " --> " in file[i]:
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is_st=True
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for char in file[i-1].strip().replace("\ufeff",""):
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if char not in ['0','1','2','3','4','5','6','7','8','9']:
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is_st=False
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break
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if is_st:
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indexlist.append(i) #get line id
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listlength=len(indexlist)
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for i in range(0,listlength-1):
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st,et=file[indexlist[i]].split(" --> ")
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id=int(file[indexlist[i]-1].strip().replace("\ufeff",""))
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text=""
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for x in range(indexlist[i]+1,indexlist[i+1]-2):
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text+=file[x]
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st=subtitle(id,st,et,text)
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st.normalize(ntype="srt")
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st.add_offset(offset=offset)
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subtitle_list.append(st)
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st,et=file[indexlist[-1]].split(" --> ")
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id=file[indexlist[-1]-1]
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text=""
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for x in range(indexlist[-1]+1,filelength):
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text+=file[x]
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st=subtitle(id,st,et,text)
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st.normalize(ntype="srt")
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st.add_offset(offset=offset)
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subtitle_list.append(st)
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return subtitle_list
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from pydub import AudioSegment
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def trim_audio(intervals, input_file_path, output_file_path):
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# load the audio file
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audio = AudioSegment.from_file(input_file_path)
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# iterate over the list of time intervals
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for i, (start_time, end_time) in enumerate(intervals):
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# extract the segment of the audio
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segment = audio[start_time*1000:end_time*1000]
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# construct the output file path
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output_file_path_i = f"{output_file_path}_{i}.wav"
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# export the segment to a file
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segment.export(output_file_path_i, format='wav')
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import re
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def sort_key(file_name):
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"""Extract the last number in the file name for sorting."""
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numbers = re.findall(r'\d+', file_name)
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if numbers:
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return int(numbers[-1])
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return -1 # In case there's no number, this ensures it goes to the start.
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def merge_audios(folder_path):
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output_file = "AI配音版.wav"
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# Get all WAV files in the folder
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files = [f for f in os.listdir(folder_path) if f.endswith('.wav')]
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# Sort files based on the last digit in their names
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sorted_files = sorted(files, key=sort_key)
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# Initialize an empty audio segment
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merged_audio = AudioSegment.empty()
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# Loop through each file, in order, and concatenate them
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for file in sorted_files:
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audio = AudioSegment.from_wav(os.path.join(folder_path, file))
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merged_audio += audio
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print(f"Merged: {file}")
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# Export the merged audio to a new file
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merged_audio.export(output_file, format="wav")
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return "AI配音版.wav"
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import shutil
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def convert_from_srt(apikey, filename, audio_full, voice, multilingual):
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subtitle_list = read_srt(filename)
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#audio_data, sr = librosa.load(audio_full, sr=44100)
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#write("audio_full.wav", sr, audio_data.astype(np.int16))
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if os.path.isdir("output"):
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shutil.rmtree("output")
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if multilingual==False:
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for i in subtitle_list:
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os.makedirs("output", exist_ok=True)
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trim_audio([[i.start_time, i.end_time]], audio_full, f"sliced_audio_{i.index}")
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print(f"正在合成第{i.index}条语音")
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print(f"语音内容:{i.text}")
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convert(apikey, i.text, f"sliced_audio_{i.index}_0.wav", voice, i.text + " " + str(i.index))
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else:
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for i in subtitle_list:
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os.makedirs("output", exist_ok=True)
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trim_audio([[i.start_time, i.end_time]], audio_full, f"sliced_audio_{i.index}")
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print(f"正在合成第{i.index}条语音")
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print(f"语音内容:{i.text.splitlines()[1]}")
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convert(apikey, i.text.splitlines()[1], f"sliced_audio_{i.index}_0.wav", voice, i.text.splitlines()[1] + " " + str(i.index))
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merge_audios("output")
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return "AI配音版.wav"
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restart_markdown = ("""
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### 若此页面无法正常显示,请点击[此链接](https://openxlab.org.cn/apps/detail/Kevin676/OpenAI-TTS)唤醒该程序!谢谢🍻
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""")
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all_voices = voices()
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with gr.Blocks() as app:
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gr.Markdown("# <center>🌊💕🎶 11Labs + OpenVoice V2 - SRT文件一键AI配音</center>")
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gr.Markdown("### <center>🌟 只需上传SRT文件和原版配音文件即可,每次一集视频AI自动配音!Developed by Kevin Wang </center>")
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with gr.Row():
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with gr.Column():
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inp0 = gr.Textbox(type='password', label='请输入您的11Labs API Key')
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inp1 = gr.File(file_count="single", label="请上传一集视频对应的SRT文件")
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inp2 = gr.Audio(label="请上传一集视频的配音文件", type="filepath")
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inp3 = gr.Dropdown(choices=[ voice.name for voice in all_voices ], label='请选择一个说话人提供基础音色', info="试听音色链接:https://huggingface.co/spaces/elevenlabs/tts", value='Rachel')
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#inp4 = gr.Dropdown(label="请选择用于分离伴奏的模型", info="UVR-HP5去除背景音乐效果更好,但会对人声造成一定的损伤", choices=["UVR-HP2", "UVR-HP5"], value="UVR-HP5")
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inp4 = gr.Checkbox(label="SRT文件是否为双语字幕", info="若为双语字幕,请打勾选择(SRT文件中需要先出现中文字幕,后英文字幕;中英字幕各占一行)")
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btn = gr.Button("一键开启AI配音吧💕", variant="primary")
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with gr.Column():
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out1 = gr.Audio(label="为您生成的AI完整配音", type="filepath")
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btn.click(convert_from_srt, [inp0, inp1, inp2, inp3, inp4], [out1])
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gr.Markdown("### <center>注意❗:请勿生成会对任何个人或组织造成侵害的内容,请尊重他人的著作权和知识产权。用户对此程序的任何使用行为与程序开发者无关。</center>")
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gr.HTML('''
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<div class="footer">
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<p>🌊🏞️🎶 - 江水东流急,滔滔无尽声。 明·顾璘
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</p>
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</div>
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''')
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app.launch(show_error=True)
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