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import os |
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import torch |
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import librosa |
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import gradio as gr |
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from scipy.io.wavfile import write |
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from transformers import WavLMModel |
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import utils |
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from models import SynthesizerTrn |
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from mel_processing import mel_spectrogram_torch |
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from speaker_encoder.voice_encoder import SpeakerEncoder |
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''' |
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def get_wavlm(): |
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os.system('gdown https://drive.google.com/uc?id=12-cB34qCTvByWT-QtOcZaqwwO21FLSqU') |
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shutil.move('WavLM-Large.pt', 'wavlm') |
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''' |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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print("Loading FreeVC...") |
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hps = utils.get_hparams_from_file("configs/freevc.json") |
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freevc = SynthesizerTrn( |
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hps.data.filter_length // 2 + 1, |
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hps.train.segment_size // hps.data.hop_length, |
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**hps.model).to(device) |
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_ = freevc.eval() |
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_ = utils.load_checkpoint("checkpoints/freevc.pth", freevc, None) |
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smodel = SpeakerEncoder('speaker_encoder/ckpt/pretrained_bak_5805000.pt') |
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print("Loading FreeVC(24k)...") |
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hps = utils.get_hparams_from_file("configs/freevc-24.json") |
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freevc_24 = SynthesizerTrn( |
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hps.data.filter_length // 2 + 1, |
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hps.train.segment_size // hps.data.hop_length, |
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**hps.model).to(device) |
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_ = freevc_24.eval() |
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_ = utils.load_checkpoint("checkpoints/freevc-24.pth", freevc_24, None) |
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print("Loading FreeVC-s...") |
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hps = utils.get_hparams_from_file("configs/freevc-s.json") |
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freevc_s = SynthesizerTrn( |
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hps.data.filter_length // 2 + 1, |
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hps.train.segment_size // hps.data.hop_length, |
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**hps.model).to(device) |
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_ = freevc_s.eval() |
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_ = utils.load_checkpoint("checkpoints/freevc-s.pth", freevc_s, None) |
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print("Loading WavLM for content...") |
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cmodel = WavLMModel.from_pretrained("microsoft/wavlm-large").to(device) |
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from openai import OpenAI |
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import ffmpeg |
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def convert(api_key, text, tgt, voice, save_path): |
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model = "FreeVC (24kHz)" |
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with torch.no_grad(): |
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wav_tgt, _ = librosa.load(tgt, sr=hps.data.sampling_rate) |
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wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20) |
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if model == "FreeVC" or model == "FreeVC (24kHz)": |
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g_tgt = smodel.embed_utterance(wav_tgt) |
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g_tgt = torch.from_numpy(g_tgt).unsqueeze(0).to(device) |
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else: |
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wav_tgt = torch.from_numpy(wav_tgt).unsqueeze(0).to(device) |
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mel_tgt = mel_spectrogram_torch( |
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wav_tgt, |
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hps.data.filter_length, |
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hps.data.n_mel_channels, |
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hps.data.sampling_rate, |
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hps.data.hop_length, |
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hps.data.win_length, |
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hps.data.mel_fmin, |
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hps.data.mel_fmax |
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) |
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client = OpenAI(api_key=api_key) |
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response = client.audio.speech.create( |
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model="tts-1", |
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voice=voice, |
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input=text, |
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) |
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response.stream_to_file("output_openai.mp3") |
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src = "output_openai.mp3" |
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wav_src, _ = librosa.load(src, sr=hps.data.sampling_rate) |
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wav_src = torch.from_numpy(wav_src).unsqueeze(0).to(device) |
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c = cmodel(wav_src).last_hidden_state.transpose(1, 2).to(device) |
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if model == "FreeVC": |
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audio = freevc.infer(c, g=g_tgt) |
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elif model == "FreeVC-s": |
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audio = freevc_s.infer(c, mel=mel_tgt) |
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else: |
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audio = freevc_24.infer(c, g=g_tgt) |
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audio = audio[0][0].data.cpu().float().numpy() |
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if model == "FreeVC" or model == "FreeVC-s": |
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write(f"output/{save_path}.wav", hps.data.sampling_rate, audio) |
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else: |
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write(f"output/{save_path}.wav", 24000, audio) |
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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(":") |
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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) |
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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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audio = AudioSegment.from_file(input_file_path) |
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for i, (start_time, end_time) in enumerate(intervals): |
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segment = audio[start_time*1000:end_time*1000] |
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output_file_path_i = f"{output_file_path}_{i}.wav" |
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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 |
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def merge_audios(folder_path): |
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output_file = "AI配音版.wav" |
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files = [f for f in os.listdir(folder_path) if f.endswith('.wav')] |
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sorted_files = sorted(files, key=sort_key) |
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merged_audio = AudioSegment.empty() |
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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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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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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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with gr.Blocks() as app: |
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gr.Markdown("# <center>🌊💕🎶 OpenAI TTS - 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OpenAI 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=['alloy', 'echo', 'fable', 'onyx', 'nova', 'shimmer'], label='请选择一个说话人提供基础音色', info="试听音色链接:https://platform.openai.com/docs/guides/text-to-speech/voice-options", value='alloy') |
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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) |