import os import torch import librosa import gradio as gr from scipy.io.wavfile import write from transformers import WavLMModel import utils from models import SynthesizerTrn from mel_processing import mel_spectrogram_torch from speaker_encoder.voice_encoder import SpeakerEncoder ''' def get_wavlm(): os.system('gdown https://drive.google.com/uc?id=12-cB34qCTvByWT-QtOcZaqwwO21FLSqU') shutil.move('WavLM-Large.pt', 'wavlm') ''' device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print("Loading FreeVC...") hps = utils.get_hparams_from_file("configs/freevc.json") freevc = SynthesizerTrn( hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, **hps.model).to(device) _ = freevc.eval() _ = utils.load_checkpoint("checkpoints/freevc.pth", freevc, None) smodel = SpeakerEncoder('speaker_encoder/ckpt/pretrained_bak_5805000.pt') print("Loading FreeVC(24k)...") hps = utils.get_hparams_from_file("configs/freevc-24.json") freevc_24 = SynthesizerTrn( hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, **hps.model).to(device) _ = freevc_24.eval() _ = utils.load_checkpoint("checkpoints/freevc-24.pth", freevc_24, None) print("Loading FreeVC-s...") hps = utils.get_hparams_from_file("configs/freevc-s.json") freevc_s = SynthesizerTrn( hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, **hps.model).to(device) _ = freevc_s.eval() _ = utils.load_checkpoint("checkpoints/freevc-s.pth", freevc_s, None) print("Loading WavLM for content...") cmodel = WavLMModel.from_pretrained("microsoft/wavlm-large").to(device) import ffmpeg import random import numpy as np from elevenlabs import voices, generate, set_api_key, UnauthenticatedRateLimitError def pad_buffer(audio): # Pad buffer to multiple of 2 bytes buffer_size = len(audio) element_size = np.dtype(np.int16).itemsize if buffer_size % element_size != 0: audio = audio + b'\0' * (element_size - (buffer_size % element_size)) return audio def generate_voice(text, voice_name): try: audio = generate( text[:250], # Limit to 250 characters voice=voice_name, model="eleven_multilingual_v2" ) with open("output" + ".mp3", mode='wb') as f: f.write(audio) return "output.mp3" except UnauthenticatedRateLimitError as e: raise gr.Error("Thanks for trying out ElevenLabs TTS! You've reached the free tier limit. Please provide an API key to continue.") except Exception as e: raise gr.Error(e) html_denoise = """

""" def convert(api_key, text, tgt, voice, save_path): model = "FreeVC (24kHz)" with torch.no_grad(): # tgt wav_tgt, _ = librosa.load(tgt, sr=hps.data.sampling_rate) wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20) if model == "FreeVC" or model == "FreeVC (24kHz)": g_tgt = smodel.embed_utterance(wav_tgt) g_tgt = torch.from_numpy(g_tgt).unsqueeze(0).to(device) else: wav_tgt = torch.from_numpy(wav_tgt).unsqueeze(0).to(device) mel_tgt = mel_spectrogram_torch( wav_tgt, hps.data.filter_length, hps.data.n_mel_channels, hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length, hps.data.mel_fmin, hps.data.mel_fmax ) # src os.environ["ELEVEN_API_KEY"] = api_key src = generate_voice(text, voice) wav_src, _ = librosa.load(src, sr=hps.data.sampling_rate) wav_src = torch.from_numpy(wav_src).unsqueeze(0).to(device) c = cmodel(wav_src).last_hidden_state.transpose(1, 2).to(device) # infer if model == "FreeVC": audio = freevc.infer(c, g=g_tgt) elif model == "FreeVC-s": audio = freevc_s.infer(c, mel=mel_tgt) else: audio = freevc_24.infer(c, g=g_tgt) audio = audio[0][0].data.cpu().float().numpy() if model == "FreeVC" or model == "FreeVC-s": write(f"output/{save_path}.wav", hps.data.sampling_rate, audio) else: write(f"output/{save_path}.wav", 24000, audio) return f"output/{save_path}.wav" class subtitle: def __init__(self,index:int, start_time, end_time, text:str): self.index = int(index) self.start_time = start_time self.end_time = end_time self.text = text.strip() def normalize(self,ntype:str,fps=30): if ntype=="prcsv": h,m,s,fs=(self.start_time.replace(';',':')).split(":")#seconds self.start_time=int(h)*3600+int(m)*60+int(s)+round(int(fs)/fps,5) h,m,s,fs=(self.end_time.replace(';',':')).split(":") self.end_time=int(h)*3600+int(m)*60+int(s)+round(int(fs)/fps,5) elif ntype=="srt": h,m,s=self.start_time.split(":") s=s.replace(",",".") self.start_time=int(h)*3600+int(m)*60+round(float(s),5) h,m,s=self.end_time.split(":") s=s.replace(",",".") self.end_time=int(h)*3600+int(m)*60+round(float(s),5) else: raise ValueError def add_offset(self,offset=0): self.start_time+=offset if self.start_time<0: self.start_time=0 self.end_time+=offset if self.end_time<0: self.end_time=0 def __str__(self) -> str: return f'id:{self.index},start:{self.start_time},end:{self.end_time},text:{self.text}' def read_srt(uploaded_file): offset=0 with open(uploaded_file.name,"r",encoding="utf-8") as f: file=f.readlines() subtitle_list=[] indexlist=[] filelength=len(file) for i in range(0,filelength): if " --> " in file[i]: is_st=True for char in file[i-1].strip().replace("\ufeff",""): if char not in ['0','1','2','3','4','5','6','7','8','9']: is_st=False break if is_st: indexlist.append(i) #get line id listlength=len(indexlist) for i in range(0,listlength-1): st,et=file[indexlist[i]].split(" --> ") id=int(file[indexlist[i]-1].strip().replace("\ufeff","")) text="" for x in range(indexlist[i]+1,indexlist[i+1]-2): text+=file[x] st=subtitle(id,st,et,text) st.normalize(ntype="srt") st.add_offset(offset=offset) subtitle_list.append(st) st,et=file[indexlist[-1]].split(" --> ") id=file[indexlist[-1]-1] text="" for x in range(indexlist[-1]+1,filelength): text+=file[x] st=subtitle(id,st,et,text) st.normalize(ntype="srt") st.add_offset(offset=offset) subtitle_list.append(st) return subtitle_list import webrtcvad from pydub import AudioSegment from pydub.utils import make_chunks def vad(audio_name, out_path_name): audio = AudioSegment.from_file(audio_name, format="wav") # Set the desired sample rate (WebRTC VAD supports only 8000, 16000, 32000, or 48000 Hz) audio = audio.set_frame_rate(48000) # Set single channel (mono) audio = audio.set_channels(1) # Initialize VAD vad = webrtcvad.Vad() # Set aggressiveness mode (an integer between 0 and 3, 3 is the most aggressive) vad.set_mode(3) # Convert pydub audio to bytes frame_duration = 30 # Duration of a frame in ms frame_width = int(audio.frame_rate * frame_duration / 1000) # width of a frame in samples frames = make_chunks(audio, frame_duration) # Perform voice activity detection voiced_frames = [] for frame in frames: if len(frame.raw_data) < frame_width * 2: # Ensure frame is correct length break is_speech = vad.is_speech(frame.raw_data, audio.frame_rate) if is_speech: voiced_frames.append(frame) # Combine voiced frames back to an audio segment voiced_audio = sum(voiced_frames, AudioSegment.silent(duration=0)) voiced_audio.export(f"{out_path_name}.wav", format="wav") def trim_audio(intervals, input_file_path, output_file_path): # load the audio file audio = AudioSegment.from_file(input_file_path) # iterate over the list of time intervals for i, (start_time, end_time) in enumerate(intervals): # extract the segment of the audio segment = audio[start_time*1000:end_time*1000] output_file_path_i = f"increased_{i}.wav" if len(segment) < 5000: # Calculate how many times to repeat the audio to make it at least 5 seconds long repeat_count = (5000 // len(segment)) + 3 # Repeat the audio longer_audio = segment * repeat_count # Save the extended audio print(f"Audio was less than 5 seconds. Extended to {len(longer_audio)} milliseconds.") longer_audio.export(output_file_path_i, format='wav') vad(f"{output_file_path_i}", f"{output_file_path}_{i}") else: print("Audio is already 5 seconds or longer.") segment.export(f"{output_file_path}_{i}.wav", format='wav') import re def sort_key(file_name): """Extract the last number in the file name for sorting.""" numbers = re.findall(r'\d+', file_name) if numbers: return int(numbers[-1]) return -1 # In case there's no number, this ensures it goes to the start. def merge_audios(folder_path): output_file = "AI配音版.wav" # Get all WAV files in the folder files = [f for f in os.listdir(folder_path) if f.endswith('.wav')] # Sort files based on the last digit in their names sorted_files = sorted(files, key=sort_key) # Initialize an empty audio segment merged_audio = AudioSegment.empty() # Loop through each file, in order, and concatenate them for file in sorted_files: audio = AudioSegment.from_wav(os.path.join(folder_path, file)) merged_audio += audio print(f"Merged: {file}") # Export the merged audio to a new file merged_audio.export(output_file, format="wav") return "AI配音版.wav" import shutil def convert_from_srt(apikey, filename, audio_full, voice, multilingual): subtitle_list = read_srt(filename) #audio_data, sr = librosa.load(audio_full, sr=44100) #write("audio_full.wav", sr, audio_data.astype(np.int16)) if os.path.isdir("output"): shutil.rmtree("output") if multilingual==False: for i in subtitle_list: try: os.makedirs("output", exist_ok=True) trim_audio([[i.start_time, i.end_time]], audio_full, f"sliced_audio_{i.index}") print(f"正在合成第{i.index}条语音") print(f"语音内容：{i.text}") convert(apikey, i.text, f"sliced_audio_{i.index}_0.wav", voice, i.text + " " + str(i.index)) except Exception: pass else: for i in subtitle_list: try: os.makedirs("output", exist_ok=True) trim_audio([[i.start_time, i.end_time]], audio_full, f"sliced_audio_{i.index}") print(f"正在合成第{i.index}条语音") print(f"语音内容：{i.text.splitlines()[1]}") convert(apikey, i.text.splitlines()[1], f"sliced_audio_{i.index}_0.wav", voice, i.text.splitlines()[1] + " " + str(i.index)) except Exception: pass merge_audios("output") return "AI配音版.wav" restart_markdown = (""" ### 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🍻 """) all_voices = voices() with gr.Blocks() as app: gr.Markdown("# 🌊💕🎶 11Labs TTS - SRT文件一键AI配音") gr.Markdown("### 🌟 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 ") with gr.Row(): with gr.Column(): inp0 = gr.Textbox(type='password', label='请输入您的11Labs API Key') inp1 = gr.File(file_count="single", label="请上传一集视频对应的SRT文件") inp2 = gr.Audio(label="请上传一集视频的配音文件", type="filepath") 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') #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") 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）") btn = gr.Button("一键开启AI配音吧💕", variant="primary") with gr.Column(): out1 = gr.Audio(label="为您生成的AI完整配音", type="filepath") btn.click(convert_from_srt, [inp0, inp1, inp2, inp3, inp4], [out1]) gr.Markdown("### 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。") gr.HTML(''' ''') app.launch(share=True, show_error=True)