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| 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.client import ElevenLabs | |
| 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(api_key, text, voice): | |
| client = ElevenLabs( | |
| api_key=api_key, # Defaults to ELEVEN_API_KEY | |
| ) | |
| audio = client.generate(text=text, voice=voice) #response.voices[0] | |
| audio = b"".join(audio) | |
| with open("output.mp3", "wb") as f: | |
| f.write(audio) | |
| return "output.mp3" | |
| html_denoise = """ | |
| <html> | |
| <head> | |
| </script> | |
| <link rel="stylesheet" href="https://gradio.s3-us-west-2.amazonaws.com/2.6.2/static/bundle.css"> | |
| </head> | |
| <body> | |
| <div id="target"></div> | |
| <script src="https://gradio.s3-us-west-2.amazonaws.com/2.6.2/static/bundle.js"></script> | |
| <script | |
| type="module" | |
| src="https://gradio.s3-us-west-2.amazonaws.com/4.15.0/gradio.js" | |
| ></script> | |
| <iframe | |
| src="https://g-app-center-40055665-8145-0zp6jbv.openxlab.space" | |
| frameBorder="0" | |
| width="1280" | |
| height="700" | |
| ></iframe> | |
| </body> | |
| </html> | |
| """ | |
| 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 | |
| src = generate_voice(api_key, 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 | |
| # get a zip file | |
| import zipfile | |
| def zip_sliced_files(directory, zip_filename): | |
| # Create a ZipFile object | |
| with zipfile.ZipFile(zip_filename, 'w') as zipf: | |
| # Iterate over all files in the directory | |
| for foldername, subfolders, filenames in os.walk(directory): | |
| for filename in filenames: | |
| # Check if the file starts with "sliced" and has a .wav extension | |
| if filename.startswith("sliced") and filename.endswith(".wav"): | |
| # Create the complete file path | |
| file_path = os.path.join(foldername, filename) | |
| # Add the file to the zip file | |
| zipf.write(file_path, arcname=filename) | |
| print(f"Added {filename} to {zip_filename}") | |
| # set speed | |
| def change_speed(audio_inp, speed=1.0): | |
| audio = AudioSegment.from_file(audio_inp) | |
| sound_with_altered_frame_rate = audio._spawn(audio.raw_data, overrides={ | |
| "frame_rate": int(audio.frame_rate * speed) | |
| }) | |
| slower_audio = sound_with_altered_frame_rate.set_frame_rate(audio.frame_rate) | |
| slower_audio.export("slower_speech.wav", format="wav") | |
| return "slower_speech.wav" | |
| # delete files first | |
| def delete_sliced_files(directory): | |
| # Iterate over all files in the directory | |
| for foldername, subfolders, filenames in os.walk(directory): | |
| for filename in filenames: | |
| # Check if the file starts with "sliced" | |
| if filename.startswith("sliced"): | |
| # Create the complete file path | |
| file_path = os.path.join(foldername, filename) | |
| # Delete the file | |
| os.remove(file_path) | |
| print(f"Deleted {filename}") | |
| def convert_from_srt(api_key, filename, audio_full, voice, multilingual): | |
| subtitle_list = read_srt(filename) | |
| delete_sliced_files("./") | |
| #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(api_key, 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(api_key, 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") | |
| zip_sliced_files("./", "参考音频.zip") | |
| return "AI配音版.wav", "参考音频.zip" | |
| 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🍻 | |
| """) | |
| import ffmpeg | |
| def denoise(video_full): | |
| if os.path.exists("audio_full.wav"): | |
| os.remove("audio_full.wav") | |
| ffmpeg.input(video_full).output("audio_full.wav", ac=2, ar=44100).run() | |
| return "audio_full.wav" | |
| with gr.Blocks() as app: | |
| gr.Markdown("# <center>🌊💕🎶 11Labs TTS - SRT文件一键AI配音</center>") | |
| 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>") | |
| with gr.Tab("📺视频转音频"): | |
| with gr.Row(): | |
| inp_video = gr.Video(label="请上传一集包含原声配音的视频", info="需要是.mp4视频文件") | |
| btn_convert = gr.Button("视频文件转音频", variant="primary") | |
| out_audio = gr.Audio(label="视频对应的音频文件，可以下载至本地后进行降噪处理", type="filepath") | |
| btn_convert.click(denoise, [inp_video], [out_audio]) | |
| with gr.Tab("🎶AI配音"): | |
| 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=["Rachel", "Alice", "Chris", "Adam"], label='请选择一个说话人提供基础音色', info="试听音色链接：https://elevenlabs.io/app/speech-synthesis", value='Chris') | |
| #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）") | |
| btn1 = 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") | |
| out2 = gr.File(label="包含所有参考音频的zip文件") | |
| inp_speed = gr.Slider(label="设置AI配音的速度", minimum=0.8, maximum=1.2, value=1.0, step=0.01) | |
| btn2 = gr.Button("一键改变AI配音速度") | |
| out3 = gr.Audio(label="变速后的AI配音", type="filepath") | |
| btn1.click(convert_from_srt, [inp0, inp1, inp2, inp3, inp4], [out1, out2]) | |
| btn2.click(change_speed, [out1, inp_speed], [out3]) | |
| 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>") | |
| gr.HTML(''' | |
| <div class="footer"> | |
| <p>🌊🏞️🎶 - 江水东流急，滔滔无尽声。 明·顾璘 | |
| </p> | |
| </div> | |
| ''') | |
| app.launch(share=False, show_error=True) |