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kevinwang676 commited on Apr 19

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Create app_share.py

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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 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 = """
+<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
+        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,2)
+              h,m,s,fs=(self.end_time.replace(';',':')).split(":")
+              self.end_time=int(h)*3600+int(m)*60+int(s)+round(int(fs)/fps,2)
+         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),2)
+             h,m,s=self.end_time.split(":")
+             s=s.replace(",",".")
+             self.end_time=int(h)*3600+int(m)*60+round(float(s),2)
+         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
+from pydub import AudioSegment
+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]
+        # construct the output file path
+        output_file_path_i = f"{output_file_path}_{i}.wav"
+        # export the segment to a file
+        segment.export(output_file_path_i, 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:
+            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))
+    else:
+        for i in subtitle_list:
+            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))
+    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("# <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.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("### <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=True, show_error=True)