Update app.py

app.py

@@ -48,8 +48,40 @@ _ = utils.load_checkpoint("checkpoints/freevc-s.pth", freevc_s, None)
 
 print("Loading WavLM for content...")
 cmodel = WavLMModel.from_pretrained("microsoft/wavlm-large").to(device)
- 
-def convert(model, src, tgt):
+
+
+from openai import OpenAI
+
+import ffmpeg
+import urllib.request
+urllib.request.urlretrieve("https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/UVR-HP2.pth", "uvr5/uvr_model/UVR-HP2.pth")
+urllib.request.urlretrieve("https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/UVR-HP5.pth", "uvr5/uvr_model/UVR-HP5.pth")
+
+from uvr5.vr import AudioPre
+weight_uvr5_root = "uvr5/uvr_model"
+uvr5_names = []
+for name in os.listdir(weight_uvr5_root):
+    if name.endswith(".pth") or "onnx" in name:
+        uvr5_names.append(name.replace(".pth", ""))
+
+func = AudioPre
+
+pre_fun_hp2 = func(
+  agg=int(10),
+  model_path=os.path.join(weight_uvr5_root, "UVR-HP2.pth"),
+  device="cuda",
+  is_half=True,
+)
+pre_fun_hp5 = func(
+  agg=int(10),
+  model_path=os.path.join(weight_uvr5_root, "UVR-HP5.pth"),
+  device="cuda",
+  is_half=True,
+)
+
+
+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)
@@ -60,7 +92,7 @@ def convert(model, src, tgt):
         else:
             wav_tgt = torch.from_numpy(wav_tgt).unsqueeze(0).to(device)
             mel_tgt = mel_spectrogram_torch(
-                wav_tgt, 
+                wav_tgt,
                 hps.data.filter_length,
                 hps.data.n_mel_channels,
                 hps.data.sampling_rate,
@@ -70,6 +102,17 @@ def convert(model, src, tgt):
                 hps.data.mel_fmax
             )
         # src
+        client = OpenAI(api_key=api_key)
+
+        response = client.audio.speech.create(
+            model="tts-1-hd",
+            voice=voice,
+            input=text,
+        )
+
+        response.stream_to_file("output_openai.mp3")
+
+        src = "output_openai.mp3"
         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)
@@ -82,22 +125,182 @@ def convert(model, src, tgt):
             audio = freevc_24.infer(c, g=g_tgt)
         audio = audio[0][0].data.cpu().float().numpy()
         if model == "FreeVC" or model == "FreeVC-s":
-            write("out.wav", hps.data.sampling_rate, audio)
+            write(f"output/{save_path}.wav", hps.data.sampling_rate, audio)
         else:
-            write("out.wav", 24000, audio)
-    out = "out.wav"
-    return out
+            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 merge_audios(input_dir):
+    output_file = "AI配音版.wav"
+    # List all .wav files in the directory
+    files = [f for f in os.listdir(input_dir) if f.endswith('.wav')]
+
+    # Sort files based on the numerical order extracted from their names
+    sorted_files = sorted(files, key=lambda x: int(re.search(r'(\d+)', x).group()))
+
+    # Initialize an empty audio segment
+    combined = AudioSegment.empty()
+
+    # Loop through the sorted list and concatenate them
+    for file in sorted_files:
+        path = os.path.join(input_dir, file)
+        audio = AudioSegment.from_wav(path)
+        combined += audio
+        print(f"Merged: {file}")
+
+    # Export the combined audio
+    combined.export(output_file, format="wav")
+    return "AI配音版.wav"
+
+import shutil
+
+def convert_from_srt(apikey, filename, video_full, voice, split_model, multilingual):
+    subtitle_list = read_srt(filename)
     
-model = gr.Dropdown(choices=["FreeVC", "FreeVC-s", "FreeVC (24kHz)"], value="FreeVC",type="value", label="Model") 
-audio1 = gr.Audio(label="Source Audio", type='filepath')
-audio2 = gr.Audio(label="Reference Audio", type='filepath')
-inputs = [model, audio1, audio2]
-outputs =  gr.Audio(label="Output Audio", type='filepath')
+    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()
+    
+    if split_model=="UVR-HP2":
+        pre_fun = pre_fun_hp2
+    else:
+        pre_fun = pre_fun_hp5
+
+    filename = "output"
+    pre_fun._path_audio_("audio_full.wav", f"./denoised/{split_model}/{filename}/", f"./denoised/{split_model}/{filename}/", "wav")
+    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]], f"./denoised/{split_model}/{filename}/vocal_audio_full.wav_10.wav", 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]], f"./denoised/{split_model}/{filename}/vocal_audio_full.wav_10.wav", 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))
+     
+    return merge_audios("output")
+
 
-title = "FreeVC"
-description = "Gradio Demo for FreeVC: Towards High-Quality Text-Free One-Shot Voice Conversion. To use it, simply upload your audio, or click the example to load. Read more at the links below. Note: It seems that the WavLM checkpoint in HuggingFace is a little different from the one used to train FreeVC, which may degrade the performance a bit. In addition, speaker similarity can be largely affected if there are too much silence in the reference audio, so please <strong>trim</strong> it before submitting."
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2210.15418' target='_blank'>Paper</a> | <a href='https://github.com/OlaWod/FreeVC' target='_blank'>Github Repo</a></p>"
+with gr.Blocks() as app:
+    gr.Markdown("# <center>🌊💕🎶 XTTS - 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OpenAI API Key')
+            inp1 = gr.File(file_count="single", label="请上传一集视频对应的SRT文件")
+            inp2 = 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")
+            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')
+            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")
+            inp5 = 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")
 
-examples=[["FreeVC", 'p225_001.wav', 'p226_002.wav'], ["FreeVC-s", 'p226_002.wav', 'p225_001.wav'], ["FreeVC (24kHz)", 'p225_001.wav', 'p226_002.wav']]
+        btn.click(convert_from_srt, [inp0, inp1, inp2, inp3, inp4, inp5], [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>
+    ''')
 
-gr.Interface(convert, inputs, outputs, title=title, description=description, article=article, examples=examples, enable_queue=True).launch()
+app.launch(share=True, show_error=True)