Delete app.py

app.py

@@ -1,142 +0,0 @@
-import os
-
-os.system(
-    'pip install "modelscope[cv]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html'
-)
-
-from modelscope.pipelines import pipeline
-from modelscope.utils.constant import Tasks
-import gradio as gr
-import datetime
-
-#获取当前北京时间
-utc_dt = datetime.datetime.utcnow()
-beijing_dt = utc_dt.astimezone(datetime.timezone(datetime.timedelta(hours=16)))
-formatted = beijing_dt.strftime("%Y-%m-%d_%H")
-print(f"北京时间: {beijing_dt.year}年{beijing_dt.month}月{beijing_dt.day}日 "
-      f"{beijing_dt.hour}时{beijing_dt.minute}分{beijing_dt.second}秒")
-#创建作品存放目录
-works_path = '../works_audio_video_transcribe/' + formatted
-if not os.path.exists(works_path):
-  os.makedirs(works_path)
-print('作品目录：' + works_path)
-
-inference_pipeline = pipeline(
-    task=Tasks.auto_speech_recognition,
-    model='damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch')
-    
-def transcript(audiofile, text_file):
-    rec_result = inference_pipeline(audio_in=audiofile)
-    print(rec_result['text'])
-    
-    with open(text_file, "w") as f:
-        f.write(rec_result['text'])
-        
-    return rec_result['text']
-
-def audio_recog(audiofile):
-    utc_dt = datetime.datetime.utcnow()
-    beijing_dt = utc_dt.astimezone(datetime.timezone(datetime.timedelta(hours=16)))
-    formatted = beijing_dt.strftime("%Y-%m-%d_%H-%M-%S")
-    print(f"开始时间: {beijing_dt.year}年{beijing_dt.month}月{beijing_dt.day}日 "
-      f"{beijing_dt.hour}时{beijing_dt.minute}分{beijing_dt.second}秒")
-      
-    print("音频文件：" + audiofile)
-    
-    filename = os.path.splitext(os.path.basename(audiofile))[0]
-    text_file = works_path + '/' + filename + '.txt'
-    
-    text_output = transcript(audiofile, text_file)
-    
-    utc_dt = datetime.datetime.utcnow()
-    beijing_dt = utc_dt.astimezone(datetime.timezone(datetime.timedelta(hours=16)))
-    formatted = beijing_dt.strftime("%Y-%m-%d_%H-%M-%S")
-    print(f"结束时间: {beijing_dt.year}年{beijing_dt.month}月{beijing_dt.day}日 "
-      f"{beijing_dt.hour}时{beijing_dt.minute}分{beijing_dt.second}秒")
-    
-    return text_output, text_file
-
-def video_recog(filepath):
-    filename = os.path.splitext(os.path.basename(filepath))[0]
-    worksfile = works_path + '/works_' + filename + '.mp4'
-    print("视频文件：" + filepath)
-    
-    utc_dt = datetime.datetime.utcnow()
-    beijing_dt = utc_dt.astimezone(datetime.timezone(datetime.timedelta(hours=16)))
-    formatted = beijing_dt.strftime("%Y-%m-%d_%H-%M-%S.%f")
-    
-    # 提取音频为mp3
-    audiofile = works_path + '/' + formatted + '.mp3'
-    os.system(f"ffmpeg -i {filepath} -vn -c:a libmp3lame -q:a 4 {audiofile}")
-    
-    #识别音频文件
-    text_output, text_file = audio_recog(audiofile)
-    
-    return text_output, text_file
-
-css_style = "#fixed_size_img {height: 240px;} " \
-            "#overview {margin: auto;max-width: 400px; max-height: 400px;}"
-
-title = "音视频识别 by宁侠"
-description = "您只需要上传一段音频或视频文件，我们的服务会快速对其进行语音识别，然后生成相应的文字。这样，您就可以轻松地记录下重要的语音内容。现在就来试试我们的音视频识别服务吧，让您的生活和工作更加便捷！"
-
-examples_path = 'examples/'
-examples = [[examples_path + 'demo_shejipuhui.mp4']]
-
-# gradio interface
-with gr.Blocks(title=title, css=css_style) as demo:
-    gr.HTML('''
-      <div style="text-align: center; max-width: 720px; margin: 0 auto;">
-                  <div
-                    style="
-                      display: inline-flex;
-                      align-items: center;
-                      gap: 0.8rem;
-                      font-size: 1.75rem;
-                    "
-                  >
-                    <h1 style="font-family:  PingFangSC; font-weight: 500; font-size: 36px; margin-bottom: 7px;">
-                      音视频识别
-                    </h1>
-                    <h1 style="font-family: PingFangSC; font-weight: 500; line-height: 1.5em; font-size: 16px; margin-bottom: 7px;">
-                      by宁侠
-                    </h1>
-      ''')
-    gr.Markdown(description)
-    
-    with gr.Tab("🔊音频识别 Audio Transcribe"):
-        with gr.Row():
-            with gr.Column():
-                audio_input = gr.Audio(label="🔊音频输入 Audio Input", type="filepath")
-                gr.Examples(['examples/paddlespeech.asr-zh.wav', 'examples/demo_shejipuhui.mp3'], [audio_input])
-                audio_recog_button = gr.Button("👂音频识别 Recognize")
-            with gr.Column():
-                audio_text_output = gr.Textbox(label="✏️识别结果 Recognition Result", max_lines=5)
-                audio_text_file = gr.File(label="✏️识别结果文件 Recognition Result File")
-                audio_subtitles_button = gr.Button("添加字幕\nGenerate Subtitles", visible=False)
-                audio_output = gr.Audio(label="🔊音频 Audio", visible=False)
-    
-    audio_recog_button.click(audio_recog, inputs=[audio_input], outputs=[audio_text_output, audio_text_file])
-#    audio_subtitles_button.click(audio_subtitles, inputs=[audio_text_input], outputs=[audio_output])
-
-    with gr.Tab("🎥视频识别 Video Transcribe"):
-        with gr.Row():
-            with gr.Column():
-                video_input = gr.Video(label="🎥视频输入 Video Input")
-                gr.Examples(['examples/demo_shejipuhui.mp4'], [video_input], label='语音识别示例 ASR Demo')
-                video_recog_button = gr.Button("👂视频识别 Recognize")
-                video_output = gr.Video(label="🎥视频 Video", visible=False)
-            with gr.Column():
-                video_text_output = gr.Textbox(label="✏️识别结果 Recognition Result", max_lines=5)
-                video_text_file = gr.File(label="✏️识别结果文件 Recognition Result File")
-                with gr.Row(visible=False):
-                    font_size = gr.Slider(minimum=10, maximum=100, value=32, step=2, label="🔠字幕字体大小 Subtitle Font Size")
-                    font_color = gr.Radio(["black", "white", "green", "red"], label="🌈字幕颜色 Subtitle Color", value='white')
-                video_subtitles_button = gr.Button("添加字幕\nGenerate Subtitles", visible=False)
-                
-    
-    video_recog_button.click(video_recog, inputs=[video_input], outputs=[video_text_output, video_text_file])
-#    video_subtitles_button.click(video_subtitles, inputs=[video_text_input], outputs=[video_output])
-
-# start gradio service in local
-demo.queue(api_open=False).launch(debug=True)