app.py

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
import gradio as gr
import datetime
import os

os.system(
    'pip install "modelscope[cv]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html'
)

#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)