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import gradio as gr |
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from modelscope.pipelines import pipeline |
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from modelscope.utils.constant import Tasks |
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from videoclipper import VideoClipper |
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if __name__ == "__main__": |
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inference_pipeline = pipeline( |
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task=Tasks.auto_speech_recognition, |
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model='damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch', |
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vad_model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch', |
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punc_model='damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch', |
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) |
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audio_clipper = VideoClipper(inference_pipeline) |
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def audio_recog(audio_input): |
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return audio_clipper.recog(audio_input) |
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def audio_clip(dest_text, start_ost, end_ost, state): |
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return audio_clipper.clip(dest_text, start_ost, end_ost, state) |
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def video_recog(video_input): |
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return audio_clipper.video_recog(video_input) |
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def video_clip(dest_text, start_ost, end_ost, state): |
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return audio_clipper.video_clip(dest_text, start_ost, end_ost, state) |
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def video_clip_addsub(dest_text, start_ost, end_ost, state, font_size, font_color): |
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return audio_clipper.video_clip(dest_text, start_ost, end_ost, state, font_size, font_color, add_sub=True) |
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top_md_1 = (""" |
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A video clip tool based on Paraformer-long's VAD, ASR, timestamp prediction, punctuation restoration abilities. |
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Get the video clip simply following steps: |
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* Step1: Upload video file (or try examples below), click **<font color="#f7802b">Recognize</font>** button |
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* Step2: Copy text segments you need to 'Text to Clip', set the subtitle settings (if you need) |
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* Step3: Click **<font color="#f7802b">Clip</font>** button or **<font color="#f7802b">Clip and Generate Subtitles</font>** button |
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""") |
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top_md_2 = (""" |
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The video had better to have size under 40Mb, |
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For video in large size, you can split the audio from it and use 'Audio Clip', |
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or **<font color="#1785c4">establish your own gradio service with the source code (recommanded)</font>** : |
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<div align="center"> |
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<div style="display:flex; gap: 0.25rem;" align="center"> |
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FunASR_APP: <a href='https://github.com/alibaba/funasr-app'><img src='https://img.shields.io/badge/Github-Code-blue'></a> |
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🌟Support Us: <a href='https://github.com/alibaba/funasr-app/stargazers'><img src='https://img.shields.io/github/stars/alibaba/funasr-app.svg?style=social'></a> |
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</div> |
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</div> |
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""") |
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top_md_3 = ("""You may understand FunASR futher with source code and paper: |
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<div align="center"> |
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<div style="display:flex; gap: 0.25rem;" align="center"> |
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FunASR: <a href='https://github.com/alibaba-damo-academy/FunASR'><img src='https://img.shields.io/badge/Github-Code-blue'></a> |
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FunASR Paper: <a href="https://arxiv.org/abs/2305.11013"><img src="https://img.shields.io/badge/Arxiv-2305.11013-orange"></a> |
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🌟Star FunASR: <a href='https://github.com/alibaba-damo-academy/FunASR/stargazers'><img src='https://img.shields.io/github/stars/alibaba-damo-academy/FunASR.svg?style=social'></a> |
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</div> |
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</div> |
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""") |
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with gr.Blocks() as demo: |
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gr.Markdown(top_md_1) |
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gr.Markdown(top_md_2) |
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gr.Markdown(top_md_3) |
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video_state = gr.State() |
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audio_state = gr.State() |
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with gr.Tab("🎥✂️视频裁剪 Video Clipping"): |
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with gr.Row(): |
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with gr.Column(): |
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video_input = gr.Video(label="🎥视频输入 Video Input") |
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gr.Examples(['examples/2022云栖大会_片段2.mp4', |
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'examples/2022云栖大会_片段.mp4', |
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'examples/为什么要多读书？这是我听过最好的答案-片段.mp4', |
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'examples/使用chatgpt_片段.mp4'], |
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[video_input]) |
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recog_button2 = gr.Button("👂识别 Recognize") |
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video_text_output = gr.Textbox(label="✏️识别结果 Recognition Result") |
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video_srt_output = gr.Textbox(label="📖SRT字幕内容 RST Subtitles") |
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with gr.Column(): |
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video_text_input = gr.Textbox(label="✏️待裁剪文本 Text to Clip (多段文本使用'#'连接)") |
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with gr.Row(): |
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video_start_ost = gr.Slider(minimum=-500, maximum=1000, value=0, step=50, label="⏪开始位置偏移 Start Offset (ms)") |
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video_end_ost = gr.Slider(minimum=-500, maximum=1000, value=100, step=50, label="⏩结束位置偏移 End Offset (ms)") |
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with gr.Row(): |
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font_size = gr.Slider(minimum=10, maximum=100, value=32, step=2, label="🔠字幕字体大小 Subtitle Font Size") |
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font_color = gr.Radio(["black", "white", "green", "red"], label="🌈字幕颜色 Subtitle Color", value='white') |
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with gr.Row(): |
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clip_button2 = gr.Button("✂️裁剪\nClip") |
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clip_button3 = gr.Button("✂️裁剪并添加字幕\nClip and Generate Subtitles") |
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video_output = gr.Video(label="🎥裁剪结果 Audio Clipped") |
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video_mess_output = gr.Textbox(label="ℹ️裁剪信息 Clipping Log") |
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video_srt_clip_output = gr.Textbox(label="📖裁剪部分SRT字幕内容 Clipped RST Subtitles") |
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with gr.Tab("🔊✂️音频裁剪 Audio Clipping"): |
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with gr.Row(): |
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with gr.Column(): |
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audio_input = gr.Audio(label="🔊音频输入 Audio Input") |
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gr.Examples(['examples/鲁肃采访片段1.wav'], [audio_input]) |
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recog_button1 = gr.Button("👂识别 Recognize") |
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audio_text_output = gr.Textbox(label="✏️识别结果 Recognition Result") |
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audio_srt_output = gr.Textbox(label="📖SRT字幕内容 RST Subtitles") |
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with gr.Column(): |
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audio_text_input = gr.Textbox(label="✏️待裁剪文本 Text to Clip (多段文本使用'#'连接)") |
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with gr.Row(): |
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audio_start_ost = gr.Slider(minimum=-500, maximum=1000, value=0, step=50, label="⏪开始位置偏移 Start Offset (ms)") |
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audio_end_ost = gr.Slider(minimum=-500, maximum=1000, value=100, step=50, label="⏩结束位置偏移 End Offset (ms)") |
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with gr.Row(): |
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clip_button1 = gr.Button("✂️裁剪 Clip") |
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audio_output = gr.Audio(label="🔊裁剪结果 Audio Clipped") |
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audio_mess_output = gr.Textbox(label="ℹ️裁剪信息 Clipping Log") |
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audio_srt_clip_output = gr.Textbox(label="📖裁剪部分SRT字幕内容 Clipped RST Subtitles") |
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recog_button1.click(audio_recog, |
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inputs=audio_input, |
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outputs=[audio_text_output, audio_srt_output, audio_state]) |
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clip_button1.click(audio_clip, |
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inputs=[audio_text_input, audio_start_ost, audio_end_ost, audio_state], |
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outputs=[audio_output, audio_mess_output, audio_srt_clip_output]) |
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recog_button2.click(video_recog, |
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inputs=video_input, |
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outputs=[video_text_output, video_srt_output, video_state]) |
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clip_button2.click(video_clip, |
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inputs=[video_text_input, video_start_ost, video_end_ost, video_state], |
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outputs=[video_output, video_mess_output, video_srt_clip_output]) |
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clip_button3.click(video_clip_addsub, |
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inputs=[video_text_input, video_start_ost, video_end_ost, video_state, font_size, font_color], |
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outputs=[video_output, video_mess_output, video_srt_clip_output]) |
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demo.queue(concurrency_count=3).launch() |
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