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