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Duplicate from kazuk/youtube-whisper

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  1. .gitattributes +34 -0
  2. README.md +14 -0
  3. app.py +66 -0
  4. requirements.txt +3 -0
.gitattributes ADDED
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README.md ADDED
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+ ---
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+ title: Youtube Whisper
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+ emoji: ⚡
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+ colorFrom: green
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+ colorTo: red
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+ sdk: gradio
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+ sdk_version: 3.16.2
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+ app_file: app.py
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+ pinned: false
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+ license: unknown
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+ duplicated_from: kazuk/youtube-whisper
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+ ---
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+
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
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+ import gradio as gr
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+ import whisper
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+ from pytube import YouTube
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+
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+ def get_audio(url):
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+ yt = YouTube(url)
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+ return yt.streams.filter(only_audio=True)[0].download(filename="tmp.mp4")
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+
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+ def get_transcript(url, model_size, lang, format):
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+
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+ model = whisper.load_model(model_size)
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+
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+ if lang == "None":
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+ lang = None
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+
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+ result = model.transcribe(get_audio(url), fp16=False, language=lang)
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+
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+ if format == "None":
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+ return result["text"]
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+ elif format == ".srt":
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+ return format_to_srt(result["segments"])
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+
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+ def format_to_srt(segments):
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+ output = ""
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+ for i, segment in enumerate(segments):
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+ output += f"{i + 1}\n"
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+ output += f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n"
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+ output += f"{segment['text']}\n\n"
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+ return output
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+
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+ def format_timestamp(t):
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+ hh = t//3600
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+ mm = (t - hh*3600)//60
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+ ss = t - hh*3600 - mm*60
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+ mi = (t - int(t))*1000
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+ return f"{int(hh):02d}:{int(mm):02d}:{int(ss):02d},{int(mi):03d}"
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+
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+
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+ langs = ["None"] + sorted(list(whisper.tokenizer.LANGUAGES.values()))
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+ model_size = list(whisper._MODELS.keys())
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+
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+ with gr.Blocks() as demo:
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+
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+ with gr.Row():
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+
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+ with gr.Column():
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+
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+ with gr.Row():
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+ url = gr.Textbox(placeholder='Youtube video URL', label='URL')
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+
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+ with gr.Row():
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+
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+ model_size = gr.Dropdown(choices=model_size, value='tiny', label="Model")
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+ lang = gr.Dropdown(choices=langs, value="None", label="Language (Optional)")
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+ format = gr.Dropdown(choices=["None", ".srt"], value="None", label="Timestamps? (Optional)")
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+
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+ with gr.Row():
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+ gr.Markdown("Larger models are more accurate, but slower. For 1min video, it'll take ~30s (tiny), ~1min (base), ~3min (small), ~5min (medium), etc.")
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+ transcribe_btn = gr.Button('Transcribe')
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+
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+ with gr.Column():
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+ outputs = gr.Textbox(placeholder='Transcription of the video', label='Transcription')
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+
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+ transcribe_btn.click(get_transcript, inputs=[url, model_size, lang, format], outputs=outputs)
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+
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+ demo.launch(debug=True)
requirements.txt ADDED
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+ transformers
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+ pytube
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+ git+https://github.com/openai/whisper.git