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| import gradio as gr | |
| import whisper | |
| from pytube import YouTube | |
| def get_audio(url): | |
| yt = YouTube(url) | |
| return yt.streams.filter(only_audio=True)[0].download(filename="tmp.mp4") | |
| def get_transcript(url, model_size, lang, format): | |
| model = whisper.load_model(model_size) | |
| if lang == "None": | |
| lang = None | |
| result = model.transcribe(get_audio(url), fp16=False, language=lang) | |
| if format == "None": | |
| return result["text"] | |
| elif format == ".srt": | |
| return format_to_srt(result["segments"]) | |
| def format_to_srt(segments): | |
| output = "" | |
| for i, segment in enumerate(segments): | |
| output += f"{i + 1}\n" | |
| output += f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n" | |
| output += f"{segment['text']}\n\n" | |
| return output | |
| def format_timestamp(t): | |
| hh = t//3600 | |
| mm = (t - hh*3600)//60 | |
| ss = t - hh*3600 - mm*60 | |
| mi = (t - int(t))*1000 | |
| return f"{int(hh):02d}:{int(mm):02d}:{int(ss):02d},{int(mi):03d}" | |
| langs = ["None"] + sorted(list(whisper.tokenizer.LANGUAGES.values())) | |
| model_size = list(whisper._MODELS.keys()) | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| url = gr.Textbox(placeholder='Youtube video URL', label='URL') | |
| with gr.Row(): | |
| model_size = gr.Dropdown(choices=model_size, value='tiny', label="Model") | |
| lang = gr.Dropdown(choices=langs, value="None", label="Language (Optional)") | |
| format = gr.Dropdown(choices=["None", ".srt"], value="None", label="Timestamps? (Optional)") | |
| with gr.Row(): | |
| gr.Markdown("Larger models are more accurate, but slower. For 1min video, it'll take ~30s (tiny), ~1min (base), ~3min (small), ~5min (medium), etc.") | |
| transcribe_btn = gr.Button('Transcribe') | |
| with gr.Column(): | |
| outputs = gr.Textbox(placeholder='Transcription of the video', label='Transcription') | |
| transcribe_btn.click(get_transcript, inputs=[url, model_size, lang, format], outputs=outputs) | |
| demo.launch(debug=True) |