import torch import gradio as gr import pytube as pt from transformers import pipeline asr = pipeline( task="automatic-speech-recognition", model="whispy/whisper_hf", chunk_length_s=30, device="cpu", ) summarizer = pipeline( "summarization", model="it5/it5-efficient-small-el32-news-summarization", ) translator = pipeline( "translation", model="Helsinki-NLP/opus-mt-it-en") def transcribe(microphone, file_upload): warn_output = "" if (microphone is not None) and (file_upload is not None): warn_output = ( "WARNING: You've uploaded an audio file and used the microphone. " "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n" ) elif (microphone is None) and (file_upload is None): return "ERROR: You have to either use the microphone or upload an audio file" file = microphone if microphone is not None else file_upload text = asr(file)["text"] translate = translator(text) translate = translate[0]["translation_text"] return warn_output + text, translate def _return_yt_html_embed(yt_url): video_id = yt_url.split("?v=")[-1] HTML_str = ( f'
' "
" ) return HTML_str def yt_transcribe(yt_url): yt = pt.YouTube(yt_url) html_embed_str = _return_yt_html_embed(yt_url) stream = yt.streams.filter(only_audio=True)[0] stream.download(filename="audio.mp3") text = asr("audio.mp3")["text"] summary = summarizer(text) summary = summary[0]["summary_text"] translate = translator(summary) translate = translate[0]["translation_text"] return html_embed_str, text, summary, translate demo = gr.Blocks() mf_transcribe = gr.Interface( fn=transcribe, inputs=[ gr.inputs.Audio(source="microphone", type="filepath", optional=True), gr.inputs.Audio(source="upload", type="filepath", optional=True), ], outputs=["text", "text"], layout="horizontal", theme="huggingface", title="Whisper Demo: Transcribe and Translate Italian Audio", description=( "Transcribe and Translate long-form microphone or audio inputs with the click of a button! Demo uses the the fine-tuned" f" [whispy/whisper_hf](https://huggingface.co/whispy/whisper_hf) and 🤗 Transformers to transcribe audio files" " of arbitrary length. It also uses another model for the translation." ), allow_flagging="never", ) yt_transcribe = gr.Interface( fn=yt_transcribe, inputs=[gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")], outputs=["html", gr.Textbox(label="Transcribed text"), gr.Textbox(label="Summarized text"), gr.Textbox(label="Translated text"), ] layout="horizontal", theme="huggingface", title="Whisper Demo: Transcribe, Summarize and Translate YouTube", description=( "Transcribe, Summarize and Translate long-form YouTube videos with the click of a button! Demo uses the the fine-tuned " f" [whispy/whisper_hf](https://huggingface.co/whispy/whisper_hf) and 🤗 Transformers to transcribe audio files of" " arbitrary length. It also uses other two models to first summarize and then translate the text input. You can try with the following examples: " f" [Video1](https://www.youtube.com/watch?v=xhWhyu8cBTk)" f" [Video2](https://www.youtube.com/watch?v=C6Vw_Z3t_2U)" ), allow_flagging="never", ) with demo: gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe and Translate Audio", "Transcribe, Summarize and Translate YouTube"]) demo.launch(enable_queue=True)