| 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'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>' | |
| " </center>" | |
| ) | |
| 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) | |