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# -*- coding: utf-8 -*- | |
"""app.ipynb | |
Automatically generated by Colaboratory. | |
Original file is located at | |
https://colab.research.google.com/drive/1BLa-ng23vT9TfwY5G535Y20WSO76Z3FD | |
""" | |
import torch | |
import gradio as gr | |
import pytube as pt | |
from transformers import pipeline | |
asr = pipeline( | |
task="automatic-speech-recognition", | |
model="Yasaman/whisper_fa", | |
chunk_length_s=30, | |
device="cpu", | |
) | |
summarizer = pipeline( | |
"summarization", | |
model="alireza7/PEGASUS-persian-base-PN-summary", | |
) | |
translator = pipeline( | |
"translation", | |
model="Helsinki-NLP/opus-mt-iir-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=[ | |
gr.Textbox(label="Transcribed text"), | |
gr.Textbox(label="Translated text"), | |
], | |
layout="horizontal", | |
theme="huggingface", | |
title="Whisper Demo: Transcribe and Translate Persian Audio", | |
description=( | |
"Transcribe and Translate long-form microphone or audio inputs with the click of a button! Demo uses the the fine-tuned" | |
f" [Yasaman/whisper_fa](https://huggingface.co/Yasaman/whisper_fa) 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" [Yasaman/whisper_fa](https://huggingface.co/Yasaman/whisper_fa) 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 example: " | |
f" [Video1](https://www.youtube.com/watch?v=qtRzP3KvQZk)" | |
), | |
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) |