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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) |