File size: 3,944 Bytes
3c8bd48 c968a60 3c8bd48 3e14be7 c7eee43 3c8bd48 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 |
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=[
gr.Textbox(label="Transcribed text"),
gr.Textbox(label="Translated 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)
|