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import torch
import gradio as gr
from transformers import pipeline
import pytube as pt
MODEL_NAME = "openai/whisper-small"
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device,
)
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"
)
file = microphone
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 = pipe(file)["text"]
return warn_output + text
def yt_transcribe(yt_url):
yt = pt.YouTube(yt_url)
stream = yt.streams.filter(only_audio=True)[0]
stream.download(filename="audio.mp3")
text = pipe("audio.mp3")["text"]
return text
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",
layout="horizontal",
theme="huggingface",
title="Whisper Audio Transcribe",
description="Transcribe long audio/ microphone input (powered by 🤗transformers) with a click of a button!",
allow_flagging="never",
)
yt_transcribe = gr.Interface(
fn=yt_transcribe,
inputs=[
gr.inputs.Textbox(
lines=1, placeholder="Paste a URL to YT video here", label="yt_url"
)
],
outputs="text",
layout="horizontal",
theme="huggingface",
title="Whisper YT Transcribe",
description="Transcribe long YouTube videos (powered by 🤗transformers) with a click of a button!",
allow_flagging="never",
)
with demo:
gr.TabbedInterface(
[mf_transcribe, yt_transcribe], ["Audio Transcribe", "YouTube Transcribe"]
)
demo.launch(enable_queue=True)
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