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import torch
import gradio as gr
import pytube as pt
from transformers import pipeline
MODEL_NAME = "openai/whisper-small.en"
device = 0 if torch.cuda.is_available() else "cpu"
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device,
)
all_special_ids = pipe.tokenizer.all_special_ids
transcribe_token_id = all_special_ids[-5]
translate_token_id = all_special_ids[-6]
def transcribe(microphone, state, task="transcribe"):
file = microphone
pipe.model.config.forced_decoder_ids = [[2, transcribe_token_id if task=="transcribe" else translate_token_id]]
text = pipe(file)["text"]
return state + "\n" + text, state + "\n" + text
mf_transcribe = gr.Interface(
fn=transcribe,
inputs=[
gr.Audio(source="microphone", type="filepath", optional=True),
gr.State(value="")
],
outputs=[
gr.Textbox(lines=15),
gr.State()]
,
layout="horizontal",
theme="huggingface",
title="Whisper Small.EN: Transcribe Audio",
live=True,
description=(
"Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
" of arbitrary length."
),
allow_flagging="never",
)
mf_transcribe.launch(enable_queue=True)
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