File size: 1,600 Bytes
59c41a7 f1d7aff 59c41a7 f1d7aff 59c41a7 aa9af15 59c41a7 |
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 |
from transformers import pipeline
asr = pipeline(task="automatic-speech-recognition",
model= "distil-whisper/distil-small.en")
import gradio as gr
demo = gr.Blocks()
def transcribe_long_form(filepath):
if filepath is None:
gr.Warning("No audio found, please retry")
return
output = asr(filepath,
max_new_tokens=256,
chunk_length_s=30,
batch_size=4,)
return output['text']
mic_transcribe = gr.Interface(
fn=transcribe_long_form,
inputs=gr.Audio(sources="microphone",
type="filepath"),
outputs=gr.Textbox(label="Transcription",
lines=7),
allow_flagging="never",
description="Speak into the microphone or upload an audio file to transcribe it into text. This model uses a state-of-the-art speech recognition algorithm to recognize spoken words and phrases")
file_transcribe = gr.Interface(
fn=transcribe_long_form,
inputs=gr.Audio(sources="upload",
type="filepath"),
outputs=gr.Textbox(label="Transcription",
lines=7),
allow_flagging="never",
description="Speak into the microphone or upload an audio file to transcribe it into text. This model uses a state-of-the-art speech recognition algorithm to recognize spoken words and phrases")
with demo:
gr.TabbedInterface(
[mic_transcribe,
file_transcribe],
["Transcribe Microphone",
"Transcribe Audio File"],
title="Speak out Loud - Automatic Speech Recognition"
)
demo.launch() |