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import gradio as gr
import torch
from transformers import pipeline
from datasets import load_dataset
# device = "cuda:0" if torch.cuda.is_available() else "cpu"
# ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# sample = ds[0]["audio"]
def transcribe_audio(sample):
pipe = pipeline(
"automatic-speech-recognition",
model="openai/whisper-small",
chunk_length_s=30,
)
# prediction = pipe(sample.copy(), batch_size=8)["text"]
prediction = pipe(sample.copy(), batch_size=8, return_timestamps=True)["chunks"]
return prediction
# we can also return timestamps for the predictions
interface = gr.Interface(
fn=transcribe_audio, # The function to be applied to the audio input
inputs=gr.Audio(type="filepath"), # Users can record or upload audio
outputs="text", # The output is the transcription (text)
title="Whisper Small ASR", # Title of your app
description="Transcription using Whisper Small." # Description of your app
)
# **This line starts the Gradio app**
interface.launch()
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