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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"
def convert_audio():
pipe = pipeline(
"automatic-speech-recognition",
model="openai/whisper-small",
chunk_length_s=30,
device=device,
)
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
sample = ds[0]["audio"]
print("Using predefined audio sample:")
audio_data = sample['array']
prediction = pipe(audio_data)["text"]
print(prediction)
return prediction
demo = gr.Interface(
fn = convert_audio,
inputs = None,
outputs = "text",
)
demo.launch(share=True)
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