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import gradio as gr | |
from transformers import pipeline | |
import numpy as np | |
transcriber = pipeline("automatic-speech-recognition", model="Oysiyl/w2v-bert-2.0-ukrainian-colab-CV16.0") | |
def transcribe(audio): | |
sr, y = audio | |
y = y.astype(np.float32) | |
y /= np.max(np.abs(y)) | |
return transcriber({"sampling_rate": sr, "raw": y})["text"] | |
demo = gr.Interface( | |
transcribe, | |
gr.Audio(sources=["microphone", "upload"]), | |
outputs="text", | |
title="Automatic Speech Recognition for Ukrainian language demo", | |
description="Say something or upload audiofile!", | |
examples=[["examples/asr_example.wav"]], | |
cache_examples=True | |
) | |
demo.launch() |