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import gradio as gr
from transformers import pipeline
import numpy as np
transcriber = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-tiny")
def transcribe(stream, new_chunk):
sr, y = new_chunk
y = y.astype(np.float32)
y /= np.max(np.abs(y))
if stream is not None:
stream = np.concatenate([stream, y])
else:
stream = y
return stream, transcriber({
"sampling_rate": sr,
"raw": stream,
}, generate_kwargs={
'num_beams': 5,
'task': 'transcribe',
'language': 'no'
})["text"]
demo = gr.Interface(
transcribe,
["state", gr.Audio(sources=["microphone"], streaming=True)],
["state", "text"],
live=True,
)
if __name__ == "__main__":
demo.launch()
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