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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() | |