| import gradio as gr | |
| from transformers import pipeline | |
| import numpy as np | |
| asr_model = "distil-whisper/distil-medium.en" | |
| asr_pipe = pipeline("automatic-speech-recognition", model=asr_model) | |
| 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, asr_pipe({"sampling_rate": sr, "raw": stream})["text"] | |
| demo = gr.Blocks() | |
| mic = gr.Interface( | |
| fn = transcribe, | |
| inputs = [ | |
| "state", gr.Audio(sources=["microphone"], streaming=True)], | |
| outputs = ["state", "text"], | |
| layout="horizontal", | |
| theme="huggingface", | |
| title="Whisper & BERT demo - Intent Classification", | |
| description=( | |
| "Transcribe audio inputs with Whisper ASR model and detect intention from the text. Use BERT NLP model to classify the intention as one of the commands to command a light." | |
| ), | |
| allow_flagging="never", | |
| live=True, | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |