app.py

#from transformers import pipeline
#import streamlit as st

#pipe = pipeline('sentiment-analysis')
#text = st.text_area('Enter some text here!')

#if text:
#    out = pipe(text)
#    st.json(out)


from transformers import pipeline
import torch

classifier = pipeline(
    "audio-classification", model="MIT/ast-finetuned-speech-commands-v2", device=device
)

from transformers.pipelines.audio_utils import ffmpeg_microphone_live


def launch_fn(
    wake_word="marvin",
    prob_threshold=0.5,
    chunk_length_s=2.0,
    stream_chunk_s=1,
    debug=False,
):
    if wake_word not in classifier.model.config.label2id.keys():
        raise ValueError(
            f"Wake word {wake_word} not in set of valid class labels, pick a wake word in the set {classifier.model.config.label2id.keys()}."
        )

    sampling_rate = classifier.feature_extractor.sampling_rate

    mic = ffmpeg_microphone_live(
        sampling_rate=sampling_rate,
        chunk_length_s=chunk_length_s,
        stream_chunk_s=stream_chunk_s,
    )

    print("Listening for wake word...")
    mic_results = classifier(mic)
    for prediction in mic_results:
        prediction = prediction[0]
        if debug:
            print(prediction)
        if prediction["label"] == wake_word:
            if prediction["score"] > prob_threshold:
                return True

launch_fn(debug=True)