app.py

from transformers import pipeline
import gradio as gr

model_id = "Teapack1/model_KWS"  # update with your model id
pipe = pipeline("audio-classification", model=model_id)


title = "Keyword Spotting Wav2Vec2"
description = "Gradio demo for finetuned Wav2Vec2 model on a custom dataset to perform keyword spotting task. Classes are scene 1, scene 2, scene 3, yes, no and stop."

def classify_audio(filepath):
    preds = pipe(filepath)
    outputs = {}
    for p in preds:
        outputs[p["label"]] = p["score"]
    return outputs


demo = gr.Blocks()

mic_transcribe = gr.Interface(
    fn=classify_audio,
    inputs=gr.Audio(sources="microphone", type="filepath"),
    outputs=gr.outputs.Label()
)

mic_transcribe = gr.Interface(
    fn=classify_audio,
    inputs=gr.Audio(sources="upload", type="filepath"),
    outputs=gr.outputs.Label()
)

with demo:
    gr.TabbedInterface(
        [mic_transcribe, file_transcribe],
        ["Transcribe Microphone", "Transcribe Audio File"],
    )