app.py

import torchaudio
import torch
from model import M11
import gradio as gr

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
model_PATH = "./model.ckpt"

classifier = M11.load_from_checkpoint(model_PATH)
classifier.eval()

def preprocess(signal, sr, device):
    # resampling the audio signal with the training sample rate
    if sr != 8_000:
        resampler = torchaudio.transforms.Resample(sr, 8_000).to(device)
        signal = resampler(signal)
    # turning the stereo signals into mono
    if signal.shape[0] > 1:
        signal = torch.mean(signal, dim=0, keepdim=True)
    
    return signal

def get_likely_index(tensor):
    # find most likely label index for each element in the batch
    return tensor.argmax(dim=-1)

def pipeline(input):
    # print('gere')
    # print(input)
    sample_rate, audio = input
    processed_audio = preprocess(torch.from_numpy(audio), sample_rate, DEVICE)

    with torch.no_grad():
        pred = get_likely_index(classifier(processed_audio.unsqueeze(0))).view(-1)
    # out_prob, score, index, text_lab = classifier.classify_file(aud.name)
    return pred[0]
  
inputs = gr.inputs.Audio(label="Input Audio", type="numpy")
outputs = "text"
title = "Threat Detection From Bengali Voice Calls"
description = "Gradio demo for Audio Classification, simply upload your audio, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2005.07143' target='_blank'>ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification</a> | <a href='https://github.com/speechbrain/speechbrain' target='_blank'>Github Repo</a></p>"
examples = [
    ['sample_audio.wav']
]
gr.Interface(pipeline, inputs, outputs, title=title, description=description, article=article, examples=examples).launch()