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import torchaudio
import torch
from model import M11
import gradio as gr
def _cut_if_necessary(signal):
if signal.shape[1] > 400000:
signal = signal[:, :400000]
return signal
def _right_pad_if_necessary(signal):
signal_length = signal.shape[1]
if signal_length < 400000:
num_missing_samples = 400000 - signal_length
last_dim_padding = (0, num_missing_samples) # will add 0 number of zeros in the left side of array and num_missing_samples number of zeros in the right part
signal = torch.nn.functional.pad(signal, last_dim_padding)
return signal
def preprocess(signal, sr, device):
# add a channel dimension for 1d samples
if len(signal.shape) == 1:
signal = signal.unsqueeze(0)
# 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)
signal = _cut_if_necessary(signal) # truncating longer signals
signal = _right_pad_if_necessary(signal) # extending shorter signals
return signal
def pipeline(audio_file):
audio_PATH = audio_file.name
audio, sample_rate = torchaudio.load(audio_PATH)
processed_audio = preprocess(audio.to(DEVICE), sample_rate, DEVICE)
with torch.no_grad():
pred = torch.exp(classifier(processed_audio.unsqueeze(0)).squeeze()) # turning log_softmax into probabilities
return {labels[i]: float(pred[i]) for i in range(3)}
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
model_PATH = "./model.ckpt"
labels = ["Threat", "Normal", "Sarcastic"]
classifier = M11.load_from_checkpoint(model_PATH).to(DEVICE)
classifier.eval()
inputs = gr.inputs.Audio(label="Input Audio", type="file")
outputs = gr.outputs.Label(num_top_classes=3)
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://github.com/khalidsaifullaah' target='_blank'>Github Repo</a></p>"
examples = [
['sample_audio.wav']
]
gr.Interface(pipeline, inputs, outputs, title=title, description=description, article=article, examples=examples).launch() |