Main.py

import torch

# To speed-up training process
torch.autograd.set_detect_anomaly(False)
torch.autograd.profiler.profile(False)
torch.autograd.profiler.emit_nvtx(False)

import warnings
# import pickle

# from Transformer import Transformer
import librosa

# import os.path as path
import json
# from tqdm import tqdm
# import time

import numpy as np
import os

warnings.filterwarnings("ignore")

with open("config.json") as json_data_file:
    data = json.load(json_data_file)

lr = data['learn_rate']
epochs = data['epochs']
batch_size = data['batch_size']
Training = data['Training']
Testing = data['Testing']
main_path = data['MainPath']

device = data['Device']
diag_attn = data['DiagAttn']

dir_path = os.path.dirname(os.path.realpath(__file__))

BestModelPath = dir_path + '/Best_GlobalModel_500_0_0.pt'

def pre_process_mfcc(mfcc):
    
    mfcc = mfcc.T

    mean_G = np.mean(mfcc, axis=0)
    std_G = np.std(mfcc, axis=0)
    mfcc = 0.5*(mfcc-mean_G)/std_G
    
    return mfcc

def wav2art(wav):
    rate = 16000
    mfcc = librosa.feature.mfcc(y = wav, sr = 16000, n_mfcc=13, hop_length=int(0.010*rate), n_fft=int(0.020*rate))
    mfcc = pre_process_mfcc(mfcc)

    mfcc = torch.tensor([mfcc]).float()

    test_model = torch.load(BestModelPath, map_location=torch.device('cpu')).float()
    test_model.eval()

    p = test_model(mfcc, 0, 0, 0)
    p = p[0].detach().numpy()

    return p