dataset_path = os.path.join('drive', 'My Drive', 'LibriSpeech Dataset')
path = {}
path['dev'] = os.path.join(dataset_path, 'LibriSpeech Dev Dataset')
path['test'] = os.path.join(dataset_path, 'LibriSpeech Test Dataset')
path['train'] = os.path.join(dataset_path, 'LibriSpeech Train Dataset')
dataset = 'dev'
input_spec = np.load(os.path.join(path[dataset], 'input_spec.npy'))
input_phase = np.load(os.path.join(path[dataset], 'input_phase.npy'))
output_spec = np.load(os.path.join(path[dataset], 'output_spec.npy'))
output_phase = np.load(os.path.join(path[dataset], 'output_phase.npy'))
dvec = np.load(os.path.join(path[dataset], 'dvec.npy'))
target_waves = []
for i in tqdm(range(output_spec.shape[0])):
target_waves.append(audio.spec2wave(output_spec[i], output_phase[i]))
val_loss = []
val_sdr = []
model = get_model()
model.compile(optimizer='adam', loss='mse')
def compute_loss_sdr(weights_path):
model.load_weights(weights_path)
predict_spec = model.predict(x={
'input_spec': input_spec,
'dvec': dvec
},
batch_size=batch_size,
verbose=1)
