def test_in_out(self):
dummy_input = T.rand(4, 20, 80) # B x T x D
dummy_hidden = [T.rand(2, 4, 128), T.rand(2, 4, 128)]
model = SpeakerEncoder(input_dim=80,
proj_dim=256,
lstm_dim=768,
num_lstm_layers=3)
# computing d vectors
output = model.forward(dummy_input)
assert output.shape[0] == 4
assert output.shape[1] == 256
output = model.inference(dummy_input)
assert output.shape[0] == 4
assert output.shape[1] == 256
# compute d vectors by passing LSTM hidden
# output = model.forward(dummy_input, dummy_hidden)
# assert output.shape[0] == 4
# assert output.shape[1] == 20
# assert output.shape[2] == 256
# check normalization
output_norm = T.nn.functional.normalize(output, dim=1, p=2)
assert_diff = (output_norm - output).sum().item()
assert output.type() == "torch.FloatTensor"
assert (abs(assert_diff) <
1e-4), f" [!] output_norm has wrong values - {assert_diff}"
# compute d for a given batch
dummy_input = T.rand(1, 240, 80) # B x T x D
output = model.compute_embedding(dummy_input,
num_frames=160,
overlap=0.5)
assert output.shape[0] == 1
assert output.shape[1] == 256
assert len(output.shape) == 2
#print(f'wav_file: {wav_file}')
if os.path.exists(wav_file):
wav_files.append(wav_file)
print(f'Count of wavs imported: {len(wav_files)}')
else:
# Parse all wav files in data_path
wav_path = data_path
wav_files = glob.glob(data_path + '/**/*.wav', recursive=True)
output_files = [
wav_file.replace(wav_path, args.output_path).replace('.wav', '.npy')
for wav_file in wav_files
]
for output_file in output_files:
os.makedirs(os.path.dirname(output_file), exist_ok=True)
model = SpeakerEncoder(**c.model)
model.load_state_dict(torch.load(args.model_path)['model'])
model.eval()
if args.use_cuda:
model.cuda()
for idx, wav_file in enumerate(tqdm(wav_files)):
mel_spec = ap.melspectrogram(ap.load_wav(wav_file)).T
mel_spec = torch.FloatTensor(mel_spec[None, :, :])
if args.use_cuda:
mel_spec = mel_spec.cuda()
embedd = model.compute_embedding(mel_spec)
np.save(output_files[idx], embedd.detach().cpu().numpy())