def set_feats_func(self):
# initialize feats_function
if self.configs["feats"]["type"] == "mfcc_kaldi":
from torchaudio.compliance.kaldi import mfcc
self.feats_func = lambda x: mfcc(torch.from_numpy(x.astype("float32").reshape(1, -1)), **self.configs["mfcc_kaldi"]).transpose(0, 1)
elif self.configs["feats"]["type"] == "fbank_kaldi":
from torchaudio.compliance.kaldi import fbank
self.feats_func = lambda x: fbank(torch.from_numpy(x.astype("float32").reshape(1, -1)), **self.configs["fbank_kaldi"]).transpose(0, 1)
elif self.configs["feats"]["type"] == "spectrogram_kaldi":
from torchaudio.compliance.kaldi import spectrogram
self.feats_func = lambda x: spectrogram(torch.from_numpy(x.astype("float32").reshape(1, -1)),
**self.configs["spectrogram_kaldi"]).transpose(0, 1)
else:
raise NotImplementedError
def get_output_fn(sound, args):
output = kaldi.spectrogram(sound,
blackman_coeff=args[1],
dither=args[2],
energy_floor=args[3],
frame_length=args[4],
frame_shift=args[5],
preemphasis_coefficient=args[6],
raw_energy=args[7],
remove_dc_offset=args[8],
round_to_power_of_two=args[9],
snip_edges=args[10],
subtract_mean=args[11],
window_type=args[12])
return output
def test_spectrogram(self):
sound, sample_rate = torchaudio.load_wav(self.test_filepath)
kaldi_output_dir = os.path.join(self.test_dirpath, 'assets', 'kaldi')
files = list(
filter(lambda x: x.startswith('spec'),
os.listdir(kaldi_output_dir)))
print('Results:', len(files))
for f in files:
print(f)
kaldi_output_path = os.path.join(kaldi_output_dir, f)
kaldi_output_dict = {
k: v
for k, v in torchaudio.kaldi_io.read_mat_ark(kaldi_output_path)
}
assert len(
kaldi_output_dict
) == 1 and 'my_id' in kaldi_output_dict, 'invalid test kaldi ark file'
kaldi_output = kaldi_output_dict['my_id']
args = f.split('-')
args[-1] = os.path.splitext(args[-1])[0]
assert len(args) == 13, 'invalid test kaldi file name'
spec_output = kaldi.spectrogram(
sound,
blackman_coeff=float(args[1]),
dither=float(args[2]),
energy_floor=float(args[3]),
frame_length=float(args[4]),
frame_shift=float(args[5]),
preemphasis_coefficient=float(args[6]),
raw_energy=args[7] == 'true',
remove_dc_offset=args[8] == 'true',
round_to_power_of_two=args[9] == 'true',
snip_edges=args[10] == 'true',
subtract_mean=args[11] == 'true',
window_type=args[12])
error = spec_output - kaldi_output
mse = error.pow(2).sum() / spec_output.numel()
max_error = torch.max(error.abs())
print('mse:', mse.item(), 'max_error:', max_error.item())
self.assertTrue(spec_output.shape, kaldi_output.shape)
self.assertTrue(
torch.allclose(spec_output, kaldi_output, atol=1e-3, rtol=0))
def _feature_fn(self, *args, **kwargs):
from torchaudio.compliance.kaldi import spectrogram
return spectrogram(*args, **kwargs)