def get_output_fn(sound, args):
output = kaldi.mfcc(sound,
blackman_coeff=args[1],
dither=0.0,
energy_floor=args[2],
frame_length=args[3],
frame_shift=args[4],
high_freq=args[5],
htk_compat=args[6],
low_freq=args[7],
num_mel_bins=args[8],
preemphasis_coefficient=args[9],
raw_energy=args[10],
remove_dc_offset=args[11],
round_to_power_of_two=args[12],
snip_edges=args[13],
subtract_mean=args[14],
use_energy=args[15],
num_ceps=args[16],
cepstral_lifter=args[17],
vtln_high=args[18],
vtln_low=args[19],
vtln_warp=args[20],
window_type=args[21])
return output
def compute_mfcc(data,
num_mel_bins=23,
frame_length=25,
frame_shift=10,
dither=0.0,
num_ceps=40,
high_freq=0.0,
low_freq=20.0):
""" Extract mfcc
Args:
data: Iterable[{key, wav, label, sample_rate}]
Returns:
Iterable[{key, feat, label}]
"""
for sample in data:
assert 'sample_rate' in sample
assert 'wav' in sample
assert 'key' in sample
assert 'label' in sample
sample_rate = sample['sample_rate']
waveform = sample['wav']
waveform = waveform * (1 << 15)
# Only keep key, feat, label
mat = kaldi.mfcc(waveform,
num_mel_bins=num_mel_bins,
frame_length=frame_length,
frame_shift=frame_shift,
dither=dither,
num_ceps=num_ceps,
high_freq=high_freq,
low_freq=low_freq,
sample_frequency=sample_rate)
yield dict(key=sample['key'], label=sample['label'], feat=mat)
def get_torchaudio_fbank_or_mfcc(
waveform: np.ndarray,
sample_rate: float,
n_bins: int = 80,
feature_type: str = "fbank",
) -> np.ndarray:
"""Get mel-filter bank or mfcc features via TorchAudio."""
try:
import torchaudio.compliance.kaldi as ta_kaldi
waveform = torch.from_numpy(waveform)
if feature_type == "fbank":
features = ta_kaldi.fbank(
waveform, num_mel_bins=n_bins, sample_frequency=sample_rate
)
else:
features = ta_kaldi.mfcc(
waveform,
num_mel_bins=n_bins,
num_ceps=40,
low_freq=20,
high_freq=-400,
sample_frequency=sample_rate,
)
return features.numpy()
except ImportError:
raise ImportError(
"Please install torchaudio to enable online feature extraction: pip install torchaudio"
)
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 plain_single_file_predict(model,
wav_dir,
train_configs,
out_dir,
window_size=400,
lookahead=200,
lookbehind=200,
regex=""):
model = model.eval().cuda()
wavs = glob(os.path.join(wav_dir, "**/*{}*.wav".format(regex)),
recursive=True)
assert len(wavs) > 0, "No file found"
for wav in wavs:
print("Processing File {}".format(wav))
audio, _ = sf.read(wav)
if train_configs["feats"]["type"] == "mfcc_kaldi":
feats_func = lambda x: mfcc(
torch.from_numpy(x.astype("float32").reshape(1, -1)), **
train_configs["mfcc_kaldi"]).transpose(0, 1)
else:
raise NotImplementedError
tot_feats = compute_feats_windowed(feats_func, audio)
tot_feats = tot_feats.detach().cpu().numpy()
pred_func = lambda x: model(torch.from_numpy(x).unsqueeze(0).cuda()
).detach().cpu().numpy()
preds = overlap_add(tot_feats,
pred_func,
window_size,
window_size // 2,
lookahead=lookahead,
lookbehind=lookbehind)
out_file = os.path.join(
out_dir,
wav.split("/")[-1].split(".wav")[0] + ".logits")
np.save(out_file, preds)
def _feature_fn(self, *args, **kwargs):
from torchaudio.compliance.kaldi import mfcc
return mfcc(*args, **kwargs)
