def test_pitch_feats(self, kwargs):
"""compute_kaldi_pitch produces numerically compatible result with compute-kaldi-pitch-feats"""
sample_rate = kwargs['sample_rate']
waveform = get_sinusoid(dtype='float32', sample_rate=sample_rate)
result = F.compute_kaldi_pitch(waveform[0], **kwargs)
waveform = get_sinusoid(dtype='int16', sample_rate=sample_rate)
wave_file = self.get_temp_path('test.wav')
save_wav(wave_file, waveform, sample_rate)
command = ['compute-kaldi-pitch-feats'] + convert_args(**kwargs) + ['scp:-', 'ark:-']
kaldi_result = run_kaldi(command, 'scp', wave_file)
self.assert_equal(result, expected=kaldi_result)
def extract_features(x, sr):
step = 0.01
fft_time = 0.05
n_mels = 128
n_mfcc = 40
n_fft = int(fft_time * sr)
hop_length = int(step * sr)
spec = AT.MelSpectrogram(sample_rate=sr,
n_fft=n_fft,
hop_length=hop_length,
n_mels=n_mels,
f_max=8000)(x)[0]
intensity = spec.mean(dim=0).log()
spec = AT.AmplitudeToDB()(spec)
mfcc = AT.MFCC(
sample_rate=sr,
n_mfcc=n_mfcc,
melkwargs={
"n_fft": n_fft,
"hop_length": hop_length,
"n_mels": n_mels,
"f_max": 8000,
},
)(x)[0]
mfcc = (mfcc - mfcc.mean(dim=1, keepdim=True)) / mfcc.std(dim=1,
keepdim=True)
pitch_feature = AF.compute_kaldi_pitch(
x,
sample_rate=sr,
frame_length=fft_time * 1000,
frame_shift=step * 1000,
snip_edges=True,
min_f0=70,
max_f0=350,
penalty_factor=0.01,
)
pitch = pitch_feature[0]
return {
"Waveform": x[0],
"MelSpectrogram": spec,
"MFCC": mfcc,
"Pitch": pitch,
"Intensity": intensity,
}
plot_pitch(waveform, sample_rate, pitch) play_audio(waveform, sample_rate) ###################################################################### # Kaldi Pitch (beta) # ------------------ # # Kaldi Pitch feature [1] is a pitch detection mechanism tuned for automatic # speech recognition (ASR) applications. This is a beta feature in ``torchaudio``, # and it is available only in ``functional``. # # 1. A pitch extraction algorithm tuned for automatic speech recognition # # Ghahremani, B. BabaAli, D. Povey, K. Riedhammer, J. Trmal and S. # Khudanpur # # 2014 IEEE International Conference on Acoustics, Speech and Signal # Processing (ICASSP), Florence, 2014, pp. 2494-2498, doi: # 10.1109/ICASSP.2014.6854049. # [`abstract <https://ieeexplore.ieee.org/document/6854049>`__], # [`paper <https://danielpovey.com/files/2014_icassp_pitch.pdf>`__] # waveform, sample_rate = get_speech_sample(resample=16000) pitch_feature = F.compute_kaldi_pitch(waveform, sample_rate) pitch, nfcc = pitch_feature[..., 0], pitch_feature[..., 1] plot_kaldi_pitch(waveform, sample_rate, pitch, nfcc) play_audio(waveform, sample_rate)
def func(tensor):
sample_rate: float = 44100.
return F.compute_kaldi_pitch(tensor, sample_rate)
