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import torch
import torchaudio.functional as F
from parameterized import parameterized
from torchaudio_unittest.common_utils import (
get_sinusoid,
load_params,
save_wav,
skipIfNoExec,
TempDirMixin,
TestBaseMixin,
)
from torchaudio_unittest.common_utils.kaldi_utils import convert_args, run_kaldi
class Kaldi(TempDirMixin, TestBaseMixin):
def assert_equal(self, output, *, expected, rtol=None, atol=None):
expected = expected.to(dtype=self.dtype, device=self.device)
self.assertEqual(output, expected, rtol=rtol, atol=atol)
@skipIfNoExec("apply-cmvn-sliding")
def test_sliding_window_cmn(self):
"""sliding_window_cmn should be numerically compatible with apply-cmvn-sliding"""
kwargs = {
"cmn_window": 600,
"min_cmn_window": 100,
"center": False,
"norm_vars": False,
}
tensor = torch.randn(40, 10, dtype=self.dtype, device=self.device)
result = F.sliding_window_cmn(tensor, **kwargs)
command = ["apply-cmvn-sliding"] + convert_args(**kwargs) + ["ark:-", "ark:-"]
kaldi_result = run_kaldi(command, "ark", tensor)
self.assert_equal(result, expected=kaldi_result)
class KaldiCPUOnly(TempDirMixin, TestBaseMixin):
def assert_equal(self, output, *, expected, rtol=None, atol=None):
expected = expected.to(dtype=self.dtype, device=self.device)
self.assertEqual(output, expected, rtol=rtol, atol=atol)
@parameterized.expand(load_params("kaldi_test_pitch_args.jsonl"))
@skipIfNoExec("compute-kaldi-pitch-feats")
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)
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