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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import numpy as np
import pytest
import torch
from nemo.collections.asr.modules.audio_preprocessing import AudioToSpectrogram, SpectrogramToAudio
try:
importlib.import_module('torchaudio')
HAVE_TORCHAUDIO = True
except ModuleNotFoundError:
HAVE_TORCHAUDIO = False
class TestAudioSpectrogram:
@pytest.mark.unit
@pytest.mark.skipif(not HAVE_TORCHAUDIO, reason="Modules in this test require torchaudio")
@pytest.mark.parametrize('fft_length', [64, 512])
@pytest.mark.parametrize('num_channels', [1, 3])
def test_audio_to_spec(self, fft_length: int, num_channels: int):
"""Test output length for audio to spectrogram.
Create signals of arbitrary length and check output
length is matching the actual transform length.
"""
hop_lengths = [fft_length // 2, fft_length // 3, fft_length // 4]
batch_size = 4
num_examples = 20
random_seed = 42
atol = 1e-6
_rng = np.random.default_rng(seed=random_seed)
for n in range(num_examples):
# Generate time-domain examples with different length
input_length = _rng.integers(low=fft_length, high=100 * fft_length, size=batch_size) # in samples
x = _rng.normal(size=(batch_size, num_channels, np.max(input_length)))
x = torch.tensor(x)
for b in range(batch_size):
x[b, :, input_length[b] :] = 0
for hop_length in hop_lengths:
# Prepare transform
audio2spec = AudioToSpectrogram(fft_length=fft_length, hop_length=hop_length)
# Transform the whole batch
batch_spec, batch_spec_len = audio2spec(input=x, input_length=torch.tensor(input_length))
for b in range(batch_size):
# Transform just the current example
b_spec, b_spec_len = audio2spec(input=x[b : b + 1, :, : input_length[b]])
actual_len = b_spec.size(-1)
# Check lengths
assert (
actual_len == b_spec_len
), f'Output length not matching for example ({n}, {b}) with length {input_length[n]} (hop_length={hop_length}): true {actual_len} vs calculated {b_spec_len}.'
assert (
actual_len == batch_spec_len[b]
), f'Output length not matching for example ({n}, {b}) with length {input_length[n]} (hop_length={hop_length}): true {actual_len} vs calculated batch len {batch_spec_len[b]}.'
# Make sure transforming a batch is the same as transforming individual examples
assert torch.allclose(
batch_spec[b, ..., :actual_len], b_spec, atol=atol
), f'Spectrograms not matching for example ({n}, {b}) with length {input_length[b]} (hop_length={hop_length})'
@pytest.mark.unit
@pytest.mark.skipif(not HAVE_TORCHAUDIO, reason="Modules in this test require torchaudio")
@pytest.mark.parametrize('fft_length', [64, 512])
@pytest.mark.parametrize('num_channels', [1, 3])
def test_spec_to_audio(self, fft_length: int, num_channels: int):
"""Test output length for spectrogram to audio.
Create signals of arbitrary length and check output
length is matching the actual transform length.
"""
hop_lengths = [fft_length // 2, fft_length // 3, fft_length // 4]
batch_size = 4
num_examples = 20
random_seed = 42
atol = 1e-6
_rng = np.random.default_rng(seed=random_seed)
for n in range(num_examples):
# Generate spectrogram examples with different lengths
input_length = _rng.integers(low=10, high=100, size=batch_size) # in frames
input_shape = (batch_size, num_channels, fft_length // 2 + 1, np.max(input_length))
spec = _rng.normal(size=input_shape) + 1j * _rng.normal(size=input_shape)
spec = torch.tensor(spec)
spec[..., 0, :] = spec[..., 0, :].real
spec[..., -1, :] = spec[..., -1, :].real
for b in range(batch_size):
spec[b, ..., input_length[b] :] = 0
for hop_length in hop_lengths:
# Prepare transform
spec2audio = SpectrogramToAudio(fft_length=fft_length, hop_length=hop_length)
# Transform the whole batch
batch_x, batch_x_len = spec2audio(input=spec, input_length=torch.tensor(input_length))
for b in range(batch_size):
# Transform just the current example
b_x, b_x_len = spec2audio(input=spec[b : b + 1, ..., : input_length[b]])
actual_len = b_x.size(-1)
# Check lengths
assert (
b_x_len == actual_len
), f'Output length not matching for example ({n}, {b}) with {input_length[b]} frames (hop_length={hop_length}): true {actual_len} vs calculated {b_x_len}.'
assert (
batch_x_len[b] == actual_len
), f'Output length not matching for example ({n}, {b}) with {input_length[b]} frames (hop_length={hop_length}): true {actual_len} vs calculated batch {batch_x_len[b]}.'
# Make sure transforming a batch is the same as transforming individual examples
if input_length[b] < spec.size(-1):
# Discard the last bit of the signal which differs due to number of frames in batch (with zero padded frames) vs individual (only valid frames).
# The reason for this difference is normalization with `window_sumsquare` of the inverse STFT. More specifically,
# batched and non-batched transform are using on a different number of frames.
tail_length = max(fft_length // 2 - hop_length, 0)
else:
tail_length = 0
valid_len = actual_len - tail_length
batch_x_valid = batch_x[b, :, :valid_len]
b_x_valid = b_x[..., :valid_len]
assert torch.allclose(
batch_x_valid, b_x_valid, atol=atol
), f'Signals not matching for example ({n}, {b}) with length {input_length[b]} (hop_length={hop_length}): max abs diff {torch.max(torch.abs(batch_x_valid-b_x_valid))} at {torch.argmax(torch.abs(batch_x_valid-b_x_valid))}'
@pytest.mark.unit
@pytest.mark.skipif(not HAVE_TORCHAUDIO, reason="Modules in this test require torchaudio")
@pytest.mark.parametrize('fft_length', [128, 1024])
@pytest.mark.parametrize('num_channels', [1, 4])
def test_audio_to_spectrogram_reconstruction(self, fft_length: int, num_channels: int):
"""Test analysis and synthesis transform result in a perfect reconstruction.
"""
batch_size = 4
num_samples = fft_length * 50
num_examples = 25
random_seed = 42
atol = 1e-6
_rng = np.random.default_rng(seed=random_seed)
hop_lengths = [fft_length // 2, fft_length // 4]
for hop_length in hop_lengths:
audio2spec = AudioToSpectrogram(fft_length=fft_length, hop_length=hop_length)
spec2audio = SpectrogramToAudio(fft_length=fft_length, hop_length=hop_length)
for n in range(num_examples):
x = _rng.normal(size=(batch_size, num_channels, num_samples))
x_spec, x_spec_length = audio2spec(input=torch.Tensor(x))
x_hat, x_hat_length = spec2audio(input=x_spec, input_length=x_spec_length)
assert np.allclose(
x_hat.cpu().detach().numpy(), x, atol=atol
), f'Reconstructed not matching for example {n} (hop length {hop_length})'
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