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#
# 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 einops import rearrange
from nemo.collections.audio.modules.transforms import AudioToSpectrogram, SpectrogramToAudio
class TestAudioSpectrogram:
@pytest.mark.unit
@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.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.parametrize('fft_length', [128, 1024])
@pytest.mark.parametrize('num_channels', [1, 4])
@pytest.mark.parametrize('magnitude_power', [0.5, 1, 2])
@pytest.mark.parametrize('scale', [0.1, 1.0])
def test_audio_to_spectrogram_reconstruction(
self, fft_length: int, num_channels: int, magnitude_power: float, scale: float
):
"""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, magnitude_power=magnitude_power, scale=scale
)
spec2audio = SpectrogramToAudio(
fft_length=fft_length, hop_length=hop_length, magnitude_power=magnitude_power, scale=scale
)
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})'
@pytest.mark.unit
@pytest.mark.parametrize('fft_length', [128, 512])
@pytest.mark.parametrize('num_channels', [1, 4])
@pytest.mark.parametrize('magnitude_power', [0.5, 1])
@pytest.mark.parametrize('scale', [0.1, 1.0])
def test_match_reference_implementation(
self, fft_length: int, num_channels: int, magnitude_power: float, scale: float
):
"""Test analysis and synthesis transforms match reference implementation."""
batch_size = 4
num_samples = fft_length * 50
num_examples = 8
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, magnitude_power=magnitude_power, scale=scale
)
spec2audio = SpectrogramToAudio(
fft_length=fft_length, hop_length=hop_length, magnitude_power=magnitude_power, scale=scale
)
# Reference implementations
ref_window = torch.hann_window(fft_length)
def audio2spec_ref(x):
# Transform each channel and batch example separately
x_spec = []
for b in range(batch_size):
for c in range(num_channels):
x_spec_bc = torch.stft(
input=x[b, c, :],
n_fft=fft_length,
hop_length=hop_length,
win_length=fft_length,
window=ref_window,
center=True,
pad_mode='constant',
normalized=False,
onesided=True,
return_complex=True,
)
x_spec.append(x_spec_bc)
x_spec = torch.stack(x_spec, dim=0)
x_spec = rearrange(x_spec, '(B C) F N -> B C F N', B=batch_size, C=num_channels)
# magnitude compression and scaling
x_spec = (
torch.pow(x_spec.abs(), magnitude_power) * torch.exp(1j * x_spec.angle())
if magnitude_power != 1
else x_spec
)
x_spec = x_spec * scale if scale != 1 else x_spec
return x_spec
def spec2audio_ref(x_spec):
# scaling and magnitude compression
x_spec = x_spec / scale if scale != 1 else x_spec
x_spec = (
torch.pow(x_spec.abs(), 1 / magnitude_power) * torch.exp(1j * x_spec.angle())
if magnitude_power != 1
else x_spec
)
# Transform each channel and batch example separately
x = []
for b in range(batch_size):
for c in range(num_channels):
x_bc = torch.istft(
input=x_spec[b, c, ...],
n_fft=fft_length,
hop_length=hop_length,
win_length=fft_length,
window=ref_window,
center=True,
normalized=False,
onesided=True,
return_complex=False,
)
x.append(x_bc)
x = torch.stack(x, dim=0)
x = rearrange(x, '(B C) T -> B C T', B=batch_size, C=num_channels)
return x
for n in range(num_examples):
x = _rng.normal(size=(batch_size, num_channels, num_samples))
# Test analysis
x_spec, x_spec_length = audio2spec(input=torch.Tensor(x))
x_spec_ref = audio2spec_ref(torch.Tensor(x))
assert torch.allclose(
x_spec, x_spec_ref, atol=atol
), f'Analysis not matching for example {n} (hop length {hop_length})'
# Test synthesis
x_hat, _ = spec2audio(input=x_spec, input_length=x_spec_length)
x_hat_ref = spec2audio_ref(x_spec_ref)
assert torch.allclose(
x_hat, x_hat_ref, atol=atol
), f'Synthesis not matching for example {n} (hop length {hop_length})'
@pytest.mark.unit
@pytest.mark.parametrize('fft_length', [13, 63])
def test_invalid_length(self, fft_length: int):
"""Test initializing transforms with invalid length."""
# Only even fft lengths are supported
with pytest.raises(ValueError):
AudioToSpectrogram(fft_length=fft_length, hop_length=fft_length // 2)
with pytest.raises(ValueError):
SpectrogramToAudio(fft_length=fft_length, hop_length=fft_length // 2)
@pytest.mark.unit
@pytest.mark.parametrize('fft_length', [32])
def test_invalid_compression(self, fft_length: int):
"""Test initializing transforms with invalid compression."""
# Compression must be positive
with pytest.raises(ValueError):
AudioToSpectrogram(fft_length=fft_length, hop_length=fft_length // 2, magnitude_power=0.0)
with pytest.raises(ValueError):
SpectrogramToAudio(fft_length=fft_length, hop_length=fft_length // 2, magnitude_power=0.0)
with pytest.raises(ValueError):
AudioToSpectrogram(fft_length=fft_length, hop_length=fft_length // 2, magnitude_power=-1.0)
with pytest.raises(ValueError):
SpectrogramToAudio(fft_length=fft_length, hop_length=fft_length // 2, magnitude_power=-1.0)
# Scaling must be positive
with pytest.raises(ValueError):
AudioToSpectrogram(fft_length=fft_length, hop_length=fft_length // 2, scale=0.0)
with pytest.raises(ValueError):
SpectrogramToAudio(fft_length=fft_length, hop_length=fft_length // 2, scale=0.0)
with pytest.raises(ValueError):
AudioToSpectrogram(fft_length=fft_length, hop_length=fft_length // 2, scale=-1.0)
with pytest.raises(ValueError):
SpectrogramToAudio(fft_length=fft_length, hop_length=fft_length // 2, scale=-1.0)
@pytest.mark.unit
@pytest.mark.parametrize('fft_length', [32])
def test_invalid_spec_to_audio_input(self, fft_length: int):
"""Test invalid input for spec to audio transform."""
s2a = SpectrogramToAudio(fft_length=fft_length, hop_length=fft_length // 2)
# Input must be complex
s2a(input=torch.randn(1, 1, fft_length // 2 + 1, 100, dtype=torch.cfloat))
# Input must be complex
with pytest.raises(ValueError):
s2a(input=torch.randn(1, 1, fft_length // 2 + 1, 100))
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