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# Copyright (c) 2020, 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 pytest
import torch
from torchmetrics.audio.snr import SignalNoiseRatio
from nemo.collections.audio.metrics.audio import AudioMetricWrapper
from nemo.collections.audio.metrics.squim import SquimMOSMetric, SquimObjectiveMetric
try:
import torchaudio
HAVE_TORCHAUDIO = True
except ModuleNotFoundError:
HAVE_TORCHAUDIO = False
class TestAudioMetricWrapper:
def test_metric_full_batch(self):
"""Test metric on batches where all examples have equal length."""
ref_metric = SignalNoiseRatio()
wrapped_metric = AudioMetricWrapper(metric=SignalNoiseRatio())
num_resets = 5
num_batches = 10
batch_size = 8
num_channels = 2
num_samples = 200
batch_shape = (batch_size, num_channels, num_samples)
for nr in range(num_resets):
for nb in range(num_batches):
target = torch.rand(*batch_shape)
preds = target + torch.rand(1) * torch.rand(*batch_shape)
# test forward for a single batch
batch_value_wrapped = wrapped_metric(preds=preds, target=target)
batch_value_ref = ref_metric(preds=preds, target=target)
assert torch.allclose(
batch_value_wrapped, batch_value_ref
), f'Metric forward not matching for batch {nb}, reset {nr}'
# test compute (over num_batches)
assert torch.allclose(
wrapped_metric.compute(), ref_metric.compute()
), f'Metric compute not matching for batch {nb}, reset {nr}'
ref_metric.reset()
wrapped_metric.reset()
def test_input_length(self):
"""Test metric on batches where examples have different length."""
ref_metric = SignalNoiseRatio()
wrapped_metric = AudioMetricWrapper(metric=SignalNoiseRatio())
num_resets = 5
num_batches = 10
batch_size = 8
num_channels = 2
num_samples = 200
batch_shape = (batch_size, num_channels, num_samples)
for nr in range(num_resets):
for nb in range(num_batches):
target = torch.rand(*batch_shape)
preds = target + torch.rand(1) * torch.rand(*batch_shape)
input_length = torch.randint(low=num_samples // 2, high=num_samples, size=(batch_size,))
# test forward for a single batch
batch_value_wrapped = wrapped_metric(preds=preds, target=target, input_length=input_length)
# compute reference value, assuming batch reduction using averaging
batch_value_ref = 0
for b_idx, b_len in enumerate(input_length):
batch_value_ref += ref_metric(preds=preds[b_idx, ..., :b_len], target=target[b_idx, ..., :b_len])
batch_value_ref /= batch_size # average
assert torch.allclose(
batch_value_wrapped, batch_value_ref
), f'Metric forward not matching for batch {nb}, reset {nr}'
# test compute (over num_batches)
assert torch.allclose(
wrapped_metric.compute(), ref_metric.compute()
), f'Metric compute not matching for batch {nb}, reset {nr}'
ref_metric.reset()
wrapped_metric.reset()
@pytest.mark.unit
@pytest.mark.parametrize('channel', [0, 1])
def test_channel(self, channel):
"""Test metric on a single channel from a batch."""
ref_metric = SignalNoiseRatio()
# select only a single channel
wrapped_metric = AudioMetricWrapper(metric=SignalNoiseRatio(), channel=channel)
num_resets = 5
num_batches = 10
batch_size = 8
num_channels = 2
num_samples = 200
batch_shape = (batch_size, num_channels, num_samples)
for nr in range(num_resets):
for nb in range(num_batches):
target = torch.rand(*batch_shape)
preds = target + torch.rand(1) * torch.rand(*batch_shape)
# varying length
input_length = torch.randint(low=num_samples // 2, high=num_samples, size=(batch_size,))
# test forward for a single batch
batch_value_wrapped = wrapped_metric(preds=preds, target=target, input_length=input_length)
# compute reference value, assuming batch reduction using averaging
batch_value_ref = 0
for b_idx, b_len in enumerate(input_length):
batch_value_ref += ref_metric(
preds=preds[b_idx, channel, :b_len], target=target[b_idx, channel, :b_len]
)
batch_value_ref /= batch_size # average
assert torch.allclose(
batch_value_wrapped, batch_value_ref
), f'Metric forward not matching for batch {nb}, reset {nr}'
# test compute (over num_batches)
assert torch.allclose(
wrapped_metric.compute(), ref_metric.compute()
), f'Metric compute not matching for batch {nb}, reset {nr}'
ref_metric.reset()
wrapped_metric.reset()
class TestSquimMetrics:
@pytest.mark.unit
@pytest.mark.parametrize('fs', [16000, 24000])
def test_squim_mos(self, fs: int):
"""Test Squim MOS metric"""
if HAVE_TORCHAUDIO:
# Setup
num_batches = 4
batch_size = 4
atol = 1e-6
# UUT
squim_mos_metric = SquimMOSMetric(fs=fs)
# Helper function
resampler = torchaudio.transforms.Resample(
orig_freq=fs,
new_freq=16000,
lowpass_filter_width=64,
rolloff=0.9475937167399596,
resampling_method='sinc_interp_kaiser',
beta=14.769656459379492,
)
squim_mos_model = torchaudio.pipelines.SQUIM_SUBJECTIVE.get_model()
def calculate_squim_mos(preds: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
if fs != 16000:
preds = resampler(preds)
target = resampler(target)
# Calculate MOS
mos_batch = squim_mos_model(preds, target)
return mos_batch
# Test
mos_sum = torch.tensor(0.0)
for n in range(num_batches):
preds = torch.randn(batch_size, fs)
target = torch.randn(batch_size, fs)
# UUT forward
squim_mos_metric.update(preds=preds, target=target)
# Golden
mos_golden = calculate_squim_mos(preds=preds, target=target)
# Accumulate
mos_sum += mos_golden.sum()
# Check the final value of the metric
mos_golden_final = mos_sum / (num_batches * batch_size)
assert torch.allclose(squim_mos_metric.compute(), mos_golden_final, atol=atol), f'Comparison failed'
else:
with pytest.raises(ModuleNotFoundError):
SquimMOSMetric(fs=fs)
@pytest.mark.unit
@pytest.mark.parametrize('metric', ['stoi', 'pesq', 'si_sdr'])
@pytest.mark.parametrize('fs', [16000, 24000])
def test_squim_objective(self, metric: str, fs: int):
"""Test Squim objective metric"""
if HAVE_TORCHAUDIO:
# Setup
num_batches = 4
batch_size = 4
atol = 1e-6
# UUT
squim_objective_metric = SquimObjectiveMetric(fs=fs, metric=metric)
# Helper function
resampler = torchaudio.transforms.Resample(
orig_freq=fs,
new_freq=16000,
lowpass_filter_width=64,
rolloff=0.9475937167399596,
resampling_method='sinc_interp_kaiser',
beta=14.769656459379492,
)
squim_objective_model = torchaudio.pipelines.SQUIM_OBJECTIVE.get_model()
def calculate_squim_objective(preds: torch.Tensor) -> torch.Tensor:
if fs != 16000:
preds = resampler(preds)
# Calculate metric
stoi_batch, pesq_batch, si_sdr_batch = squim_objective_model(preds)
if metric == 'stoi':
return stoi_batch
elif metric == 'pesq':
return pesq_batch
elif metric == 'si_sdr':
return si_sdr_batch
else:
raise ValueError(f'Unknown metric {metric}')
# Test
metric_sum = torch.tensor(0.0)
for n in range(num_batches):
preds = torch.randn(batch_size, fs)
# UUT forward
squim_objective_metric.update(preds=preds, target=None)
# Golden
metric_golden = calculate_squim_objective(preds=preds)
# Accumulate
metric_sum += metric_golden.sum()
# Check the final value of the metric
metric_golden_final = metric_sum / (num_batches * batch_size)
assert torch.allclose(
squim_objective_metric.compute(), metric_golden_final, atol=atol
), f'Comparison failed'
else:
with pytest.raises(ModuleNotFoundError):
SquimObjectiveMetric(fs=fs, metric=metric)
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