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audio metrics: SNR, SI_SDR, SI_SNR (#292)
* add snr, si_sdr, si_snr * format * add noqa: F401 to __init__.py * remove types in doc, change estimate to preds, remove EPS * update functional.rst * update CHANGELOG.md * switch preds and target * switch preds and target in Example * add SNR, SI_SNR, SI_SDR module implementation * add test * add module doc * use _check_same_shape * to alphabetical order * update test * move Base to the top of Audio * add soundfile * gcc * fix mocking * image * doctest * mypy * fix requirements * fix dtype * something * update * adjust * Apply suggestions from code review * update test_snr * update test_si_snr * new snr: use torch.finfo(preds.dtype).eps * update test_snr.py * new si_sdr imp * update test_si_sdr * update test_si_snr * remove pb_bss_eval * add museval * update test files * remove museval * add funcs update return None annotation * add 'Setup ffmpeg' * update "Setup ffmpeg" * use setup-conda@v1 * multi-OS * update atol to 1e-5 * Apply suggestions from code review * change atol to 1e-2 * update * fix 'Setup Linux' not activated * add sudo * reduce Time to 100 to reduce the test time * increase timeoutInMinutes to 40 * install ffmpeg * timeout-minutes to 55 * +git * show-error-codes * .detach().cpu().numpy() first * add numpy * numpy * ignore_errors torchmetrics.audio.* * solve mypy no-redef error * remove --quiet * pypesq * apt * add # type: ignore * try without test_si_snr & test_si_sdr * test_import_speechmetrics * test_speechmetrics_si_sdr * test_si_sdr_functional * test audio only * install libsndfile1 * add sisnr sisdr test * test all & add quiet & remove test_speechmetrics * remove sudo & install libsndfile1 * add test * update * fix tests * typing * fix typing * fix bus error * SRMRpy * pesq * gcc * comment -u root cuda 10.2 whoami * env Co-authored-by: quancs <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Nicki Skafte <[email protected]> Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Jirka <[email protected]> Co-authored-by: Jirka Borovec <[email protected]> Co-authored-by: Justus Schock <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,131 @@ | ||
| # Copyright The PyTorch Lightning team. | ||
| # | ||
| # 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. | ||
| from collections import namedtuple | ||
| from functools import partial | ||
|
|
||
| import pytest | ||
| import speechmetrics | ||
| import torch | ||
| from torch import Tensor | ||
|
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||
| from tests.helpers import seed_all | ||
| from tests.helpers.testers import BATCH_SIZE, NUM_BATCHES, MetricTester | ||
| from torchmetrics.audio import SI_SDR | ||
| from torchmetrics.functional import si_sdr | ||
| from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_1_6 | ||
|
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| seed_all(42) | ||
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| Time = 100 | ||
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| Input = namedtuple('Input', ["preds", "target"]) | ||
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| inputs = Input( | ||
| preds=torch.rand(NUM_BATCHES, BATCH_SIZE, 1, Time), | ||
| target=torch.rand(NUM_BATCHES, BATCH_SIZE, 1, Time), | ||
| ) | ||
|
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| speechmetrics_sisdr = speechmetrics.load('sisdr') | ||
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| def speechmetrics_si_sdr(preds: Tensor, target: Tensor, zero_mean: bool): | ||
| # shape: preds [BATCH_SIZE, 1, Time] , target [BATCH_SIZE, 1, Time] | ||
| # or shape: preds [NUM_BATCHES*BATCH_SIZE, 1, Time] , target [NUM_BATCHES*BATCH_SIZE, 1, Time] | ||
| if zero_mean: | ||
| preds = preds - preds.mean(dim=2, keepdim=True) | ||
| target = target - target.mean(dim=2, keepdim=True) | ||
| target = target.detach().cpu().numpy() | ||
| preds = preds.detach().cpu().numpy() | ||
| mss = [] | ||
| for i in range(preds.shape[0]): | ||
| ms = [] | ||
| for j in range(preds.shape[1]): | ||
| metric = speechmetrics_sisdr(preds[i, j], target[i, j], rate=16000) | ||
| ms.append(metric['sisdr'][0]) | ||
| mss.append(ms) | ||
| return torch.tensor(mss) | ||
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|
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| def average_metric(preds, target, metric_func): | ||
| # shape: preds [BATCH_SIZE, 1, Time] , target [BATCH_SIZE, 1, Time] | ||
| # or shape: preds [NUM_BATCHES*BATCH_SIZE, 1, Time] , target [NUM_BATCHES*BATCH_SIZE, 1, Time] | ||
| return metric_func(preds, target).mean() | ||
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| speechmetrics_si_sdr_zero_mean = partial(speechmetrics_si_sdr, zero_mean=True) | ||
| speechmetrics_si_sdr_no_zero_mean = partial(speechmetrics_si_sdr, zero_mean=False) | ||
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| @pytest.mark.parametrize( | ||
| "preds, target, sk_metric, zero_mean", | ||
| [ | ||
| (inputs.preds, inputs.target, speechmetrics_si_sdr_zero_mean, True), | ||
| (inputs.preds, inputs.target, speechmetrics_si_sdr_no_zero_mean, False), | ||
| ], | ||
| ) | ||
| class TestSISDR(MetricTester): | ||
| atol = 1e-2 | ||
|
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| @pytest.mark.parametrize("ddp", [True, False]) | ||
| @pytest.mark.parametrize("dist_sync_on_step", [True, False]) | ||
| def test_si_sdr(self, preds, target, sk_metric, zero_mean, ddp, dist_sync_on_step): | ||
| self.run_class_metric_test( | ||
| ddp, | ||
| preds, | ||
| target, | ||
| SI_SDR, | ||
| sk_metric=partial(average_metric, metric_func=sk_metric), | ||
| dist_sync_on_step=dist_sync_on_step, | ||
| metric_args=dict(zero_mean=zero_mean), | ||
| ) | ||
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| def test_si_sdr_functional(self, preds, target, sk_metric, zero_mean): | ||
| self.run_functional_metric_test( | ||
| preds, | ||
| target, | ||
| si_sdr, | ||
| sk_metric, | ||
| metric_args=dict(zero_mean=zero_mean), | ||
| ) | ||
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| def test_si_sdr_differentiability(self, preds, target, sk_metric, zero_mean): | ||
| self.run_differentiability_test( | ||
| preds=preds, | ||
| target=target, | ||
| metric_module=SI_SDR, | ||
| metric_functional=si_sdr, | ||
| metric_args={'zero_mean': zero_mean} | ||
| ) | ||
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| @pytest.mark.skipif( | ||
| not _TORCH_GREATER_EQUAL_1_6, reason='half support of core operations on not support before pytorch v1.6' | ||
| ) | ||
| def test_si_sdr_half_cpu(self, preds, target, sk_metric, zero_mean): | ||
| pytest.xfail("SI-SDR metric does not support cpu + half precision") | ||
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| @pytest.mark.skipif(not torch.cuda.is_available(), reason='test requires cuda') | ||
| def test_si_sdr_half_gpu(self, preds, target, sk_metric, zero_mean): | ||
| self.run_precision_test_gpu( | ||
| preds=preds, | ||
| target=target, | ||
| metric_module=SI_SDR, | ||
| metric_functional=si_sdr, | ||
| metric_args={'zero_mean': zero_mean} | ||
| ) | ||
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| def test_error_on_different_shape(metric_class=SI_SDR): | ||
| metric = metric_class() | ||
| with pytest.raises(RuntimeError, match='Predictions and targets are expected to have the same shape'): | ||
| metric(torch.randn(100, ), torch.randn(50, )) |
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