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[Audio] Metric with Squim objective and MOS (#9751)
* Metric with Squim Objective and MOS Signed-off-by: Ante Jukić <[email protected]> * Removed utility functions Signed-off-by: Ante Jukić <[email protected]> --------- Signed-off-by: Ante Jukić <[email protected]>
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| Original file line number | Diff line number | Diff line change |
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| # Copyright (c) 2024, 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. | ||
|
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| from typing import Any | ||
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| import torch | ||
| from torchmetrics import Metric | ||
| from nemo.utils import logging | ||
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| try: | ||
| import torchaudio | ||
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| HAVE_TORCHAUDIO = True | ||
| except ModuleNotFoundError: | ||
| HAVE_TORCHAUDIO = False | ||
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|
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| class SquimMOSMetric(Metric): | ||
| """A metric calculating the average Torchaudio Squim MOS. | ||
| Args: | ||
| fs: sampling rate of the input signals | ||
| """ | ||
|
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| sample_rate: int = 16000 # sample rate of the model | ||
| mos_sum: torch.Tensor | ||
| num_examples: torch.Tensor | ||
| higher_is_better: bool = True | ||
|
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| def __init__(self, fs: int, **kwargs: Any): | ||
| super().__init__(**kwargs) | ||
|
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| if not HAVE_TORCHAUDIO: | ||
| raise ModuleNotFoundError(f"{self.__class__.__name__} metric needs `torchaudio`.") | ||
|
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| if fs != self.sample_rate: | ||
| # Resampler: kaiser_best | ||
| self._squim_mos_metric_resampler = torchaudio.transforms.Resample( | ||
| orig_freq=fs, | ||
| new_freq=self.sample_rate, | ||
| lowpass_filter_width=64, | ||
| rolloff=0.9475937167399596, | ||
| resampling_method='sinc_interp_kaiser', | ||
| beta=14.769656459379492, | ||
| ) | ||
| logging.warning('Input signals will be resampled from fs=%d to %d Hz', fs, self.sample_rate) | ||
| self.fs = fs | ||
|
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| # MOS model | ||
| self._squim_mos_metric_model = torchaudio.pipelines.SQUIM_SUBJECTIVE.get_model() | ||
|
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| self.add_state('mos_sum', default=torch.tensor(0.0), dist_reduce_fx='sum') | ||
| self.add_state('num_examples', default=torch.tensor(0), dist_reduce_fx='sum') | ||
| logging.debug('Setup metric %s with input fs=%s', self.__class__.__name__, self.fs) | ||
|
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| def update(self, preds: torch.Tensor, target: torch.Tensor) -> None: | ||
| """Update the metric by calculating the MOS score for the current batch. | ||
| Args: | ||
| preds: tensor with predictions, shape (B, T) | ||
| target: tensor with target signals, shape (B, T). Target can be a non-matching reference. | ||
| """ | ||
| if self.fs != self.sample_rate: | ||
| preds = self._squim_mos_metric_resampler(preds) | ||
| target = self._squim_mos_metric_resampler(target) | ||
|
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||
| if preds.ndim == 1: | ||
| # Unsqueeze batch dimension | ||
| preds = preds.unsqueeze(0) | ||
| target = target.unsqueeze(0) | ||
| elif preds.ndim > 2: | ||
| raise ValueError(f'Expected 1D or 2D signals, got {preds.ndim}D signals') | ||
|
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| mos_batch = self._squim_mos_metric_model(preds, target) | ||
|
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| self.mos_sum += mos_batch.sum() | ||
| self.num_examples += mos_batch.numel() | ||
|
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| def compute(self) -> torch.Tensor: | ||
| """Compute the underlying metric.""" | ||
| return self.mos_sum / self.num_examples | ||
|
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| def state_dict(self, *args, **kwargs): | ||
| """Do not save the MOS model and resampler in the state dict.""" | ||
| state_dict = super().state_dict(*args, **kwargs) | ||
| # Do not include resampler or mos_model in the state dict | ||
| remove_keys = [ | ||
| key | ||
| for key in state_dict.keys() | ||
| if '_squim_mos_metric_resampler' in key or '_squim_mos_metric_model' in key | ||
| ] | ||
| for key in remove_keys: | ||
| del state_dict[key] | ||
| return state_dict | ||
|
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|
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| class SquimObjectiveMetric(Metric): | ||
| """A metric calculating the average Torchaudio Squim objective metric. | ||
| Args: | ||
| fs: sampling rate of the input signals | ||
| metric: the objective metric to calculate. One of 'stoi', 'pesq', 'si_sdr' | ||
| """ | ||
|
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| sample_rate: int = 16000 # sample rate of the model | ||
| metric_sum: torch.Tensor | ||
| num_examples: torch.Tensor | ||
| higher_is_better: bool = True | ||
|
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| def __init__(self, fs: int, metric: str, **kwargs: Any): | ||
| super().__init__(**kwargs) | ||
|
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| if not HAVE_TORCHAUDIO: | ||
| raise ModuleNotFoundError(f"{self.__class__.__name__} needs `torchaudio`.") | ||
|
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| if fs != self.sample_rate: | ||
| # Resampler: kaiser_best | ||
| self._squim_objective_metric_resampler = torchaudio.transforms.Resample( | ||
| orig_freq=fs, | ||
| new_freq=self.sample_rate, | ||
| lowpass_filter_width=64, | ||
| rolloff=0.9475937167399596, | ||
| resampling_method='sinc_interp_kaiser', | ||
| beta=14.769656459379492, | ||
| ) | ||
| logging.warning('Input signals will be resampled from fs=%d to %d Hz', fs, self.sample_rate) | ||
| self.fs = fs | ||
|
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| if metric not in ['stoi', 'pesq', 'si_sdr']: | ||
| raise ValueError(f'Unsupported metric {metric}. Supported metrics are "stoi", "pesq", "si_sdr".') | ||
|
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| self.metric = metric | ||
|
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| # Objective model | ||
| self._squim_objective_metric_model = torchaudio.pipelines.SQUIM_OBJECTIVE.get_model() | ||
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| self.add_state('metric_sum', default=torch.tensor(0.0), dist_reduce_fx='sum') | ||
| self.add_state('num_examples', default=torch.tensor(0), dist_reduce_fx='sum') | ||
| logging.debug('Setup %s with metric=%s, input fs=%s', self.__class__.__name__, self.metric, self.fs) | ||
|
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| def update(self, preds: torch.Tensor, target: Any = None) -> None: | ||
| """Update the metric by calculating the selected metric score for the current batch. | ||
| Args: | ||
| preds: tensor with predictions, shape (B, T) | ||
| target: None, not used. Keeping for interfacfe compatibility with other metrics. | ||
| """ | ||
| if self.fs != self.sample_rate: | ||
| preds = self._squim_objective_metric_resampler(preds) | ||
|
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| if preds.ndim == 1: | ||
| # Unsqueeze batch dimension | ||
| preds = preds.unsqueeze(0) | ||
| elif preds.ndim > 2: | ||
| raise ValueError(f'Expected 1D or 2D signals, got {preds.ndim}D signals') | ||
|
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| stoi_batch, pesq_batch, si_sdr_batch = self._squim_objective_metric_model(preds) | ||
|
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| if self.metric == 'stoi': | ||
| metric_batch = stoi_batch | ||
| elif self.metric == 'pesq': | ||
| metric_batch = pesq_batch | ||
| elif self.metric == 'si_sdr': | ||
| metric_batch = si_sdr_batch | ||
| else: | ||
| raise ValueError(f'Unknown metric {self.metric}') | ||
|
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| self.metric_sum += metric_batch.sum() | ||
| self.num_examples += metric_batch.numel() | ||
|
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| def compute(self) -> torch.Tensor: | ||
| """Compute the underlying metric.""" | ||
| return self.metric_sum / self.num_examples | ||
|
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| def state_dict(self, *args, **kwargs): | ||
| """Do not save the MOS model and resampler in the state dict.""" | ||
| state_dict = super().state_dict(*args, **kwargs) | ||
| # Do not include resampler or mos_model in the state dict | ||
| remove_keys = [ | ||
| key | ||
| for key in state_dict.keys() | ||
| if '_squim_objective_metric_resampler' in key or '_squim_objective_metric_model' in key | ||
| ] | ||
| for key in remove_keys: | ||
| del state_dict[key] | ||
| return state_dict |
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