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| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import torch | |
| import torchmetrics | |
| from ..data.audio_utils import convert_audio | |
| from ..modules.chroma import ChromaExtractor | |
| class ChromaCosineSimilarityMetric(torchmetrics.Metric): | |
| """Chroma cosine similarity metric. | |
| This metric extracts a chromagram for a reference waveform and | |
| a generated waveform and compares each frame using the cosine similarity | |
| function. The output is the mean cosine similarity. | |
| Args: | |
| sample_rate (int): Sample rate used by the chroma extractor. | |
| n_chroma (int): Number of chroma used by the chroma extractor. | |
| radix2_exp (int): Exponent for the chroma extractor. | |
| argmax (bool): Whether the chroma extractor uses argmax. | |
| eps (float): Epsilon for cosine similarity computation. | |
| """ | |
| def __init__(self, sample_rate: int, n_chroma: int, radix2_exp: int, argmax: bool, eps: float = 1e-8): | |
| super().__init__() | |
| self.chroma_sample_rate = sample_rate | |
| self.n_chroma = n_chroma | |
| self.eps = eps | |
| self.chroma_extractor = ChromaExtractor(sample_rate=self.chroma_sample_rate, n_chroma=self.n_chroma, | |
| radix2_exp=radix2_exp, argmax=argmax) | |
| self.add_state("cosine_sum", default=torch.tensor(0.), dist_reduce_fx="sum") | |
| self.add_state("weight", default=torch.tensor(0.), dist_reduce_fx="sum") | |
| def update(self, preds: torch.Tensor, targets: torch.Tensor, | |
| sizes: torch.Tensor, sample_rates: torch.Tensor) -> None: | |
| """Compute cosine similarity between chromagrams and accumulate scores over the dataset.""" | |
| if preds.size(0) == 0: | |
| return | |
| assert preds.shape == targets.shape, ( | |
| f"Preds and target shapes mismatch: preds={preds.shape}, targets={targets.shape}") | |
| assert preds.size(0) == sizes.size(0), ( | |
| f"Number of items in preds ({preds.shape}) mismatch ", | |
| f"with sizes ({sizes.shape})") | |
| assert preds.size(0) == sample_rates.size(0), ( | |
| f"Number of items in preds ({preds.shape}) mismatch ", | |
| f"with sample_rates ({sample_rates.shape})") | |
| assert torch.all(sample_rates == sample_rates[0].item()), "All sample rates are not the same in the batch" | |
| device = self.weight.device | |
| preds, targets = preds.to(device), targets.to(device) # type: ignore | |
| sample_rate = sample_rates[0].item() | |
| preds = convert_audio(preds, from_rate=sample_rate, to_rate=self.chroma_sample_rate, to_channels=1) | |
| targets = convert_audio(targets, from_rate=sample_rate, to_rate=self.chroma_sample_rate, to_channels=1) | |
| gt_chroma = self.chroma_extractor(targets) | |
| gen_chroma = self.chroma_extractor(preds) | |
| chroma_lens = (sizes / self.chroma_extractor.winhop).ceil().int() | |
| for i in range(len(gt_chroma)): | |
| t = int(chroma_lens[i].item()) | |
| cosine_sim = torch.nn.functional.cosine_similarity( | |
| gt_chroma[i, :t], gen_chroma[i, :t], dim=1, eps=self.eps) | |
| self.cosine_sum += cosine_sim.sum(dim=0) # type: ignore | |
| self.weight += torch.tensor(t) # type: ignore | |
| def compute(self) -> float: | |
| """Computes the average cosine similarty across all generated/target chromagrams pairs.""" | |
| assert self.weight.item() > 0, "Unable to compute with total number of comparisons <= 0" # type: ignore | |
| return (self.cosine_sum / self.weight).item() # type: ignore | |