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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import logging | |
| import os | |
| import sys | |
| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| from .. import FairseqDataset | |
| logger = logging.getLogger(__name__) | |
| class RawAudioDataset(FairseqDataset): | |
| def __init__( | |
| self, | |
| sample_rate, | |
| max_sample_size=None, | |
| min_sample_size=0, | |
| shuffle=True, | |
| pad=False, | |
| normalize=False, | |
| ): | |
| super().__init__() | |
| self.sample_rate = sample_rate | |
| self.sizes = [] | |
| self.max_sample_size = ( | |
| max_sample_size if max_sample_size is not None else sys.maxsize | |
| ) | |
| self.min_sample_size = min_sample_size | |
| self.pad = pad | |
| self.shuffle = shuffle | |
| self.normalize = normalize | |
| def __getitem__(self, index): | |
| raise NotImplementedError() | |
| def __len__(self): | |
| return len(self.sizes) | |
| def postprocess(self, feats, curr_sample_rate): | |
| if feats.dim() == 2: | |
| feats = feats.mean(-1) | |
| if curr_sample_rate != self.sample_rate: | |
| raise Exception(f"sample rate: {curr_sample_rate}, need {self.sample_rate}") | |
| assert feats.dim() == 1, feats.dim() | |
| if self.normalize: | |
| with torch.no_grad(): | |
| feats = F.layer_norm(feats, feats.shape) | |
| return feats | |
| def crop_to_max_size(self, wav, target_size): | |
| size = len(wav) | |
| diff = size - target_size | |
| if diff <= 0: | |
| return wav | |
| start = np.random.randint(0, diff + 1) | |
| end = size - diff + start | |
| return wav[start:end] | |
| def collater(self, samples): | |
| samples = [s for s in samples if s["source"] is not None] | |
| if len(samples) == 0: | |
| return {} | |
| sources = [s["source"] for s in samples] | |
| sizes = [len(s) for s in sources] | |
| if self.pad: | |
| target_size = min(max(sizes), self.max_sample_size) | |
| else: | |
| target_size = min(min(sizes), self.max_sample_size) | |
| collated_sources = sources[0].new_zeros(len(sources), target_size) | |
| padding_mask = ( | |
| torch.BoolTensor(collated_sources.shape).fill_(False) if self.pad else None | |
| ) | |
| for i, (source, size) in enumerate(zip(sources, sizes)): | |
| diff = size - target_size | |
| if diff == 0: | |
| collated_sources[i] = source | |
| elif diff < 0: | |
| assert self.pad | |
| collated_sources[i] = torch.cat( | |
| [source, source.new_full((-diff,), 0.0)] | |
| ) | |
| padding_mask[i, diff:] = True | |
| else: | |
| collated_sources[i] = self.crop_to_max_size(source, target_size) | |
| input = {"source": collated_sources} | |
| if self.pad: | |
| input["padding_mask"] = padding_mask | |
| return {"id": torch.LongTensor([s["id"] for s in samples]), "net_input": input} | |
| def num_tokens(self, index): | |
| return self.size(index) | |
| def size(self, index): | |
| """Return an example's size as a float or tuple. This value is used when | |
| filtering a dataset with ``--max-positions``.""" | |
| if self.pad: | |
| return self.sizes[index] | |
| return min(self.sizes[index], self.max_sample_size) | |
| def ordered_indices(self): | |
| """Return an ordered list of indices. Batches will be constructed based | |
| on this order.""" | |
| if self.shuffle: | |
| order = [np.random.permutation(len(self))] | |
| else: | |
| order = [np.arange(len(self))] | |
| order.append(self.sizes) | |
| return np.lexsort(order)[::-1] | |
| class FileAudioDataset(RawAudioDataset): | |
| def __init__( | |
| self, | |
| manifest_path, | |
| sample_rate, | |
| max_sample_size=None, | |
| min_sample_size=0, | |
| shuffle=True, | |
| pad=False, | |
| normalize=False, | |
| ): | |
| super().__init__( | |
| sample_rate=sample_rate, | |
| max_sample_size=max_sample_size, | |
| min_sample_size=min_sample_size, | |
| shuffle=shuffle, | |
| pad=pad, | |
| normalize=normalize, | |
| ) | |
| self.fnames = [] | |
| self.line_inds = set() | |
| skipped = 0 | |
| with open(manifest_path, "r") as f: | |
| self.root_dir = f.readline().strip() | |
| for i, line in enumerate(f): | |
| items = line.strip().split("\t") | |
| assert len(items) == 2, line | |
| sz = int(items[1]) | |
| if min_sample_size is not None and sz < min_sample_size: | |
| skipped += 1 | |
| continue | |
| self.fnames.append(items[0]) | |
| self.line_inds.add(i) | |
| self.sizes.append(sz) | |
| logger.info(f"loaded {len(self.fnames)}, skipped {skipped} samples") | |
| def __getitem__(self, index): | |
| import soundfile as sf | |
| fname = os.path.join(self.root_dir, self.fnames[index]) | |
| wav, curr_sample_rate = sf.read(fname) | |
| feats = torch.from_numpy(wav).float() | |
| feats = self.postprocess(feats, curr_sample_rate) | |
| return {"id": index, "source": feats} | |