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