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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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