File size: 13,679 Bytes
6a62ffb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 |
# 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 io
import numpy as np
import torch
import torch.nn.functional as F
from .. import FairseqDataset
from ..data_utils import compute_mask_indices, get_buckets, get_bucketed_sizes
from fairseq.data.audio.audio_utils import (
parse_path,
read_from_stored_zip,
is_sf_audio_data,
)
from fairseq.data.text_compressor import TextCompressor, TextCompressionLevel
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,
compute_mask_indices=False,
**mask_compute_kwargs,
):
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
self.compute_mask_indices = compute_mask_indices
if self.compute_mask_indices:
self.mask_compute_kwargs = mask_compute_kwargs
self._features_size_map = {}
self._C = mask_compute_kwargs["encoder_embed_dim"]
self._conv_feature_layers = eval(mask_compute_kwargs["conv_feature_layers"])
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 _compute_mask_indices(self, dims, padding_mask):
B, T, C = dims
mask_indices, mask_channel_indices = None, None
if self.mask_compute_kwargs["mask_prob"] > 0:
mask_indices = compute_mask_indices(
(B, T),
padding_mask,
self.mask_compute_kwargs["mask_prob"],
self.mask_compute_kwargs["mask_length"],
self.mask_compute_kwargs["mask_selection"],
self.mask_compute_kwargs["mask_other"],
min_masks=2,
no_overlap=self.mask_compute_kwargs["no_mask_overlap"],
min_space=self.mask_compute_kwargs["mask_min_space"],
)
mask_indices = torch.from_numpy(mask_indices)
if self.mask_compute_kwargs["mask_channel_prob"] > 0:
mask_channel_indices = compute_mask_indices(
(B, C),
None,
self.mask_compute_kwargs["mask_channel_prob"],
self.mask_compute_kwargs["mask_channel_length"],
self.mask_compute_kwargs["mask_channel_selection"],
self.mask_compute_kwargs["mask_channel_other"],
no_overlap=self.mask_compute_kwargs["no_mask_channel_overlap"],
min_space=self.mask_compute_kwargs["mask_channel_min_space"],
)
mask_channel_indices = (
torch.from_numpy(mask_channel_indices).unsqueeze(1).expand(-1, T, -1)
)
return mask_indices, mask_channel_indices
@staticmethod
def _bucket_tensor(tensor, num_pad, value):
return F.pad(tensor, (0, num_pad), value=value)
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}
out = {"id": torch.LongTensor([s["id"] for s in samples])}
if self.pad:
input["padding_mask"] = padding_mask
if hasattr(self, "num_buckets") and self.num_buckets > 0:
assert self.pad, "Cannot bucket without padding first."
bucket = max(self._bucketed_sizes[s["id"]] for s in samples)
num_pad = bucket - collated_sources.size(-1)
if num_pad:
input["source"] = self._bucket_tensor(collated_sources, num_pad, 0)
input["padding_mask"] = self._bucket_tensor(padding_mask, num_pad, True)
if self.compute_mask_indices:
B = input["source"].size(0)
T = self._get_mask_indices_dims(input["source"].size(-1))
padding_mask_reshaped = input["padding_mask"].clone()
extra = padding_mask_reshaped.size(1) % T
if extra > 0:
padding_mask_reshaped = padding_mask_reshaped[:, :-extra]
padding_mask_reshaped = padding_mask_reshaped.view(
padding_mask_reshaped.size(0), T, -1
)
padding_mask_reshaped = padding_mask_reshaped.all(-1)
input["padding_count"] = padding_mask_reshaped.sum(-1).max().item()
mask_indices, mask_channel_indices = self._compute_mask_indices(
(B, T, self._C),
padding_mask_reshaped,
)
input["mask_indices"] = mask_indices
input["mask_channel_indices"] = mask_channel_indices
out["sample_size"] = mask_indices.sum().item()
out["net_input"] = input
return out
def _get_mask_indices_dims(self, size, padding=0, dilation=1):
if size not in self._features_size_map:
L_in = size
for (_, kernel_size, stride) in self._conv_feature_layers:
L_out = L_in + 2 * padding - dilation * (kernel_size - 1) - 1
L_out = 1 + L_out // stride
L_in = L_out
self._features_size_map[size] = L_out
return self._features_size_map[size]
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))]
order.append(
np.minimum(
np.array(self.sizes),
self.max_sample_size,
)
)
return np.lexsort(order)[::-1]
else:
return np.arange(len(self))
def set_bucket_info(self, num_buckets):
self.num_buckets = num_buckets
if self.num_buckets > 0:
self._collated_sizes = np.minimum(
np.array(self.sizes),
self.max_sample_size,
)
self.buckets = get_buckets(
self._collated_sizes,
self.num_buckets,
)
self._bucketed_sizes = get_bucketed_sizes(
self._collated_sizes, self.buckets
)
logger.info(
f"{len(self.buckets)} bucket(s) for the audio dataset: "
f"{self.buckets}"
)
class FileAudioDataset(RawAudioDataset):
def __init__(
self,
manifest_path,
sample_rate,
max_sample_size=None,
min_sample_size=0,
shuffle=True,
pad=False,
normalize=False,
num_buckets=0,
compute_mask_indices=False,
text_compression_level=TextCompressionLevel.none,
**mask_compute_kwargs,
):
super().__init__(
sample_rate=sample_rate,
max_sample_size=max_sample_size,
min_sample_size=min_sample_size,
shuffle=shuffle,
pad=pad,
normalize=normalize,
compute_mask_indices=compute_mask_indices,
**mask_compute_kwargs,
)
self.text_compressor = TextCompressor(level=text_compression_level)
skipped = 0
self.fnames = []
sizes = []
self.skipped_indices = set()
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
self.skipped_indices.add(i)
continue
self.fnames.append(self.text_compressor.compress(items[0]))
sizes.append(sz)
logger.info(f"loaded {len(self.fnames)}, skipped {skipped} samples")
self.sizes = np.array(sizes, dtype=np.int64)
try:
import pyarrow
self.fnames = pyarrow.array(self.fnames)
except:
logger.debug(
"Could not create a pyarrow array. Please install pyarrow for better performance"
)
pass
self.set_bucket_info(num_buckets)
def __getitem__(self, index):
import soundfile as sf
fn = self.fnames[index]
fn = fn if isinstance(self.fnames, list) else fn.as_py()
fn = self.text_compressor.decompress(fn)
path_or_fp = os.path.join(self.root_dir, fn)
_path, slice_ptr = parse_path(path_or_fp)
if len(slice_ptr) == 2:
byte_data = read_from_stored_zip(_path, slice_ptr[0], slice_ptr[1])
assert is_sf_audio_data(byte_data)
path_or_fp = io.BytesIO(byte_data)
wav, curr_sample_rate = sf.read(path_or_fp, dtype="float32")
feats = torch.from_numpy(wav).float()
feats = self.postprocess(feats, curr_sample_rate)
return {"id": index, "source": feats}
class BinarizedAudioDataset(RawAudioDataset):
def __init__(
self,
data_dir,
split,
sample_rate,
max_sample_size=None,
min_sample_size=0,
shuffle=True,
pad=False,
normalize=False,
num_buckets=0,
compute_mask_indices=False,
**mask_compute_kwargs,
):
super().__init__(
sample_rate=sample_rate,
max_sample_size=max_sample_size,
min_sample_size=min_sample_size,
shuffle=shuffle,
pad=pad,
normalize=normalize,
compute_mask_indices=compute_mask_indices,
**mask_compute_kwargs,
)
from fairseq.data import data_utils, Dictionary
self.fnames_dict = Dictionary.load(os.path.join(data_dir, "dict.txt"))
root_path = os.path.join(data_dir, f"{split}.root")
if os.path.exists(root_path):
with open(root_path, "r") as f:
self.root_dir = next(f).strip()
else:
self.root_dir = None
fnames_path = os.path.join(data_dir, split)
self.fnames = data_utils.load_indexed_dataset(fnames_path, self.fnames_dict)
lengths_path = os.path.join(data_dir, f"{split}.lengths")
with open(lengths_path, "r") as f:
for line in f:
sz = int(line.rstrip())
assert (
sz >= min_sample_size
), f"Min sample size is not supported for binarized dataset, but found a sample with size {sz}"
self.sizes.append(sz)
self.sizes = np.array(self.sizes, dtype=np.int64)
self.set_bucket_info(num_buckets)
logger.info(f"loaded {len(self.fnames)} samples")
def __getitem__(self, index):
import soundfile as sf
fname = self.fnames_dict.string(self.fnames[index], separator="")
if self.root_dir:
fname = os.path.join(self.root_dir, fname)
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}
|