def load_dataset(self,
split: str,
task_cfg: FairseqDataclass = None,
**kwargs):
data_path = self.cfg.data
task_cfg = task_cfg or self.cfg
# upgrade old task
if isinstance(task_cfg, Namespace):
if not hasattr(task_cfg, "autoregressive"):
task_cfg.autoregressive = not task_cfg.criterion == 'ctc'
manifest = os.path.join(data_path, "{}.tsv".format(split))
self.datasets[split] = FileAudioDataset(
manifest,
sample_rate=task_cfg.sample_rate,
max_sample_size=self.cfg.max_sample_size,
min_sample_size=self.cfg.max_sample_size,
min_length=self.cfg.min_sample_size,
pad=task_cfg.labels is not None or task_cfg.enable_padding,
normalize=task_cfg.normalize,
)
if task_cfg.labels:
label_path = os.path.join(data_path, f"{split}.{task_cfg.labels}")
labels = []
with open(label_path, "r") as f:
for line in f:
labels.append(line)
process_label = LabelEncoder(self.target_dictionary)
self.datasets[split] = AddTargetDataset(
self.datasets[split],
labels,
pad=self.target_dictionary.pad(),
eos=self.target_dictionary.eos(),
batch_targets=True,
process_label=process_label,
add_to_input=task_cfg.autoregressive,
)
def load_dataset(self, split, **kwargs):
"""Load a given dataset split.
Args:
split (str): name of the split (e.g., train, valid, test)
"""
manifest = os.path.join(self.args.data, "{}.tsv".format(split))
self.datasets[split] = FileAudioDataset(
manifest,
sample_rate=self.args.sample_rate,
max_sample_size=self.args.max_sample_size,
min_sample_size=self.args.min_sample_size
if not self.args.no_min_cropping else self.args.max_sample_size,
min_length=self.args.min_sample_size,
pad=self.args.labels is not None or self.args.enable_padding,
normalize=self.args.normalize,
)
if self.args.labels:
dict_path = os.path.join(self.args.data,
f"dict.{self.args.labels}.txt")
self._target_dictionary = Dictionary.load(dict_path)
label_path = os.path.join(self.args.data,
f"{split}.{self.args.labels}")
labels = []
with open(label_path, "r") as f:
for line in f:
labels.append(line)
process_label = LabelEncoder(self.target_dictionary)
self.datasets[split] = AddTargetDataset(
self.datasets[split],
labels,
pad=self.target_dictionary.pad(),
eos=self.target_dictionary.eos(),
batch_targets=True,
process_label=process_label,
add_to_input=not self.is_ctc,
)
def load_dataset(self, split: str, task_cfg: FairseqDataclass = None, **kwargs):
data_path = self.cfg.data
task_cfg = task_cfg or self.cfg
# upgrade old task
if isinstance(task_cfg, Namespace):
if not hasattr(task_cfg, "autoregressive"):
task_cfg.autoregressive = not task_cfg.criterion == "ctc"
if getattr(task_cfg, 'binarized_dataset', False):
self.datasets[split] = BinarizedAudioDataset(
data_path,
split=split,
sample_rate=task_cfg.get("sample_rate", self.cfg.sample_rate),
max_sample_size=self.cfg.max_sample_size,
min_sample_size=self.cfg.min_sample_size,
pad=task_cfg.labels is not None or task_cfg.enable_padding,
normalize=task_cfg.normalize,
num_buckets=self.cfg.num_batch_buckets or int(self.cfg.tpu),
compute_mask_indices=(self.cfg.precompute_mask_indices or self.cfg.tpu),
**self._get_mask_precompute_kwargs(task_cfg),
)
else:
manifest_path = os.path.join(data_path, "{}.tsv".format(split))
self.datasets[split] = FileAudioDataset(
manifest_path=manifest_path,
sample_rate=task_cfg.get("sample_rate", self.cfg.sample_rate),
max_sample_size=self.cfg.max_sample_size,
min_sample_size=self.cfg.min_sample_size,
pad=task_cfg.labels is not None or task_cfg.enable_padding,
normalize=task_cfg.normalize,
num_buckets=self.cfg.num_batch_buckets or int(self.cfg.tpu),
compute_mask_indices=(self.cfg.precompute_mask_indices or self.cfg.tpu),
**self._get_mask_precompute_kwargs(task_cfg),
)
if self.cfg.tpu and task_cfg["mask_channel_prob"] == 0.0:
logger.info(
"Pretraining on TPUs may suffer convergence "
"issues when training with `mask_channel_prob` value of "
"0. You may want to set this to a low value close to 0."
)
if task_cfg.labels:
label_path = os.path.join(data_path, f"{split}.{task_cfg.labels}")
skipped_indices = getattr(self.datasets[split], 'skipped_indices', set())
with open(label_path, "r") as f:
labels = [
line
for i, line in enumerate(f)
if i not in skipped_indices
]
assert len(labels) == len(self.datasets[split]), (
f"labels length ({len(labels)}) and dataset length "
f"({len(self.datasets[split])}) do not match"
)
process_label = LabelEncoder(self.target_dictionary)
self.datasets[split] = AddTargetDataset(
self.datasets[split],
labels,
pad=self.target_dictionary.pad(),
eos=self.target_dictionary.eos(),
batch_targets=True,
process_label=process_label,
add_to_input=task_cfg.get("autoregressive", False),
)
def load_dataset(self,
split: str,
task_cfg: FairseqDataclass = None,
**kwargs):
data_path_parent = self.cfg.data
task_cfg = task_cfg or self.cfg
data_path_list = [
os.path.join(data_path_parent, path)
for path in os.listdir(data_path_parent)
]
# upgrade old task
if isinstance(task_cfg, Namespace):
if not hasattr(task_cfg, "autoregressive"):
task_cfg.autoregressive = not task_cfg.criterion == "ctc"
dataset_map = OrderedDict()
datasets_lengths = []
for data_path in data_path_list:
if getattr(task_cfg, "binarized_dataset", False):
dataset_map[data_path] = BinarizedAudioDataset(
data_path,
split=split,
sample_rate=task_cfg.get("sample_rate",
self.cfg.sample_rate),
max_sample_size=self.cfg.max_sample_size,
min_sample_size=self.cfg.min_sample_size,
pad=task_cfg.labels is not None or task_cfg.enable_padding,
normalize=task_cfg.normalize,
num_buckets=self.cfg.num_batch_buckets
or int(self.cfg.tpu),
compute_mask_indices=(self.cfg.precompute_mask_indices
or self.cfg.tpu),
**self._get_mask_precompute_kwargs(task_cfg),
)
else:
manifest_path = os.path.join(data_path, "{}.tsv".format(split))
dataset_map[data_path] = FileAudioDataset(
manifest_path=manifest_path,
sample_rate=task_cfg.get("sample_rate",
self.cfg.sample_rate),
max_sample_size=self.cfg.max_sample_size,
min_sample_size=self.cfg.min_sample_size,
pad=task_cfg.labels is not None or task_cfg.enable_padding,
normalize=task_cfg.normalize,
num_buckets=self.cfg.num_batch_buckets
or int(self.cfg.tpu),
compute_mask_indices=(self.cfg.precompute_mask_indices
or self.cfg.tpu),
**self._get_mask_precompute_kwargs(task_cfg),
)
if self.cfg.tpu and task_cfg["mask_channel_prob"] == 0.0:
logger.info(
"Pretraining on TPUs may suffer convergence "
"issues when training with `mask_channel_prob` value of "
"0. You may want to set this to a low value close to 0.")
if task_cfg.labels:
label_path = os.path.join(data_path,
f"{split}.{task_cfg.labels}")
if os.path.exists(label_path):
skipped_indices = getattr(dataset_map[data_path],
"skipped_indices", set())
with open(label_path, "r") as f:
labels = [
line for i, line in enumerate(f)
if i not in skipped_indices
]
assert len(labels) == len(dataset_map[data_path]), (
f"labels length ({len(labels)}) and dataset length "
f"({len(dataset_map[data_path])}) do not match")
process_label = LabelEncoder(self.target_dictionary)
dataset_map[data_path] = AddTargetDataset(
dataset_map[data_path],
labels,
pad=self.target_dictionary.pad(),
eos=self.target_dictionary.eos(),
batch_targets=True,
process_label=process_label,
add_to_input=task_cfg.get("autoregressive", False),
)
datasets_lengths.append(
sum(dataset_map[data_path].sizes) / task_cfg.sample_rate /
3600)
datasets_lengths = np.array(datasets_lengths)
self.sample_probs = self._get_sample_prob(datasets_lengths)
size_ratio = (self.sample_probs *
datasets_lengths.sum()) / datasets_lengths
for id, data_path in enumerate(data_path_list):
logger.info(
"Up/Down Sampling ratio by datasets: {} : {:.2f} to prob:{:.2f}".\
format(data_path.split('/')[-1], size_ratio[id],self.sample_probs[id])
)
self.datasets[split] = MultiCorpusSampledDataset(
dataset_map, sampling_func=self.dataset_sampler)
logger.info('{} {} examples'.format(split, len(self.datasets[split])))