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PaddleSpeech/paddlespeech/s2t/io/speechbrain/sampler.py

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# Copyright (c) 2023 speechbrain Authors. All Rights Reserved.
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Modified from speechbrain 2023 (https://github.com/speechbrain/speechbrain/blob/develop/speechbrain/dataio/sampler.py)
"""compatible samplers.
These determine the order of iteration through a dataset.
Authors:
* Aku Rouhe 2020
* Samuele Cornell 2020
* Ralf Leibold 2020
* Artem Ploujnikov 2021
* Andreas Nautsch 2021
"""
import logging
from collections import Counter
from typing import List
import numpy as np
import paddle
from paddle.io import RandomSampler
from paddle.io import Sampler
from paddle.io import WeightedRandomSampler
from scipy.stats import lognorm
from paddlespeech.s2t.io.speechbrain.dataset import DynamicItemDataset
logger = logging.getLogger(__name__)
class ReproducibleRandomSampler(RandomSampler):
"""A modification of RandomSampler which always returns the same values.
Also look at `paddle.io.RandomSampler`. This has mostly
the same behaviour and arguments, except for adding 'seed' and 'epoch' and
not supporting 'generator'.
Note
----
Call `set_epoch` before every epoch. Otherwise, the sampler will produce the
same sequence of indices every epoch.
Arguments
---------
data_source : Dataset
The data source to sample indices for.
seed : int
The base seed to use for the random number generator. It is recommended
to use a value which has a good mix of 0 and 1 bits.
epoch : int
The epoch to start at.
"""
def __init__(self, data_source, seed=563375142, epoch=0, **kwargs):
if "generator" in kwargs:
MSG = ("Cannot give a separate generator when using " +
"ReproducibleRandomSampler")
raise ValueError(MSG)
super().__init__(data_source, **kwargs)
self.seed = int(seed)
self.epoch = epoch
self.gen = paddle.seed(1)
def set_epoch(self, epoch):
"""
You can also just access self.epoch, but we maintain this interface
to mirror paddle.io.DistributedBatchSampler
"""
self.epoch = epoch
def __iter__(self):
self.gen.manual_seed(self.seed + self.epoch)
return super().__iter__()
class ReproducibleWeightedRandomSampler(WeightedRandomSampler):
"""A reproducible modification of WeightedRandomSampler.
Also look at `paddle.io.WeightedRandomSampler`. This has the
the same behaviour and arguments, except for adding 'seed' and 'epoch' and
not supporting 'generator'.
Note
----
Call `set_epoch` before every epoch. Otherwise, the sampler will produce the
same sequence of indices every epoch.
Arguments
---------
weights : sequence of float
Weights for each index. Doesn't need to sum to one.
num_samples : int
Number of samples to draw
replacement : bool
To draw with replacement or not (within an epoch of num_samples).
seed : int
The base seed to use for the random number generator. It is recommended
to use a value which has a good mix of 0 and 1 bits.
epoch : int
The epoch to start at.
"""
def __init__(
self,
weights,
num_samples,
replacement,
seed=129491412,
epoch=0,
**kwargs, ):
if "generator" in kwargs:
MSG = ("Cannot give a separate generator when using " +
"ReproducibleRandomSampler")
raise ValueError(MSG)
super().__init__(weights, num_samples, replacement, **kwargs)
self.seed = int(seed)
self.epoch = epoch
self.gen = paddle.seed(1)
def set_epoch(self, epoch):
"""
You can also just access self.epoch, but we maintain this interface
to mirror paddle.io.DistributedBatchSampler
"""
self.epoch = epoch
def __iter__(self):
self.gen.manual_seed(self.seed + self.epoch)
return super().__iter__()
class DynamicBatchSampler(Sampler):
"""This BatchSampler batches examples together by grouping them by their length.
Every example in the batch have approximately the same length and
thus padding is minimized.
This enables faster training on datasets
where length of examples can vary significantly (e.g Librispeech).
Inspired by: https://www.tensorflow.org/api_docs/python/tf/data/experimental/bucket_by_sequence_length
Dynamic batching is performed by specifying a max_batch_length which is the
upper limit for the sum of the length of examples in a batch:
e.g., if ex1 has length 4, ex2 length 5 and if max_batch_length is set to 6
ex1 and ex2 will be placed, alone, in two distinct batches.
Length for each example can be obtained in two manners.
If the input dataset is a DynamicItemDataset it can be obtained by specifying a
length_func. Default assumes a "duration" entry is in the annotation.
Length for each example can also be passed to this class upon instantiation
by specifying a list containing the length for each example and passing it to
lengths_list.
Examples are grouped together by defining a set of possible discrete intervals
(buckets). Examples whose length fall into these intervals can be batched together.
The number of buckets can be specified by using the arg num_buckets.
There is usually an optimal range for the value of this argument.
If num_buckets == 1, all examples can be batched together. You have maximum randomization
but your training speed will be slower due to the fact that a large amount of the values will be padding
as long and short examples can be batched together.
As the number of buckets grows only examples with similar
length can be grouped together.
This trades-off speed with randomization.
TLDR: Low number -> better randomization, High number -> faster training.
NOTE THAT: if set too high the training speed will decrease. If num_buckets -> number of examples in the
dataset the batch size will be small impacting training speed and possibly performance.
The buckets can also be specified by passing a list to the bucket_boundaries
argument instead of specifying a left_bucket_length and a bucket_length_multiplier.
"""
def __init__(
self,
dataset,
max_batch_length: int,
num_buckets: int=None,
length_func=lambda x: x["duration"],
shuffle: bool=True,
batch_ordering: str="random",
max_batch_ex: int=None,
bucket_boundaries: List[int]=[],
lengths_list: List[int]=None,
seed: int=42,
epoch: int=0,
drop_last: bool=False,
verbose: bool=False, ):
self._dataset = dataset
self._ex_lengths = {}
ex_ids = self._dataset.data_ids
self.verbose = verbose
# We do not put a default on num_buckets to encourage users to play with this parameter
if num_buckets is None and len(bucket_boundaries) == 0:
raise RuntimeError(
"Please specify either num_buckets or bucket boundaries."
"Check the docs, and/or the tutorial !")
if lengths_list is not None:
# take length of examples from this argument and bypass length_key
for indx in range(len(lengths_list)):
self._ex_lengths[str(indx)] = lengths_list[indx]
else:
# use length func
if not isinstance(dataset, DynamicItemDataset):
raise NotImplementedError(
"Dataset should be a DynamicItemDataset when using length function"
)
for indx in range(len(self._dataset)):
self._ex_lengths[str(indx)] = length_func(
self._dataset.data[ex_ids[indx]])
if len(bucket_boundaries) > 0:
if not all([x >= 0 for x in bucket_boundaries]):
raise ValueError(
"All elements in bucket boundaries should be non-negative (>= 0)."
)
if not len(set(bucket_boundaries)) == len(bucket_boundaries):
raise ValueError(
"Bucket_boundaries should not contain duplicates.")
np.testing.assert_array_equal(
np.array(bucket_boundaries),
np.array(sorted(bucket_boundaries)),
err_msg="The arg bucket_boundaries should be an ascending sorted list of non negative values values!",
)
self._bucket_boundaries = np.array(sorted(bucket_boundaries))
else:
# use num_buckets
self._bucket_boundaries = np.array(
self._get_boundaries_through_warping(
max_batch_length=max_batch_length,
num_quantiles=num_buckets, ))
self._max_batch_length = max_batch_length
self._shuffle_ex = shuffle
self._batch_ordering = batch_ordering
self._seed = seed
self._drop_last = drop_last
if max_batch_ex is None:
max_batch_ex = np.inf
self._max_batch_ex = max_batch_ex
# Calculate bucket lengths - how often does one bucket boundary fit into max_batch_length?
self._bucket_lens = [
max(1, int(max_batch_length / self._bucket_boundaries[i]))
for i in range(len(self._bucket_boundaries))
] + [1]
self._epoch = epoch
self._generate_batches()
def get_durations(self, batch):
"""Gets durations of the elements in the batch."""
return [self._ex_lengths[str(idx)] for idx in batch]
def _get_boundaries_through_warping(
self,
max_batch_length: int,
num_quantiles: int, ) -> List[int]:
# NOTE: the following lines do not cover that there is only one example in the dataset
# warp frames (duration) distribution of train data
logger.info("Batch quantisation in latent space")
# linspace set-up
num_boundaries = num_quantiles + 1
# create latent linearly equal spaced buckets
latent_boundaries = np.linspace(
1 / num_boundaries,
num_quantiles / num_boundaries,
num_quantiles, )
# get quantiles using lognormal distribution
quantiles = lognorm.ppf(latent_boundaries, 1)
# scale up to max_batch_length
bucket_boundaries = quantiles * max_batch_length / quantiles[-1]
# compute resulting bucket length multipliers
length_multipliers = [
bucket_boundaries[x + 1] / bucket_boundaries[x]
for x in range(num_quantiles - 1)
]
# logging
logger.info(
"Latent bucket boundary - buckets: {} - length multipliers: {}".
format(
list(map("{:.2f}".format, bucket_boundaries)),
list(map("{:.2f}".format, length_multipliers)), ))
return list(sorted(bucket_boundaries))
def _permute_batches(self):
if self._batch_ordering == "random":
# deterministically shuffle based on epoch and seed
gen = paddle.seed(1)
gen.manual_seed(self._seed + self._epoch)
sampler = paddle.randperm(
len(self._batches)).tolist() # type: ignore
tmp = []
for idx in sampler:
tmp.append(self._batches[idx])
self._batches = tmp
elif self._batch_ordering == "ascending":
self._batches = sorted(
self._batches,
key=lambda x: max([self._ex_lengths[str(idx)] for idx in x]), )
elif self._batch_ordering == "descending":
self._batches = sorted(
self._batches,
key=lambda x: max([self._ex_lengths[str(idx)] for idx in x]),
reverse=True, )
else:
raise NotImplementedError
def _generate_batches(self):
logger.info("DynamicBatchSampler: Generating dynamic batches")
if self._shuffle_ex:
# deterministically shuffle based on epoch and seed
gen = paddle.seed(1)
gen.manual_seed(self._seed + self._epoch)
sampler = paddle.randperm(
len(self._dataset)).tolist() # type: ignore
else:
# take examples as they are: e.g. they have been sorted
sampler = range(len(self._dataset)) # type: ignore
self._batches = []
bucket_batches = [[] for i in self._bucket_lens]
stats_tracker = [{
"min": np.inf,
"max": -np.inf,
"tot": 0,
"n_ex": 0
} for i in self._bucket_lens]
for idx in sampler:
# length of pre-sampled audio
item_len = self._ex_lengths[str(idx)]
# bucket to fill up most padding
bucket_id = np.searchsorted(self._bucket_boundaries, item_len)
# fill audio's duration into that bucket
bucket_batches[bucket_id].append(idx)
stats_tracker[bucket_id]["min"] = min(
stats_tracker[bucket_id]["min"], item_len)
stats_tracker[bucket_id]["max"] = max(
stats_tracker[bucket_id]["max"], item_len)
stats_tracker[bucket_id]["tot"] += item_len
stats_tracker[bucket_id]["n_ex"] += 1
# track #samples - why not duration/#frames; rounded up?
# keep track of durations, if necessary
if (len(bucket_batches[bucket_id]) >= self._bucket_lens[bucket_id]
or len(bucket_batches[bucket_id]) >= self._max_batch_ex):
self._batches.append(bucket_batches[bucket_id])
bucket_batches[bucket_id] = []
# keep track of durations
# Dump remaining batches
if not self._drop_last:
for batch in bucket_batches:
if batch:
self._batches.append(batch)
self._permute_batches() # possibly reorder batches
if self._epoch == 0: # only log at first epoch
# frames per batch & their padding remaining
boundaries = [0] + self._bucket_boundaries.tolist()
for bucket_indx in range(len(self._bucket_boundaries)):
try:
num_batches = stats_tracker[bucket_indx]["tot"] // (
self._max_batch_length)
pad_factor = (stats_tracker[bucket_indx]["max"] -
stats_tracker[bucket_indx]["min"]) / (
stats_tracker[bucket_indx]["tot"] /
stats_tracker[bucket_indx]["n_ex"])
except ZeroDivisionError:
num_batches = 0
pad_factor = 0
logger.info((
"DynamicBatchSampler: Bucket {} with boundary {:.1f}-{:.1f} and "
+
"batch_size {}: Num Examples {:.1f}, Num Full Batches {:.3f}, Pad Factor {:.3f}."
).format(
bucket_indx,
boundaries[bucket_indx],
boundaries[bucket_indx + 1],
self._bucket_lens[bucket_indx],
stats_tracker[bucket_indx]["n_ex"],
num_batches,
pad_factor * 100, ))
if self.verbose:
batch_stats = {
"tot_frames": [],
"tot_pad_frames": [],
"pad_%": [],
}
for batch in self._batches:
tot_frames = sum(
[self._ex_lengths[str(idx)] for idx in batch])
batch_stats["tot_frames"].append(tot_frames)
max_frames = max(
[self._ex_lengths[str(idx)] for idx in batch])
tot_pad = sum([
max_frames - self._ex_lengths[str(idx)] for idx in batch
])
batch_stats["tot_pad_frames"].append(tot_pad)
batch_stats["pad_%"].append(tot_pad / tot_frames * 100)
padding_details = "Batch {} with {:.1f} frames with {} files - {:.1f} padding, {:.2f} (%) of total."
padding_details = "DynamicBatchSampler: " + padding_details
for i in range(len(self._batches)):
logger.info(
padding_details.format(
i,
batch_stats["tot_frames"][i],
len(self._batches[i]),
batch_stats["tot_pad_frames"][i],
batch_stats["pad_%"][i], ))
def __iter__(self):
for batch in self._batches:
yield batch
if self._shuffle_ex: # re-generate examples if ex_ordering == "random"
self._generate_batches()
if self._batch_ordering == "random":
# we randomly permute the batches only --> faster
self._permute_batches()
def set_epoch(self, epoch):
"""
You can also just access self.epoch, but we maintain this interface
to mirror paddle.io.DistributedBatchSampler
"""
self._epoch = epoch
self._generate_batches()
def __len__(self):
return len(self._batches)
class BalancingDataSampler(ReproducibleWeightedRandomSampler):
"""A data sampler that takes a single key from the dataset and
ensures an approximately equal distribution by that key
Arguments
---------
dataset: DynamicItemDataset
the dataset form which samples will be drawn
key: str
the key from which samples will be taken
num_samples : int
Number of samples to draw
replacement : bool
To draw with replacement or not (within an epoch of num_samples).
seed : int
The base seed to use for the random number generator. It is recommended
to use a value which has a good mix of 0 and 1 bits.
epoch : int
The epoch to start at.
"""
def __init__(
self,
dataset,
key,
num_samples=None,
replacement=True,
seed=563375142,
epoch=0,
**kwargs, ):
self.dataset = dataset
self.key = key
if not num_samples:
num_samples = len(dataset)
weights = self._compute_weights()
super().__init__(weights, num_samples, replacement, seed, epoch,
**kwargs)
def _compute_weights(self):
with self.dataset.output_keys_as([self.key]):
class_ids = [item[self.key] for item in self.dataset]
class_counter = Counter(class_ids)
weights = 1 / paddle.to_tensor(
[class_counter[class_id] for class_id in class_ids])
return weights
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