# 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