Source code for espnet2.iterators.chunk_iter_factory

import logging
import re
from collections import defaultdict
from copy import deepcopy
from math import inf
from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union

import numpy as np
import torch
from typeguard import typechecked

from espnet2.iterators.abs_iter_factory import AbsIterFactory
from espnet2.iterators.sequence_iter_factory import SequenceIterFactory
from espnet2.samplers.abs_sampler import AbsSampler

DEFAULT_EXCLUDED_KEY_PREFIXES = ("utt2category", "utt2fs")


[docs]class ChunkIterFactory(AbsIterFactory): """Creates chunks from a sequence Examples: >>> batches = [["id1"], ["id2"], ...] >>> batch_size = 128 >>> chunk_length = 1000 >>> iter_factory = ChunkIterFactory(dataset, batches, batch_size, chunk_length) >>> it = iter_factory.build_iter(epoch) >>> for ids, batch in it: ... ... - The number of mini-batches are varied in each epochs and we can't get the number in advance because IterFactory doesn't be given to the length information. - Since the first reason, "num_iters_per_epoch" can't be implemented for this iterator. Instead of it, "num_samples_per_epoch" is implemented. """ @typechecked def __init__( self, dataset, batch_size: int, batches: Union[AbsSampler, Sequence[Sequence[Any]]], chunk_length: Union[int, str], chunk_shift_ratio: float = 0.5, num_cache_chunks: int = 1024, num_samples_per_epoch: Optional[int] = None, seed: int = 0, shuffle: bool = False, num_workers: int = 0, collate_fn=None, pin_memory: bool = False, excluded_key_prefixes: Optional[List[str]] = None, discard_short_samples: bool = True, default_fs: Optional[int] = None, chunk_max_abs_length: Optional[int] = None, ): assert all(len(x) == 1 for x in batches), "batch-size must be 1" self.per_sample_iter_factory = SequenceIterFactory( dataset=dataset, batches=batches, num_iters_per_epoch=num_samples_per_epoch, seed=seed, shuffle=shuffle, num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory, ) self.num_cache_chunks = max(num_cache_chunks, batch_size) if isinstance(chunk_length, str): if len(chunk_length) == 0: raise ValueError("e.g. 5,8 or 3-5: but got empty string") self.chunk_lengths = [] for x in chunk_length.split(","): try: sps = list(map(int, x.split("-"))) except ValueError: raise ValueError(f"e.g. 5,8 or 3-5: but got {chunk_length}") if len(sps) > 2: raise ValueError(f"e.g. 5,8 or 3-5: but got {chunk_length}") elif len(sps) == 2: # Append all numbers between the range into the candidates self.chunk_lengths += list(range(sps[0], sps[1] + 1)) else: self.chunk_lengths += [sps[0]] else: # Single candidates: Fixed chunk length self.chunk_lengths = [chunk_length] if chunk_max_abs_length is None: self.chunk_max_abs_length = inf else: self.chunk_max_abs_length = chunk_max_abs_length self.chunk_shift_ratio = chunk_shift_ratio self.batch_size = batch_size self.seed = seed self.shuffle = shuffle # Default sampling frequency used to decide the chunk length # in case that different batches have different sampling frequencies # (If None, the chunk length is always fixed) self.default_fs = default_fs # Whether to discard samples that shorter than the shortest chunk length self.discard_short_samples = discard_short_samples self.collate_fn = collate_fn # keys that satisfy either condition below will be excluded from the length # consistency check: # - exactly match one of the prefixes in `excluded_key_prefixes` # - have one of the prefixes in `excluded_key_prefixes` and end with numbers if excluded_key_prefixes is None: _excluded_key_prefixes = DEFAULT_EXCLUDED_KEY_PREFIXES else: _excluded_key_prefixes = deepcopy(excluded_key_prefixes) for k in DEFAULT_EXCLUDED_KEY_PREFIXES: if k not in _excluded_key_prefixes: _excluded_key_prefixes.append(k) self.excluded_key_pattern = ( "(" + "[0-9]*)|(".join(_excluded_key_prefixes) + "[0-9]*)" ) if self.excluded_key_pattern: logging.info( f"Data keys with the following patterns will be excluded from the " f"length consistency check:\n{self.excluded_key_pattern}" )
[docs] def build_iter( self, epoch: int, shuffle: Optional[bool] = None, ) -> Iterator[Tuple[List[str], Dict[str, torch.Tensor]]]: per_sample_loader = self.per_sample_iter_factory.build_iter(epoch, shuffle) if shuffle is None: shuffle = self.shuffle state = np.random.RandomState(epoch + self.seed) # NOTE(kamo): # This iterator supports multiple chunk lengths and # keep chunks for each lengths here until collecting specified numbers cache_chunks_dict = defaultdict(dict) cache_id_list_dict = defaultdict(dict) for ids, batch in per_sample_loader: # Must be per-sample-loader assert len(ids) == 1, f"Must be per-sample-loader: {len(ids)}" assert all(len(x) == 1 for x in batch.values()) # Get keys of sequence data sequence_keys = [] for key in batch: if key + "_lengths" in batch: sequence_keys.append(key) # Remove lengths data and get the first sample batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")} id_ = ids[0] for key in sequence_keys: if self.excluded_key_pattern is not None and re.fullmatch( self.excluded_key_pattern, key ): # ignore length inconsistency for `excluded_key_prefixes` continue if len(batch[key]) != len(batch[sequence_keys[0]]): raise RuntimeError( f"All sequences must has same length: " f"{len(batch[key])} != {len(batch[sequence_keys[0]])}" ) # Get sampling frequency of the batch to recalculate the chunk length fs = batch.get("utt2fs", torch.LongTensor([16000])).type(torch.int64).item() default_fs = fs if self.default_fs is None else self.default_fs assert fs % default_fs == 0 or default_fs % fs == 0 L = len(batch[sequence_keys[0]]) # Select chunk length chunk_lengths = [lg * fs // default_fs for lg in self.chunk_lengths] chunk_lengths = [ min(lg, self.chunk_max_abs_length) for lg in chunk_lengths if lg < L ] if len(chunk_lengths) == 0 and getattr(self, "discard_short_samples", True): logging.warning( f"The length of '{id_}' is {L}, but it is shorter than " f"any candidates of chunk-length: {self.chunk_lengths}" ) continue # Convert numpy array to number category = ( batch.get("utt2category", torch.LongTensor([0])) .type(torch.int64) .item() ) if len(chunk_lengths) == 0: # keep the batch as is cache_id_list = cache_id_list_dict[category].setdefault(0, []) cache_chunks = cache_chunks_dict[category].setdefault(0, {}) Z, N, W, S = 0, 1, L, 0 else: W = int(state.choice(chunk_lengths, 1)) cache_id_list = cache_id_list_dict[category].setdefault(W, []) cache_chunks = cache_chunks_dict[category].setdefault(W, {}) # Shift width to the next chunk S = int(W * self.chunk_shift_ratio) # Number of chunks N = (L - W) // S + 1 if shuffle: Z = state.randint(0, (L - W) % S + 1) else: Z = 0 # Split a sequence into chunks. # Note that the marginal frames divided by chunk length are discarded for k, v in batch.items(): if k not in cache_chunks: cache_chunks[k] = [] if k in sequence_keys: # Shift chunks with overlapped length for data augmentation if self.excluded_key_pattern is not None and re.fullmatch( self.excluded_key_pattern, k ): for _ in range(N): cache_chunks[k].append(v) else: cache_chunks[k] += [ v[Z + i * S : Z + i * S + W] for i in range(N) ] else: # If not sequence, use whole data instead of chunk cache_chunks[k] += [v for _ in range(N)] cache_id_list += [id_ for _ in range(N)] if len(cache_id_list) > self.num_cache_chunks: cache_id_list, cache_chunks = yield from self._generate_mini_batches( cache_id_list, cache_chunks, shuffle, state, ) if len(chunk_lengths) == 0: W = 0 cache_id_list_dict[category][W] = cache_id_list cache_chunks_dict[category][W] = cache_chunks else: for category in cache_id_list_dict.keys(): for W in cache_id_list_dict[category]: cache_id_list = cache_id_list_dict[category].setdefault(W, []) cache_chunks = cache_chunks_dict[category].setdefault(W, {}) yield from self._generate_mini_batches( cache_id_list, cache_chunks, shuffle, state, )
[docs] def prepare_for_collate(self, id_list, batches): return [ (id_, {k: vs[i].numpy() for k, vs in batches.items()}) for i, id_ in enumerate(id_list) ]
def _generate_mini_batches( self, id_list: List[str], batches: Dict[str, List[torch.Tensor]], shuffle: bool, state: np.random.RandomState, ): if shuffle: indices = np.arange(0, len(id_list)) state.shuffle(indices) batches = {k: [v[i] for i in indices] for k, v in batches.items()} id_list = [id_list[i] for i in indices] bs = self.batch_size while len(id_list) >= bs: # Make mini-batch and yield if self.discard_short_samples: yield ( id_list[:bs], {k: torch.stack(v[:bs], 0) for k, v in batches.items()}, ) else: yield self.collate_fn( self.prepare_for_collate( id_list[:bs], {k: v[:bs] for k, v in batches.items()} ) ) id_list = id_list[bs:] batches = {k: v[bs:] for k, v in batches.items()} return id_list, batches