xiongxinlei
2 years ago
parent
6f7e9656fe
commit
d7da629302
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sys
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import random
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import numpy as np
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import kaldi_python_io as k_io
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from paddle.io import Dataset
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from paddlespeech.vector.utils.data_utils import batch_pad_right
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import paddlespeech.vector.utils as utils
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from paddlespeech.vector.utils.utils import read_map_file
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from paddlespeech.vector import _logger as log
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def ark_collate_fn(batch):
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"""
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Custom collate function] for kaldi feats dataset
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Args:
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min_chunk_size: min chunk size of a utterance
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max_chunk_size: max chunk size of a utterance
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Returns:
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ark_collate_fn: collate funtion for dataloader
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"""
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data = []
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target = []
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for items in batch:
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for x, y in zip(items[0], items[1]):
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data.append(np.array(x))
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target.append(y)
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data, lengths = batch_pad_right(data)
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return np.array(data, dtype=np.float32), \
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np.array(lengths, dtype=np.float32), \
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np.array(target, dtype=np.long).reshape((len(target), 1))
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class KaldiArkDataset(Dataset):
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"""
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Dataset used to load kaldi ark/scp files.
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"""
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def __init__(self, scp_file, label2utt, min_item_size=1,
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max_item_size=1, repeat=50, min_chunk_size=200,
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max_chunk_size=400, select_by_speaker=True):
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self.scp_file = scp_file
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self.scp_reader = None
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self.repeat = repeat
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self.min_item_size = min_item_size
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self.max_item_size = max_item_size
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self.min_chunk_size = min_chunk_size
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self.max_chunk_size = max_chunk_size
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self._collate_fn = ark_collate_fn
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self._is_select_by_speaker = select_by_speaker
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if utils.is_exist(self.scp_file):
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self.scp_reader = k_io.ScriptReader(self.scp_file)
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label2utts, utt2label = read_map_file(label2utt, key_func=int)
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self.utt_info = list(label2utts.items()) if self._is_select_by_speaker else list(utt2label.items())
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@property
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def collate_fn(self):
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"""
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Return a collate funtion.
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"""
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return self._collate_fn
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def _random_chunk(self, length):
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chunk_size = random.randint(self.min_chunk_size, self.max_chunk_size)
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if chunk_size >= length:
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return 0, length
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start = random.randint(0, length - chunk_size)
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end = start + chunk_size
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return start, end
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def _select_by_speaker(self, index):
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if self.scp_reader is None or not self.utt_info:
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return []
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index = index % (len(self.utt_info))
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inputs = []
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labels = []
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item_size = random.randint(self.min_item_size, self.max_item_size)
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for loop_idx in range(item_size):
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try:
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utt_index = random.randint(0, len(self.utt_info[index][1])) \
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% len(self.utt_info[index][1])
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key = self.utt_info[index][1][utt_index]
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except:
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print(index, utt_index, len(self.utt_info[index][1]))
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sys.exit(-1)
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x = self.scp_reader[key]
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x = np.transpose(x)
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bg, end = self._random_chunk(x.shape[-1])
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inputs.append(x[:, bg: end])
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labels.append(self.utt_info[index][0])
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return inputs, labels
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def _select_by_utt(self, index):
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if self.scp_reader is None or len(self.utt_info) == 0:
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return {}
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index = index % (len(self.utt_info))
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key = self.utt_info[index][0]
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x = self.scp_reader[key]
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x = np.transpose(x)
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bg, end = self._random_chunk(x.shape[-1])
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y = self.utt_info[index][1]
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return [x[:, bg: end]], [y]
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def __getitem__(self, index):
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if self._is_select_by_speaker:
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return self._select_by_speaker(index)
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else:
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return self._select_by_utt(index)
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def __len__(self):
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return len(self.utt_info) * self.repeat
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def __iter__(self):
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self._start = 0
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return self
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def __next__(self):
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if self._start < len(self):
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ret = self[self._start]
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self._start += 1
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return ret
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else:
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raise StopIteration
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@ -0,0 +1,91 @@
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Load nnet3 training egs which generated by kaldi
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"""
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import random
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import numpy as np
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import kaldi_python_io as k_io
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from paddle.io import Dataset
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import paddlespeech.vector.utils.utils as utils
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from paddlespeech.vector import _logger as log
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class KaldiEgsDataset(Dataset):
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"""
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Dataset used to load kaldi nnet3 egs files.
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"""
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def __init__(self, egs_list_file, egs_idx, transforms=None):
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self.scp_reader = None
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self.subset_idx = egs_idx - 1
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self.transforms = transforms
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if not utils.is_exist(egs_list_file):
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return
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self.egs_files = []
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with open(egs_list_file, 'r') as in_fh:
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for line in in_fh:
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if line.strip():
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self.egs_files.append(line.strip())
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self.next_subset()
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def next_subset(self, target_index=None, delta_index=None):
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"""
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Use next specific subset
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Args:
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target_index: target egs index
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delta_index: incremental value of egs index
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"""
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if self.egs_files:
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if target_index:
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self.subset_idx = target_index
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else:
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delta_index = delta_index if delta_index else 1
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self.subset_idx += delta_index
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log.info("egs dataset subset index: %d" % (self.subset_idx))
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egs_file = self.egs_files[self.subset_idx % len(self.egs_files)]
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if utils.is_exist(egs_file):
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self.scp_reader = k_io.Nnet3EgsScriptReader(egs_file)
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else:
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log.warning("No such file or directory: %s" % (egs_file))
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def __getitem__(self, index):
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if self.scp_reader is None:
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return {}
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index %= len(self)
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in_dict, out_dict = self.scp_reader[index]
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x = np.array(in_dict['matrix'])
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x = np.transpose(x)
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y = np.array(out_dict['matrix'][0][0][0], dtype=np.int).reshape((1,))
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if self.transforms is not None:
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idx = random.randint(0, len(self.transforms) - 1)
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x = self.transforms[idx](x)
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return x, y
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def __len__(self):
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return len(self.scp_reader)
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def __iter__(self):
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self._start = 0
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return self
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def __next__(self):
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if self._start < len(self):
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ret = self[self._start]
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self._start += 1
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return ret
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else:
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raise StopIteration
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