xiongxinlei
2 years ago
parent
0e87037f2c
commit
993d6783d7
@ -1,25 +0,0 @@
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stage=-1
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stop_stage=100
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TARGET_DIR=${MAIN_ROOT}/dataset
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. utils/parse_options.sh || exit -1;
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src=$1
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mkdir -p data/{dev,test}
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if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
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# download data, generate manifests
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# create data/{dev,test} directory to store the manifest files
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python3 ${TARGET_DIR}/voxceleb/voxceleb1.py \
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--manifest_prefix="data/manifest" \
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--target_dir="${src}"
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if [ $? -ne 0 ]; then
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echo "Prepare Voxceleb failed. Terminated."
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exit 1
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fi
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mv data/manifest.dev data/dev
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mv data/voxceleb1.dev.meta data/dev
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mv data/manifest.test data/test
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mv data/voxceleb1.test.meta data/test
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fi
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@ -1,142 +0,0 @@
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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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@ -1,143 +0,0 @@
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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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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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return KaldiArkDataset
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@ -1,91 +0,0 @@
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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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# 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
|
||||
# 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
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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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@ -1,125 +0,0 @@
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
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#
|
||||
# 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.
|
||||
|
||||
"""
|
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data utilities
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"""
|
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import os
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import sys
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import numpy
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import paddle
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def pad_right_to(array, target_shape, mode="constant", value=0):
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"""
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This function takes a numpy array of arbitrary shape and pads it to target
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shape by appending values on the right.
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Args:
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array: input numpy array. Input array whose dimension we need to pad.
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target_shape : (list, tuple). Target shape we want for the target array its len must be equal to array.ndim
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mode : str. Pad mode, please refer to numpy.pad documentation.
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value : float. Pad value, please refer to numpy.pad documentation.
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Returns:
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array: numpy.array. Padded array.
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valid_vals : list. List containing proportion for each dimension of original, non-padded values.
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"""
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assert len(target_shape) == array.ndim
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pads = [] # this contains the abs length of the padding for each dimension.
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valid_vals = [] # thic contains the relative lengths for each dimension.
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i = 0 # iterating over target_shape ndims
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while i < len(target_shape):
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assert (
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target_shape[i] >= array.shape[i]
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), "Target shape must be >= original shape for every dim"
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pads.append([0, target_shape[i] - array.shape[i]])
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valid_vals.append(array.shape[i] / target_shape[i])
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i += 1
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array = numpy.pad(array, pads, mode=mode, constant_values=value)
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return array, valid_vals
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|
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|
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def batch_pad_right(arrays, mode="constant", value=0):
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"""Given a list of numpy arrays it batches them together by padding to the right
|
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on each dimension in order to get same length for all.
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|
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Args:
|
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arrays : list. List of array we wish to pad together.
|
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mode : str. Padding mode see numpy.pad documentation.
|
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value : float. Padding value see numpy.pad documentation.
|
||||
|
||||
Returns:
|
||||
array : numpy.array. Padded array.
|
||||
valid_vals : list. List containing proportion for each dimension of original, non-padded values.
|
||||
"""
|
||||
|
||||
if not len(arrays):
|
||||
raise IndexError("arrays list must not be empty")
|
||||
|
||||
if len(arrays) == 1:
|
||||
# if there is only one array in the batch we simply unsqueeze it.
|
||||
return numpy.expand_dims(arrays[0], axis=0), numpy.array([1.0])
|
||||
|
||||
if not (
|
||||
any(
|
||||
[arrays[i].ndim == arrays[0].ndim for i in range(1, len(arrays))]
|
||||
)
|
||||
):
|
||||
raise IndexError("All arrays must have same number of dimensions")
|
||||
|
||||
# FIXME we limit the support here: we allow padding of only the last dimension
|
||||
# need to remove this when feat extraction is updated to handle multichannel.
|
||||
max_shape = []
|
||||
for dim in range(arrays[0].ndim):
|
||||
if dim != (arrays[0].ndim - 1):
|
||||
if not all(
|
||||
[x.shape[dim] == arrays[0].shape[dim] for x in arrays[1:]]
|
||||
):
|
||||
raise EnvironmentError(
|
||||
"arrays should have same dimensions except for last one"
|
||||
)
|
||||
max_shape.append(max([x.shape[dim] for x in arrays]))
|
||||
|
||||
batched = []
|
||||
valid = []
|
||||
for t in arrays:
|
||||
# for each array we apply pad_right_to
|
||||
padded, valid_percent = pad_right_to(
|
||||
t, max_shape, mode=mode, value=value
|
||||
)
|
||||
batched.append(padded)
|
||||
valid.append(valid_percent[-1])
|
||||
|
||||
batched = numpy.stack(batched)
|
||||
|
||||
return batched, numpy.array(valid)
|
||||
|
||||
|
||||
def length_to_mask(length, max_len=None, dtype=None):
|
||||
"""Creates a binary mask for each sequence.
|
||||
"""
|
||||
assert len(length.shape) == 1
|
||||
|
||||
if max_len is None:
|
||||
max_len = paddle.cast(paddle.max(length), dtype="int64") # using arange to generate mask
|
||||
mask = paddle.arange(max_len, dtype=length.dtype).expand([paddle.shape(length)[0], max_len]) < length.unsqueeze(1)
|
||||
|
||||
if dtype is None:
|
||||
dtype = length.dtype
|
||||
|
||||
mask = paddle.cast(mask, dtype=dtype)
|
||||
return mask
|
||||
@ -1,132 +0,0 @@
|
||||
# Copyright (c) 2021 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.
|
||||
|
||||
"""
|
||||
utilities
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
import paddle
|
||||
import numpy as np
|
||||
|
||||
from paddlespeech.vector import _logger as log
|
||||
|
||||
|
||||
def exit_if_not_exist(in_path):
|
||||
"""
|
||||
Check the existence of a file or directory, if not exit, exit the program.
|
||||
|
||||
Args:
|
||||
in_path: input dicrector
|
||||
"""
|
||||
if not is_exist(in_path):
|
||||
sys.exit(-1)
|
||||
|
||||
|
||||
def is_exist(in_path):
|
||||
"""
|
||||
Check the existence of a file or directory
|
||||
|
||||
Args:
|
||||
in_path: input dicrector
|
||||
|
||||
Returns:
|
||||
True or False
|
||||
"""
|
||||
if not os.path.exists(in_path):
|
||||
log.error("No such file or directory: %s" % (in_path))
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def get_latest_file(target_dir):
|
||||
"""
|
||||
Get the latest file in target directory
|
||||
|
||||
Args:
|
||||
target_dir: target directory
|
||||
|
||||
Returns:
|
||||
latest_file: a string or None
|
||||
"""
|
||||
items = os.listdir(target_dir)
|
||||
items.sort(key=lambda fn: os.path.getmtime(os.path.join(target_dir, fn)) \
|
||||
if not os.path.isdir(os.path.join(target_dir, fn)) else 0)
|
||||
latest_file = None if not items else os.path.join(target_dir, items[-1])
|
||||
return latest_file
|
||||
|
||||
|
||||
def avg_models(models):
|
||||
"""
|
||||
merge multiple models
|
||||
"""
|
||||
checkpoint_dict = paddle.load(models[0])
|
||||
final_state_dict = checkpoint_dict
|
||||
|
||||
if len(models) > 1:
|
||||
for model in models[1:]:
|
||||
checkpoint_dict = paddle.load(model)
|
||||
for k, v in checkpoint_dict.items():
|
||||
final_state_dict[k] += v
|
||||
for k in final_state_dict.keys():
|
||||
final_state_dict[k] /= float(len(models))
|
||||
if np.any(np.isnan(final_state_dict[k])):
|
||||
print("Nan in %s" % (k))
|
||||
|
||||
return final_state_dict
|
||||
|
||||
def Q_from_tokens(token_num):
|
||||
"""
|
||||
get prior model, data from uniform, would support others(guassian) in future
|
||||
"""
|
||||
freq = [1] * token_num
|
||||
Q = paddle.to_tensor(freq, dtype = 'float64')
|
||||
return Q / Q.sum()
|
||||
|
||||
|
||||
def read_map_file(map_file, key_func=None, value_func=None, values_func=None):
|
||||
""" Read map file. First colume is key, the rest columes are values.
|
||||
|
||||
Args:
|
||||
map_file: map file
|
||||
key_func: convert function for key
|
||||
value_func: convert function for each value
|
||||
values_func: convert function for values
|
||||
|
||||
Returns:
|
||||
dict: key 2 value
|
||||
dict: value 2 key
|
||||
"""
|
||||
if not is_exist(map_file):
|
||||
sys.exit(0)
|
||||
|
||||
key2val = {}
|
||||
val2key = {}
|
||||
with open(map_file, 'r') as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
items = line.split()
|
||||
assert len(items) >= 2
|
||||
key = items[0] if not key_func else key_func(items[0])
|
||||
values = items[1:] if not value_func else [value_func(item) for item in items[1:]]
|
||||
if values_func:
|
||||
values = values_func(values)
|
||||
key2val[key] = values
|
||||
for value in values:
|
||||
val2key[value] = key
|
||||
|
||||
return key2val, val2key
|
||||
Loading…
Reference in new issue