184 lines
6.3 KiB
Python
184 lines
6.3 KiB
Python
# Copyright (c) 2020, NVIDIA CORPORATION. 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.
|
|
|
|
import numpy as np
|
|
import torch
|
|
from nvidia.dali.plugin.base_iterator import LastBatchPolicy
|
|
from nvidia.dali.plugin.pytorch import DALIGenericIterator
|
|
|
|
from common.helpers import print_once
|
|
from common.text import _clean_text, punctuation_map
|
|
|
|
|
|
def normalize_string(s, symbols, punct_map):
|
|
"""
|
|
Normalizes string.
|
|
Example:
|
|
'call me at 8:00 pm!' -> 'call me at eight zero pm'
|
|
"""
|
|
labels = set(symbols)
|
|
try:
|
|
text = _clean_text(s, ["english_cleaners"], punct_map).strip()
|
|
return ''.join([tok for tok in text if all(t in labels for t in tok)])
|
|
except Exception as e:
|
|
print_once(f"WARNING: Normalizing failed: {s} {e}")
|
|
|
|
|
|
class DaliIterator(object):
|
|
"""Returns batches of data.
|
|
|
|
Batches are in the form:
|
|
(preprocessed_signal, preprocessed_signal_length, transcript,
|
|
transcript_length)
|
|
|
|
This iterator is not meant to be the entry point to a Dali pipeline.
|
|
Use DataLoader instead.
|
|
"""
|
|
|
|
def __init__(self, dali_pipelines, transcripts, symbols, batch_size,
|
|
reader_name, train_iterator: bool):
|
|
self.transcripts = transcripts
|
|
self.symbols = symbols
|
|
self.batch_size = batch_size
|
|
|
|
# in train pipeline shard_size is set to divisable by batch_size,
|
|
# so PARTIAL policy is safe
|
|
self.dali_it = DALIGenericIterator(
|
|
dali_pipelines,
|
|
["audio", "label", "audio_shape"],
|
|
reader_name=reader_name,
|
|
dynamic_shape=True,
|
|
auto_reset=True,
|
|
last_batch_policy=LastBatchPolicy.DROP)
|
|
|
|
@staticmethod
|
|
def _str2list(s: str):
|
|
"""
|
|
Returns list of floats, that represents given string.
|
|
'0.' denotes separator
|
|
'1.' denotes 'a'
|
|
'27.' denotes "'"
|
|
Assumes, that the string is lower case.
|
|
"""
|
|
list = []
|
|
for c in s:
|
|
if c == "'":
|
|
list.append(27.)
|
|
else:
|
|
list.append(max(0., ord(c) - 96.))
|
|
return list
|
|
|
|
@staticmethod
|
|
def _pad_lists(lists: list, pad_val=0):
|
|
"""
|
|
Pads lists, so that all have the same size.
|
|
Returns list with actual sizes of corresponding input lists
|
|
"""
|
|
max_length = 0
|
|
sizes = []
|
|
for li in lists:
|
|
sizes.append(len(li))
|
|
max_length = max_length if len(li) < max_length else len(li)
|
|
for li in lists:
|
|
li += [pad_val] * (max_length - len(li))
|
|
return sizes
|
|
|
|
def _gen_transcripts(self, labels, normalize_transcripts: bool = True):
|
|
"""
|
|
Generate transcripts in format expected by NN
|
|
"""
|
|
if normalize_transcripts:
|
|
lists = [
|
|
self._str2list(normalize_string(self.transcripts[lab.item()],
|
|
self.symbols, punctuation_map(self.symbols)))
|
|
for lab in labels]
|
|
else:
|
|
lists = [self._str2list(self.transcripts[lab.item()])
|
|
for lab in labels]
|
|
|
|
sizes = self._pad_lists(lists)
|
|
return (torch.tensor(lists).cuda(),
|
|
torch.tensor(sizes, dtype=torch.int32).cuda())
|
|
|
|
def __next__(self):
|
|
data = self.dali_it.__next__()
|
|
transcripts, transcripts_lengths = self._gen_transcripts(
|
|
data[0]["label"])
|
|
return (data[0]["audio"], data[0]["audio_shape"][:, 1], transcripts,
|
|
transcripts_lengths)
|
|
|
|
def next(self):
|
|
return self.__next__()
|
|
|
|
def __iter__(self):
|
|
return self
|
|
|
|
|
|
# TODO: refactor
|
|
class SyntheticDataIterator(object):
|
|
def __init__(self, batch_size, nfeatures, feat_min=-5., feat_max=0.,
|
|
txt_min=0., txt_max=23., feat_lens_max=1760, txt_lens_max=231,
|
|
regenerate=False):
|
|
"""
|
|
Args:
|
|
batch_size
|
|
nfeatures: number of features for melfbanks
|
|
feat_min: minimum value in `feat` tensor, used for randomization
|
|
feat_max: maximum value in `feat` tensor, used for randomization
|
|
txt_min: minimum value in `txt` tensor, used for randomization
|
|
txt_max: maximum value in `txt` tensor, used for randomization
|
|
regenerate: If True, regenerate random tensors for every iterator
|
|
step. If False, generate them only at start.
|
|
"""
|
|
self.batch_size = batch_size
|
|
self.nfeatures = nfeatures
|
|
self.feat_min = feat_min
|
|
self.feat_max = feat_max
|
|
self.feat_lens_max = feat_lens_max
|
|
self.txt_min = txt_min
|
|
self.txt_max = txt_max
|
|
self.txt_lens_max = txt_lens_max
|
|
self.regenerate = regenerate
|
|
|
|
if not self.regenerate:
|
|
(self.feat, self.feat_lens, self.txt, self.txt_lens
|
|
) = self._generate_sample()
|
|
|
|
def _generate_sample(self):
|
|
feat = ((self.feat_max - self.feat_min)
|
|
* np.random.random_sample(
|
|
(self.batch_size, self.nfeatures, self.feat_lens_max))
|
|
+ self.feat_min)
|
|
feat_lens = np.random.randint(0, int(self.feat_lens_max) - 1,
|
|
size=self.batch_size)
|
|
txt = (self.txt_max - self.txt_min) * np.random.random_sample(
|
|
(self.batch_size, self.txt_lens_max)) + self.txt_min
|
|
txt_lens = np.random.randint(0, int(self.txt_lens_max) - 1,
|
|
size=self.batch_size)
|
|
return (torch.Tensor(feat).cuda(),
|
|
torch.Tensor(feat_lens).cuda(),
|
|
torch.Tensor(txt).cuda(),
|
|
torch.Tensor(txt_lens).cuda())
|
|
|
|
def __next__(self):
|
|
if self.regenerate:
|
|
return self._generate_sample()
|
|
return self.feat, self.feat_lens, self.txt, self.txt_lens
|
|
|
|
def next(self):
|
|
return self.__next__()
|
|
|
|
def __iter__(self):
|
|
return self
|