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PaddleSpeech/deepspeech/io/reader.py

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# 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.
from collections import OrderedDict
import kaldiio
import numpy as np
import soundfile
from deepspeech.frontend.augmentor.augmentation import AugmentationPipeline
from deepspeech.utils.log import Log
__all__ = ["LoadInputsAndTargets"]
logger = Log(__name__).getlog()
class LoadInputsAndTargets():
"""Create a mini-batch from a list of dicts
>>> batch = [('utt1',
... dict(input=[dict(feat='some.ark:123',
... filetype='mat',
... name='input1',
... shape=[100, 80])],
... output=[dict(tokenid='1 2 3 4',
... name='target1',
... shape=[4, 31])]]))
>>> l = LoadInputsAndTargets()
>>> feat, target = l(batch)
:param: str mode: Specify the task mode, "asr" or "tts"
:param: str preprocess_conf: The path of a json file for pre-processing
:param: bool load_input: If False, not to load the input data
:param: bool load_output: If False, not to load the output data
:param: bool sort_in_input_length: Sort the mini-batch in descending order
of the input length
:param: bool use_speaker_embedding: Used for tts mode only
:param: bool use_second_target: Used for tts mode only
:param: dict preprocess_args: Set some optional arguments for preprocessing
:param: Optional[dict] preprocess_args: Used for tts mode only
"""
def __init__(
self,
mode="asr",
preprocess_conf=None,
load_input=True,
load_output=True,
sort_in_input_length=True,
preprocess_args=None,
keep_all_data_on_mem=False, ):
self._loaders = {}
if mode not in ["asr"]:
raise ValueError("Only asr are allowed: mode={}".format(mode))
if preprocess_conf is not None:
with open(preprocess_conf, 'r') as fin:
self.preprocessing = AugmentationPipeline(fin.read())
logger.warning(
"[Experimental feature] Some preprocessing will be done "
"for the mini-batch creation using {}".format(
self.preprocessing))
else:
# If conf doesn't exist, this function don't touch anything.
self.preprocessing = None
self.mode = mode
self.load_output = load_output
self.load_input = load_input
self.sort_in_input_length = sort_in_input_length
if preprocess_args is None:
self.preprocess_args = {}
else:
assert isinstance(preprocess_args, dict), type(preprocess_args)
self.preprocess_args = dict(preprocess_args)
self.keep_all_data_on_mem = keep_all_data_on_mem
def __call__(self, batch, return_uttid=False):
"""Function to load inputs and targets from list of dicts
:param List[Tuple[str, dict]] batch: list of dict which is subset of
loaded data.json
:param bool return_uttid: return utterance ID information for visualization
:return: list of input token id sequences [(L_1), (L_2), ..., (L_B)]
:return: list of input feature sequences
[(T_1, D), (T_2, D), ..., (T_B, D)]
:rtype: list of float ndarray
:return: list of target token id sequences [(L_1), (L_2), ..., (L_B)]
:rtype: list of int ndarray
"""
x_feats_dict = OrderedDict() # OrderedDict[str, List[np.ndarray]]
y_feats_dict = OrderedDict() # OrderedDict[str, List[np.ndarray]]
uttid_list = [] # List[str]
for uttid, info in batch:
uttid_list.append(uttid)
if self.load_input:
# Note(kamo): This for-loop is for multiple inputs
for idx, inp in enumerate(info["input"]):
# {"input":
# [{"feat": "some/path.h5:F01_050C0101_PED_REAL",
# "filetype": "hdf5",
# "name": "input1", ...}], ...}
x = self._get_from_loader(
filepath=inp["feat"],
filetype=inp.get("filetype", "mat"))
x_feats_dict.setdefault(inp["name"], []).append(x)
if self.load_output:
for idx, inp in enumerate(info["output"]):
if "tokenid" in inp:
# ======= Legacy format for output =======
# {"output": [{"tokenid": "1 2 3 4"}])
x = np.fromiter(
map(int, inp["tokenid"].split()), dtype=np.int64)
else:
# ======= New format =======
# {"input":
# [{"feat": "some/path.h5:F01_050C0101_PED_REAL",
# "filetype": "hdf5",
# "name": "target1", ...}], ...}
x = self._get_from_loader(
filepath=inp["feat"],
filetype=inp.get("filetype", "mat"))
y_feats_dict.setdefault(inp["name"], []).append(x)
if self.mode == "asr":
return_batch, uttid_list = self._create_batch_asr(
x_feats_dict, y_feats_dict, uttid_list)
else:
raise NotImplementedError(self.mode)
if self.preprocessing is not None:
# Apply pre-processing all input features
for x_name in return_batch.keys():
if x_name.startswith("input"):
return_batch[x_name] = self.preprocessing(
return_batch[x_name], uttid_list,
**self.preprocess_args)
if return_uttid:
return tuple(return_batch.values()), uttid_list
# Doesn't return the names now.
return tuple(return_batch.values())
def _create_batch_asr(self, x_feats_dict, y_feats_dict, uttid_list):
"""Create a OrderedDict for the mini-batch
:param OrderedDict x_feats_dict:
e.g. {"input1": [ndarray, ndarray, ...],
"input2": [ndarray, ndarray, ...]}
:param OrderedDict y_feats_dict:
e.g. {"target1": [ndarray, ndarray, ...],
"target2": [ndarray, ndarray, ...]}
:param: List[str] uttid_list:
Give uttid_list to sort in the same order as the mini-batch
:return: batch, uttid_list
:rtype: Tuple[OrderedDict, List[str]]
"""
# handle single-input and multi-input (paralell) asr mode
xs = list(x_feats_dict.values())
if self.load_output:
ys = list(y_feats_dict.values())
assert len(xs[0]) == len(ys[0]), (len(xs[0]), len(ys[0]))
# get index of non-zero length samples
nonzero_idx = list(
filter(lambda i: len(ys[0][i]) > 0, range(len(ys[0]))))
for n in range(1, len(y_feats_dict)):
nonzero_idx = filter(lambda i: len(ys[n][i]) > 0, nonzero_idx)
else:
# Note(kamo): Be careful not to make nonzero_idx to a generator
nonzero_idx = list(range(len(xs[0])))
if self.sort_in_input_length:
# sort in input lengths based on the first input
nonzero_sorted_idx = sorted(
nonzero_idx, key=lambda i: -len(xs[0][i]))
else:
nonzero_sorted_idx = nonzero_idx
if len(nonzero_sorted_idx) != len(xs[0]):
logger.warning(
"Target sequences include empty tokenid (batch {} -> {}).".
format(len(xs[0]), len(nonzero_sorted_idx)))
# remove zero-length samples
xs = [[x[i] for i in nonzero_sorted_idx] for x in xs]
uttid_list = [uttid_list[i] for i in nonzero_sorted_idx]
x_names = list(x_feats_dict.keys())
if self.load_output:
ys = [[y[i] for i in nonzero_sorted_idx] for y in ys]
y_names = list(y_feats_dict.keys())
# Keeping x_name and y_name, e.g. input1, for future extension
return_batch = OrderedDict([
* [(x_name, x) for x_name, x in zip(x_names, xs)],
* [(y_name, y) for y_name, y in zip(y_names, ys)],
])
else:
return_batch = OrderedDict(
[(x_name, x) for x_name, x in zip(x_names, xs)])
return return_batch, uttid_list
def _get_from_loader(self, filepath, filetype):
"""Return ndarray
In order to make the fds to be opened only at the first referring,
the loader are stored in self._loaders
>>> ndarray = loader.get_from_loader(
... 'some/path.h5:F01_050C0101_PED_REAL', filetype='hdf5')
:param: str filepath:
:param: str filetype:
:return:
:rtype: np.ndarray
"""
if filetype == "hdf5":
# e.g.
# {"input": [{"feat": "some/path.h5:F01_050C0101_PED_REAL",
# "filetype": "hdf5",
# -> filepath = "some/path.h5", key = "F01_050C0101_PED_REAL"
filepath, key = filepath.split(":", 1)
loader = self._loaders.get(filepath)
if loader is None:
# To avoid disk access, create loader only for the first time
loader = h5py.File(filepath, "r")
self._loaders[filepath] = loader
return loader[key][()]
elif filetype == "sound.hdf5":
# e.g.
# {"input": [{"feat": "some/path.h5:F01_050C0101_PED_REAL",
# "filetype": "sound.hdf5",
# -> filepath = "some/path.h5", key = "F01_050C0101_PED_REAL"
filepath, key = filepath.split(":", 1)
loader = self._loaders.get(filepath)
if loader is None:
# To avoid disk access, create loader only for the first time
loader = SoundHDF5File(filepath, "r", dtype="int16")
self._loaders[filepath] = loader
array, rate = loader[key]
return array
elif filetype == "sound":
# e.g.
# {"input": [{"feat": "some/path.wav",
# "filetype": "sound"},
# Assume PCM16
if not self.keep_all_data_on_mem:
array, _ = soundfile.read(filepath, dtype="int16")
return array
if filepath not in self._loaders:
array, _ = soundfile.read(filepath, dtype="int16")
self._loaders[filepath] = array
return self._loaders[filepath]
elif filetype == "npz":
# e.g.
# {"input": [{"feat": "some/path.npz:F01_050C0101_PED_REAL",
# "filetype": "npz",
filepath, key = filepath.split(":", 1)
loader = self._loaders.get(filepath)
if loader is None:
# To avoid disk access, create loader only for the first time
loader = np.load(filepath)
self._loaders[filepath] = loader
return loader[key]
elif filetype == "npy":
# e.g.
# {"input": [{"feat": "some/path.npy",
# "filetype": "npy"},
if not self.keep_all_data_on_mem:
return np.load(filepath)
if filepath not in self._loaders:
self._loaders[filepath] = np.load(filepath)
return self._loaders[filepath]
elif filetype in ["mat", "vec"]:
# e.g.
# {"input": [{"feat": "some/path.ark:123",
# "filetype": "mat"}]},
# In this case, "123" indicates the starting points of the matrix
# load_mat can load both matrix and vector
if not self.keep_all_data_on_mem:
return kaldiio.load_mat(filepath)
if filepath not in self._loaders:
self._loaders[filepath] = kaldiio.load_mat(filepath)
return self._loaders[filepath]
elif filetype == "scp":
# e.g.
# {"input": [{"feat": "some/path.scp:F01_050C0101_PED_REAL",
# "filetype": "scp",
filepath, key = filepath.split(":", 1)
loader = self._loaders.get(filepath)
if loader is None:
# To avoid disk access, create loader only for the first time
loader = kaldiio.load_scp(filepath)
self._loaders[filepath] = loader
return loader[key]
else:
raise NotImplementedError(
"Not supported: loader_type={}".format(filetype))
def file_type(self, filepath):
suffix = filepath.split(":")[0].split('.')[-1]
if suffix == 'ark':
return 'mat'
elif suffix == 'scp':
return 'scp'
elif suffix == 'npy':
return 'npy'
elif suffix == 'npz':
return 'npz'
elif suffix in ['wav', 'flac']:
# PCM16
return 'sound'
else:
raise ValueError(f"Not support filetype: {suffix}")
class SoundHDF5File():
"""Collecting sound files to a HDF5 file
>>> f = SoundHDF5File('a.flac.h5', mode='a')
>>> array = np.random.randint(0, 100, 100, dtype=np.int16)
>>> f['id'] = (array, 16000)
>>> array, rate = f['id']
:param: str filepath:
:param: str mode:
:param: str format: The type used when saving wav. flac, nist, htk, etc.
:param: str dtype:
"""
def __init__(self,
filepath,
mode="r+",
format=None,
dtype="int16",
**kwargs):
self.filepath = filepath
self.mode = mode
self.dtype = dtype
self.file = h5py.File(filepath, mode, **kwargs)
if format is None:
# filepath = a.flac.h5 -> format = flac
second_ext = os.path.splitext(os.path.splitext(filepath)[0])[1]
format = second_ext[1:]
if format.upper() not in soundfile.available_formats():
# If not found, flac is selected
format = "flac"
# This format affects only saving
self.format = format
def __repr__(self):
return '<SoundHDF5 file "{}" (mode {}, format {}, type {})>'.format(
self.filepath, self.mode, self.format, self.dtype)
def create_dataset(self, name, shape=None, data=None, **kwds):
f = io.BytesIO()
array, rate = data
soundfile.write(f, array, rate, format=self.format)
self.file.create_dataset(
name, shape=shape, data=np.void(f.getvalue()), **kwds)
def __setitem__(self, name, data):
self.create_dataset(name, data=data)
def __getitem__(self, key):
data = self.file[key][()]
f = io.BytesIO(data.tobytes())
array, rate = soundfile.read(f, dtype=self.dtype)
return array, rate
def keys(self):
return self.file.keys()
def values(self):
for k in self.file:
yield self[k]
def items(self):
for k in self.file:
yield k, self[k]
def __iter__(self):
return iter(self.file)
def __contains__(self, item):
return item in self.file
def __len__(self, item):
return len(self.file)
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.file.close()
def close(self):
self.file.close()