You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
427 lines
16 KiB
427 lines
16 KiB
# 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.
|
|
# Modified from espnet(https://github.com/espnet/espnet)
|
|
from collections import OrderedDict
|
|
|
|
import kaldiio
|
|
import numpy as np
|
|
import soundfile
|
|
|
|
from paddlespeech.s2t.frontend.augmentor.augmentation import AugmentationPipeline as Transformation
|
|
from paddlespeech.s2t.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:
|
|
self.preprocessing = Transformation(preprocess_conf)
|
|
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].lower()
|
|
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()
|