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PaddleSpeech/paddlespeech/s2t/transform/transformation.py

158 lines
6.6 KiB

3 years ago
# 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)
"""Transformation module."""
import copy
import io
import logging
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from collections import OrderedDict
from collections.abc import Sequence
from inspect import signature
import yaml
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
import_alias = dict(
identity="paddlespeech.s2t.transform.transform_interface:Identity",
time_warp="paddlespeech.s2t.transform.spec_augment:TimeWarp",
time_mask="paddlespeech.s2t.transform.spec_augment:TimeMask",
freq_mask="paddlespeech.s2t.transform.spec_augment:FreqMask",
spec_augment="paddlespeech.s2t.transform.spec_augment:SpecAugment",
speed_perturbation="paddlespeech.s2t.transform.perturb:SpeedPerturbation",
volume_perturbation="paddlespeech.s2t.transform.perturb:VolumePerturbation",
noise_injection="paddlespeech.s2t.transform.perturb:NoiseInjection",
bandpass_perturbation="paddlespeech.s2t.transform.perturb:BandpassPerturbation",
rir_convolve="paddlespeech.s2t.transform.perturb:RIRConvolve",
delta="paddlespeech.s2t.transform.add_deltas:AddDeltas",
cmvn="paddlespeech.s2t.transform.cmvn:CMVN",
utterance_cmvn="paddlespeech.s2t.transform.cmvn:UtteranceCMVN",
fbank="paddlespeech.s2t.transform.spectrogram:LogMelSpectrogram",
spectrogram="paddlespeech.s2t.transform.spectrogram:Spectrogram",
stft="paddlespeech.s2t.transform.spectrogram:Stft",
istft="paddlespeech.s2t.transform.spectrogram:IStft",
stft2fbank="paddlespeech.s2t.transform.spectrogram:Stft2LogMelSpectrogram",
wpe="paddlespeech.s2t.transform.wpe:WPE",
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channel_selector="paddlespeech.s2t.transform.channel_selector:ChannelSelector",
fbank_kaldi="paddlespeech.s2t.transform.spectrogram:LogMelSpectrogramKaldi",
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cmvn_json="paddlespeech.s2t.transform.cmvn:GlobalCMVN")
class Transformation():
"""Apply some functions to the mini-batch
Examples:
>>> kwargs = {"process": [{"type": "fbank",
... "n_mels": 80,
... "fs": 16000},
... {"type": "cmvn",
... "stats": "data/train/cmvn.ark",
... "norm_vars": True},
... {"type": "delta", "window": 2, "order": 2}]}
>>> transform = Transformation(kwargs)
>>> bs = 10
>>> xs = [np.random.randn(100, 80).astype(np.float32)
... for _ in range(bs)]
>>> xs = transform(xs)
"""
def __init__(self, conffile=None):
if conffile is not None:
if isinstance(conffile, dict):
self.conf = copy.deepcopy(conffile)
else:
with io.open(conffile, encoding="utf-8") as f:
self.conf = yaml.safe_load(f)
assert isinstance(self.conf, dict), type(self.conf)
else:
self.conf = {"mode": "sequential", "process": []}
self.functions = OrderedDict()
if self.conf.get("mode", "sequential") == "sequential":
for idx, process in enumerate(self.conf["process"]):
assert isinstance(process, dict), type(process)
opts = dict(process)
process_type = opts.pop("type")
class_obj = dynamic_import(process_type, import_alias)
# TODO(karita): assert issubclass(class_obj, TransformInterface)
try:
self.functions[idx] = class_obj(**opts)
except TypeError:
try:
signa = signature(class_obj)
except ValueError:
# Some function, e.g. built-in function, are failed
pass
else:
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logging.error("Expected signature: {}({})".format(
class_obj.__name__, signa))
raise
else:
raise NotImplementedError(
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"Not supporting mode={}".format(self.conf["mode"]))
def __repr__(self):
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rep = "\n" + "\n".join(" {}: {}".format(k, v)
for k, v in self.functions.items())
return "{}({})".format(self.__class__.__name__, rep)
def __call__(self, xs, uttid_list=None, **kwargs):
"""Return new mini-batch
:param Union[Sequence[np.ndarray], np.ndarray] xs:
:param Union[Sequence[str], str] uttid_list:
:return: batch:
:rtype: List[np.ndarray]
"""
if not isinstance(xs, Sequence):
is_batch = False
xs = [xs]
else:
is_batch = True
if isinstance(uttid_list, str):
uttid_list = [uttid_list for _ in range(len(xs))]
if self.conf.get("mode", "sequential") == "sequential":
for idx in range(len(self.conf["process"])):
func = self.functions[idx]
# TODO(karita): use TrainingTrans and UttTrans to check __call__ args
# Derive only the args which the func has
try:
param = signature(func).parameters
except ValueError:
# Some function, e.g. built-in function, are failed
param = {}
_kwargs = {k: v for k, v in kwargs.items() if k in param}
try:
if uttid_list is not None and "uttid" in param:
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xs = [
func(x, u, **_kwargs)
for x, u in zip(xs, uttid_list)
]
else:
xs = [func(x, **_kwargs) for x in xs]
except Exception:
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logging.fatal("Catch a exception from {}th func: {}".format(
idx, func))
raise
else:
raise NotImplementedError(
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"Not supporting mode={}".format(self.conf["mode"]))
if is_batch:
return xs
else:
return xs[0]