# 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.
import io
import numpy as np
from paddlespeech.s2t.frontend.augmentor.augmentation import AugmentationPipeline
from paddlespeech.s2t.frontend.featurizer.speech_featurizer import SpeechFeaturizer
from paddlespeech.s2t.frontend.normalizer import FeatureNormalizer
from paddlespeech.s2t.frontend.speech import SpeechSegment
from paddlespeech.s2t.frontend.utility import IGNORE_ID
from paddlespeech.s2t.frontend.utility import TarLocalData
from paddlespeech.s2t.io.reader import LoadInputsAndTargets
from paddlespeech.s2t.io.utility import pad_list
from paddlespeech.s2t.utils.log import Log
__all__ = ["SpeechCollator", "TripletSpeechCollator"]
logger = Log(__name__).getlog()
def _tokenids(text, keep_transcription_text):
# for training text is token ids
tokens = text # token ids
if keep_transcription_text:
# text is string, convert to unicode ord
assert isinstance(text, str), (type(text), text)
tokens = [ord(t) for t in text]
tokens = np.array(tokens, dtype=np.int64)
return tokens
class SpeechCollatorBase():
def __init__(
self,
aug_file,
mean_std_filepath,
vocab_filepath,
spm_model_prefix,
random_seed=0,
unit_type="char",
spectrum_type='linear', # 'linear', 'mfcc', 'fbank'
feat_dim=0, # 'mfcc', 'fbank'
delta_delta=False, # 'mfcc', 'fbank'
stride_ms=10.0, # ms
window_ms=20.0, # ms
n_fft=None, # fft points
max_freq=None, # None for samplerate/2
target_sample_rate=16000, # target sample rate
use_dB_normalization=True,
target_dB=-20,
dither=1.0,
keep_transcription_text=True):
"""SpeechCollator Collator
Args:
unit_type(str): token unit type, e.g. char, word, spm
vocab_filepath (str): vocab file path.
mean_std_filepath (str): mean and std file path, which suffix is *.npy
spm_model_prefix (str): spm model prefix, need if `unit_type` is spm.
augmentation_config (str, optional): augmentation json str. Defaults to '{}'.
stride_ms (float, optional): stride size in ms. Defaults to 10.0.
window_ms (float, optional): window size in ms. Defaults to 20.0.
n_fft (int, optional): fft points for rfft. Defaults to None.
max_freq (int, optional): max cut freq. Defaults to None.
target_sample_rate (int, optional): target sample rate which used for training. Defaults to 16000.
spectrum_type (str, optional): 'linear', 'mfcc' or 'fbank'. Defaults to 'linear'.
feat_dim (int, optional): audio feature dim, using by 'mfcc' or 'fbank'. Defaults to None.
delta_delta (bool, optional): audio feature with delta-delta, using by 'fbank' or 'mfcc'. Defaults to False.
use_dB_normalization (bool, optional): do dB normalization. Defaults to True.
target_dB (int, optional): target dB. Defaults to -20.
random_seed (int, optional): for random generator. Defaults to 0.
keep_transcription_text (bool, optional): True, when not in training mode, will not do tokenizer; Defaults to False.
if ``keep_transcription_text`` is False, text is token ids else is raw string.
Do augmentations
Padding audio features with zeros to make them have the same shape (or
a user-defined shape) within one batch.
"""
self.keep_transcription_text = keep_transcription_text
self.train_mode = not keep_transcription_text
self.stride_ms = stride_ms
self.window_ms = window_ms
self.feat_dim = feat_dim
self.loader = LoadInputsAndTargets()
# only for tar filetype
self._local_data = TarLocalData(tar2info={}, tar2object={})
self.augmentation = AugmentationPipeline(
preprocess_conf=aug_file.read(), random_seed=random_seed)
self._normalizer = FeatureNormalizer(
mean_std_filepath) if mean_std_filepath else None
self._speech_featurizer = SpeechFeaturizer(
unit_type=unit_type,
vocab_filepath=vocab_filepath,
spm_model_prefix=spm_model_prefix,
spectrum_type=spectrum_type,
feat_dim=feat_dim,
delta_delta=delta_delta,
stride_ms=stride_ms,
window_ms=window_ms,
n_fft=n_fft,
max_freq=max_freq,
target_sample_rate=target_sample_rate,
use_dB_normalization=use_dB_normalization,
target_dB=target_dB,
dither=dither)
self.feature_size = self._speech_featurizer.audio_feature.feature_size
self.text_feature = self._speech_featurizer.text_feature
self.vocab_dict = self.text_feature.vocab_dict
self.vocab_list = self.text_feature.vocab_list
self.vocab_size = self.text_feature.vocab_size
def process_utterance(self, audio_file, transcript):
"""Load, augment, featurize and normalize for speech data.
:param audio_file: Filepath or file object of audio file.
:type audio_file: str | file
:param transcript: Transcription text.
:type transcript: str
:return: Tuple of audio feature tensor and data of transcription part,
where transcription part could be token ids or text.
:rtype: tuple of (2darray, list)
"""
filetype = self.loader.file_type(audio_file)
if filetype != 'sound':
spectrum = self.loader._get_from_loader(audio_file, filetype)
feat_dim = spectrum.shape[1]
assert feat_dim == self.feat_dim, f"expect feat dim {self.feat_dim}, but got {feat_dim}"
if self.keep_transcription_text:
transcript_part = transcript
else:
text_ids = self.text_feature.featurize(transcript)
transcript_part = text_ids
else:
# read audio
speech_segment = SpeechSegment.from_file(
audio_file, transcript, infos=self._local_data)
# audio augment
self.augmentation.transform_audio(speech_segment)
# extract speech feature
spectrum, transcript_part = self._speech_featurizer.featurize(
speech_segment, self.keep_transcription_text)
# CMVN spectrum
if self._normalizer:
spectrum = self._normalizer.apply(spectrum)
# spectrum augment
spectrum = self.augmentation.transform_feature(spectrum)
return spectrum, transcript_part
def __call__(self, batch):
"""batch examples
Args:
batch (List[Dict]): batch is [dict(audio, text, ...)]
audio (np.ndarray) shape (T, D)
text (List[int] or str): shape (U,)
Returns:
tuple(utts, xs_pad, ilens, ys_pad, olens): batched data.
utts: (B,)
xs_pad : (B, Tmax, D)
ilens: (B,)
ys_pad : (B, Umax)
olens: (B,)
"""
audios = []
audio_lens = []
texts = []
text_lens = []
utts = []
tids = [] # tokenids
for idx, item in enumerate(batch):
utts.append(item['utt'])
audio = item['input'][0]['feat']
text = item['output'][0]['text']
audio, text = self.process_utterance(audio, text)
audios.append(audio) # [T, D]
audio_lens.append(audio.shape[0])
tokens = _tokenids(text, self.keep_transcription_text)
texts.append(tokens)
text_lens.append(tokens.shape[0])
#[B, T, D]
xs_pad = pad_list(audios, 0.0).astype(np.float32)
ilens = np.array(audio_lens).astype(np.int64)
ys_pad = pad_list(texts, IGNORE_ID).astype(np.int64)
olens = np.array(text_lens).astype(np.int64)
return utts, xs_pad, ilens, ys_pad, olens
class SpeechCollator(SpeechCollatorBase):
@classmethod
def from_config(cls, config):
"""Build a SpeechCollator object from a config.
Args:
config (yacs.config.CfgNode): configs object.
Returns:
SpeechCollator: collator object.
"""
assert 'augmentation_config' in config
assert 'keep_transcription_text' in config
assert 'mean_std_filepath' in config
assert 'vocab_filepath' in config
assert 'spectrum_type' in config
assert 'n_fft' in config
assert config
if isinstance(config.augmentation_config, (str, bytes)):
if config.augmentation_config:
aug_file = io.open(
config.augmentation_config, mode='r', encoding='utf8')
else:
aug_file = io.StringIO(initial_value='{}', newline='')
else:
aug_file = config.augmentation_config
assert isinstance(aug_file, io.StringIO)
speech_collator = cls(
aug_file=aug_file,
random_seed=0,
mean_std_filepath=config.mean_std_filepath,
unit_type=config.unit_type,
vocab_filepath=config.vocab_filepath,
spm_model_prefix=config.spm_model_prefix,
spectrum_type=config.spectrum_type,
feat_dim=config.feat_dim,
delta_delta=config.delta_delta,
stride_ms=config.stride_ms,
window_ms=config.window_ms,
n_fft=config.n_fft,
max_freq=config.max_freq,
target_sample_rate=config.target_sample_rate,
use_dB_normalization=config.use_dB_normalization,
target_dB=config.target_dB,
dither=config.dither,
keep_transcription_text=config.keep_transcription_text)
return speech_collator
class TripletSpeechCollator(SpeechCollator):
def process_utterance(self, audio_file, translation, transcript):
"""Load, augment, featurize and normalize for speech data.
:param audio_file: Filepath or file object of audio file.
:type audio_file: str | file
:param translation: translation text.
:type translation: str
:return: Tuple of audio feature tensor and data of translation part,
where translation part could be token ids or text.
:rtype: tuple of (2darray, list)
"""
spectrum, translation_part = super().process_utterance(audio_file,
translation)
transcript_part = self._speech_featurizer.text_featurize(
transcript, self.keep_transcription_text)
return spectrum, translation_part, transcript_part
def __call__(self, batch):
"""batch examples
Args:
batch (List[Dict]): batch is [dict(audio, text, ...)]
audio (np.ndarray) shape (T, D)
text (List[int] or str): shape (U,)
Returns:
tuple(utts, xs_pad, ilens, ys_pad, olens): batched data.
utts: (B,)
xs_pad : (B, Tmax, D)
ilens: (B,)
ys_pad : [(B, Umax), (B, Umax)]
olens: [(B,), (B,)]
"""
utts = []
audios = []
audio_lens = []
translation_text = []
translation_text_lens = []
transcription_text = []
transcription_text_lens = []
for idx, item in enumerate(batch):
utts.append(item['utt'])
audio = item['input'][0]['feat']
translation = item['output'][0]['text']
transcription = item['output'][1]['text']
audio, translation, transcription = self.process_utterance(
audio, translation, transcription)
audios.append(audio) # [T, D]
audio_lens.append(audio.shape[0])
tokens = [[], []]
for idx, text in enumerate([translation, transcription]):
tokens[idx] = _tokenids(text, self.keep_transcription_text)
translation_text.append(tokens[0])
translation_text_lens.append(tokens[0].shape[0])
transcription_text.append(tokens[1])
transcription_text_lens.append(tokens[1].shape[0])
xs_pad = pad_list(audios, 0.0).astype(np.float32) #[B, T, D]
ilens = np.array(audio_lens).astype(np.int64)
padded_translation = pad_list(translation_text,
IGNORE_ID).astype(np.int64)
translation_lens = np.array(translation_text_lens).astype(np.int64)
padded_transcription = pad_list(transcription_text,
IGNORE_ID).astype(np.int64)
transcription_lens = np.array(transcription_text_lens).astype(np.int64)
ys_pad = (padded_translation, padded_transcription)
olens = (translation_lens, transcription_lens)
return utts, xs_pad, ilens, ys_pad, olens