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416 lines
16 KiB
416 lines
16 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import io
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from typing import Optional
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import numpy as np
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from yacs.config import CfgNode
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from paddlespeech.s2t.frontend.augmentor.augmentation import AugmentationPipeline
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from paddlespeech.s2t.frontend.featurizer.speech_featurizer import SpeechFeaturizer
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from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
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from paddlespeech.s2t.frontend.normalizer import FeatureNormalizer
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from paddlespeech.s2t.frontend.speech import SpeechSegment
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from paddlespeech.s2t.frontend.utility import IGNORE_ID
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from paddlespeech.s2t.frontend.utility import TarLocalData
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from paddlespeech.s2t.io.reader import LoadInputsAndTargets
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from paddlespeech.s2t.io.utility import pad_list
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from paddlespeech.s2t.utils.log import Log
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__all__ = ["SpeechCollator", "TripletSpeechCollator"]
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logger = Log(__name__).getlog()
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def _tokenids(text, keep_transcription_text):
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# for training text is token ids
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tokens = text # token ids
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if keep_transcription_text:
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# text is string, convert to unicode ord
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assert isinstance(text, str), (type(text), text)
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tokens = [ord(t) for t in text]
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tokens = np.array(tokens, dtype=np.int64)
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return tokens
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class TextCollatorSpm():
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def __init__(self, unit_type, vocab_filepath, spm_model_prefix):
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assert (vocab_filepath is not None)
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self.text_featurizer = TextFeaturizer(
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unit_type=unit_type,
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vocab_filepath=vocab_filepath,
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spm_model_prefix=spm_model_prefix)
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self.eos_id = self.text_featurizer.eos_id
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self.blank_id = self.text_featurizer.blank_id
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def __call__(self, batch):
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"""
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return type [List, np.array [B, T], np.array [B, T], np.array[B]]
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"""
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keys = []
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texts = []
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texts_input = []
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texts_output = []
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text_lens = []
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for idx, item in enumerate(batch):
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key = item.split(" ")[0].strip()
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text = " ".join(item.split(" ")[1:])
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keys.append(key)
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token_ids = self.text_featurizer.featurize(text)
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texts_input.append(
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np.array([self.eos_id] + token_ids).astype(np.int64))
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texts_output.append(
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np.array(token_ids + [self.eos_id]).astype(np.int64))
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text_lens.append(len(token_ids) + 1)
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ys_input_pad = pad_list(texts_input, self.blank_id).astype(np.int64)
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ys_output_pad = pad_list(texts_output, self.blank_id).astype(np.int64)
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y_lens = np.array(text_lens).astype(np.int64)
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return keys, ys_input_pad, ys_output_pad, y_lens
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class SpeechCollatorBase():
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def __init__(
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self,
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aug_file,
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mean_std_filepath,
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vocab_filepath,
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spm_model_prefix,
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random_seed=0,
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unit_type="char",
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spectrum_type='linear', # 'linear', 'mfcc', 'fbank'
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feat_dim=0, # 'mfcc', 'fbank'
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delta_delta=False, # 'mfcc', 'fbank'
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stride_ms=10.0, # ms
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window_ms=20.0, # ms
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n_fft=None, # fft points
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max_freq=None, # None for samplerate/2
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target_sample_rate=16000, # target sample rate
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use_dB_normalization=True,
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target_dB=-20,
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dither=1.0,
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keep_transcription_text=True):
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"""SpeechCollator Collator
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Args:
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unit_type(str): token unit type, e.g. char, word, spm
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vocab_filepath (str): vocab file path.
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mean_std_filepath (str): mean and std file path, which suffix is *.npy
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spm_model_prefix (str): spm model prefix, need if `unit_type` is spm.
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augmentation_config (str, optional): augmentation json str. Defaults to '{}'.
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stride_ms (float, optional): stride size in ms. Defaults to 10.0.
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window_ms (float, optional): window size in ms. Defaults to 20.0.
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n_fft (int, optional): fft points for rfft. Defaults to None.
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max_freq (int, optional): max cut freq. Defaults to None.
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target_sample_rate (int, optional): target sample rate which used for training. Defaults to 16000.
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spectrum_type (str, optional): 'linear', 'mfcc' or 'fbank'. Defaults to 'linear'.
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feat_dim (int, optional): audio feature dim, using by 'mfcc' or 'fbank'. Defaults to None.
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delta_delta (bool, optional): audio feature with delta-delta, using by 'fbank' or 'mfcc'. Defaults to False.
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use_dB_normalization (bool, optional): do dB normalization. Defaults to True.
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target_dB (int, optional): target dB. Defaults to -20.
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random_seed (int, optional): for random generator. Defaults to 0.
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keep_transcription_text (bool, optional): True, when not in training mode, will not do tokenizer; Defaults to False.
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if ``keep_transcription_text`` is False, text is token ids else is raw string.
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Do augmentations
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Padding audio features with zeros to make them have the same shape (or
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a user-defined shape) within one batch.
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"""
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self.keep_transcription_text = keep_transcription_text
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self.train_mode = not keep_transcription_text
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self.stride_ms = stride_ms
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self.window_ms = window_ms
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self.feat_dim = feat_dim
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self.loader = LoadInputsAndTargets()
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# only for tar filetype
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self._local_data = TarLocalData(tar2info={}, tar2object={})
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self.augmentation = AugmentationPipeline(
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preprocess_conf=aug_file.read(), random_seed=random_seed)
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self._normalizer = FeatureNormalizer(
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mean_std_filepath) if mean_std_filepath else None
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self._speech_featurizer = SpeechFeaturizer(
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unit_type=unit_type,
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vocab_filepath=vocab_filepath,
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spm_model_prefix=spm_model_prefix,
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spectrum_type=spectrum_type,
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feat_dim=feat_dim,
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delta_delta=delta_delta,
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stride_ms=stride_ms,
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window_ms=window_ms,
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n_fft=n_fft,
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max_freq=max_freq,
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target_sample_rate=target_sample_rate,
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use_dB_normalization=use_dB_normalization,
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target_dB=target_dB,
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dither=dither)
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self.feature_size = self._speech_featurizer.audio_feature.feature_size
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self.text_feature = self._speech_featurizer.text_feature
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self.vocab_dict = self.text_feature.vocab_dict
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self.vocab_list = self.text_feature.vocab_list
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self.vocab_size = self.text_feature.vocab_size
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def process_utterance(self, audio_file, transcript):
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"""Load, augment, featurize and normalize for speech data.
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:param audio_file: Filepath or file object of audio file.
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:type audio_file: str | file
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:param transcript: Transcription text.
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:type transcript: str
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:return: Tuple of audio feature tensor and data of transcription part,
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where transcription part could be token ids or text.
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:rtype: tuple of (2darray, list)
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"""
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filetype = self.loader.file_type(audio_file)
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if filetype != 'sound':
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spectrum = self.loader._get_from_loader(audio_file, filetype)
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feat_dim = spectrum.shape[1]
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assert feat_dim == self.feat_dim, f"expect feat dim {self.feat_dim}, but got {feat_dim}"
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if self.keep_transcription_text:
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transcript_part = transcript
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else:
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text_ids = self.text_feature.featurize(transcript)
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transcript_part = text_ids
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else:
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# read audio
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speech_segment = SpeechSegment.from_file(
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audio_file, transcript, infos=self._local_data)
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# audio augment
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self.augmentation.transform_audio(speech_segment)
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# extract speech feature
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spectrum, transcript_part = self._speech_featurizer.featurize(
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speech_segment, self.keep_transcription_text)
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# CMVN spectrum
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if self._normalizer:
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spectrum = self._normalizer.apply(spectrum)
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# spectrum augment
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spectrum = self.augmentation.transform_feature(spectrum)
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return spectrum, transcript_part
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def __call__(self, batch):
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"""batch examples
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Args:
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batch (List[Dict]): batch is [dict(audio, text, ...)]
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audio (np.ndarray) shape (T, D)
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text (List[int] or str): shape (U,)
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Returns:
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tuple(utts, xs_pad, ilens, ys_pad, olens): batched data.
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utts: (B,)
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xs_pad : (B, Tmax, D)
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ilens: (B,)
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ys_pad : (B, Umax)
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olens: (B,)
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"""
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audios = []
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audio_lens = []
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texts = []
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text_lens = []
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utts = []
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tids = [] # tokenids
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for idx, item in enumerate(batch):
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utts.append(item['utt'])
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audio = item['feat']
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text = item['text']
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audio, text = self.process_utterance(audio, text)
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audios.append(audio) # [T, D]
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audio_lens.append(audio.shape[0])
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tokens = _tokenids(text, self.keep_transcription_text)
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texts.append(tokens)
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text_lens.append(tokens.shape[0])
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#[B, T, D]
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xs_pad = pad_list(audios, 0.0).astype(np.float32)
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ilens = np.array(audio_lens).astype(np.int64)
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ys_pad = pad_list(texts, IGNORE_ID).astype(np.int64)
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olens = np.array(text_lens).astype(np.int64)
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return utts, xs_pad, ilens, ys_pad, olens
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class SpeechCollator(SpeechCollatorBase):
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@classmethod
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def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
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default = CfgNode(
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dict(
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augmentation_config="",
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random_seed=0,
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mean_std_filepath="",
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unit_type="char",
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vocab_filepath="",
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spm_model_prefix="",
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spectrum_type='linear', # 'linear', 'mfcc', 'fbank'
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feat_dim=0, # 'mfcc', 'fbank'
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delta_delta=False, # 'mfcc', 'fbank'
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stride_ms=10.0, # ms
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window_ms=20.0, # ms
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n_fft=None, # fft points
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max_freq=None, # None for samplerate/2
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target_sample_rate=16000, # target sample rate
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use_dB_normalization=True,
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target_dB=-20,
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dither=1.0, # feature dither
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keep_transcription_text=False))
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if config is not None:
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config.merge_from_other_cfg(default)
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return default
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@classmethod
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def from_config(cls, config):
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"""Build a SpeechCollator object from a config.
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Args:
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config (yacs.config.CfgNode): configs object.
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Returns:
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SpeechCollator: collator object.
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"""
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assert 'augmentation_config' in config.collator
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assert 'keep_transcription_text' in config.collator
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assert 'mean_std_filepath' in config.collator
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assert 'vocab_filepath' in config.collator
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assert 'spectrum_type' in config.collator
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assert 'n_fft' in config.collator
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assert config.collator
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if isinstance(config.collator.augmentation_config, (str, bytes)):
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if config.collator.augmentation_config:
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aug_file = io.open(
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config.collator.augmentation_config,
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mode='r',
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encoding='utf8')
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else:
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aug_file = io.StringIO(initial_value='{}', newline='')
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else:
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aug_file = config.collator.augmentation_config
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assert isinstance(aug_file, io.StringIO)
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speech_collator = cls(
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aug_file=aug_file,
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random_seed=0,
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mean_std_filepath=config.collator.mean_std_filepath,
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unit_type=config.collator.unit_type,
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vocab_filepath=config.collator.vocab_filepath,
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spm_model_prefix=config.collator.spm_model_prefix,
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spectrum_type=config.collator.spectrum_type,
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feat_dim=config.collator.feat_dim,
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delta_delta=config.collator.delta_delta,
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stride_ms=config.collator.stride_ms,
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window_ms=config.collator.window_ms,
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n_fft=config.collator.n_fft,
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max_freq=config.collator.max_freq,
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target_sample_rate=config.collator.target_sample_rate,
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use_dB_normalization=config.collator.use_dB_normalization,
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target_dB=config.collator.target_dB,
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dither=config.collator.dither,
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keep_transcription_text=config.collator.keep_transcription_text)
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return speech_collator
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class TripletSpeechCollator(SpeechCollator):
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def process_utterance(self, audio_file, translation, transcript):
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"""Load, augment, featurize and normalize for speech data.
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:param audio_file: Filepath or file object of audio file.
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:type audio_file: str | file
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:param translation: translation text.
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:type translation: str
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:return: Tuple of audio feature tensor and data of translation part,
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where translation part could be token ids or text.
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:rtype: tuple of (2darray, list)
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"""
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spectrum, translation_part = super().process_utterance(audio_file,
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translation)
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transcript_part = self._speech_featurizer.text_featurize(
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transcript, self.keep_transcription_text)
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return spectrum, translation_part, transcript_part
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def __call__(self, batch):
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"""batch examples
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Args:
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batch (List[Dict]): batch is [dict(audio, text, ...)]
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audio (np.ndarray) shape (T, D)
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text (List[int] or str): shape (U,)
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Returns:
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tuple(utts, xs_pad, ilens, ys_pad, olens): batched data.
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utts: (B,)
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xs_pad : (B, Tmax, D)
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ilens: (B,)
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ys_pad : [(B, Umax), (B, Umax)]
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olens: [(B,), (B,)]
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"""
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utts = []
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audios = []
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audio_lens = []
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translation_text = []
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translation_text_lens = []
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transcription_text = []
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transcription_text_lens = []
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for idx, item in enumerate(batch):
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utts.append(item['utt'])
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audio = item['feat']
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translation = item['text']
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transcription = item['text1']
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audio, translation, transcription = self.process_utterance(
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audio, translation, transcription)
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audios.append(audio) # [T, D]
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audio_lens.append(audio.shape[0])
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tokens = [[], []]
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for idx, text in enumerate([translation, transcription]):
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tokens[idx] = _tokenids(text, self.keep_transcription_text)
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translation_text.append(tokens[0])
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translation_text_lens.append(tokens[0].shape[0])
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transcription_text.append(tokens[1])
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transcription_text_lens.append(tokens[1].shape[0])
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xs_pad = pad_list(audios, 0.0).astype(np.float32) #[B, T, D]
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ilens = np.array(audio_lens).astype(np.int64)
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padded_translation = pad_list(translation_text,
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IGNORE_ID).astype(np.int64)
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translation_lens = np.array(translation_text_lens).astype(np.int64)
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padded_transcription = pad_list(transcription_text,
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IGNORE_ID).astype(np.int64)
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transcription_lens = np.array(transcription_text_lens).astype(np.int64)
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ys_pad = (padded_translation, padded_transcription)
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olens = (translation_lens, transcription_lens)
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return utts, xs_pad, ilens, ys_pad, olens
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