def __init__(self,
vocab_filepath,
mean_std_filepath,
augmentation_config='{}',
max_duration=float('inf'),
min_duration=0.0,
stride_ms=10.0,
window_ms=20.0,
max_freq=None,
specgram_type='linear',
use_dB_normalization=True,
num_threads=multiprocessing.cpu_count() // 2,
random_seed=0):
self._max_duration = max_duration
self._min_duration = min_duration
self._normalizer = FeatureNormalizer(mean_std_filepath)
self._augmentation_pipeline = AugmentationPipeline(
augmentation_config=augmentation_config, random_seed=random_seed)
self._speech_featurizer = SpeechFeaturizer(
vocab_filepath=vocab_filepath,
specgram_type=specgram_type,
stride_ms=stride_ms,
window_ms=window_ms,
max_freq=max_freq,
use_dB_normalization=use_dB_normalization)
self._num_threads = num_threads
self._rng = random.Random(random_seed)
self._epoch = 0
# for caching tar files info
self._local_data = local()
self._local_data.tar2info = {}
self._local_data.tar2object = {}
def compute_mean_std(manifest_path, num_samples, output_path):
normalizer = FeatureNormalizer(mean_std_filepath=None,
manifest_path=manifest_path,
num_samples=num_samples)
# 将计算的结果保存的文件中
normalizer.write_to_file(output_path)
print('计算的均值和标准值已保存在 %s！' % output_path)
def __init__(self,
vocab_filepath,
mean_std_filepath,
augmentation_config='{}',
max_duration=float('inf'),
min_duration=0.0,
stride_ms=10.0,
window_ms=20.0,
use_dB_normalization=True,
random_seed=0,
keep_transcription_text=False,
place=paddle.CPUPlace(),
is_training=True):
self._max_duration = max_duration
self._min_duration = min_duration
self._normalizer = FeatureNormalizer(mean_std_filepath)
self._augmentation_pipeline = AugmentationPipeline(
augmentation_config=augmentation_config, random_seed=random_seed)
self._speech_featurizer = SpeechFeaturizer(
vocab_filepath=vocab_filepath,
stride_ms=stride_ms,
window_ms=window_ms,
use_dB_normalization=use_dB_normalization)
self._rng = random.Random(random_seed)
self._keep_transcription_text = keep_transcription_text
self.epoch = 0
self._is_training = is_training
# for caching tar files info
self._local_data = local()
self._local_data.tar2info = {}
self._local_data.tar2object = {}
self._place = place
def get_audio_mfcc_features(txt_files,
wav_files,
n_input,
n_context,
word_num_map,
txt_labels=None,
specgram_type='mfcc',
mean_std_filepath='data/aishell/mean_std.npz'):
""" Get MFCC/linear specgram features. The dim of MFCC is 39, contains 13 mfcc + 13 delta1 + 13 delta2.
Linear specgram contains 161 features in different frequency section.
:param txt_files:
:param wav_files:
:param n_input:
:param n_context:
:param word_num_map:
:param txt_labels:
:return:
"""
audio_features = []
audio_features_len = []
text_vector = []
text_vector_len = []
if txt_files != None:
txt_labels = txt_files
get_feature = AudioFeaturizer(specgram_type)
normalizer = FeatureNormalizer(mean_std_filepath)
for txt_obj, wav_file in zip(txt_labels, wav_files):
# Turn inputs into features
if specgram_type == 'mfcc':
audio_data = audiofile_to_input_vector(
wav_file, n_input, n_context) # get mfcc feature ( ???, 741 )
elif specgram_type == 'linear':
speech_segment = SpeechSegment.from_file(wav_file, "")
specgram = get_feature.featurize(speech_segment)
audio_data = normalizer.apply(specgram)
audio_data = np.transpose(
audio_data) # get linear specgram feature, (?, 161)
audio_data = audio_data.astype('float32')
audio_features.append(audio_data)
audio_features_len.append(np.int32(len(audio_data)))
target = []
if txt_files != None: # txt_obj是文件
target = trans_text_ch_to_vector(txt_obj, word_num_map)
else:
target = trans_text_ch_to_vector(None, word_num_map,
txt_obj) # txt_obj是labels
text_vector.append(target)
text_vector_len.append(len(target))
audio_features = np.asarray(audio_features)
audio_features_len = np.asarray(audio_features_len)
text_vector = np.asarray(text_vector)
text_vector_len = np.asarray(text_vector_len)
return audio_features, audio_features_len, text_vector, text_vector_len
def compute_mean_std(manifest_path, num_samples, output_path):
# 随机取指定的数量计算平均值归一化
normalizer = FeatureNormalizer(mean_std_filepath=None,
manifest_path=manifest_path,
num_samples=num_samples,
num_workers=args.num_workers)
# 将计算的结果保存的文件中
normalizer.write_to_file(output_path)
print('计算的均值和标准值已保存在 %s！' % output_path)
def __init__(self,
vocab_filepath,
mean_std_filepath,
stride_ms=10.0,
window_ms=20.0,
use_dB_normalization=True):
self._normalizer = FeatureNormalizer(mean_std_filepath)
self._speech_featurizer = SpeechFeaturizer(
vocab_filepath=vocab_filepath,
stride_ms=stride_ms,
window_ms=window_ms,
use_dB_normalization=use_dB_normalization)
def __init__(self,
manifest,
vocab_filepath,
mean_std_filepath,
augmentation_config='{}',
max_duration=float('inf'),
min_duration=0.0,
stride_ms=10.0,
window_ms=20.0,
max_freq=None,
specgram_type='linear',
use_dB_normalization=True,
random_seed=0,
keep_transcription_text=False,
segmented=False):
self._max_duration = max_duration
self._min_duration = min_duration
self._segmented = segmented
self._keep_transcription_text = keep_transcription_text
if isinstance(manifest, str) and os.path.isfile(manifest):
self.manifest = pd.read_csv(manifest)
elif isinstance(manifest, pd.DataFrame):
self.manifest = manifest
else:
raise BaseException(
"{} is neither an valide path or a pandas DataFrame object".
format(manifest))
# duration filtering
self.manifest = self.manifest[
(self.manifest.duration >= self._min_duration)
& (self.manifest.duration <= self._max_duration)]
self.manifest = self.manifest.sort_values(by=["duration"],
ascending=True)
self._normalizer = FeatureNormalizer(mean_std_filepath)
self._augmentation_pipeline = AugmentationPipeline(
augmentation_config=augmentation_config, random_seed=random_seed)
self._speech_featurizer = SpeechFeaturizer(
vocab_filepath=vocab_filepath,
specgram_type=specgram_type,
stride_ms=stride_ms,
window_ms=window_ms,
max_freq=max_freq,
use_dB_normalization=use_dB_normalization)
def main():
print_arguments(args)
audio_featurizer = AudioFeaturizer(specgram_type=args.specgram_type)
def augment_and_featurize(audio_segment):
return audio_featurizer.featurize(audio_segment)
normalizer = FeatureNormalizer(
mean_std_filepath=None,
manifest_path=args.manifest_path,
featurize_func=augment_and_featurize,
num_samples=args.num_samples)
normalizer.write_to_file(args.output_path)
def main():
print_arguments(args)
augmentation_pipeline = AugmentationPipeline('{}')
audio_featurizer = AudioFeaturizer(specgram_type=args.specgram_type)
def augment_and_featurize(audio_segment):
augmentation_pipeline.transform_audio(audio_segment)
return audio_featurizer.featurize(audio_segment)
# 随机取指定的数量计算平均值归一化
normalizer = FeatureNormalizer(mean_std_filepath=None,
manifest_path=args.manifest_path,
featurize_func=augment_and_featurize,
num_samples=args.num_samples)
# 将计算的结果保存的文件中
normalizer.write_to_file(args.output_path)
class AudioInferProcess(object):
"""
识别程序所使用的是对音频预处理的工具
:param vocab_filepath: 词汇表文件路径
:type vocab_filepath: str
:param mean_std_filepath: 平均值和标准差的文件路径
:type mean_std_filepath: str
:param stride_ms: 生成帧的跨步大小(以毫秒为单位)
:type stride_ms: float
:param window_ms: 用于生成帧的窗口大小(毫秒)
:type window_ms: float
:param use_dB_normalization: 提取特征前是否将音频归一化至-20 dB
:type use_dB_normalization: bool
"""
def __init__(self,
vocab_filepath,
mean_std_filepath,
stride_ms=10.0,
window_ms=20.0,
use_dB_normalization=True):
self._normalizer = FeatureNormalizer(mean_std_filepath)
self._speech_featurizer = SpeechFeaturizer(
vocab_filepath=vocab_filepath,
stride_ms=stride_ms,
window_ms=window_ms,
use_dB_normalization=use_dB_normalization)
def process_utterance(self, audio_file):
"""对语音数据加载、预处理
:param audio_file: 音频文件的文件路径或文件对象
:type audio_file: str | file
:return: 预处理的音频数据
:rtype: 2darray
"""
speech_segment = SpeechSegment.from_file(audio_file, "")
specgram, _ = self._speech_featurizer.featurize(speech_segment, False)
specgram = self._normalizer.apply(specgram)
return specgram
@property
def vocab_size(self):
"""返回词汇表大小
:return: 词汇表大小
:rtype: int
"""
return self._speech_featurizer.vocab_size
@property
def vocab_list(self):
"""返回词汇表列表
:return: 词汇表列表
:rtype: list
"""
return self._speech_featurizer.vocab_list
class DataGenerator(object):
"""
DataGenerator provides basic audio data preprocessing pipeline, and offers
data reader interfaces of PaddlePaddle requirements.
:param vocab_filepath: Vocabulary filepath for indexing tokenized
transcripts.
:type vocab_filepath: basestring
:param mean_std_filepath: File containing the pre-computed mean and stddev.
:type mean_std_filepath: None|basestring
:param augmentation_config: Augmentation configuration in json string.
Details see AugmentationPipeline.__doc__.
:type augmentation_config: str
:param max_duration: Audio with duration (in seconds) greater than
this will be discarded.
:type max_duration: float
:param min_duration: Audio with duration (in seconds) smaller than
this will be discarded.
:type min_duration: float
:param stride_ms: Striding size (in milliseconds) for generating frames.
:type stride_ms: float
:param window_ms: Window size (in milliseconds) for generating frames.
:type window_ms: float
:param max_freq: Used when specgram_type is 'linear', only FFT bins
corresponding to frequencies between [0, max_freq] are
returned.
:types max_freq: None|float
:param specgram_type: Specgram feature type. Options: 'linear'.
:type specgram_type: str
:param use_dB_normalization: Whether to normalize the audio to -20 dB
before extracting the features.
:type use_dB_normalization: bool
:param random_seed: Random seed.
:type random_seed: int
:param keep_transcription_text: If set to True, transcription text will
be passed forward directly without
converting to index sequence.
:type keep_transcription_text: bool
:param place: The place to run the program.
:type place: CPUPlace or CUDAPlace
:param is_training: If set to True, generate text data for training,
otherwise, generate text data for infer.
:type is_training: bool
"""
def __init__(self,
vocab_filepath,
mean_std_filepath,
augmentation_config='{}',
max_duration=float('inf'),
min_duration=0.0,
stride_ms=10.0,
window_ms=20.0,
max_freq=None,
specgram_type='linear',
use_dB_normalization=True,
random_seed=0,
keep_transcription_text=False,
place=fluid.CPUPlace(),
is_training=True):
self._max_duration = max_duration
self._min_duration = min_duration
self._normalizer = FeatureNormalizer(mean_std_filepath)
self._augmentation_pipeline = AugmentationPipeline(
augmentation_config=augmentation_config, random_seed=random_seed)
self._speech_featurizer = SpeechFeaturizer(
vocab_filepath=vocab_filepath,
specgram_type=specgram_type,
stride_ms=stride_ms,
window_ms=window_ms,
max_freq=max_freq,
use_dB_normalization=use_dB_normalization)
self._rng = random.Random(random_seed)
self._keep_transcription_text = keep_transcription_text
self._epoch = 0
self._is_training = is_training
# for caching tar files info
self._local_data = local()
self._local_data.tar2info = {}
self._local_data.tar2object = {}
self._place = place
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: basestring | file
:param transcript: Transcription text.
:type transcript: basestring
: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)
"""
try:
is_str = isinstance(audio_file, basestring)
except:
is_str = isinstance(audio_file, str)
if is_str and audio_file.startswith('tar:'):
speech_segment = SpeechSegment.from_file(
self._subfile_from_tar(audio_file), transcript)
else:
speech_segment = SpeechSegment.from_file(audio_file, transcript)
self._augmentation_pipeline.transform_audio(speech_segment)
specgram, transcript_part = self._speech_featurizer.featurize(
speech_segment, self._keep_transcription_text)
specgram = self._normalizer.apply(specgram)
return specgram, transcript_part
def batch_reader_creator(self,
manifest_path,
batch_size,
padding_to=-1,
flatten=False,
sortagrad=False,
shuffle_method="batch_shuffle"):
"""
Batch data reader creator for audio data. Return a callable generator
function to produce batches of data.
Audio features within one batch will be padded with zeros to have the
same shape, or a user-defined shape.
:param manifest_path: Filepath of manifest for audio files.
:type manifest_path: basestring
:param batch_size: Number of instances in a batch.
:type batch_size: int
:param padding_to: If set -1, the maximun shape in the batch
will be used as the target shape for padding.
Otherwise, `padding_to` will be the target shape.
:type padding_to: int
:param flatten: If set True, audio features will be flatten to 1darray.
:type flatten: bool
:param sortagrad: If set True, sort the instances by audio duration
in the first epoch for speed up training.
:type sortagrad: bool
:param shuffle_method: Shuffle method. Options:
'' or None: no shuffle.
'instance_shuffle': instance-wise shuffle.
'batch_shuffle': similarly-sized instances are
put into batches, and then
batch-wise shuffle the batches.
For more details, please see
``_batch_shuffle.__doc__``.
'batch_shuffle_clipped': 'batch_shuffle' with
head shift and tail
clipping. For more
details, please see
``_batch_shuffle``.
If sortagrad is True, shuffle is disabled
for the first epoch.
:type shuffle_method: None|str
:return: Batch reader function, producing batches of data when called.
:rtype: callable
"""
def batch_reader():
# read manifest
manifest = read_manifest(manifest_path=manifest_path,
max_duration=self._max_duration,
min_duration=self._min_duration)
# sort (by duration) or batch-wise shuffle the manifest
if self._epoch == 0 and sortagrad:
manifest.sort(key=lambda x: x["duration"])
manifest.reverse()
else:
if shuffle_method == "batch_shuffle":
manifest = self._batch_shuffle(manifest,
batch_size,
clipped=False)
elif shuffle_method == "batch_shuffle_clipped":
manifest = self._batch_shuffle(manifest,
batch_size,
clipped=True)
elif shuffle_method == "instance_shuffle":
self._rng.shuffle(manifest)
elif shuffle_method is None:
pass
else:
raise ValueError("Unknown shuffle method %s." %
shuffle_method)
# prepare batches
batch = []
instance_reader = self._instance_reader_creator(manifest)
for instance in instance_reader():
batch.append(instance)
if len(batch) == batch_size:
yield self._padding_batch(batch, padding_to, flatten)
batch = []
if len(batch) >= 1:
yield self._padding_batch(batch, padding_to, flatten)
self._epoch += 1
return batch_reader
@property
def feeding(self):
"""Returns data reader's feeding dict.
:return: Data feeding dict.
:rtype: dict
"""
feeding_dict = {"audio_spectrogram": 0, "transcript_text": 1}
return feeding_dict
@property
def vocab_size(self):
"""Return the vocabulary size.
:return: Vocabulary size.
:rtype: int
"""
return self._speech_featurizer.vocab_size
@property
def vocab_list(self):
"""Return the vocabulary in list.
:return: Vocabulary in list.
:rtype: list
"""
return self._speech_featurizer.vocab_list
def _parse_tar(self, file):
"""Parse a tar file to get a tarfile object
and a map containing tarinfoes
"""
result = {}
f = tarfile.open(file)
for tarinfo in f.getmembers():
result[tarinfo.name] = tarinfo
return f, result
def _subfile_from_tar(self, file):
"""Get subfile object from tar.
It will return a subfile object from tar file
and cached tar file info for next reading request.
"""
tarpath, filename = file.split(':', 1)[1].split('#', 1)
if 'tar2info' not in self._local_data.__dict__:
self._local_data.tar2info = {}
if 'tar2object' not in self._local_data.__dict__:
self._local_data.tar2object = {}
if tarpath not in self._local_data.tar2info:
object, infoes = self._parse_tar(tarpath)
self._local_data.tar2info[tarpath] = infoes
self._local_data.tar2object[tarpath] = object
return self._local_data.tar2object[tarpath].extractfile(
self._local_data.tar2info[tarpath][filename])
def _instance_reader_creator(self, manifest):
"""
Instance reader creator. Create a callable function to produce
instances of data.
Instance: a tuple of ndarray of audio spectrogram and a list of
token indices for transcript.
"""
def reader():
for instance in manifest:
inst = self.process_utterance(instance["audio_filepath"],
instance["text"]),
yield inst[0]
return reader
def _padding_batch(self, batch, padding_to=-1, flatten=False):
"""
Padding audio features with zeros to make them have the same shape (or
a user-defined shape) within one bach.
If ``padding_to`` is -1, the maximun shape in the batch will be used
as the target shape for padding. Otherwise, `padding_to` will be the
target shape (only refers to the second axis).
If `flatten` is True, features will be flatten to 1darray.
"""
new_batch = []
# get target shape
max_length = max([audio.shape[1] for audio, text in batch])
if padding_to != -1:
if padding_to < max_length:
raise ValueError(
"If padding_to is not -1, it should be larger "
"than any instance's shape in the batch")
max_length = padding_to
# padding
padded_audios = []
texts, text_lens = [], []
audio_lens = []
masks = []
for audio, text in batch:
padded_audio = np.zeros([audio.shape[0], max_length])
padded_audio[:, :audio.shape[1]] = audio
if flatten:
padded_audio = padded_audio.flatten()
padded_audios.append(padded_audio)
if self._is_training:
texts += text
else:
texts.append(text)
text_lens.append(len(text))
audio_lens.append(audio.shape[1])
mask_shape0 = (audio.shape[0] - 1) // 2 + 1
mask_shape1 = (audio.shape[1] - 1) // 3 + 1
mask_max_len = (max_length - 1) // 3 + 1
mask_ones = np.ones((mask_shape0, mask_shape1))
mask_zeros = np.zeros((mask_shape0, mask_max_len - mask_shape1))
mask = np.repeat(np.reshape(
np.concatenate((mask_ones, mask_zeros), axis=1),
(1, mask_shape0, mask_max_len)),
32,
axis=0)
masks.append(mask)
padded_audios = np.array(padded_audios).astype('float32')
if self._is_training:
texts = np.expand_dims(np.array(texts).astype('int32'), axis=-1)
texts = fluid.create_lod_tensor(texts,
recursive_seq_lens=[text_lens],
place=self._place)
audio_lens = np.array(audio_lens).astype('int64').reshape([-1, 1])
masks = np.array(masks).astype('float32')
return padded_audios, texts, audio_lens, masks
def _batch_shuffle(self, manifest, batch_size, clipped=False):
"""Put similarly-sized instances into minibatches for better efficiency
and make a batch-wise shuffle.
1. Sort the audio clips by duration.
2. Generate a random number `k`, k in [0, batch_size).
3. Randomly shift `k` instances in order to create different batches
for different epochs. Create minibatches.
4. Shuffle the minibatches.
:param manifest: Manifest contents. List of dict.
:type manifest: list
:param batch_size: Batch size. This size is also used for generate
a random number for batch shuffle.
:type batch_size: int
:param clipped: Whether to clip the heading (small shift) and trailing
(incomplete batch) instances.
:type clipped: bool
:return: Batch shuffled mainifest.
:rtype: list
"""
manifest.sort(key=lambda x: x["duration"])
shift_len = self._rng.randint(0, batch_size - 1)
batch_manifest = zip(*[iter(manifest[shift_len:])] * batch_size)
self._rng.shuffle(batch_manifest)
batch_manifest = [item for batch in batch_manifest for item in batch]
if clipped:
res_len = len(manifest) - shift_len - len(batch_manifest)
batch_manifest.extend(manifest[-res_len:])
batch_manifest.extend(manifest[0:shift_len])
return batch_manifest
class SpecgramGenerator(DynamicLengthGenerator):
"""audio specgram generator"""
def __init__(self,
manifest,
vocab_filepath,
mean_std_filepath,
augmentation_config='{}',
max_duration=float('inf'),
min_duration=0.0,
stride_ms=10.0,
window_ms=20.0,
max_freq=None,
specgram_type='linear',
use_dB_normalization=True,
random_seed=0,
keep_transcription_text=False,
segmented=False):
self._max_duration = max_duration
self._min_duration = min_duration
self._segmented = segmented
self._keep_transcription_text = keep_transcription_text
if isinstance(manifest, str) and os.path.isfile(manifest):
self.manifest = pd.read_csv(manifest)
elif isinstance(manifest, pd.DataFrame):
self.manifest = manifest
else:
raise BaseException(
"{} is neither an valide path or a pandas DataFrame object".
format(manifest))
# duration filtering
self.manifest = self.manifest[
(self.manifest.duration >= self._min_duration)
& (self.manifest.duration <= self._max_duration)]
self.manifest = self.manifest.sort_values(by=["duration"],
ascending=True)
self._normalizer = FeatureNormalizer(mean_std_filepath)
self._augmentation_pipeline = AugmentationPipeline(
augmentation_config=augmentation_config, random_seed=random_seed)
self._speech_featurizer = SpeechFeaturizer(
vocab_filepath=vocab_filepath,
specgram_type=specgram_type,
stride_ms=stride_ms,
window_ms=window_ms,
max_freq=max_freq,
use_dB_normalization=use_dB_normalization)
def __len__(self):
return len(self.manifest)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
instance = self.manifest.iloc[idx]
if self._segmented is True:
specgram, transcript = self.process_utterance(
instance["audio_path"], instance["text"], segments_info=None)
else:
specgram, transcript = self.process_utterance(
instance["audio_path"],
instance["text"],
segments_info={
"start": instance["st"],
"end": instance["et"]
})
uttid = instance["uttid"]
sample = {
"uttid": uttid,
"specgrams": specgram,
"text": transcript,
"trans": instance["text"]
}
return sample
def process_utterance(self,
audio_file,
transcript,
uttid=None,
segments_info=None):
"""Load, augment, featurize and normalize for speech data.
:param audio_file: Filepath or file object of audio file.
:type audio_file: basestring | file
:param transcript: Transcription text.
:type transcript: basestring
: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)
"""
if isinstance(audio_file, str) and audio_file.startswith('tar:'):
speech_segment = SpeechSegment.from_file(
self._subfile_from_tar(audio_file), transcript)
elif segments_info is None:
speech_segment = SpeechSegment.from_file(audio_file, transcript)
else:
speech_segment = SpeechSegment.slice_from_file(
audio_file, transcript, **segments_info)
# augment speech. i.e. add noise, speedup
self._augmentation_pipeline.transform_audio(speech_segment)
specgram, transcript_part = self._speech_featurizer.featurize(
speech_segment, self._keep_transcription_text)
specgram = self._normalizer.apply(specgram)
return specgram, transcript_part
class DataGenerator(object):
"""
DataGenerator provides basic audio data preprocessing pipeline, and offers
data reader interfaces of PaddlePaddle requirements.
:param vocab_filepath: Vocabulary filepath for indexing tokenized
transcripts.
:type vocab_filepath: str
:param mean_std_filepath: File containing the pre-computed mean and stddev.
:type mean_std_filepath: None|str
:param augmentation_config: Augmentation configuration in json string.
Details see AugmentationPipeline.__doc__.
:type augmentation_config: str
:param max_duration: Audio with duration (in seconds) greater than
this will be discarded.
:type max_duration: float
:param min_duration: Audio with duration (in seconds) smaller than
this will be discarded.
:type min_duration: float
:param stride_ms: Striding size (in milliseconds) for generating frames.
:type stride_ms: float
:param window_ms: Window size (in milliseconds) for generating frames.
:type window_ms: float
:param max_freq: Used when specgram_type is 'linear', only FFT bins
corresponding to frequencies between [0, max_freq] are
returned.
:types max_freq: None|float
:param specgram_type: Specgram feature type. Options: 'linear'.
:type specgram_type: str
:param use_dB_normalization: Whether to normalize the audio to -20 dB
before extracting the features.
:type use_dB_normalization: bool
:param random_seed: Random seed.
:type random_seed: int
:param keep_transcription_text: If set to True, transcription text will
be passed forward directly without
converting to index sequence.
:type keep_transcription_text: bool
:param place: The place to run the program.
:type place: CPUPlace or CUDAPlace
:param is_training: If set to True, generate text data for training,
otherwise, generate text data for infer.
:type is_training: bool
"""
def __init__(self,
vocab_filepath,
mean_std_filepath,
augmentation_config='{}',
max_duration=float('inf'),
min_duration=0.0,
stride_ms=10.0,
window_ms=20.0,
max_freq=None,
specgram_type='linear',
use_dB_normalization=True,
random_seed=0,
keep_transcription_text=False,
place=fluid.CPUPlace(),
is_training=True):
self._max_duration = max_duration
self._min_duration = min_duration
self._normalizer = FeatureNormalizer(mean_std_filepath)
self._augmentation_pipeline = AugmentationPipeline(
augmentation_config=augmentation_config, random_seed=random_seed)
self._speech_featurizer = SpeechFeaturizer(
vocab_filepath=vocab_filepath,
specgram_type=specgram_type,
stride_ms=stride_ms,
window_ms=window_ms,
max_freq=max_freq,
use_dB_normalization=use_dB_normalization)
self._rng = random.Random(random_seed)
self._keep_transcription_text = keep_transcription_text
self._epoch = 0
self._is_training = is_training
# for caching tar files info
self._local_data = local()
self._local_data.tar2info = {}
self._local_data.tar2object = {}
self._place = place
def process_utterance(self, audio_file, transcript):
"""对语音数据加载、扩充、特征化和归一化
:param audio_file: 音频文件的文件路径或文件对象
:type audio_file: str | file
:param transcript: 音频对应的文本
:type transcript: str
:return: 经过归一化等预处理的音频数据，音频文件对应文本的ID
:rtype: tuple of (2darray, list)
"""
speech_segment = SpeechSegment.from_file(audio_file, transcript)
self._augmentation_pipeline.transform_audio(speech_segment)
specgram, transcript_part = self._speech_featurizer.featurize(
speech_segment, self._keep_transcription_text)
specgram = self._normalizer.apply(specgram)
return specgram, transcript_part
def batch_reader_creator(self,
manifest_path,
batch_size,
padding_to=-1,
flatten=False,
shuffle_method="batch_shuffle"):
"""
Batch data reader creator for audio data. Return a callable generator
function to produce batches of data.
Audio features within one batch will be padded with zeros to have the
same shape, or a user-defined shape.
:param manifest_path: Filepath of manifest for audio files.
:type manifest_path: str
:param batch_size: Number of instances in a batch.
:type batch_size: int
:param padding_to: If set -1, the maximun shape in the batch
will be used as the target shape for padding.
Otherwise, `padding_to` will be the target shape.
:type padding_to: int
:param flatten: If set True, audio features will be flatten to 1darray.
:type flatten: bool
:param shuffle_method: Shuffle method. Options:
'' or None: no shuffle.
'instance_shuffle': instance-wise shuffle.
'batch_shuffle': similarly-sized instances are
put into batches, and then
batch-wise shuffle the batches.
For more details, please see
``_batch_shuffle.__doc__``.
'batch_shuffle_clipped': 'batch_shuffle' with
head shift and tail
clipping. For more
details, please see
``_batch_shuffle``.
If sortagrad is True, shuffle is disabled
for the first epoch.
:type shuffle_method: None|str
:return: Batch reader function, producing batches of data when called.
:rtype: callable
"""
def batch_reader():
# 读取数据列表
manifest = read_manifest(manifest_path=manifest_path,
max_duration=self._max_duration,
min_duration=self._min_duration)
# 将数据列表长到短排序
if self._epoch == 0:
manifest.sort(key=lambda x: x["duration"])
manifest.reverse()
else:
if shuffle_method == "batch_shuffle":
manifest = self._batch_shuffle(manifest,
batch_size,
clipped=False)
elif shuffle_method == "batch_shuffle_clipped":
manifest = self._batch_shuffle(manifest,
batch_size,
clipped=True)
elif shuffle_method == "instance_shuffle":
self._rng.shuffle(manifest)
elif shuffle_method is None:
pass
else:
raise ValueError("Unknown shuffle method %s." %
shuffle_method)
# 准备批量数据
batch = []
instance_reader = self._instance_reader_creator(manifest)
for instance in instance_reader():
batch.append(instance)
if len(batch) == batch_size:
yield self._padding_batch(batch, padding_to, flatten)
batch = []
if len(batch) >= 1:
yield self._padding_batch(batch, padding_to, flatten)
self._epoch += 1
return batch_reader
@property
def feeding(self):
"""返回数据读取器的exe读取字典
:return: 数据读取字典
:rtype: dict
"""
feeding_dict = {"audio_spectrogram": 0, "transcript_text": 1}
return feeding_dict
@property
def vocab_size(self):
"""返回词汇表大小
:return: 词汇表大小
:rtype: int
"""
return self._speech_featurizer.vocab_size
@property
def vocab_list(self):
"""返回词汇表列表
:return: 词汇表列表
:rtype: list
"""
return self._speech_featurizer.vocab_list
def _instance_reader_creator(self, manifest):
"""
创建一个数据生成器reader
Instance: 生成器得到的数据是一个元组，包含了经过预处理音频数据和音频对应文本的ID
"""
def reader():
for instance in manifest:
inst = self.process_utterance(instance["audio_filepath"],
instance["text"])
yield inst
return reader
def _padding_batch(self, batch, padding_to=-1, flatten=False):
"""
用零填充音频功能，使它们在同一个batch具有相同的形状(或一个用户定义的形状)
如果padding_to为-1，则批处理中的最大形状将被使用 作为填充的目标形状。
否则，' padding_to '将是目标形状(仅指第二轴)。
如果“flatten”为True，特征将被flatten为一维数据
"""
# 获取目标形状
max_length = max([audio.shape[1] for audio, text in batch])
if padding_to != -1:
if padding_to < max_length:
raise ValueError("如果padding_to不是-1，它应该大于批处理中任何实例的形状")
max_length = padding_to
# 填充操作
padded_audios = []
texts, text_lens = [], []
audio_lens = []
masks = []
for audio, text in batch:
padded_audio = np.zeros([audio.shape[0], max_length])
padded_audio[:, :audio.shape[1]] = audio
if flatten:
padded_audio = padded_audio.flatten()
padded_audios.append(padded_audio)
if self._is_training:
texts += text
else:
texts.append(text)
text_lens.append(len(text))
audio_lens.append(audio.shape[1])
mask_shape0 = (audio.shape[0] - 1) // 2 + 1
mask_shape1 = (audio.shape[1] - 1) // 3 + 1
mask_max_len = (max_length - 1) // 3 + 1
mask_ones = np.ones((mask_shape0, mask_shape1))
mask_zeros = np.zeros((mask_shape0, mask_max_len - mask_shape1))
mask = np.repeat(np.reshape(
np.concatenate((mask_ones, mask_zeros), axis=1),
(1, mask_shape0, mask_max_len)),
32,
axis=0)
masks.append(mask)
padded_audios = np.array(padded_audios).astype('float32')
if self._is_training:
texts = np.expand_dims(np.array(texts).astype('int32'), axis=-1)
texts = fluid.create_lod_tensor(texts,
recursive_seq_lens=[text_lens],
place=self._place)
audio_lens = np.array(audio_lens).astype('int64').reshape([-1, 1])
masks = np.array(masks).astype('float32')
return padded_audios, texts, audio_lens, masks
def _batch_shuffle(self, manifest, batch_size, clipped=False):
"""将大小相似的实例放入小批量中可以提高效率，并进行批量打乱
1. 按持续时间对音频剪辑进行排序
2. 生成一个随机数k， k的范围[0,batch_size)
3. 随机移动k实例，为不同的epoch训练创建不同的批次
4. 打乱minibatches.
:param manifest: 数据列表
:type manifest: list
:param batch_size: 批量大小。这个大小还用于为批量洗牌生成一个随机数。
:type batch_size: int
:param clipped: 是否剪辑头部(小移位)和尾部(不完整批处理)实例。
:type clipped: bool
:return: Batch shuffled mainifest.
:rtype: list
"""
manifest.sort(key=lambda x: x["duration"])
shift_len = self._rng.randint(0, batch_size - 1)
batch_manifest = list(zip(*[iter(manifest[shift_len:])] * batch_size))
self._rng.shuffle(batch_manifest)
batch_manifest = [item for batch in batch_manifest for item in batch]
if not clipped:
res_len = len(manifest) - shift_len - len(batch_manifest)
batch_manifest.extend(manifest[-res_len:])
batch_manifest.extend(manifest[0:shift_len])
return batch_manifest
'ctc_beam_search',
'结果解码方法',
choices=['ctc_beam_search', 'ctc_greedy'])
add_arg('lang_model_path', str, 'lm/zh_giga.no_cna_cmn.prune01244.klm',
"语言模型文件路径")
args = parser.parse_args()
print_arguments(args)
# 加载数据字典
with open(args.dataset_vocab, 'r', encoding='utf-8') as f:
labels = eval(f.read())
vocabulary = [labels[i] for i in range(len(labels))]
# 提取音频特征器和归一化器
audio_featurizer = AudioFeaturizer()
normalizer = FeatureNormalizer(mean_std_filepath=args.mean_std_path)
# 创建模型
model = DeepSpeech2Model(feat_size=audio_featurizer.feature_dim(),
dict_size=len(vocabulary),
num_conv_layers=args.num_conv_layers,
num_rnn_layers=args.num_rnn_layers,
rnn_size=args.rnn_layer_size)
model.set_state_dict(
paddle.load(os.path.join(args.model_path, 'model.pdparams')))
model.eval()
# 集束搜索方法的处理
if args.decoder == "ctc_beam_search":
try:
from decoders.beam_search_decoder import BeamSearchDecoder
class DataGenerator(object):
"""
DataGenerator provides basic audio data preprocessing pipeline, and offers
data reader interfaces of PaddlePaddle requirements.
:param vocab_filepath: Vocabulary filepath for indexing tokenized
transcripts.
:type vocab_filepath: basestring
:param mean_std_filepath: File containing the pre-computed mean and stddev.
:type mean_std_filepath: None|basestring
:param augmentation_config: Augmentation configuration in json string.
Details see AugmentationPipeline.__doc__.
:type augmentation_config: str
:param max_duration: Audio with duration (in seconds) greater than
this will be discarded.
:type max_duration: float
:param min_duration: Audio with duration (in seconds) smaller than
this will be discarded.
:type min_duration: float
:param stride_ms: Striding size (in milliseconds) for generating frames.
:type stride_ms: float
:param window_ms: Window size (in milliseconds) for generating frames.
:type window_ms: float
:param max_freq: Used when specgram_type is 'linear', only FFT bins
corresponding to frequencies between [0, max_freq] are
returned.
:types max_freq: None|float
:param specgram_type: Specgram feature type. Options: 'linear'.
:type specgram_type: str
:param use_dB_normalization: Whether to normalize the audio to -20 dB
before extracting the features.
:type use_dB_normalization: bool
:param num_threads: Number of CPU threads for processing data.
:type num_threads: int
:param random_seed: Random seed.
:type random_seed: int
"""
def __init__(self,
vocab_filepath,
mean_std_filepath,
augmentation_config='{}',
max_duration=float('inf'),
min_duration=0.0,
stride_ms=10.0,
window_ms=20.0,
max_freq=None,
specgram_type='linear',
use_dB_normalization=True,
num_threads=multiprocessing.cpu_count() // 2,
random_seed=0):
self._max_duration = max_duration
self._min_duration = min_duration
self._normalizer = FeatureNormalizer(mean_std_filepath)
self._augmentation_pipeline = AugmentationPipeline(
augmentation_config=augmentation_config, random_seed=random_seed)
self._speech_featurizer = SpeechFeaturizer(
vocab_filepath=vocab_filepath,
specgram_type=specgram_type,
stride_ms=stride_ms,
window_ms=window_ms,
max_freq=max_freq,
use_dB_normalization=use_dB_normalization)
self._num_threads = num_threads
self._rng = random.Random(random_seed)
self._epoch = 0
# for caching tar files info
self._local_data = local()
self._local_data.tar2info = {}
self._local_data.tar2object = {}
def process_utterance(self, filename, transcript):
"""Load, augment, featurize and normalize for speech data.
:param filename: Audio filepath
:type filename: basestring | file
:param transcript: Transcription text.
:type transcript: basestring
:return: Tuple of audio feature tensor and list of token ids for
transcription.
:rtype: tuple of (2darray, list)
"""
speech_segment = SpeechSegment.from_file(filename, transcript)
self._augmentation_pipeline.transform_audio(speech_segment)
specgram, text_ids = self._speech_featurizer.featurize(speech_segment)
specgram = self._normalizer.apply(specgram)
return specgram, text_ids
def batch_reader_creator(self,
manifest_path,
batch_size,
min_batch_size=1,
padding_to=-1,
flatten=False,
sortagrad=False,
shuffle_method="batch_shuffle"):
"""
Batch data reader creator for audio data. Return a callable generator
function to produce batches of data.
Audio features within one batch will be padded with zeros to have the
same shape, or a user-defined shape.
:param manifest_path: Filepath of manifest for audio files.
:type manifest_path: basestring
:param batch_size: Number of instances in a batch.
:type batch_size: int
:param min_batch_size: Any batch with batch size smaller than this will
be discarded. (To be deprecated in the future.)
:type min_batch_size: int
:param padding_to: If set -1, the maximun shape in the batch
will be used as the target shape for padding.
Otherwise, `padding_to` will be the target shape.
:type padding_to: int
:param flatten: If set True, audio features will be flatten to 1darray.
:type flatten: bool
:param sortagrad: If set True, sort the instances by audio duration
in the first epoch for speed up training.
:type sortagrad: bool
:param shuffle_method: Shuffle method. Options:
'' or None: no shuffle.
'instance_shuffle': instance-wise shuffle.
'batch_shuffle': similarly-sized instances are
put into batches, and then
batch-wise shuffle the batches.
For more details, please see
``_batch_shuffle.__doc__``.
'batch_shuffle_clipped': 'batch_shuffle' with
head shift and tail
clipping. For more
details, please see
``_batch_shuffle``.
If sortagrad is True, shuffle is disabled
for the first epoch.
:type shuffle_method: None|str
:return: Batch reader function, producing batches of data when called.
:rtype: callable
"""
def batch_reader():
# read manifest
manifest = read_manifest(manifest_path=manifest_path,
max_duration=self._max_duration,
min_duration=self._min_duration)
# sort (by duration) or batch-wise shuffle the manifest
if self._epoch == 0 and sortagrad:
manifest.sort(key=lambda x: x["duration"])
else:
if shuffle_method == "batch_shuffle":
manifest = self._batch_shuffle(manifest,
batch_size,
clipped=False)
elif shuffle_method == "batch_shuffle_clipped":
manifest = self._batch_shuffle(manifest,
batch_size,
clipped=True)
elif shuffle_method == "instance_shuffle":
self._rng.shuffle(manifest)
elif shuffle_method == None:
pass
else:
raise ValueError("Unknown shuffle method %s." %
shuffle_method)
# prepare batches
instance_reader = self._instance_reader_creator(manifest)
batch = []
for instance in instance_reader():
batch.append(instance)
if len(batch) == batch_size:
yield self._padding_batch(batch, padding_to, flatten)
batch = []
if len(batch) >= min_batch_size:
yield self._padding_batch(batch, padding_to, flatten)
self._epoch += 1
return batch_reader
@property
def feeding(self):
"""Returns data reader's feeding dict.
:return: Data feeding dict.
:rtype: dict
"""
return {"audio_spectrogram": 0, "transcript_text": 1}
@property
def vocab_size(self):
"""Return the vocabulary size.
:return: Vocabulary size.
:rtype: int
"""
return self._speech_featurizer.vocab_size
@property
def vocab_list(self):
"""Return the vocabulary in list.
:return: Vocabulary in list.
:rtype: list
"""
return self._speech_featurizer.vocab_list
def _parse_tar(self, file):
"""Parse a tar file to get a tarfile object
and a map containing tarinfoes
"""
result = {}
f = tarfile.open(file)
for tarinfo in f.getmembers():
result[tarinfo.name] = tarinfo
return f, result
def _get_file_object(self, file):
"""Get file object by file path.
If file startwith tar, it will return a tar file object
and cached tar file info for next reading request.
It will return file directly, if the type of file is not str.
"""
if file.startswith('tar:'):
tarpath, filename = file.split(':', 1)[1].split('#', 1)
if 'tar2info' not in self._local_data.__dict__:
self._local_data.tar2info = {}
if 'tar2object' not in self._local_data.__dict__:
self._local_data.tar2object = {}
if tarpath not in self._local_data.tar2info:
object, infoes = self._parse_tar(tarpath)
self._local_data.tar2info[tarpath] = infoes
self._local_data.tar2object[tarpath] = object
return self._local_data.tar2object[tarpath].extractfile(
self._local_data.tar2info[tarpath][filename])
else:
return open(file, 'r')
def _instance_reader_creator(self, manifest):
"""
Instance reader creator. Create a callable function to produce
instances of data.
Instance: a tuple of ndarray of audio spectrogram and a list of
token indices for transcript.
"""
def reader():
for instance in manifest:
yield instance
def mapper(instance):
return self.process_utterance(
self._get_file_object(instance["audio_filepath"]),
instance["text"])
return paddle.reader.xmap_readers(mapper,
reader,
self._num_threads,
1024,
order=True)
def _padding_batch(self, batch, padding_to=-1, flatten=False):
"""
Padding audio features with zeros to make them have the same shape (or
a user-defined shape) within one bach.
If ``padding_to`` is -1, the maximun shape in the batch will be used
as the target shape for padding. Otherwise, `padding_to` will be the
target shape (only refers to the second axis).
If `flatten` is True, features will be flatten to 1darray.
"""
new_batch = []
# get target shape
max_length = max([audio.shape[1] for audio, text in batch])
if padding_to != -1:
if padding_to < max_length:
raise ValueError(
"If padding_to is not -1, it should be larger "
"than any instance's shape in the batch")
max_length = padding_to
# padding
for audio, text in batch:
padded_audio = np.zeros([audio.shape[0], max_length])
padded_audio[:, :audio.shape[1]] = audio
if flatten:
padded_audio = padded_audio.flatten()
new_batch.append((padded_audio, text))
return new_batch
def _batch_shuffle(self, manifest, batch_size, clipped=False):
"""Put similarly-sized instances into minibatches for better efficiency
and make a batch-wise shuffle.
1. Sort the audio clips by duration.
2. Generate a random number `k`, k in [0, batch_size).
3. Randomly shift `k` instances in order to create different batches
for different epochs. Create minibatches.
4. Shuffle the minibatches.
:param manifest: Manifest contents. List of dict.
:type manifest: list
:param batch_size: Batch size. This size is also used for generate
a random number for batch shuffle.
:type batch_size: int
:param clipped: Whether to clip the heading (small shift) and trailing
(incomplete batch) instances.
:type clipped: bool
:return: Batch shuffled mainifest.
:rtype: list
"""
manifest.sort(key=lambda x: x["duration"])
shift_len = self._rng.randint(0, batch_size - 1)
batch_manifest = zip(*[iter(manifest[shift_len:])] * batch_size)
self._rng.shuffle(batch_manifest)
batch_manifest = list(sum(batch_manifest, ()))
if not clipped:
res_len = len(manifest) - shift_len - len(batch_manifest)
batch_manifest.extend(manifest[-res_len:])
batch_manifest.extend(manifest[0:shift_len])
return batch_manifest