athena.data.datasets.speech_synthesis

audio dataset

Module Contents

Classes

SpeechSynthesisDatasetBuilder

SpeechSynthesisDatasetBuilder

class athena.data.datasets.speech_synthesis.SpeechSynthesisDatasetBuilder(config=None)

Bases: athena.data.datasets.base.BaseDatasetBuilder

SpeechSynthesisDatasetBuilder

default_config

num_class

@property

Returns:the max_index of the vocabulary Return type:int

speaker_list

return the speaker list

audio_featurizer_func

return the audio_featurizer function

feat_dim

return the number of feature dims

sample_type

@property

Returns:sample_type of the dataset:

{
    "input": tf.int32,
    "input_length": tf.int32,
    "output_length": tf.int32,
    "output": tf.float32,
    "speaker": tf.int32
}

Return type:dict

sample_shape

@property

Returns:sample_shape of the dataset:

{
    "input": tf.TensorShape([None]),
    "input_length": tf.TensorShape([]),
    "output_length": tf.TensorShape([]),
    "output": tf.TensorShape([None, feature_dim]),
    "speaker": tf.TensorShape([])
}

Return type:dict

sample_signature

@property

Returns:sample_signature of the dataset:

{
    "input": tf.TensorSpec(shape=(None, None), dtype=tf.int32),
    "input_length": tf.TensorSpec(shape=(None), dtype=tf.int32),
    "output_length": tf.TensorSpec(shape=(None), dtype=tf.int32),
    "output": tf.TensorSpec(shape=(None, None, feature_dim),
                            dtype=tf.float32),
    "speaker": tf.TensorSpec(shape=(None), dtype=tf.int32)
}

Return type:dict

reload_config(self, config)

reload the config

preprocess_data(self, file_path)

generate a list of tuples (wav_filename, wav_length_ms, transcript, speaker).

load_csv(self, file_path)

load csv file

__getitem__(self, index)

get a sample

Parameters:index (int) – index of the entries Returns:sample:

{
    "input": text,
    "input_length": text_length,
    "output_length": audio_feat_length,
    "output": audio_feat,
    "speaker": self.speakers_dict[speaker]
}

Return type:dict

__len__(self)

return the number of data samples

filter_sample_by_unk(self)

filter samples which contain unk

filter_sample_by_input_length(self)

filter samples by input length

The length of filterd samples will be in [min_length, max_length)

Returns:a filtered list of tuples (wav_filename, wav_len, transcript, speaker) Return type:entries

filter_sample_by_output_length(self)

filter samples by output length

The length of filterd samples will be in [min_length, max_length)

Returns:a filtered list of tuples (wav_filename, wav_len, transcripts, speaker) Return type:entries

compute_cmvn_if_necessary(self, is_necessary=True)

compute cmvn file