tensorbay.label.label_sentence

Word, LabeledSentence, SentenceSubcatalog.

SentenceSubcatalog defines the subcatalog for audio transcripted sentence type of labels.

Word is a word within a phonetic transcription sentence, containing the content of the word, the start and end time in the audio.

LabeledSentence is the transcripted sentence type of label. which is often used for tasks such as automatic speech recognition.

class tensorbay.label.label_sentence.SentenceSubcatalog(is_sample=False, sample_rate=None, lexicon=None)[source]

Bases: tensorbay.label.basic.SubcatalogBase, tensorbay.label.supports.AttributesMixin

This class defines the subcatalog for audio transcripted sentence type of labels.

Parameters

is_sample (bool) – A boolen value indicates whether time format is sample related.
sample_rate (int) – The number of samples of audio carried per second.
lexicon (List[List[str]]) – A list consists all of text and phone.

Return type

None

description

The description of the entire sentence subcatalog.

Type: str

is_sample

A boolen value indicates whether time format is sample related.

Type: bool

sample_rate

The number of samples of audio carried per second.

Type: int

lexicon

A list consists all of text and phone.

Type: List[List[str]]

attributes

All the possible attributes in the corresponding dataset stored in a NameList with the attribute names as keys and the AttributeInfo as values.

Type: tensorbay.utility.name.NameList[tensorbay.label.attributes.AttributeInfo]

Raises

TypeError – When sample_rate is None and is_sample is True.

Parameters

is_sample (bool) –
sample_rate (int) –
lexicon (List[List[str]]) –

Return type

None

Examples

Initialization Method 1: Init from SentenceSubcatalog.__init__().

>>> SentenceSubcatalog(True, 16000, [["mean", "m", "iy", "n"]])
SentenceSubcatalog(
  (is_sample): True,
  (sample_rate): 16000,
  (lexicon): [...]
)

Initialization Method 2: Init from SentenceSubcatalog.loads() method.

>>> contents = {
...     "isSample": True,
...     "sampleRate": 16000,
...     "lexicon": [["mean", "m", "iy", "n"]],
...     "attributes": [{"name": "gender", "enum": ["male", "female"]}],
... }
>>> SentenceSubcatalog.loads(contents)
SentenceSubcatalog(
  (is_sample): True,
  (sample_rate): 16000,
  (attributes): NameList [...],
  (lexicon): [...]
)

dumps()[source]

Dumps the information of this SentenceSubcatalog into a dict.

Returns: A dict containing all information of this SentenceSubcatalog.
Return type: Dict[str, Any]

Examples

>>> sentence_subcatalog = SentenceSubcatalog(True, 16000, [["mean", "m", "iy", "n"]])
>>> sentence_subcatalog.dumps()
{'isSample': True, 'sampleRate': 16000, 'lexicon': [['mean', 'm', 'iy', 'n']]}

append_lexicon(lexemes)[source]

Add lexemes to lexicon.

Parameters: lexemes (List[str]) – A list consists of text and phone.
Return type: None

Examples

>>> sentence_subcatalog = SentenceSubcatalog(True, 16000, [["mean", "m", "iy", "n"]])
>>> sentence_subcatalog.append_lexicon(["example"])
>>> sentence_subcatalog.lexicon
[['mean', 'm', 'iy', 'n'], ['example']]

class tensorbay.label.label_sentence.Word(text, begin=None, end=None)[source]

Bases: tensorbay.utility.repr.ReprMixin, tensorbay.utility.attr.AttrsMixin

This class defines the concept of word.

Word is a word within a phonetic transcription sentence, containing the content of the word, the start and end time in the audio.

Parameters

text (str) – The content of the word.
begin (float) – The begin time of the word in the audio.
end (float) – The end time of the word in the audio.

text

The content of the word.

Type: str

begin

The begin time of the word in the audio.

Type: float

end

The end time of the word in the audio.

Type: float

Examples

>>> Word(text="example", begin=1, end=2)
Word(
  (text): 'example',
  (begin): 1,
  (end): 2
)

classmethod loads(contents)[source]

Loads a Word from a dict containing the information of the word.

Parameters: contents (Dict[str, Union[str, float]]) – A dict containing the information of the word
Returns: The loaded Word object.
Return type: tensorbay.label.label_sentence._T

Examples

>>> contents = {"text": "Hello, World", "begin": 1, "end": 2}
>>> Word.loads(contents)
Word(
  (text): 'Hello, World',
  (begin): 1,
  (end): 2
)

dumps()[source]

Dumps the current word into a dict.

Returns: A dict containing all the information of the word
Return type: Dict[str, Union[str, float]]

Examples

>>> word = Word(text="example", begin=1, end=2)
>>> word.dumps()
{'text': 'example', 'begin': 1, 'end': 2}

class tensorbay.label.label_sentence.LabeledSentence(sentence=None, spell=None, phone=None, *, attributes=None)[source]

Bases: tensorbay.label.basic._LabelBase

This class defines the concept of phonetic transcription lable.

LabeledSentence is the transcripted sentence type of label. which is often used for tasks such as automatic speech recognition.

Parameters

sentence (List[tensorbay.label.label_sentence.Word]) – A list of sentence.
spell (List[tensorbay.label.label_sentence.Word]) – A list of spell, only exists in Chinese language.
phone (List[tensorbay.label.label_sentence.Word]) – A list of phone.
attributes (Dict[str, Union[str, int, float, bool, List[Union[str, int, float, bool]]]]) – The attributes of the label.

sentence

The transcripted sentence.

Type: List[tensorbay.label.label_sentence.Word]

spell

The spell within the sentence, only exists in Chinese language.

Type: List[tensorbay.label.label_sentence.Word]

phone

The phone of the sentence label.

Type: List[tensorbay.label.label_sentence.Word]

attributes

The attributes of the label.

Type: Dict[str, Union[str, int, float, bool, List[Union[str, int, float, bool]]]]

Examples

>>> sentence = [Word(text="qi1shi2", begin=1, end=2)]
>>> spell = [Word(text="qi1", begin=1, end=2)]
>>> phone = [Word(text="q", begin=1, end=2)]
>>> LabeledSentence(
...     sentence,
...     spell,
...     phone,
...     attributes={"key": "value"},
... )
LabeledSentence(
  (sentence): [
    Word(
      (text): 'qi1shi2',
      (begin): 1,
      (end): 2
    )
  ],
  (spell): [
    Word(
      (text): 'qi1',
      (begin): 1,
      (end): 2
    )
  ],
  (phone): [
    Word(
      (text): 'q',
      (begin): 1,
      (end): 2
    )
  ],
  (attributes): {
    'key': 'value'
  }
)

classmethod loads(contents)[source]

Loads a LabeledSentence from a dict containing the information of the label.

Parameters: contents (Dict[str, Any]) – A dict containing the information of the sentence label.
Returns: The loaded LabeledSentence object.
Return type: tensorbay.label.label_sentence._T

Examples

>>> contents = {
...     "sentence": [{"text": "qi1shi2", "begin": 1, "end": 2}],
...     "spell": [{"text": "qi1", "begin": 1, "end": 2}],
...     "phone": [{"text": "q", "begin": 1, "end": 2}],
...     "attributes": {"key": "value"},
... }
>>> LabeledSentence.loads(contents)
LabeledSentence(
  (sentence): [
    Word(
      (text): 'qi1shi2',
      (begin): 1,
      (end): 2
    )
  ],
  (spell): [
    Word(
      (text): 'qi1',
      (begin): 1,
      (end): 2
    )
  ],
  (phone): [
    Word(
      (text): 'q',
      (begin): 1,
      (end): 2
    )
  ],
  (attributes): {
    'key': 'value'
  }
)

dumps()[source]

Dumps the current label into a dict.

Returns: A dict containing all the information of the sentence label.
Return type: Dict[str, Any]

Examples

>>> sentence = [Word(text="qi1shi2", begin=1, end=2)]
>>> spell = [Word(text="qi1", begin=1, end=2)]
>>> phone = [Word(text="q", begin=1, end=2)]
>>> labeledsentence = LabeledSentence(
...     sentence,
...     spell,
...     phone,
...     attributes={"key": "value"},
... )
>>> labeledsentence.dumps()
{
    'attributes': {'key': 'value'},
    'sentence': [{'text': 'qi1shi2', 'begin': 1, 'end': 2}],
    'spell': [{'text': 'qi1', 'begin': 1, 'end': 2}],
    'phone': [{'text': 'q', 'begin': 1, 'end': 2}]
}