WhisperTokenizer
classkeras_hub.tokenizers.WhisperTokenizer(
vocabulary=None, merges=None, special_tokens=None, language_tokens=None, **kwargs
)
Whisper text tokenizer using Byte-Pair Encoding subword segmentation.
This tokenizer class will tokenize raw strings into integer sequences and
is based on keras_hub.tokenizers.BytePairTokenizer
.
This tokenizer does not provide truncation or padding of inputs.
Arguments
from_preset
methodWhisperTokenizer.from_preset(preset, config_file="tokenizer.json", **kwargs)
Instantiate a keras_hub.models.Tokenizer
from a model preset.
A preset is a directory of configs, weights and other file assets used
to save and load a pre-trained model. The preset
can be passed as
one of:
'bert_base_en'
'kaggle://user/bert/keras/bert_base_en'
'hf://user/bert_base_en'
'./bert_base_en'
For any Tokenizer
subclass, you can run cls.presets.keys()
to list
all built-in presets available on the class.
This constructor can be called in one of two ways. Either from the base
class like keras_hub.models.Tokenizer.from_preset()
, or from
a model class like keras_hub.models.GemmaTokenizer.from_preset()
.
If calling from the base class, the subclass of the returning object
will be inferred from the config in the preset directory.
Arguments
True
, the weights will be loaded into the
model architecture. If False
, the weights will be randomly
initialized.Examples
# Load a preset tokenizer.
tokenizer = keras_hub.tokenizer.Tokenizer.from_preset("bert_base_en")
# Tokenize some input.
tokenizer("The quick brown fox tripped.")
# Detokenize some input.
tokenizer.detokenize([5, 6, 7, 8, 9])
Preset name | Parameters | Description |
---|---|---|
whisper_tiny_en | 37.18M | 4-layer Whisper model. Trained on 438,000 hours of labelled English speech data. |
whisper_base_en | 124.44M | 6-layer Whisper model. Trained on 438,000 hours of labelled English speech data. |
whisper_small_en | 241.73M | 12-layer Whisper model. Trained on 438,000 hours of labelled English speech data. |
whisper_medium_en | 763.86M | 24-layer Whisper model. Trained on 438,000 hours of labelled English speech data. |
whisper_tiny_multi | 37.76M | 4-layer Whisper model. Trained on 680,000 hours of labelled multilingual speech data. |
whisper_base_multi | 72.59M | 6-layer Whisper model. Trained on 680,000 hours of labelled multilingual speech data. |
whisper_small_multi | 241.73M | 12-layer Whisper model. Trained on 680,000 hours of labelled multilingual speech data. |
whisper_medium_multi | 763.86M | 24-layer Whisper model. Trained on 680,000 hours of labelled multilingual speech data. |
whisper_large_multi | 1.54B | 32-layer Whisper model. Trained on 680,000 hours of labelled multilingual speech data. |
whisper_large_multi_v2 | 1.54B | 32-layer Whisper model. Trained for 2.5 epochs on 680,000 hours of labelled multilingual speech data. An improved of whisper_large_multi . |