WhisperTokenizer

• Original Link : https://keras.io/api/keras_hub/models/whisper/whisper_tokenizer/
• Last Checked at : 2024-11-26

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WhisperTokenizer class

keras_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

• vocabulary: string or dict, maps token to integer ids. If it is a string, it should be the file path to a json file.
• merges: string or list, contains the merge rule. If it is a string, it should be the file path to merge rules. The merge rule file should have one merge rule per line. Every merge rule contains merge entities separated by a space.
• special_tokens: string or dict, maps special tokens to integer IDs. If it is a string, it should be the path to a JSON file.
• language_tokens: string or dict, maps language tokens to integer IDs. If not None, the tokenizer will be assumed to be a multilingual tokenizer.

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from_preset method

WhisperTokenizer.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:

1. a built-in preset identifier like 'bert_base_en'
2. a Kaggle Models handle like 'kaggle://user/bert/keras/bert_base_en'
3. a Hugging Face handle like 'hf://user/bert_base_en'
4. a path to a local preset directory like './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

• preset: string. A built-in preset identifier, a Kaggle Models handle, a Hugging Face handle, or a path to a local directory.
• load_weights: bool. If 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])