WhisperBackbone
classkeras_hub.models.WhisperBackbone(
vocabulary_size,
num_layers,
num_heads,
hidden_dim,
intermediate_dim,
num_mels=80,
dropout=0.0,
max_encoder_sequence_length=3000,
max_decoder_sequence_length=448,
dtype=None,
**kwargs
)
A Whisper encoder-decoder network for speech.
This class implements a Transformer-based encoder-decoder model as described in "Robust Speech Recognition via Large-Scale Weak Supervision". It includes the embedding lookups and transformer layers, but not the head for predicting the next token.
The default constructor gives a fully customizable, randomly initialized Whisper
model with any number of layers, heads, and embedding dimensions. To load
preset architectures and weights, use the from_preset()
constructor.
Disclaimer: Pre-trained models are provided on an "as is" basis, without warranties or conditions of any kind. The underlying model is provided by a third party and subject to a separate license, available here.
Arguments
80
.max_encoder_sequence_length // 2
as the sequence length for the
positional embedding layer.keras.mixed_precision.DTypePolicy
. The dtype to use
for model computations and weights. Note that some computations,
such as softmax and layer normalization, will always be done at
float32 precision regardless of dtype.Examples
input_data = {
"encoder_features": np.ones(shape=(1, 12, 80), dtype="int32"),
"decoder_token_ids": np.ones(shape=(1, 12), dtype="int32"),
"decoder_padding_mask": np.array(
[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]
),
}
# Randomly initialized Whisper encoder-decoder model with a custom config.
model = keras_hub.models.WhisperBackbone(
vocabulary_size=51864,
num_layers=4,
num_heads=4,
hidden_dim=256,
intermediate_dim=512,
max_encoder_sequence_length=128,
max_decoder_sequence_length=128,
)
model(input_data)
from_preset
methodWhisperBackbone.from_preset(preset, load_weights=True, **kwargs)
Instantiate a keras_hub.models.Backbone
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 a
one of:
'bert_base_en'
'kaggle://user/bert/keras/bert_base_en'
'hf://user/bert_base_en'
'./bert_base_en'
This constructor can be called in one of two ways. Either from the base
class like keras_hub.models.Backbone.from_preset()
, or from
a model class like keras_hub.models.GemmaBackbone.from_preset()
.
If calling from the base class, the subclass of the returning object
will be inferred from the config in the preset directory.
For any Backbone
subclass, you can run cls.presets.keys()
to list
all built-in presets available on the class.
Arguments
True
, the weights will be loaded into the
model architecture. If False
, the weights will be randomly
initialized.Examples
# Load a Gemma backbone with pre-trained weights.
model = keras_hub.models.Backbone.from_preset(
"gemma_2b_en",
)
# Load a Bert backbone with a pre-trained config and random weights.
model = keras_hub.models.Backbone.from_preset(
"bert_base_en",
load_weights=False,
)
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 . |