# coding=utf-8
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""" Speech2Text model configuration """
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"facebook/s2t-small-librispeech-asr": "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json",
# See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}
[docs]class Speech2TextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.Speech2TextModel`. It is used
to instantiate an Speech2Text model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the Speech2Text
`facebook/s2t-small-librispeech-asr <https://huggingface.co/facebook/s2t-small-librispeech-asr>`__ architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
Args:
vocab_size (:obj:`int`, `optional`, defaults to 50265):
Vocabulary size of the Speech2Text model. Defines the number of different tokens that can be represented by
the :obj:`inputs_ids` passed when calling :class:`~transformers.Speech2TextModel`
d_model (:obj:`int`, `optional`, defaults to 1024):
Dimensionality of the layers and the pooler layer.
encoder_layers (:obj:`int`, `optional`, defaults to 12):
Number of encoder layers.
decoder_layers (:obj:`int`, `optional`, defaults to 12):
Number of decoder layers.
encoder_attention_heads (:obj:`int`, `optional`, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (:obj:`int`, `optional`, defaults to 16):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string,
:obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported.
dropout (:obj:`float`, `optional`, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (:obj:`float`, `optional`, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (:obj:`float`, `optional`, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
classifier_dropout (:obj:`float`, `optional`, defaults to 0.0):
The dropout ratio for classifier.
init_std (:obj:`float`, `optional`, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
encoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0):
The LayerDrop probability for the encoder. See the `LayerDrop paper <see
https://arxiv.org/abs/1909.11556>`__ for more details.
decoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0):
The LayerDrop probability for the decoder. See the `LayerDrop paper <see
https://arxiv.org/abs/1909.11556>`__ for more details.
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not the model should return the last key/values attentions (not used by all models).
max_source_positions (:obj:`int`, `optional`, defaults to 6000):
The maximum sequence length of log-mel filter-bank features that this model might ever be used with.
max_target_positions: (:obj:`int`, `optional`, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
num_conv_layers (:obj:`int`, `optional`, defaults to 2):
Number of 1D convolutional layers in the conv module.
conv_kernel_sizes (:obj:`Tuple[int]`, `optional`, defaults to :obj:`(5, 5)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the conv module. The length
of :obj:`conv_kernel_sizes` has to match :obj:`num_conv_layers`.
conv_channels (:obj:`int`, `optional`, defaults to 1024):
An integer defining the number of output channels of each convolution layers except the final one in the
conv module.
input_feat_per_channel (:obj:`int`, `optional`, defaults to 80):
An integer specifying the size of feature vector. This is also the dimensions of log-mel filter-bank
features.
input_channels (:obj:`int`, `optional`, defaults to 1):
An integer specifying number of input channels of the input feature vector.
Example::
>>> from transformers import Speech2TextModel, Speech2TextConfig
>>> # Initializing a Speech2Text s2t_transformer_s style configuration
>>> configuration = Speech2TextConfig()
>>> # Initializing a model from the s2t_transformer_s style configuration
>>> model = Speech2TextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
"""
model_type = "speech_to_text"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=10000,
encoder_layers=12,
encoder_ffn_dim=2048,
encoder_attention_heads=4,
decoder_layers=6,
decoder_ffn_dim=2048,
decoder_attention_heads=4,
encoder_layerdrop=0.0,
decoder_layerdrop=0.0,
use_cache=True,
is_encoder_decoder=True,
activation_function="relu",
d_model=256,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
init_std=0.02,
decoder_start_token_id=2,
classifier_dropout=0.0,
scale_embedding=True,
gradient_checkpointing=False,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
max_source_positions=6000,
max_target_positions=1024,
num_conv_layers=2,
conv_kernel_sizes=(5, 5),
conv_channels=1024,
input_feat_per_channel=80,
input_channels=1,
**kwargs
):
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
is_encoder_decoder=is_encoder_decoder,
decoder_start_token_id=decoder_start_token_id,
**kwargs,
)
self.vocab_size = vocab_size
self.d_model = d_model
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.init_std = init_std
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.classifier_dropout = classifier_dropout
self.use_cache = use_cache
self.num_hidden_layers = encoder_layers
self.gradient_checkpointing = gradient_checkpointing
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
self.max_source_positions = max_source_positions
self.max_target_positions = max_target_positions
self.num_conv_layers = num_conv_layers
self.conv_kernel_sizes = list(conv_kernel_sizes)
self.conv_channels = conv_channels
self.input_feat_per_channel = input_feat_per_channel
self.input_channels = input_channels
if len(self.conv_kernel_sizes) != self.num_conv_layers:
raise ValueError(
"Configuration for convolutional module is incorrect."
"It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers`"
f"but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes)}`,"
f"`config.num_conv_layers = {self.num_conv_layers}`."
)
@property
def num_attention_heads(self) -> int:
return self.encoder_attention_heads
@property
def hidden_size(self) -> int:
return self.d_model