| | """ |
| | MERT model configuration |
| | """ |
| |
|
| | import functools |
| | import operator |
| |
|
| | from transformers.configuration_utils import PretrainedConfig |
| | from transformers.utils import logging |
| |
|
| | logger = logging.get_logger(__name__) |
| |
|
| |
|
| |
|
| | class MERTConfig(PretrainedConfig): |
| | r""" |
| | """ |
| | model_type = "mert_model" |
| |
|
| | def __init__( |
| | self, |
| | vocab_size=32, |
| | hidden_size=768, |
| | num_hidden_layers=12, |
| | num_attention_heads=12, |
| | intermediate_size=3072, |
| | hidden_act="gelu", |
| | hidden_dropout=0.1, |
| | activation_dropout=0.1, |
| | attention_dropout=0.1, |
| | feat_proj_layer_norm=True, |
| | feat_proj_dropout=0.0, |
| | final_dropout=0.1, |
| | layerdrop=0.1, |
| | initializer_range=0.02, |
| | layer_norm_eps=1e-5, |
| | feat_extract_norm="group", |
| | feat_extract_activation="gelu", |
| | conv_dim=(512, 512, 512, 512, 512, 512, 512), |
| | conv_stride=(5, 2, 2, 2, 2, 2, 2), |
| | conv_kernel=(10, 3, 3, 3, 3, 2, 2), |
| | conv_bias=False, |
| | num_conv_pos_embeddings=128, |
| | num_conv_pos_embedding_groups=16, |
| | do_stable_layer_norm=False, |
| | apply_spec_augment=True, |
| | mask_time_prob=0.05, |
| | mask_time_length=10, |
| | mask_time_min_masks=2, |
| | mask_feature_prob=0.0, |
| | mask_feature_length=10, |
| | mask_feature_min_masks=0, |
| | ctc_loss_reduction="sum", |
| | ctc_zero_infinity=False, |
| | use_weighted_layer_sum=False, |
| | classifier_proj_size=256, |
| | pad_token_id=0, |
| | bos_token_id=1, |
| | eos_token_id=2, |
| | feature_extractor_cqt=False, |
| | feature_extractor_cqt_bins=336, |
| | deepnorm=False, |
| | attention_relax=-1.0, |
| | **kwargs |
| | ): |
| | super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id) |
| | self.hidden_size = hidden_size |
| | self.feat_extract_norm = feat_extract_norm |
| | self.feat_extract_activation = feat_extract_activation |
| | self.conv_dim = list(conv_dim) |
| | self.conv_stride = list(conv_stride) |
| | self.conv_kernel = list(conv_kernel) |
| | self.conv_bias = conv_bias |
| | self.num_conv_pos_embeddings = num_conv_pos_embeddings |
| | self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups |
| | self.num_feat_extract_layers = len(self.conv_dim) |
| | self.num_hidden_layers = num_hidden_layers |
| | self.intermediate_size = intermediate_size |
| | self.hidden_act = hidden_act |
| | self.num_attention_heads = num_attention_heads |
| | self.hidden_dropout = hidden_dropout |
| | self.attention_dropout = attention_dropout |
| | self.activation_dropout = activation_dropout |
| | self.feat_proj_layer_norm = feat_proj_layer_norm |
| | self.feat_proj_dropout = feat_proj_dropout |
| | self.final_dropout = final_dropout |
| | self.layerdrop = layerdrop |
| | self.layer_norm_eps = layer_norm_eps |
| | self.initializer_range = initializer_range |
| | self.vocab_size = vocab_size |
| | self.do_stable_layer_norm = do_stable_layer_norm |
| | self.use_weighted_layer_sum = use_weighted_layer_sum |
| | self.classifier_proj_size = classifier_proj_size |
| |
|
| | if ( |
| | (len(self.conv_stride) != self.num_feat_extract_layers) |
| | or (len(self.conv_kernel) != self.num_feat_extract_layers) |
| | or (len(self.conv_dim) != self.num_feat_extract_layers) |
| | ): |
| | raise ValueError( |
| | "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` ==" |
| | " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) =" |
| | f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`," |
| | f" `len(config.conv_kernel) = {len(self.conv_kernel)}`." |
| | ) |
| |
|
| | |
| | self.apply_spec_augment = apply_spec_augment |
| | self.mask_time_prob = mask_time_prob |
| | self.mask_time_length = mask_time_length |
| | self.mask_time_min_masks = mask_time_min_masks |
| | self.mask_feature_prob = mask_feature_prob |
| | self.mask_feature_length = mask_feature_length |
| | self.mask_feature_min_masks = mask_feature_min_masks |
| |
|
| | |
| | self.ctc_loss_reduction = ctc_loss_reduction |
| | self.ctc_zero_infinity = ctc_zero_infinity |
| |
|
| | |
| | self.feature_extractor_cqt = feature_extractor_cqt |
| | self.feature_extractor_cqt_bins = feature_extractor_cqt_bins |
| |
|
| | |
| | self.deepnorm = deepnorm |
| |
|
| | self.attention_relax = attention_relax |
| |
|
| | @property |
| | def inputs_to_logits_ratio(self): |
| | return functools.reduce(operator.mul, self.conv_stride, 1) |
| |
|
