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