Ansh9728/distilhubert-finetuned-gtzan

Model Details

Model Description

DistilHuBERT by NTU Speech Processing & Machine Learning Lab

The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.

Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model.

Model Architecture and Objective

HubertForSequenceClassification( (hubert): HubertModel( (feature_extractor): HubertFeatureEncoder( (conv_layers): ModuleList( (0): HubertGroupNormConvLayer( (conv): Conv1d(1, 512, kernel_size=(10,), stride=(5,), bias=False) (activation): GELUActivation() (layer_norm): GroupNorm(512, 512, eps=1e-05, affine=True) ) (1-4): 4 x HubertNoLayerNormConvLayer( (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) (activation): GELUActivation() ) (5-6): 2 x HubertNoLayerNormConvLayer( (conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) (activation): GELUActivation() ) ) ) (feature_projection): HubertFeatureProjection( (projection): Linear(in_features=512, out_features=768, bias=True) (dropout): Dropout(p=0.0, inplace=False) ) (encoder): HubertEncoder( (pos_conv_embed): HubertPositionalConvEmbedding( (conv): ParametrizedConv1d( 768, 768, kernel_size=(128,), stride=(1,), padding=(64,), groups=16 (parametrizations): ModuleDict( (weight): ParametrizationList( (0): _WeightNorm() ) ) ) (padding): HubertSamePadLayer() (activation): GELUActivation() ) (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) (layers): ModuleList( (0-1): 2 x HubertEncoderLayer( (attention): HubertSdpaAttention( (k_proj): Linear(in_features=768, out_features=768, bias=True) (v_proj): Linear(in_features=768, out_features=768, bias=True) (q_proj): Linear(in_features=768, out_features=768, bias=True) (out_proj): Linear(in_features=768, out_features=768, bias=True) ) (dropout): Dropout(p=0.1, inplace=False) (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (feed_forward): HubertFeedForward( (intermediate_dropout): Dropout(p=0.1, inplace=False) (intermediate_dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() (output_dense): Linear(in_features=3072, out_features=768, bias=True) (output_dropout): Dropout(p=0.1, inplace=False) ) (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) ) ) ) ) (projector): Linear(in_features=768, out_features=256, bias=True) (classifier): Linear(in_features=256, out_features=10, bias=True) )