Edit model card

wavlm-libri-clean-100h-base

This model is a fine-tuned version of microsoft/wavlm-base on the LIBRISPEECH_ASR - CLEAN dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0829
  • Wer: 0.0675

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0003
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • total_train_batch_size: 32
  • total_eval_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 3.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
2.8805 0.34 300 2.8686 1.0
0.2459 0.67 600 0.1858 0.1554
0.1114 1.01 900 0.1379 0.1191
0.0867 1.35 1200 0.1130 0.0961
0.0698 1.68 1500 0.1032 0.0877
0.0663 2.02 1800 0.0959 0.0785
0.0451 2.35 2100 0.0887 0.0748
0.0392 2.69 2400 0.0859 0.0698

Framework versions

  • Transformers 4.15.0.dev0
  • Pytorch 1.9.0+cu111
  • Datasets 1.16.2.dev0
  • Tokenizers 0.10.3
Downloads last month
832
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.