Fine-tuning of wav2vec2-base on 100h of Librispeech training data. Results on "clean" data are very similar to the ones of the official model. However, the result on "other" is significantly worse - the model seems to have overfitting to the "clean" data.

Model was trained on librispeech-clean-train.100 with following hyper-parameters:

• 2 GPUs Titan RTX
• Total update steps 13000
• Batch size per GPU: 32 corresponding to a total batch size of ca. ~1500 seconds
• Adam with linear decaying learning rate with 3000 warmup steps
• dynamic grouping for batch
• fp16
• attention_mask was not used during training

Check: https://wandb.ai/patrickvonplaten/huggingface/reports/Project-Dashboard--Vmlldzo1MDI2MTU?accessToken=69z0mrkoxs1msgh71p4nntr9shi6mll8rhtbo6c56yynygw0scp11d8z9o1xd0uk

Result (WER) on Librispeech test: