readme: 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](https://huggingface.co/facebook/wav2vec2-base-100h). 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:
  
  | "clean" | "other" |
  |---|---|
  | 6.5 | 18.7 |