readme: ---
  language: en
  datasets:
  - librispeech_asr
  tags:
  - audio
  - automatic-speech-recognition
  license: apache-2.0
  widget:
  - example_title: IEMOCAP sample 1
    src: https://cdn-media.huggingface.co/speech_samples/IEMOCAP_Ses01F_impro03_F013.wav
  - example_title: IEMOCAP sample 2
    src: https://cdn-media.huggingface.co/speech_samples/IEMOCAP_Ses01F_impro04_F000.wav
  - example_title: LibriSpeech sample 1
    src: https://cdn-media.huggingface.co/speech_samples/LibriSpeech_61-70968-0000.flac
  - example_title: LibriSpeech sample 2
    src: https://cdn-media.huggingface.co/speech_samples/LibriSpeech_61-70968-0001.flac
  - example_title: VoxCeleb sample 1
    src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav
  - example_title: VoxCeleb sample 2
    src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb_00004.wav
  ---
  
  Second fine-tuning try of `wav2vec2-base`. Results are similar to the ones reported in https://huggingface.co/facebook/wav2vec2-base-100h.
  
  Model was trained on *librispeech-clean-train.100* with following hyper-parameters:
  
  - 2 GPUs Titan RTX
  - Total update steps 11000
  - Batch size per GPU: 32 corresponding to a *total batch size* of ca. ~750 seconds
  - Adam with linear decaying learning rate with 3000 warmup steps
  - dynamic padding for batch
  - fp16
  - attention_mask was **not** used during training
  
  Check: https://wandb.ai/patrickvonplaten/huggingface/runs/1yrpescx?workspace=user-patrickvonplaten
  
  *Result (WER)* on Librispeech:
  
  | "clean" (% rel difference to results in paper) | "other" (% rel difference to results in paper) |
  |---|---|
  | 6.2 (-1.6%) | 15.2 (-11.2%)|