readme: ---
  language:
  - nl
  tags:
  - automatic-speech-recognition
  - hf-asr-leaderboard
  - model_for_talk
  - mozilla-foundation/common_voice_8_0
  - nl
  - robust-speech-event
  - vl
  datasets:
  - mozilla-foundation/common_voice_8_0
  - multilingual_librispeech
  model-index:
  - name: xls-r-nl-v1-cv8-lm
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: nl
      metrics:
      - name: Test WER
        type: wer
        value: 6.69
      - name: Test CER
        type: cer
        value: 1.97
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: nl
      metrics:
      - name: Test WER
        type: wer
        value: 20.79
      - name: Test CER
        type: cer
        value: 10.72
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: nl
      metrics:
      - name: Test WER
        type: wer
        value: 19.71
  ---
  
  # XLS-R-based CTC model with 5-gram language model from Common Voice
  
  This model is a version of [facebook/wav2vec2-xls-r-2b-22-to-16](https://huggingface.co/facebook/wav2vec2-xls-r-2b-22-to-16) fine-tuned mainly on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a small 5-gram language model is added based on the Common Voice training corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):
  - Wer: 0.0669
  - Cer: 0.0197
  
  ## Model description
  
  The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the final result. 
  
  To improve accuracy, a beam decoder is used; the beams are scored based on 5-gram language model trained on the Common Voice 8 corpus.
  
  ## Intended uses & limitations
  
  This model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation).
  
  ## Training and evaluation data
  
  0. The model was initialized with [the 2B parameter model from Facebook](facebook/wav2vec2-xls-r-2b-22-to-16).
  1. The model was then trained `2000` iterations (batch size 32) on [the `dutch` configuration of the `multilingual_librispeech` dataset](https://huggingface.co/datasets/multilingual_librispeech/).
  1. The model was then trained `2000` iterations (batch size 32) on [the `nl` configuration of the `common_voice_8_0` dataset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0).
  2. The model was then trained `6000` iterations (batch size 32) on [the `cgn` dataset](https://taalmaterialen.ivdnt.org/download/tstc-corpus-gesproken-nederlands/).
  3. The model was then trained `6000` iterations (batch size 32) on [the `nl` configuation of the `common_voice_8_0` dataset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0).
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.2.dev0
  - Tokenizers 0.11.0
  