readme: ---
  language:
  - hsb
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - hsb
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: wav2vec2-large-xls-r-300m-hsb-v3
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: hsb
      metrics:
      - name: Test WER
        type: wer
        value: 0.4763681592039801
      - name: Test CER
        type: cer
        value: 0.11194945177476305
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: hsb
      metrics:
      - name: Test WER
        type: wer
        value: NA
      - name: Test CER
        type: cer
        value: NA
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # wav2vec2-large-xls-r-300m-hsb-v3
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HSB dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6549
  - Wer: 0.4827
  
  ### Evaluation Commands
  
  1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
  
  python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v3 --dataset mozilla-foundation/common_voice_8_0 --config hsb --split test --log_outputs
  
  2. To evaluate on speech-recognition-community-v2/dev_data
  
  Upper Sorbian (hsb) language not found in speech-recognition-community-v2/dev_data!
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.00045
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_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: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 8.8951        | 3.23  | 100  | 3.6396          | 1.0    |
  | 3.314         | 6.45  | 200  | 3.2331          | 1.0    |
  | 3.1931        | 9.68  | 300  | 3.0947          | 0.9906 |
  | 1.7079        | 12.9  | 400  | 0.8865          | 0.8499 |
  | 0.6859        | 16.13 | 500  | 0.7994          | 0.7529 |
  | 0.4804        | 19.35 | 600  | 0.7783          | 0.7069 |
  | 0.3506        | 22.58 | 700  | 0.6904          | 0.6321 |
  | 0.2695        | 25.81 | 800  | 0.6519          | 0.5926 |
  | 0.222         | 29.03 | 900  | 0.7041          | 0.5720 |
  | 0.1828        | 32.26 | 1000 | 0.6608          | 0.5513 |
  | 0.1474        | 35.48 | 1100 | 0.7129          | 0.5319 |
  | 0.1269        | 38.71 | 1200 | 0.6664          | 0.5056 |
  | 0.1077        | 41.94 | 1300 | 0.6712          | 0.4942 |
  | 0.0934        | 45.16 | 1400 | 0.6467          | 0.4879 |
  | 0.0819        | 48.39 | 1500 | 0.6549          | 0.4827 |
  
  
  ### Framework versions
  
  - Transformers 4.16.1
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.2
  - Tokenizers 0.11.0
  