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-v2
    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: 0.4654228855721393
      - name: Test CER
        type: cer
        value: 0.11351049990708047
    - 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-v2
  
  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: 0.5328
  - Wer: 0.4596
  
  ### 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-v2 --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) 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    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 8.5979        | 3.23  | 100  | 3.5602          | 1.0    |
  | 3.303         | 6.45  | 200  | 3.2238          | 1.0    |
  | 3.2034        | 9.68  | 300  | 3.2002          | 0.9888 |
  | 2.7986        | 12.9  | 400  | 1.2408          | 0.9210 |
  | 1.3869        | 16.13 | 500  | 0.7973          | 0.7462 |
  | 1.0228        | 19.35 | 600  | 0.6722          | 0.6788 |
  | 0.8311        | 22.58 | 700  | 0.6100          | 0.6150 |
  | 0.717         | 25.81 | 800  | 0.6236          | 0.6013 |
  | 0.6264        | 29.03 | 900  | 0.6031          | 0.5575 |
  | 0.5494        | 32.26 | 1000 | 0.5656          | 0.5309 |
  | 0.4781        | 35.48 | 1100 | 0.5289          | 0.4996 |
  | 0.4311        | 38.71 | 1200 | 0.5375          | 0.4768 |
  | 0.3902        | 41.94 | 1300 | 0.5246          | 0.4703 |
  | 0.3508        | 45.16 | 1400 | 0.5382          | 0.4696 |
  | 0.3199        | 48.39 | 1500 | 0.5328          | 0.4596 |
  
  
  ### Framework versions
  
  - Transformers 4.16.1
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.2
  - Tokenizers 0.11.0
   