readme: ---
  license: apache-2.0
  language: as
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning
  - as
  - robust-speech-event
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_7_0
  model-index:
  - name: XLS-R-300M - Assamese
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 7
        type: mozilla-foundation/common_voice_7_0
        args: as
      metrics:
      - name: Test WER
        type: wer
        value: 72.64
      - name: Test CER
        type: cer
        value: 27.35
  ---
  
  # wav2vec2-large-xls-r-300m-assamese
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_7_0 dataset.
  It achieves the following results on the evaluation set:
  
  - WER: 0.7954545454545454
  - CER: 0.32341269841269843
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  To compute the evaluation parameters
  
  ```bash
  cd wav2vec2-large-xls-r-300m-assamese; python eval.py --model_id ./ --dataset mozilla-foundation/common_voice_7_0 --config as --split test --log_outputs
  ```
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  
  - learning_rate: 3e-4
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: not given
  - 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: 400
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Wer      |
  |:-------------:|:------:|:----:|:---------------:|:------:  |
  | 1.584065      | NA     | 400  | 1.584065        | 0.915512 |
  | 1.658865      | Na     | 800  | 1.658865        | 0.805096 |
  | 1.882352      | NA     | 1200 | 1.882352        | 0.820742 |
  | 1.881240      | NA     | 1600 | 1.881240        | 0.810907 |
  | 2.159748      | NA     | 2000 | 2.159748        | 0.804202 |
  | 1.992871      | NA     | 2400 | 1.992871        | 0.803308 |
  | 2.201436      | NA     | 2800 | 2.201436        | 0.802861 |
  | 2.165218      | NA     | 3200 | 2.165218        | 0.793920 |
  | 2.253643      | NA     | 3600 | 2.253643        | 0.796603 |
  | 2.265880      | NA     | 4000 | 2.265880        | 0.790344 |
  | 2.293935      | NA     | 4400 | 2.293935        | 0.797050 |
  | 2.288851      | NA     | 4800 | 2.288851        | 0.784086 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.10.0+cu113
  - Datasets 1.13.3
  - Tokenizers 0.10.3
  