readme: ---
  language:
  - mr
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_9_0
  - generated_from_trainer
  datasets:
  - mozilla-foundation/common_voice_9_0
  metrics:
  - wer
  model-index:
  - name: XLS-R-300M - Marathi
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: mozilla-foundation/common_voice_9_0
        name: Common Voice 9
        args: mr
      metrics:
      - type: wer
        value: 23.841
        name: Test WER
      - name: Test CER
        type: cer
        value: 5.522
  ---
  
  <!-- 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. -->
  
  # 
  
  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_9_0 - MR dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3642
  - Wer: 0.4190
  - Cer: 0.0946
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 7.5e-05
  - train_batch_size: 64
  - eval_batch_size: 64
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - training_steps: 6124
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Wer    | Cer    |
  |:-------------:|:------:|:----:|:---------------:|:------:|:------:|
  | 3.5184        | 12.9   | 400  | 3.4210          | 1.0    | 1.0    |
  | 2.3797        | 25.81  | 800  | 1.1068          | 0.8389 | 0.2584 |
  | 1.5022        | 38.71  | 1200 | 0.5278          | 0.6280 | 0.1517 |
  | 1.3181        | 51.61  | 1600 | 0.4254          | 0.5587 | 0.1297 |
  | 1.2037        | 64.52  | 2000 | 0.3836          | 0.5143 | 0.1176 |
  | 1.1245        | 77.42  | 2400 | 0.3643          | 0.4871 | 0.1111 |
  | 1.0582        | 90.32  | 2800 | 0.3562          | 0.4676 | 0.1062 |
  | 1.0027        | 103.23 | 3200 | 0.3530          | 0.4625 | 0.1058 |
  | 0.9382        | 116.13 | 3600 | 0.3388          | 0.4442 | 0.1002 |
  | 0.8915        | 129.03 | 4000 | 0.3430          | 0.4427 | 0.1000 |
  | 0.853         | 141.94 | 4400 | 0.3536          | 0.4375 | 0.1000 |
  | 0.8127        | 154.84 | 4800 | 0.3511          | 0.4344 | 0.0986 |
  | 0.7861        | 167.74 | 5200 | 0.3595          | 0.4372 | 0.0993 |
  | 0.7619        | 180.65 | 5600 | 0.3628          | 0.4316 | 0.0985 |
  | 0.7537        | 193.55 | 6000 | 0.3633          | 0.4174 | 0.0943 |
  
  
  ### Framework versions
  
  - Transformers 4.19.0.dev0
  - Pytorch 1.11.0+cu102
  - Datasets 2.1.1.dev0
  - Tokenizers 0.12.1
  