readme: ---
  language:
  - mr
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - mr
  - robust-speech-event
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: wav2vec2-large-xls-r-300m-mr-v2
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: mr
      metrics:
      - name: Test WER
        type: wer
        value: 0.49378259125551544
      - name: Test CER
        type: cer
        value: 0.12470799640610962
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: mr
      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-mr-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 - MR dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8729
  - Wer: 0.4942
  
  ### 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-mr-v2 --dataset mozilla-foundation/common_voice_8_0 --config mr --split test --log_outputs
  
  2. To evaluate on speech-recognition-community-v2/dev_data
  
  python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-mr-v2 --dataset speech-recognition-community-v2/dev_data --config mr --split validation --chunk_length_s 10 --stride_length_s 1
  
  Note: Marathi language not found in speech-recognition-community-v2/dev_data!
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.000333
  - 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: 1000
  - num_epochs: 200
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Wer    |
  |:-------------:|:------:|:----:|:---------------:|:------:|
  | 8.4934        | 9.09   | 200  | 3.7326          | 1.0    |
  | 3.4234        | 18.18  | 400  | 3.3383          | 0.9996 |
  | 3.2628        | 27.27  | 600  | 2.7482          | 0.9992 |
  | 1.7743        | 36.36  | 800  | 0.6755          | 0.6787 |
  | 1.0346        | 45.45  | 1000 | 0.6067          | 0.6193 |
  | 0.8137        | 54.55  | 1200 | 0.6228          | 0.5612 |
  | 0.6637        | 63.64  | 1400 | 0.5976          | 0.5495 |
  | 0.5563        | 72.73  | 1600 | 0.7009          | 0.5383 |
  | 0.4844        | 81.82  | 1800 | 0.6662          | 0.5287 |
  | 0.4057        | 90.91  | 2000 | 0.6911          | 0.5303 |
  | 0.3582        | 100.0  | 2200 | 0.7207          | 0.5327 |
  | 0.3163        | 109.09 | 2400 | 0.7107          | 0.5118 |
  | 0.2761        | 118.18 | 2600 | 0.7538          | 0.5118 |
  | 0.2415        | 127.27 | 2800 | 0.7850          | 0.5178 |
  | 0.2127        | 136.36 | 3000 | 0.8016          | 0.5034 |
  | 0.1873        | 145.45 | 3200 | 0.8302          | 0.5187 |
  | 0.1723        | 154.55 | 3400 | 0.9085          | 0.5223 |
  | 0.1498        | 163.64 | 3600 | 0.8396          | 0.5126 |
  | 0.1425        | 172.73 | 3800 | 0.8776          | 0.5094 |
  | 0.1258        | 181.82 | 4000 | 0.8651          | 0.5014 |
  | 0.117         | 190.91 | 4200 | 0.8772          | 0.4970 |
  | 0.1093        | 200.0  | 4400 | 0.8729          | 0.4942 |
  
  
  ### Framework versions
  
  - Transformers 4.16.1
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.2
  - Tokenizers 0.11.0
  
  