readme: ---
  language:
  - or
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_7_0
  - or
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_7_0
  model-index:
  - name: XLS-R-300M - Odia
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 7
        type: mozilla-foundation/common_voice_7_0
        args: or
      metrics:
      - name: Test WER
        type: wer
        value: 97.91
      - name: Test CER
        type: cer
        value: 247.09
  ---
  
  <!-- 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-odia
  
  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_7_0 - OR dataset.
  It achieves the following results on the evaluation set:
  
  ```
  python eval.py --model_id ./ --dataset mozilla-foundation/common_voice_7_0 --config as --split test --log_outputs
  ```
  
  - WER: 1.0921052631578947
  - CER: 2.5547945205479454
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  Training machine details
  
  - Platform: Linux-5.11.0-37-generic-x86_64-with-glibc2.10
  - CPU cores: 60
  - Python version: 3.8.8
  - PyTorch version: 1.10.1+cu102
  - GPU is visible: True
  - Transformers version: 4.16.0.dev0
  - Datasets version: 1.17.1.dev0
  - soundfile version: 0.10.3
  
  Training script
  
  ```bash
  python run_speech_recognition_ctc.py \
  	--dataset_name="mozilla-foundation/common_voice_7_0" \
  	--model_name_or_path="facebook/wav2vec2-xls-r-300m" \
  	--dataset_config_name="or" \
  	--output_dir="./wav2vec2-large-xls-r-300m-odia" \
  	--overwrite_output_dir \
  	--num_train_epochs="120" \
  	--per_device_train_batch_size="16" \
  	--per_device_eval_batch_size="16" \
  	--gradient_accumulation_steps="2" \
  	--learning_rate="7.5e-5" \
  	--warmup_steps="500" \
  	--length_column_name="input_length" \
  	--evaluation_strategy="steps" \
  	--text_column_name="sentence" \
  	--chars_to_ignore , ? . ! \- \; \: \" “ % ‘ ” � — \’ … \– \' \’ \– \
  	--save_steps="500" \
  	--eval_steps="500" \
  	--logging_steps="100" \
  	--layerdrop="0.0" \
  	--activation_dropout="0.1" \
  	--save_total_limit="3" \
  	--freeze_feature_encoder \
  	--feat_proj_dropout="0.0" \
  	--mask_time_prob="0.75" \
  	--mask_time_length="10" \
  	--mask_feature_prob="0.25" \
  	--mask_feature_length="64" \
  	--gradient_checkpointing \
  	--use_auth_token \
  	--fp16 \
  	--group_by_length \
  	--do_train --do_eval \
    --push_to_hub
  ```
      
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 7.5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - 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: 120.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  |    |   eval_loss |   eval_wer |   eval_runtime |   eval_samples_per_second |   eval_steps_per_second |   epoch |
  |---:|------------:|-----------:|---------------:|--------------------------:|------------------------:|--------:|
  |  0 |    3.35224  |   0.998972 |         5.0475 |                    22.189 |                   1.387 |   29.41 |
  |  1 |    1.33679  |   0.938335 |         5.0633 |                    22.12  |                   1.382 |   58.82 |
  |  2 |    0.737202 |   0.957862 |         5.0913 |                    21.998 |                   1.375 |   88.24 |
  |  3 |    0.658212 |   0.96814  |         5.0953 |                    21.981 |                   1.374 |  117.65 |
  |  4 |    0.658    |   0.9712   |         5.0953 |                    22.115 |                   1.382 |  120    |
  
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.17.1.dev0
  - Tokenizers 0.11.0
  