readme: ---
  language:
  - te
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - openslr
  - google/fleurs
  metrics:
  - wer
  model-index:
  - name: whisper-small-telugu-large-data
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: google/fleurs
        type: openslr
        config: te_in
        split: None
      metrics:
      - name: Wer
        type: wer
        value: 38.84604916991744
  ---
  
  <!-- 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. -->
  
  # whisper-small-telugu-large-data
  
  This [model](steja/whisper-small-telugu-large-data) is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs and openslr dataset in telugu.
  It achieves the following results on the evaluation set (google/fleurs, test set):
  - Loss: 0.3310
  - Wer: 38.8460
  
  [openai/whisper-small](https://huggingface.co/openai/whisper-small) has the following zero shot performance on google/fleurs test set:
  - Wer: 117.91
  
  ## 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: 1e-05
  - train_batch_size: 4
  - eval_batch_size: 16
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 4
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 32
  - total_eval_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - training_steps: 5000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer     |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|
  | 0.128         | 2.27  | 500  | 0.2015          | 45.1692 |
  | 0.0462        | 4.55  | 1000 | 0.1877          | 41.1050 |
  | 0.0184        | 6.82  | 1500 | 0.2241          | 40.5153 |
  | 0.0045        | 9.09  | 2000 | 0.2590          | 39.7260 |
  | 0.0019        | 11.36 | 2500 | 0.2824          | 39.0819 |
  | 0.0006        | 13.64 | 3000 | 0.3002          | 38.9096 |
  | 0.0002        | 15.91 | 3500 | 0.3141          | 38.5920 |
  | 0.0001        | 18.18 | 4000 | 0.3232          | 38.7463 |
  | 0.0001        | 20.45 | 4500 | 0.3289          | 38.8370 |
  | 0.0001        | 22.73 | 5000 | 0.3310          | 38.8460 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  