readme: ---
  language:
  - es
  license: apache-2.0
  tags:
  - hf-asr-leaderboard
  - generated_from_trainer
  datasets:
  - facebook/multilingual_librispeech
  metrics:
  - wer
  model-index:
  - name: Whisper Small Es - Sanchit Gandhi
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Multilingual LibriSpeech
        type: facebook/multilingual_librispeech
        args: 'config: es, split: test'
      metrics:
      - name: Wer
        type: wer
        value: 4.124340388026983
  ---
  
  <!-- 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 Es - Sanchit Gandhi
  
  This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Multilingual LibriSpeech dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1024
  - Wer: 4.1243
  
  ## 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: 2
  - eval_batch_size: 4
  - seed: 42
  - gradient_accumulation_steps: 16
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - training_steps: 5000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.1887        | 0.2   | 1000 | 0.1329          | 5.4249 |
  | 0.123         | 0.4   | 2000 | 0.1212          | 4.9639 |
  | 0.1594        | 0.6   | 3000 | 0.1144          | 4.3210 |
  | 0.1777        | 0.8   | 4000 | 0.1116          | 4.5379 |
  | 0.2469        | 1.0   | 5000 | 0.1024          | 4.1243 |
  
  
  ### Framework versions
  
  - Transformers 4.25.0.dev0
  - Pytorch 1.12.0
  - Datasets 2.6.2.dev0
  - Tokenizers 0.12.1
  