metadata

language:
  - gl
license: apache-2.0
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
model-index:
  - name: Whisper Small GL - Santiago Paramés-Estévez
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 11.0
          type: mozilla-foundation/common_voice_11_0
          config: gl
          split: test
          args: 'config: gl, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 15.233405065386526

Whisper Small GL - Santiago Paramés-Estévez

This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:

Loss: 0.3179
Wer: 15.2334

Model description

This model was fine-tuned using Sanchit Gandhi's notebook: https://huggingface.co./blog/fine-tune-whisper

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: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0707	2.69	1000	0.2596	16.4915
0.0063	5.38	2000	0.2952	15.8583
0.0014	8.06	3000	0.3105	15.2624
0.0011	10.75	4000	0.3179	15.2334

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.0+cu117
Datasets 2.7.1
Tokenizers 0.13.2