metadata

library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_17_0
metrics:
  - wer
model-index:
  - name: Whisper Small Alb - Sumitesh
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 11.0
          type: mozilla-foundation/common_voice_17_0
          config: sq
          split: None
          args: 'config: sq, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 52.63324873096447
language:
  - sq
pipeline_tag: automatic-speech-recognition

Whisper Small Alb - Sumitesh

This model is a fine-tuned version of openai/whisper-small on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:

Loss: 1.2013
Wer: 52.6332

Model description

This is a speech to text model finetuned over Whisper model by OpenAI.

Intended uses & limitations

This is free to use for learning or commercial purposes. I don't plan to monetize this ever or make it private. My goal is to make whisper more localized which is why i have this model public.

Training and evaluation data

This model is trained on common_voice_17 dataset. It is an open source multilingual dataset.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
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
0.005	15.1515	1000	0.9955	53.7437
0.0003	30.3030	2000	1.1066	52.5698
0.0001	45.4545	3000	1.1585	52.8553
0.0001	60.6061	4000	1.1889	52.7284
0.0001	75.7576	5000	1.2013	52.6332

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1