metadata

language:
  - hi
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_1_0
metrics:
  - wer
model-index:
  - name: Whisper Small EN - erenozaltun-common1
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 1.0
          type: mozilla-foundation/common_voice_1_0
          config: en
          split: None
          args: 'config: en, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 21.97714323793023

Whisper Small EN - erenozaltun-common1

This model is a fine-tuned version of openai/whisper-small on the Common Voice 1.0 dataset. It achieves the following results on the evaluation set:

Loss: 0.3868
Wer: 21.9771

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: 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: 2000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1749	0.6588	500	0.3715	21.4957
0.0885	1.3175	1000	0.3665	21.5340
0.0905	1.9763	1500	0.3668	21.8322
0.0387	2.6350	2000	0.3868	21.9771

Framework versions

Transformers 4.42.3
Pytorch 2.1.2+cu121
Datasets 2.18.0
Tokenizers 0.19.1