metadata

license: apache-2.0
base_model: openai/whisper-small
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - google/fleurs
metrics:
  - wer
model-index:
  - name: Whisper small shona
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: google/fleurs sn_zw
          type: google/fleurs
          config: sn_zw
          split: test
          args: sn_zw
        metrics:
          - name: Wer
            type: wer
            value: 49.90958408679928

Whisper small shona

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

Loss: 1.1220
Wer: 49.9096

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: 8
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 3
gradient_accumulation_steps: 2
total_train_batch_size: 48
total_eval_batch_size: 48
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
0.0064	24.24	400	0.9630	50.7233
0.001	48.48	800	1.0617	49.9397
0.0005	72.73	1200	1.1016	49.9397
0.0004	96.97	1600	1.1220	49.9096
0.0003	121.21	2000	1.1298	50.0422

Framework versions

Transformers 4.37.1
Pytorch 1.12.0+cu102
Datasets 2.16.1
Tokenizers 0.15.1