metadata

license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - google/fleurs
metrics:
  - wer
base_model: openai/whisper-small
model-index:
  - name: Whisper_small_Yoruba
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: google/fleurs yo_ng
          type: google/fleurs
          config: yo_ng
          split: test
        metrics:
          - type: wer
            value: 67.88663748364095
            name: Wer

Whisper_small_Yoruba

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

Loss: 1.6773
Wer: 67.8866

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: 4
gradient_accumulation_steps: 2
total_train_batch_size: 64
total_eval_batch_size: 64
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
0.013	36.35	400	1.4068	72.9681
0.0008	72.7	800	1.5546	68.4507
0.0003	109.09	1200	1.6400	67.9137
0.0002	145.43	1600	1.6773	67.8866
0.0002	181.78	2000	1.6901	68.1123

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu117
Datasets 2.7.1
Tokenizers 0.13.2