metadata

license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - google/fleurs
metrics:
  - wer
model-index:
  - name: Whisper Medium MS - Augmented
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: google/fleurs
          type: google/fleurs
          config: ms_my
          split: test
          args: ms_my
        metrics:
          - type: wer
            value: 9.578362255965294
            name: WER
          - type: cer
            value: 2.8109053797929726
            name: CER

Whisper Medium MS - Augmented

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

Loss: 0.2066
Wer: 9.5784
Cer: 2.8109

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

Training:

google/fleurs (train+validation)

Evaluation:

google/fleurs (test)

Training procedure

Datasets were augmented on-the-fly using audiomentations via PitchShift and TimeStretch transformations at p=0.3.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
training_steps: 1000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.0876	2.15	200	0.1949	10.3105	3.0685
0.0064	4.3	400	0.1974	9.7004	2.9596
0.0014	6.45	600	0.2031	9.6190	2.8955
0.001	8.6	800	0.2058	9.6055	2.8440
0.0009	10.75	1000	0.2066	9.5784	2.8109

Framework versions

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