metadata

language:
  - ml
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: Whisper Base ML - Bharat Ramanathan
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0
          type: mozilla-foundation/common_voice_11_0
          config: ml
          split: test
        metrics:
          - type: wer
            value: 34.16
            name: WER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: google/fleurs
          type: google/fleurs
          config: ml_in
          split: test
        metrics:
          - type: wer
            value: 53.29
            name: WER

Whisper Base ML - Bharat Ramanathan

This model is a fine-tuned version of openai/whisper-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2456
Wer: 48.0535

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: 64
eval_batch_size: 32
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
0.7249	4.02	500	0.3786	70.8029
0.3377	4.02	1000	0.2477	56.2044
0.25	9.01	1500	0.2241	49.5134
0.2009	14.01	2000	0.2158	46.9586
0.1674	19.0	2500	0.2188	49.3917
0.142	23.02	3000	0.2194	49.6350
0.123	28.01	3500	0.2280	49.7567
0.1103	33.01	4000	0.2424	51.4599
0.0999	38.0	4500	0.2435	50.6083
0.0951	42.02	5000	0.2456	48.0535

Framework versions

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