metadata

license: apache-2.0
tags:
  - generated_from_trainer
  - hf-asr-leaderboard
  - whisper-event
metrics:
  - wer
model-index:
  - name: openai/whisper-medium
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0 ca
          type: mozilla-foundation/common_voice_11_0
          args: 'config: ml, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 16.15101446793939
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: google/fleurs ca
          type: google/fleurs
          args: 'config: ml, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 20.4
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: projecte-aina/parlament_parla clean
          type: projecte-aina/parlament_parla
          args: 'config: ml, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 21.14

openai/whisper-base

This is an automatic speech recognition model that also does punctuation and casing. This model is for research only, we do not recommend using this model on production environments. See our learnings when training these models.

This model is a fine-tuned version of openai/whisper-base on the mozilla-foundation/common_voice_11_0 ca dataset. It achieves the following results on the evaluation set:

Loss: 0.3608
Wer: 16.1510

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: 2
eval_batch_size: 1
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: 40000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.4841	0.1	4000	0.5078	26.7974
0.3116	0.2	8000	0.4524	22.9455
0.3971	0.3	12000	0.4281	21.5427
0.2965	0.4	16000	0.4037	20.3082
0.2634	1.09	20000	0.3875	18.7980
0.2163	1.19	24000	0.3754	17.8170
0.3182	1.29	28000	0.3695	16.8587
0.2201	1.39	32000	0.3613	16.5785
0.155	2.08	36000	0.3633	16.3959
0.0904	2.18	40000	0.3608	16.1510

Framework versions

Transformers 4.25.1
Pytorch 1.10.0+cu102
Datasets 2.8.0
Tokenizers 0.13.2