softcatala/whisper-medium-ca

openai/whisper-medium

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-medium on the mozilla-foundation/common_voice_11_0 ca dataset. It achieves the following results on the evaluation set:

• Loss: 0.2029
• Wer: 8.3235

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: 20000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.2652	0.1	2000	0.3469	15.3537
0.3273	0.2	4000	0.3151	14.1141
0.2696	0.3	6000	0.2955	13.2472
0.1725	0.4	8000	0.2787	11.6834
0.1741	0.5	10000	0.2648	11.0088
0.2037	0.6	12000	0.2470	10.1909
0.1586	0.7	14000	0.2333	9.4096
0.1548	0.8	16000	0.2184	8.9724
0.1799	1.08	18000	0.2064	8.2830
0.1165	1.18	20000	0.2029	8.3235

Framework versions

• Transformers 4.25.1
• Pytorch 1.10.0+cu102
• Datasets 2.7.1
• Tokenizers 0.13.2