whisper-base-oshiwambo-speech

This model is a fine-tuned version of openai/whisper-base on meyabase/crowd-oshiwambo-speech-greetings dataset. It achieves the following results on the evaluation set:

• Loss: 0.0834
• Wer: 80.9524
• Cer: 58.9623
• Word Acc: 82.2917
• Sent Acc: 54.2857
• Precision: 0.5097
• Recall: 0.7524
• F1 Score: 0.6077

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: 4
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• training_steps: 10000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer	Word Acc	Sent Acc	Precision	Recall	F1 Score
0.0099	117.65	1000	0.0777	46.6667	31.6038	69.1358	11.4286	0.6914	0.5333	0.6022
0.0105	235.29	2000	0.0806	47.6190	33.2547	71.4286	11.4286	0.7143	0.5238	0.6044
0.0106	352.94	3000	0.0795	44.7619	34.6698	76.3158	25.7143	0.7632	0.5524	0.6409
0.0092	470.59	4000	0.0793	42.8571	35.8491	81.0811	31.4286	0.8108	0.5714	0.6704
0.0099	588.24	5000	0.0806	92.3810	69.8113	81.7073	42.8571	0.4752	0.6381	0.5447
0.0094	705.88	6000	0.0800	28.5714	22.1698	83.3333	48.5714	0.8333	0.7143	0.7692
0.0093	823.53	7000	0.0796	24.7619	16.2736	82.2917	54.2857	0.8229	0.7524	0.7861
0.0095	941.18	8000	0.0815	82.8571	59.1981	80.2083	51.4286	0.4968	0.7333	0.5923
0.01	1058.82	9000	0.0815	24.7619	16.5094	82.2917	54.2857	0.8229	0.7524	0.7861
0.0088	1176.47	10000	0.0834	80.9524	58.9623	82.2917	54.2857	0.5097	0.7524	0.6077

Framework versions

• Transformers 4.30.0.dev0
• Pytorch 2.0.0
• Datasets 2.12.0
• Tokenizers 0.13.3