Kwimp/TLT_spec_augment_speed_whisper_small

whisper small finetuned Specaug whole data, speed non-native

This model is a fine-tuned version of openai/whisper-small on the LTL2021 dataset. It achieves the following results on the evaluation set:

• Loss: 0.4079
• Wer: 20.1022
• Cer: 14.2547
• Sub Rate: 4.8584
• Del Rate: 4.8082
• Ins Rate: 10.4356

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: 32
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 2048
• training_steps: 4000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer	Sub Rate	Del Rate	Ins Rate
5.1251	1.6087	500	2.9965	17.8718	12.9923	5.6252	6.6825	5.5641
3.1950	3.2158	1000	1.9016	17.2164	12.4481	5.0594	5.9027	6.2544
1.7948	4.8245	1500	1.0489	19.2459	14.1208	4.9567	5.8044	8.4848
0.9167	6.4316	2000	0.4769	18.9029	13.7232	4.9065	5.0682	8.9283
0.7342	8.0386	2500	0.4290	20.4539	14.8371	4.9393	4.7208	10.7939
0.7254	9.6473	3000	0.4157	19.1520	13.6238	4.8038	4.7689	9.5793
0.7044	11.2544	3500	0.4095	20.0891	14.2597	4.8344	4.8213	10.4334
0.6828	12.8631	4000	0.4079	20.1022	14.2547	4.8584	4.8082	10.4356

Framework versions

• Transformers 5.8.1
• Pytorch 2.5.1+cu121
• Datasets 4.8.5
• Tokenizers 0.22.2