README.md

metadata

library_name: transformers
language:
  - lg
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - Grain
metrics:
  - wer
model-index:
  - name: Whisper-small-lg-finetuned
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Grain
          type: Grain
        metrics:
          - name: Wer
            type: wer
            value: 0.003958390868084323

Whisper-small-lg-finetuned

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

• Loss: 0.0014
• Wer: 0.0040
• Cer: 0.0013

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: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 80
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
1.9276	1.0	1296	0.4841	1.1593	0.4773
0.2693	2.0	2592	0.0967	1.2498	0.5777
0.0668	3.0	3888	0.0300	1.1842	0.5634
0.0234	4.0	5184	0.0162	0.8390	0.3714
0.0117	5.0	6480	0.0108	0.8158	0.3744
0.0072	6.0	7776	0.0094	0.4896	0.2288
0.0051	7.0	9072	0.0086	0.2434	0.1106
0.005	8.0	10368	0.0090	0.2317	0.1230
0.0041	9.0	11664	0.0064	0.1364	0.0608
0.0029	10.0	12960	0.0064	0.0704	0.0215
0.0025	11.0	14256	0.0053	0.0756	0.0495
0.0018	12.0	15552	0.0059	0.0699	0.0313
0.0022	13.0	16848	0.0036	0.0238	0.0095
0.0018	14.0	18144	0.0053	0.0426	0.0195
0.0013	15.0	19440	0.0051	0.0203	0.0059
0.0017	16.0	20736	0.0028	0.0255	0.0124
0.0009	17.0	22032	0.0031	0.0254	0.0116
0.001	18.0	23328	0.0038	0.0105	0.0031
0.0014	19.0	24624	0.0022	0.0109	0.0034
0.001	20.0	25920	0.0015	0.0108	0.0037
0.0009	21.0	27216	0.0036	0.0170	0.0047
0.0005	22.0	28512	0.0014	0.0091	0.0032
0.0007	23.0	29808	0.0014	0.0101	0.0031
0.001	24.0	31104	0.0020	0.0108	0.0035
0.0004	25.0	32400	0.0015	0.0093	0.0030
0.0006	26.0	33696	0.0022	0.0174	0.0076
0.0007	27.0	34992	0.0020	0.0122	0.0079
0.0006	28.0	36288	0.0016	0.0081	0.0029
0.0004	29.0	37584	0.0020	0.0110	0.0031
0.0007	30.0	38880	0.0015	0.0106	0.0037
0.0005	31.0	40176	0.0025	0.0116	0.0032
0.0005	32.0	41472	0.0016	0.0097	0.0027
0.0003	33.0	42768	0.0010	0.0087	0.0034
0.0004	34.0	44064	0.0015	0.0116	0.0062
0.0002	35.0	45360	0.0010	0.0047	0.0020
0.0001	36.0	46656	0.0009	0.0052	0.0020
0.0006	37.0	47952	0.0027	0.0097	0.0031
0.0003	38.0	49248	0.0017	0.0054	0.0016
0.0002	39.0	50544	0.0013	0.0066	0.0023
0.0003	40.0	51840	0.0023	0.0072	0.0023
0.0002	41.0	53136	0.0012	0.0044	0.0018
0.0003	42.0	54432	0.0035	0.0075	0.0031
0.0003	43.0	55728	0.0035	0.0073	0.0024
0.0001	44.0	57024	0.0014	0.0047	0.0016
0.0	45.0	58320	0.0014	0.0040	0.0013

Framework versions

• Transformers 4.45.2
• Pytorch 2.1.0+cu118
• Datasets 3.0.1
• Tokenizers 0.20.1