README.md

metadata

library_name: transformers
language:
  - en
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
  - generated_from_trainer
datasets:
  - WillHeld/india_accent_cv
metrics:
  - wer
model-index:
  - name: Whisper Indian English Acccent
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Indian English Accent
          type: WillHeld/india_accent_cv
          args: 'split: train'
        metrics:
          - type: wer
            value: 7.5056000168263415
            name: Wer

Whisper Indian English Acccent

This model is a fine-tuned version of openai/whisper-large-v3-turbo on the Indian English Accent dataset. It achieves the following results on the evaluation set:

• Loss: 0.2065
• Wer: 7.5056

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: 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: 500
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.342	0.1943	1000	0.3226	14.1310
0.2741	0.3885	2000	0.3130	13.9553
0.2576	0.5828	3000	0.2967	12.9931
0.2825	0.7770	4000	0.2692	12.3390
0.2295	0.9713	5000	0.2565	11.8331
0.1489	1.1655	6000	0.2498	11.6933
0.1485	1.3598	7000	0.2452	11.1411
0.1385	1.5540	8000	0.2346	10.4428
0.1253	1.7483	9000	0.2254	10.1852
0.1297	1.9425	10000	0.2144	9.7109
0.0594	2.1368	11000	0.2174	9.5363
0.0629	2.3310	12000	0.2136	9.8276
0.0654	2.5253	13000	0.2102	9.4301
0.0625	2.7195	14000	0.2075	8.9432
0.0574	2.9138	15000	0.2009	8.7802
0.0276	3.1080	16000	0.2050	8.4594
0.0251	3.3023	17000	0.2046	8.5951
0.0246	3.4965	18000	0.2035	8.1187
0.0259	3.6908	19000	0.2002	8.0588
0.021	3.8850	20000	0.1951	7.9147
0.0072	4.0793	21000	0.2053	7.7548
0.0067	4.2735	22000	0.2085	7.4972
0.0067	4.4678	23000	0.2094	7.6970
0.0062	4.6620	24000	0.2071	7.7433
0.0046	4.8563	25000	0.2065	7.5056

Framework versions

• Transformers 4.49.0
• Pytorch 2.2.0a0+81ea7a4
• Datasets 3.3.2
• Tokenizers 0.21.0