whisper-large-v2-multiset-vi – AI Model by geninhu | AlphaNeural AI

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geninhu/
whisper-large-v2-multiset-vi

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automatic-speech-recognition

generated_from_trainer

mozilla-foundation/common_voice_11_0

endpoints_compatible

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openai/whisper-large-v2

This model is a fine-tuned version of openai/whisper-large-v2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4041
Wer: 15.7710
Cer: 7.6691

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

Training data:

Evaluation data:

mozilla-foundation/common_voice_11_0

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-07
train_batch_size: 8
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 3
total_train_batch_size: 24
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.3983	0.1	500	0.5338	19.5876	10.6391
0.2277	1.08	1000	0.4134	16.5826	8.2668
0.172	2.05	1500	0.3968	16.3084	7.9787
0.1823	3.03	2000	0.3956	16.1768	7.8159
0.1445	4.0	2500	0.3955	16.0342	7.7438
0.147	4.1	3000	0.3965	15.8807	7.7145
0.1292	5.08	3500	0.4000	15.8587	7.7065
0.1187	6.05	4000	0.4029	15.7491	7.6398
0.1368	7.03	4500	0.4041	15.7600	7.6558
0.1231	8.0	5000	0.4041	15.7710	7.6691

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.0+cu117
Datasets 2.7.1.dev0
Tokenizers 0.13.2