whisper-large-seiching – AI Model by seiching | AlphaNeural AI

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seiching/
whisper-large-seiching

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automatic-speech-recognition

generated_from_trainer

mozilla-foundation/common_voice_13_0

endpoints_compatible

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Whisper large zh - seiching

This model is a fine-tuned version of openai/whisper-large on the Common Voice 13 dataset. It achieves the following results on the evaluation set:

Loss: 0.2457
Wer Ortho: 40.3316
Wer: 39.9281

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: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant_with_warmup
lr_scheduler_warmup_steps: 50
training_steps: 4000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
0.0361	0.69	500	0.1989	38.3627	37.9517
0.0105	1.38	1000	0.2217	39.0259	38.9100
0.0208	2.06	1500	0.2299	39.6891	39.3292
0.0091	2.75	2000	0.2264	39.8964	39.4091
0.0153	3.44	2500	0.2363	39.8135	39.3891
0.0191	4.13	3000	0.2415	40.1865	40.0080
0.0061	4.81	3500	0.2542	41.1813	39.9281
0.0107	5.5	4000	0.2457	40.3316	39.9281

Framework versions

Transformers 4.30.2
Pytorch 1.13.1+cu117
Datasets 2.13.2
Tokenizers 0.13.3