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alexziweiwang/
pure-start-epoch1

Name: pure-start-epoch1
Availability: InStock

transformers

pytorch

wav2vec2

generated_from_trainer

endpoints_compatible

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pure-start-epoch1

This model is a fine-tuned version of yongjian/wav2vec2-large-a on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 21.0050
Acc: 0.095
Wer: 1.0
Correct: 19
Total: 200
Strlen: 200

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: 9e-06
train_batch_size: 2
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss	Acc	Wer	Correct	Total	Strlen
No log	0.02	5	67.2752	0.0	1.0119	0	200	200
131.0548	0.04	10	66.2796	0.0	1.0257	0	200	200
131.0548	0.06	15	65.2071	0.005	1.0237	1	200	200
145.0859	0.08	20	64.0987	0.035	1.0198	7	200	200
145.0859	0.11	25	62.9734	0.07	1.0119	14	200	200
110.0012	0.13	30	61.8288	0.09	1.0119	18	200	200
110.0012	0.15	35	60.6565	0.09	1.0119	18	200	200
122.6164	0.17	40	59.4606	0.095	1.0119	19	200	200
122.6164	0.19	45	58.2224	0.095	1.0099	19	200	200
125.942	0.21	50	56.9514	0.095	1.0020	19	200	200
125.942	0.23	55	55.5923	0.095	1.0	19	200	200
111.2271	0.25	60	54.1423	0.095	1.0	19	200	200
111.2271	0.27	65	52.6174	0.095	1.0	19	200	200
137.2356	0.3	70	51.0340	0.095	1.0	19	200	200
137.2356	0.32	75	49.4034	0.095	1.0	19	200	200
112.2532	0.34	80	47.7291	0.095	1.0	19	200	200
112.2532	0.36	85	46.0281	0.095	1.0	19	200	200
88.3973	0.38	90	44.2361	0.095	1.0	19	200	200
88.3973	0.4	95	42.4925	0.095	1.0	19	200	200
88.7175	0.42	100	40.7673	0.095	1.0	19	200	200
88.7175	0.44	105	39.0848	0.095	1.0	19	200	200
90.857	0.46	110	37.4890	0.095	1.0	19	200	200
90.857	0.48	115	35.8966	0.095	1.0	19	200	200
77.5782	0.51	120	34.2822	0.1	1.0	20	200	200
77.5782	0.53	125	32.7953	0.1	1.0	20	200	200
80.2378	0.55	130	31.4560	0.1	1.0	20	200	200
80.2378	0.57	135	30.1651	0.1	1.0	20	200	200
73.5042	0.59	140	29.0069	0.095	1.0	19	200	200
73.5042	0.61	145	28.0349	0.095	1.0	19	200	200
71.5632	0.63	150	27.1812	0.095	1.0	19	200	200
71.5632	0.65	155	26.4012	0.095	1.0	19	200	200
76.5337	0.67	160	25.6924	0.095	1.0	19	200	200
76.5337	0.7	165	25.0184	0.095	1.0	19	200	200
54.6507	0.72	170	24.4100	0.095	1.0	19	200	200
54.6507	0.74	175	23.8273	0.095	1.0	19	200	200
57.1606	0.76	180	23.2988	0.095	1.0	19	200	200
57.1606	0.78	185	22.8731	0.095	1.0	19	200	200
56.0855	0.8	190	22.5336	0.095	1.0	19	200	200
56.0855	0.82	195	22.2334	0.095	1.0	19	200	200
55.2475	0.84	200	21.9555	0.095	1.0	19	200	200
55.2475	0.86	205	21.7112	0.095	1.0	19	200	200
47.9988	0.89	210	21.5123	0.095	1.0	19	200	200
47.9988	0.91	215	21.3407	0.095	1.0	19	200	200
55.1394	0.93	220	21.1965	0.095	1.0	19	200	200
55.1394	0.95	225	21.1028	0.095	1.0	19	200	200
48.0323	0.97	230	21.0376	0.095	1.0	19	200	200
48.0323	0.99	235	21.0050	0.095	1.0	19	200	200

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu113
Datasets 1.18.3
Tokenizers 0.13.2

alexziweiwang/pure-start-epoch1

pure-start-epoch1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

alexziweiwang/
pure-start-epoch1