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alexziweiwang/
base-on-torgo0003

Name: base-on-torgo0003
Availability: InStock

transformers

pytorch

wav2vec2

automatic-speech-recognition

generated_from_trainer

endpoints_compatible

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base-on-torgo0003

This model is a fine-tuned version of yongjian/wav2vec2-large-a on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6579
Wer: 0.7547

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: 0.0001
train_batch_size: 4
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_steps: 1000
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
28.1611	0.46	500	3.4550	1.0163
3.2238	0.92	1000	2.8781	1.0411
2.8617	1.39	1500	2.9896	1.0028
2.5841	1.85	2000	2.3744	1.2896
2.2029	2.31	2500	1.8598	1.2722
1.9976	2.77	3000	1.6505	1.2513
1.7817	3.23	3500	1.5291	1.2294
1.6484	3.69	4000	1.4635	1.2106
1.56	4.16	4500	1.4251	1.1989
1.417	4.62	5000	1.4040	1.1904
1.2884	5.08	5500	1.2734	1.1568
1.2788	5.54	6000	1.2242	1.1384
1.2159	6.0	6500	1.0561	1.1349
1.1125	6.46	7000	1.1001	1.1175
1.1495	6.93	7500	1.0409	1.1112
1.0222	7.39	8000	1.0525	1.0952
1.0104	7.85	8500	1.0184	1.1048
0.9956	8.31	9000	1.0438	1.1196
0.8747	8.77	9500	1.0736	1.1005
0.8437	9.23	10000	1.0041	1.0768
0.861	9.7	10500	0.9407	1.0496
0.8238	10.16	11000	0.9237	1.0697
0.7806	10.62	11500	0.8706	1.0343
0.7475	11.08	12000	0.9576	1.0407
0.6963	11.54	12500	0.9195	1.0159
0.7624	12.0	13000	0.8102	1.0060
0.6311	12.47	13500	0.8208	0.9897
0.6649	12.93	14000	0.7699	0.9968
0.6025	13.39	14500	0.7834	0.9547
0.5691	13.85	15000	0.7414	0.9632
0.532	14.31	15500	0.7056	0.9473
0.5572	14.77	16000	0.8136	0.9929
0.5455	15.24	16500	0.7355	0.9264
0.5369	15.7	17000	0.7531	0.9352
0.4771	16.16	17500	0.7527	0.9228
0.4778	16.62	18000	0.7312	0.9218
0.4384	17.08	18500	0.6774	0.8913
0.4619	17.54	19000	0.6888	0.8896
0.4341	18.01	19500	0.7068	0.9030
0.4164	18.47	20000	0.6484	0.8754
0.3883	18.93	20500	0.6388	0.8676
0.4135	19.39	21000	0.6732	0.8683
0.4121	19.85	21500	0.6354	0.8591
0.3694	20.31	22000	0.6751	0.8581
0.367	20.78	22500	0.6487	0.8411
0.3798	21.24	23000	0.5955	0.8312
0.3249	21.7	23500	0.6209	0.8230
0.3182	22.16	24000	0.7341	0.8212
0.3196	22.62	24500	0.6533	0.8106
0.297	23.08	25000	0.7163	0.8177
0.3021	23.55	25500	0.7209	0.8149
0.3248	24.01	26000	0.6268	0.8018
0.3013	24.47	26500	0.7014	0.7915
0.2986	24.93	27000	0.7306	0.8028
0.2913	25.39	27500	0.6866	0.7912
0.2706	25.85	28000	0.6860	0.7851
0.2572	26.32	28500	0.6478	0.7752
0.2794	26.78	29000	0.6308	0.7703
0.2796	27.24	29500	0.6302	0.7653
0.2604	27.7	30000	0.6638	0.7621
0.2367	28.16	30500	0.6492	0.7593
0.2383	28.62	31000	0.6560	0.7614
0.2495	29.09	31500	0.6577	0.7593
0.2513	29.55	32000	0.6579	0.7547

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu113
Datasets 1.18.3
Tokenizers 0.13.2

alexziweiwang/base-on-torgo0003

base-on-torgo0003

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

alexziweiwang/
base-on-torgo0003