You can deploy this model and start earning money today!

alexziweiwang/
combined-onTorgoSentenceModel-TestedSpeakerM01

Name: combined-onTorgoSentenceModel-TestedSpeakerM01
Availability: InStock

transformers

pytorch

wav2vec2

automatic-speech-recognition

generated_from_trainer

endpoints_compatible

Views

No views yet

combined-onTorgoSentenceModel-TestedSpeakerM01

This model is a fine-tuned version of alexziweiwang/torgo-sentences-TestedSpeakerM01 on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.3823
Wer: 0.8064

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
28.7173	0.35	500	3.0048	0.9974
2.5941	0.69	1000	2.1227	1.1821
1.9528	1.04	1500	1.8921	1.2128
1.5166	1.39	2000	1.3280	1.1795
1.3394	1.74	2500	1.3925	1.0923
1.2284	2.08	3000	1.4628	1.1064
1.0785	2.43	3500	1.3823	1.0923
1.0343	2.78	4000	1.2196	1.0923
0.9979	3.12	4500	1.4429	1.0038
0.8963	3.47	5000	1.3360	0.9731
0.9169	3.82	5500	1.4309	0.9949
0.7811	4.17	6000	1.4731	0.9974
0.7811	4.51	6500	1.5859	0.9808
0.7818	4.86	7000	1.3996	0.9923
0.6952	5.21	7500	1.2495	1.0167
0.6708	5.56	8000	1.7330	1.0103
0.6724	5.9	8500	1.0807	0.9731
0.6321	6.25	9000	1.4906	0.9833
0.6564	6.6	9500	1.2854	0.9218
0.6099	6.94	10000	1.4289	0.9321
0.5643	7.29	10500	1.5796	0.9449
0.5612	7.64	11000	1.3855	0.9013
0.5746	7.99	11500	0.9218	0.8936
0.5198	8.33	12000	1.0982	0.9436
0.5754	8.68	12500	1.1686	0.9449
0.5102	9.03	13000	1.1525	0.9321
0.4721	9.38	13500	1.2355	0.8551
0.4715	9.72	14000	1.2203	0.8897
0.4961	10.07	14500	1.3226	0.8821
0.4256	10.42	15000	1.4078	0.8795
0.449	10.76	15500	1.1023	0.8731
0.4141	11.11	16000	1.0989	0.9026
0.4325	11.46	16500	1.3532	0.8936
0.4034	11.81	17000	1.0583	0.8705
0.4027	12.15	17500	1.0765	0.8692
0.4031	12.5	18000	1.4424	0.8679
0.4129	12.85	18500	1.1504	0.8654
0.4291	13.19	19000	1.3790	0.8692
0.3512	13.54	19500	1.2891	0.9077
0.3499	13.89	20000	1.3068	0.8577
0.3331	14.24	20500	1.5288	0.8833
0.3626	14.58	21000	1.1218	0.9090
0.495	14.93	21500	1.2152	0.8641
0.3129	15.28	22000	1.4101	0.8974
0.2945	15.62	22500	1.2965	0.8615
0.3335	15.97	23000	1.3362	0.8282
0.2902	16.32	23500	1.2603	0.8679
0.294	16.67	24000	1.4634	0.8603
0.2561	17.01	24500	1.0413	0.8333
0.2654	17.36	25000	1.5734	0.8679
0.2724	17.71	25500	1.6416	0.8756
0.2581	18.06	26000	1.3018	0.8551
0.2329	18.4	26500	1.5288	0.8654
0.2484	18.75	27000	1.4365	0.8410
0.2578	19.1	27500	1.4501	0.8564
0.2237	19.44	28000	1.3333	0.8436
0.2274	19.79	28500	1.3387	0.8256
0.2253	20.14	29000	1.4898	0.8551
0.2189	20.49	29500	1.6225	0.8692
0.2006	20.83	30000	1.6085	0.8551
0.2263	21.18	30500	1.5435	0.85
0.2032	21.53	31000	1.3926	0.8372
0.2128	21.88	31500	1.4497	0.8397
0.1776	22.22	32000	1.5413	0.8372
0.1962	22.57	32500	1.4021	0.8410
0.1823	22.92	33000	1.5397	0.8410
0.1634	23.26	33500	1.5256	0.8141
0.1971	23.61	34000	1.4673	0.8308
0.1926	23.96	34500	1.4941	0.8167
0.1695	24.31	35000	1.5740	0.7949
0.1555	24.65	35500	1.5857	0.8269
0.1714	25.0	36000	1.4801	0.8333
0.1393	25.35	36500	1.5619	0.8359
0.1364	25.69	37000	1.5414	0.8385
0.1571	26.04	37500	1.4345	0.8205
0.1605	26.39	38000	1.4427	0.8192
0.1473	26.74	38500	1.4327	0.8179
0.1575	27.08	39000	1.3873	0.8090
0.1454	27.43	39500	1.3101	0.8103
0.1197	27.78	40000	1.3747	0.8038
0.1301	28.12	40500	1.3558	0.7962
0.1416	28.47	41000	1.2969	0.7962
0.1642	28.82	41500	1.3601	0.8
0.1234	29.17	42000	1.3910	0.8051
0.1262	29.51	42500	1.3941	0.8077
0.1408	29.86	43000	1.3823	0.8064

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu113
Datasets 1.18.3
Tokenizers 0.13.2

alexziweiwang/combined-onTorgoSentenceModel-TestedSpeakerM01

combined-onTorgoSentenceModel-TestedSpeakerM01

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

alexziweiwang/
combined-onTorgoSentenceModel-TestedSpeakerM01