optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: float32

Training results

Train Loss	Train Accuracy	Train Wermet	Validation Loss	Validation Accuracy	Validation Wermet	Epoch
5.2961	0.0113	1.9043	3.9402	0.0116	0.9526	0
4.6207	0.0121	0.8740	3.7957	0.0120	0.9397	1
4.4142	0.0128	0.8473	3.6045	0.0124	0.8988	2
4.1915	0.0135	0.8361	3.4445	0.0128	0.9019	3
4.0072	0.0140	0.8260	3.3268	0.0131	0.8816	4
3.8559	0.0145	0.8084	3.2440	0.0133	0.8592	5
3.7359	0.0149	0.7986	3.1751	0.0135	0.8598	6
3.6368	0.0152	0.7891	3.1298	0.0136	0.8398	7
3.5465	0.0154	0.7775	3.0736	0.0138	0.8606	8
3.4710	0.0157	0.7681	3.0318	0.0138	0.8455	9
3.3988	0.0159	0.7603	3.0159	0.0139	0.8770	10
3.3279	0.0162	0.7504	2.9672	0.0141	0.8241	11
3.2611	0.0164	0.7397	2.9541	0.0141	0.8676	12
3.1996	0.0167	0.7284	2.8913	0.0144	0.7990	13
3.1311	0.0169	0.7162	2.8671	0.0145	0.7934	14
3.0590	0.0172	0.7044	2.8241	0.0146	0.7907	15
2.9692	0.0177	0.6843	2.7517	0.0149	0.7645	16
2.8783	0.0181	0.6630	2.6682	0.0152	0.7263	17
2.7622	0.0187	0.6417	2.5586	0.0156	0.7220	18
2.6164	0.0194	0.6138	2.4121	0.0161	0.6909	19
2.4405	0.0203	0.5838	2.2417	0.0167	0.6527	20
2.2404	0.0213	0.5486	2.1401	0.0170	0.6662	21
2.0196	0.0225	0.5086	1.8907	0.0180	0.5774	22
1.7917	0.0237	0.4665	1.7073	0.0186	0.5446	23
1.5286	0.0253	0.4182	1.5139	0.0194	0.4919	24
1.2991	0.0267	0.3736	1.3605	0.0200	0.4570	25
1.1117	0.0279	0.3336	1.2304	0.0205	0.4262	26
0.9643	0.0289	0.2986	1.1387	0.0209	0.4040	27
0.8404	0.0298	0.2663	1.0514	0.0213	0.3776	28
0.7408	0.0305	0.2408	0.9883	0.0216	0.3596	29
0.6542	0.0311	0.2155	0.9281	0.0218	0.3418	30
0.5800	0.0316	0.1936	0.8801	0.0221	0.3269	31
0.5168	0.0321	0.1737	0.8401	0.0222	0.3168	32
0.4595	0.0326	0.1552	0.8071	0.0224	0.3077	33
0.4080	0.0330	0.1375	0.7825	0.0225	0.2994	34
0.3646	0.0333	0.1225	0.7550	0.0226	0.2887	35
0.3234	0.0337	0.1095	0.7369	0.0227	0.2847	36
0.2878	0.0340	0.0950	0.7270	0.0228	0.2796	37
0.2542	0.0343	0.0823	0.7096	0.0229	0.2728	38
0.2238	0.0346	0.0718	0.6963	0.0229	0.2697	39
0.1974	0.0348	0.0609	0.6857	0.0230	0.2669	40
0.1714	0.0351	0.0500	0.6843	0.0230	0.2663	41
0.1488	0.0353	0.0411	0.6770	0.0230	0.2630	42
0.1296	0.0355	0.0339	0.6754	0.0231	0.2612	43
0.1117	0.0356	0.0270	0.6702	0.0231	0.2585	44
0.0954	0.0358	0.0211	0.6695	0.0231	0.2574	45
0.0822	0.0359	0.0163	0.6711	0.0231	0.2572	46
0.0715	0.0360	0.0137	0.6685	0.0231	0.2583	47
0.0591	0.0361	0.0093	0.6696	0.0231	0.2590	48
0.0494	0.0361	0.0068	0.6663	0.0232	0.2609	49
0.0412	0.0362	0.0051	0.6726	0.0231	0.2577	50
0.0343	0.0362	0.0042	0.6756	0.0232	0.2609	51
0.0287	0.0362	0.0031	0.6700	0.0232	0.2549	52
0.0245	0.0362	0.0035	0.6796	0.0232	0.2639	53
0.0297	0.0362	0.0054	0.6695	0.0232	0.2557	54
0.0249	0.0362	0.0039	0.6700	0.0232	0.2554	55
0.0177	0.0362	0.0026	0.6673	0.0233	0.2504	56
0.0138	0.0362	0.0023	0.6763	0.0232	0.2526	57
0.0114	0.0362	0.0020	0.6770	0.0232	0.2509	58
0.0098	0.0362	0.0020	0.6751	0.0233	0.2494	59

Framework versions

Transformers 4.33.0.dev0
TensorFlow 2.13.0
Tokenizers 0.13.3

bigmorning/whisper_syl_noforce__0060

whisper_syl_noforce__0060

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

bigmorning/
whisper_syl_noforce__0060