jjyaoao/Echotune_clean_test

This model is a fine-tuned version of facebook/data2vec-audio-base-960h on the librispeech_asr dataset. It achieves the following results on the evaluation set:

• Loss: 0.0679
• Wer Ortho: 0.0369
• Wer: 0.0374

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: 6e-05
• train_batch_size: 12
• eval_batch_size: 12
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 34246.8
• num_epochs: 12

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
0.0602	0.21	500	0.0476	0.0435	0.0439
0.0478	0.42	1000	0.0436	0.0411	0.0414
0.0492	0.63	1500	0.0443	0.0412	0.0415
0.0426	0.84	2000	0.0439	0.0401	0.0403
0.0386	1.05	2500	0.0445	0.0391	0.0395
0.0409	1.26	3000	0.0438	0.0394	0.0399
0.0437	1.47	3500	0.0444	0.0389	0.0393
0.0349	1.68	4000	0.0450	0.0392	0.0396
0.0469	1.89	4500	0.0442	0.0374	0.0378
0.033	2.1	5000	0.0454	0.0359	0.0363
0.0395	2.31	5500	0.0462	0.0363	0.0367
0.0321	2.52	6000	0.0457	0.0365	0.0369
0.0385	2.73	6500	0.0455	0.0355	0.0358
0.0378	2.94	7000	0.0449	0.0361	0.0366
0.0435	3.15	7500	0.0440	0.0355	0.0360
0.0436	3.36	8000	0.0466	0.0339	0.0344
0.0394	3.57	8500	0.0480	0.0345	0.0350
0.0448	3.78	9000	0.0478	0.0338	0.0342
0.0451	3.99	9500	0.0460	0.0355	0.0361
0.035	4.2	10000	0.0485	0.0369	0.0374
0.0387	4.41	10500	0.0487	0.0358	0.0362
0.0479	4.62	11000	0.0496	0.0363	0.0368
0.0456	4.83	11500	0.0491	0.0359	0.0365
0.0372	5.04	12000	0.0507	0.0355	0.0360
0.0395	5.25	12500	0.0526	0.0353	0.0356
0.0323	5.46	13000	0.0515	0.0368	0.0373
0.0354	5.67	13500	0.0524	0.0338	0.0343
0.031	5.88	14000	0.0531	0.0349	0.0357
0.0295	6.09	14500	0.0560	0.0344	0.0349
0.032	6.31	15000	0.0564	0.0364	0.0369
0.0462	6.52	15500	0.0548	0.0358	0.0365
0.0467	6.73	16000	0.0562	0.0347	0.0352
0.0437	6.94	16500	0.0573	0.0354	0.0359
0.0357	7.15	17000	0.0561	0.0359	0.0362
0.0297	7.36	17500	0.0602	0.0347	0.0351
0.0388	7.57	18000	0.0552	0.0341	0.0345
0.0392	7.78	18500	0.0533	0.0326	0.0331
0.0419	7.99	19000	0.0535	0.0343	0.0349
0.0326	8.2	19500	0.0614	0.0374	0.0378
0.0423	8.41	20000	0.0585	0.0341	0.0346
0.0326	8.62	20500	0.0586	0.0356	0.0362
0.0448	8.83	21000	0.0637	0.0371	0.0375
0.0763	9.04	21500	0.0607	0.0359	0.0364
0.0317	9.25	22000	0.0635	0.0400	0.0405
0.0326	9.46	22500	0.0603	0.0368	0.0372
0.0393	9.67	23000	0.0665	0.0380	0.0385
0.0341	9.88	23500	0.0664	0.0408	0.0413
0.0351	10.09	24000	0.0638	0.0384	0.0388
0.0412	10.3	24500	0.0687	0.0380	0.0384
0.0359	10.51	25000	0.0634	0.0379	0.0385
0.047	10.72	25500	0.0652	0.0373	0.0378
0.0346	10.93	26000	0.0671	0.0390	0.0396
0.0366	11.14	26500	0.0664	0.0387	0.0393
0.0359	11.35	27000	0.0669	0.0369	0.0374
0.0366	11.56	27500	0.0705	0.0358	0.0364
0.054	11.77	28000	0.0659	0.0383	0.0390
0.0335	11.98	28500	0.0679	0.0369	0.0374

Framework versions

• Transformers 4.32.0.dev0
• Pytorch 2.0.0+cu117
• Datasets 2.13.1
• Tokenizers 0.13.3