You can deploy this model and start earning money today!

yesj1234/
xlsr_mid_en-k_1

Name: xlsr_mid_en-k_1
Availability: InStock

transformers

pytorch

wav2vec2

automatic-speech-recognition

./sample_speech.py

generated_from_trainer

facebook/wav2vec2-large-xlsr-53

finetune

apache-2.0

endpoints_compatible

Views

No views yet

en-xlsr

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the ./SAMPLE_SPEECH.PY - NA dataset. It achieves the following results on the evaluation set:

Loss: 0.4835
Cer: 0.1119
Wer: 0.2446

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: 0.0003
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 32
total_eval_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Cer	Wer
2.9534	0.22	100	2.9533	1.0	1.0
2.933	0.44	200	2.9231	1.0	1.0
2.904	0.65	300	2.8851	1.0	1.0
2.3607	0.87	400	2.1546	0.6799	0.9976
1.1725	1.09	500	0.9899	0.2665	0.6191
0.9865	1.31	600	0.8060	0.2126	0.5064
0.8959	1.53	700	0.7131	0.1980	0.4607
0.7743	1.74	800	0.6663	0.1799	0.4370
0.7805	1.96	900	0.6159	0.1683	0.3997
0.6562	2.18	1000	0.6186	0.1537	0.3705
0.6223	2.4	1100	0.5698	0.1496	0.3552
0.5627	2.62	1200	0.5555	0.1446	0.3372
0.5476	2.84	1300	0.5435	0.1416	0.3307
0.5002	3.05	1400	0.5304	0.1436	0.3393
0.5174	3.27	1500	0.5377	0.1485	0.3357
0.4745	3.49	1600	0.5289	0.1340	0.3132
0.5239	3.71	1700	0.5112	0.1395	0.3239
0.5115	3.93	1800	0.5079	0.1342	0.3094
0.4471	4.14	1900	0.5131	0.1301	0.2965
0.4455	4.36	2000	0.5015	0.1278	0.2931
0.4199	4.58	2100	0.4954	0.1299	0.2962
0.4699	4.8	2200	0.4827	0.1268	0.2890
0.3521	5.02	2300	0.4857	0.1217	0.2782
0.3976	5.23	2400	0.4936	0.1231	0.2802
0.365	5.45	2500	0.4906	0.1221	0.2774
0.3857	5.67	2600	0.4843	0.1202	0.2757
0.3578	5.89	2700	0.4857	0.1196	0.2708
0.3298	6.11	2800	0.4867	0.1197	0.2689
0.3099	6.32	2900	0.4924	0.1237	0.2770
0.3606	6.54	3000	0.4851	0.1189	0.2684
0.3807	6.76	3100	0.4700	0.1196	0.2656
0.3286	6.98	3200	0.4770	0.1205	0.2730
0.3318	7.2	3300	0.4845	0.1166	0.2579
0.2936	7.42	3400	0.4909	0.1159	0.2570
0.3119	7.63	3500	0.4899	0.1150	0.2539
0.3142	7.85	3600	0.4782	0.1143	0.2550
0.2935	8.07	3700	0.4885	0.1153	0.2527
0.2805	8.29	3800	0.4906	0.1143	0.2529
0.254	8.51	3900	0.4822	0.1144	0.2538
0.2855	8.72	4000	0.4852	0.1123	0.2476
0.2661	8.94	4100	0.4847	0.1132	0.2496
0.2524	9.16	4200	0.4900	0.1116	0.2442
0.253	9.38	4300	0.4888	0.1120	0.2458
0.2591	9.6	4400	0.4813	0.1125	0.2458
0.2583	9.81	4500	0.4844	0.1114	0.2435

Framework versions

Transformers 4.34.0
Pytorch 2.1.0+cu121
Datasets 2.14.5
Tokenizers 0.14.1

yesj1234/xlsr_mid_en-k_1

en-xlsr

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

yesj1234/
xlsr_mid_en-k_1