Model_ALL_Wav2Vec2

rossevine/Model_ALL_Wav2Vec2

Model_ALL_Wav2Vec2 is a automatic speech recognition model from rossevine. Use it when you need speech turned into text. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

Downloads · 30 days

18% of all-time downloads

All-time downloads

Public

Repo size

50.7 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.arpa4 GB · 76%

At a glance

Task: Automatic Speech Recognition
Library: transformers
Model type: wav2vec2
Access: Public
Created: Aug 29, 2023
Updated: Sep 4, 2023
SHA: 20633edb

Model card

From the Hugging Face model README

Model_ALL_Wav2Vec2

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.7779
Wer: 0.1975
Cer: 0.0813

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: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.8385	0.67	400	0.5656	0.3049	0.1100
0.3291	1.34	800	0.5395	0.3184	0.1128
0.258	2.01	1200	0.4904	0.2770	0.1030
0.217	2.68	1600	0.4673	0.2814	0.1073
0.1956	3.35	2000	0.5108	0.2697	0.1021
0.1872	4.02	2400	0.5531	0.2735	0.1050
0.168	4.69	2800	0.5113	0.2536	0.0967
0.1476	5.36	3200	0.6744	0.2420	0.0941
0.1531	6.04	3600	0.6433	0.2492	0.0962
0.1271	6.71	4000	0.5360	0.2392	0.0928
0.1362	7.38	4400	0.5451	0.2458	0.0958
0.1169	8.05	4800	0.6710	0.2470	0.0965
0.117	8.72	5200	0.5291	0.2480	0.0990
0.1146	9.39	5600	0.6168	0.2372	0.0927
0.1028	10.06	6000	0.5437	0.2294	0.0914
0.0918	10.73	6400	0.6350	0.2392	0.0947
0.1037	11.4	6800	0.6351	0.2346	0.0920
0.0926	12.07	7200	0.6677	0.2316	0.0924
0.0861	12.74	7600	0.5842	0.2301	0.0934
0.0791	13.41	8000	0.5862	0.2286	0.0916
0.08	14.08	8400	0.6183	0.2227	0.0900
0.0707	14.75	8800	0.5985	0.2351	0.0955
0.0719	15.42	9200	0.6327	0.2200	0.0897
0.0674	16.09	9600	0.6184	0.2193	0.0889
0.0612	16.76	10000	0.5501	0.2224	0.0912
0.0607	17.44	10400	0.5404	0.2233	0.0916
0.0612	18.11	10800	0.6111	0.2193	0.0889
0.0542	18.78	11200	0.6610	0.2196	0.0893
0.0517	19.45	11600	0.6083	0.2199	0.0905
0.0478	20.12	12000	0.6500	0.2130	0.0874
0.0464	20.79	12400	0.6671	0.2144	0.0863
0.0395	21.46	12800	0.7239	0.2113	0.0864
0.0391	22.13	13200	0.7791	0.2084	0.0851
0.0362	22.8	13600	0.6682	0.2083	0.0855
0.0396	23.47	14000	0.6608	0.2065	0.0848
0.0346	24.14	14400	0.7438	0.2065	0.0856
0.0368	24.81	14800	0.7382	0.2066	0.0842
0.0273	25.48	15200	0.7486	0.2020	0.0841
0.0286	26.15	15600	0.7566	0.2029	0.0838
0.0268	26.82	16000	0.7680	0.2015	0.0828
0.0248	27.49	16400	0.7499	0.1994	0.0813
0.0253	28.16	16800	0.7511	0.1998	0.0820
0.0228	28.83	17200	0.7686	0.1985	0.0820
0.0212	29.51	17600	0.7779	0.1975	0.0813

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu117
Datasets 1.18.3
Tokenizers 0.13.3

Task: Automatic Speech Recognition
Library: transformers
Type: wav2vec2
Created: Aug 29, 2023
Updated: Sep 4, 2023