Wave2Vec2_OV_Vie

duyhngoc/Wave2Vec2_OV_Vie

Wave2Vec2_OV_Vie is a automatic speech recognition model from duyhngoc. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.

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

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At a glance

Task: Automatic Speech Recognition
Library: transformers
License: apache-2.0
Model type: wav2vec2
Access: Public
Created: Jun 21, 2023
Updated: Jun 25, 2023
SHA: d8dfdd82

Datasets

vivos

Model card

From the Hugging Face model README

Wave2Vec2_OV_Vie

This model is a fine-tuned version of facebook/wav2vec2-base on the VIVOS - NA dataset. It achieves the following results on the evaluation set:

Loss: 3.5894
Wer: 1.0

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: 15.0

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	0.27	100	3.9210	1.0
No log	0.55	200	3.4375	1.0
No log	0.82	300	3.4356	1.0
No log	1.1	400	3.4045	1.0
4.1866	1.37	500	3.4694	1.0
4.1866	1.65	600	3.6266	1.0
4.1866	1.92	700	3.5694	1.0
4.1866	2.19	800	3.5733	1.0
4.1866	2.47	900	3.6381	1.0
3.4376	2.74	1000	3.6604	1.0
3.4376	3.02	1100	3.5868	1.0
3.4376	3.29	1200	3.4988	1.0
3.4376	3.57	1300	3.5409	1.0
3.4376	3.84	1400	3.4883	1.0
3.4365	4.12	1500	3.6125	1.0
3.4365	4.39	1600	3.6123	1.0
3.4365	4.66	1700	3.5978	1.0
3.4365	4.94	1800	3.5693	1.0
3.4365	5.21	1900	3.5659	1.0
3.4339	5.49	2000	3.6234	1.0
3.4339	5.76	2100	3.5997	1.0
3.4339	6.04	2200	3.6529	1.0
3.4339	6.31	2300	3.5780	1.0
3.4339	6.58	2400	3.5844	1.0
3.4333	6.86	2500	3.5792	1.0
3.4333	7.13	2600	3.5468	1.0
3.4333	7.41	2700	3.5691	1.0
3.4333	7.68	2800	3.5408	1.0
3.4333	7.96	2900	3.5482	1.0
3.4294	8.23	3000	3.6070	1.0
3.4294	8.5	3100	3.5905	1.0
3.4294	8.78	3200	3.6018	1.0
3.4294	9.05	3300	3.6326	1.0
3.4294	9.33	3400	3.6214	1.0
3.4293	9.6	3500	3.6372	1.0
3.4293	9.88	3600	3.6215	1.0
3.4293	10.15	3700	3.5106	1.0
3.4293	10.43	3800	3.5066	1.0
3.4293	10.7	3900	3.5352	1.0
3.4295	10.97	4000	3.5129	1.0
3.4295	11.25	4100	3.6384	1.0
3.4295	11.52	4200	3.6019	1.0
3.4295	11.8	4300	3.5876	1.0
3.4295	12.07	4400	3.6207	1.0
3.4252	12.35	4500	3.5998	1.0
3.4252	12.62	4600	3.6216	1.0
3.4252	12.89	4700	3.6073	1.0
3.4252	13.17	4800	3.5567	1.0
3.4252	13.44	4900	3.5745	1.0
3.4274	13.72	5000	3.5738	1.0
3.4274	13.99	5100	3.5914	1.0
3.4274	14.27	5200	3.6004	1.0
3.4274	14.54	5300	3.5968	1.0
3.4274	14.81	5400	3.5908	1.0

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.13.0
Tokenizers 0.13.3

Task: Automatic Speech Recognition
Library: transformers
Type: wav2vec2
License: apache-2.0
Created: Jun 21, 2023
Updated: Jun 25, 2023