way2vec2-VNmese

ducha07/way2vec2-VNmese

way2vec2-VNmese is a automatic speech recognition model from ducha07. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as cc-by-nc-4.0.

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

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At a glance

Task: Automatic Speech Recognition
Library: transformers
License: cc-by-nc-4.0
Model type: wav2vec2
Access: Public
Created: Jan 11, 2024
Updated: Jan 12, 2024
SHA: 9ceac0d6

Base models

facebook/mms-1b-all

Datasets

ducha07/audio_HTV_thoisu

Model card

From the Hugging Face model README

ASR4-for-40-epochs

This model is a fine-tuned version of facebook/mms-1b-all on the HTV news dataset. It achieves the following results on the evaluation set:

Loss: 0.4791
Wer: 0.2684

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: 0.001
train_batch_size: 32
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 40
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
5.1111	0.92	100	0.7687	0.4387
1.1201	1.83	200	0.6388	0.3767
0.9734	2.75	300	0.6319	0.3658
0.9297	3.67	400	0.5740	0.3373
0.9142	4.59	500	0.5591	0.3268
0.8462	5.5	600	0.5627	0.3227
0.8366	6.42	700	0.5491	0.3158
0.8272	7.34	800	0.5398	0.3243
0.8137	8.26	900	0.5363	0.3113
0.7643	9.17	1000	0.5528	0.3117
0.7738	10.09	1100	0.5194	0.3285
0.7622	11.01	1200	0.5348	0.3043
0.707	11.93	1300	0.5179	0.2909
0.7242	12.84	1400	0.5153	0.3138
0.7093	13.76	1500	0.5116	0.2951
0.673	14.68	1600	0.5002	0.2941
0.6877	15.6	1700	0.4958	0.3050
0.6665	16.51	1800	0.5032	0.2865
0.6507	17.43	1900	0.4871	0.2809
0.6308	18.35	2000	0.4953	0.2947
0.6507	19.27	2100	0.4998	0.2837
0.6027	20.18	2200	0.4963	0.2868
0.623	21.1	2300	0.4955	0.2953
0.6047	22.02	2400	0.5034	0.2852
0.5825	22.94	2500	0.4781	0.2795
0.585	23.85	2600	0.4851	0.2843
0.5838	24.77	2700	0.4957	0.2742
0.5718	25.69	2800	0.4885	0.2810
0.5646	26.61	2900	0.4778	0.2724
0.5476	27.52	3000	0.4914	0.2751
0.5333	28.44	3100	0.4879	0.2788
0.5533	29.36	3200	0.4820	0.2726
0.5321	30.28	3300	0.4816	0.2686
0.5161	31.19	3400	0.4865	0.2812
0.5326	32.11	3500	0.4818	0.2704
0.5188	33.03	3600	0.4816	0.2669
0.506	33.94	3700	0.4804	0.2755
0.5122	34.86	3800	0.4803	0.2667
0.506	35.78	3900	0.4785	0.2708
0.5064	36.7	4000	0.4755	0.2730
0.4997	37.61	4100	0.4804	0.2708
0.4904	38.53	4200	0.4772	0.2678
0.4774	39.45	4300	0.4791	0.2684

Framework versions

Transformers 4.37.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0

Task: Automatic Speech Recognition
Library: transformers
Type: wav2vec2
License: cc-by-nc-4.0
Languages: vi
Created: Jan 11, 2024
Updated: Jan 12, 2024