Wav2vec2-xls-r-1b-korean Open-source Korean Speech Recognition Model - Free Deployment for Accurate Korean Speech Recognition

Wav2vec2 Xls R 1b Korean

Developed by anantoj

This model is a Korean automatic speech recognition model fine-tuned on the KRESNIK/ZEROTH_KOREAN - CLEAN dataset based on facebook/wav2vec2-xls-r-1b

Speech Recognition

Transformers

KoreanOpen Source License:Apache-2.0 #Korean speech recognition #Large parameter model #Low word error rate

Downloads 20

Release Time : 3/2/2022

Model Overview

This is an automatic speech recognition model optimized for Korean, based on the 1-billion-parameter XLS-R architecture and fine-tuned on Korean datasets

Model Features

Large-scale pre-training

Based on the 1-billion-parameter XLS-R architecture with powerful speech feature extraction capabilities

Korean optimization

Specially fine-tuned on Korean datasets to adapt to Korean speech characteristics

Efficient training

Uses mixed-precision training and gradient accumulation techniques to improve training efficiency

Model Capabilities

Korean speech recognition

Speech-to-text

Audio content understanding

Use Cases

Speech transcription

Korean meeting minutes

Automatically convert Korean meeting recordings into text transcripts

Achieved WER of 82.07 and CER of 42.12 on test set

Voice assistant

Used as the speech recognition module for Korean voice assistants

Educational technology

Korean learning applications

Help learners practice Korean pronunciation and listening

🚀 Wav2Vec2 XLS - R 1B Korean

This model is a fine - tuned version of [facebook/wav2vec2 - xls - r - 1b](https://huggingface.co/facebook/wav2vec2 - xls - r - 1b) on the KRESNIK/ZEROTH_KOREAN - CLEAN dataset. It is designed for automatic speech recognition tasks, offering a solution for converting spoken Korean language into text.

🚀 Quick Start

This model is a fine - tuned version of [facebook/wav2vec2 - xls - r - 1b](https://huggingface.co/facebook/wav2vec2 - xls - r - 1b) on the KRESNIK/ZEROTH_KOREAN - CLEAN dataset. It achieves the following results on the evaluation set:

Loss: 0.0639
Wer: 0.0449

✨ Features

Fine - tuned Model: Based on the pre - trained [facebook/wav2vec2 - xls - r - 1b](https://huggingface.co/facebook/wav2vec2 - xls - r - 1b), it has been fine - tuned on the KRESNIK/ZEROTH_KOREAN - CLEAN dataset for better performance in Korean speech recognition.
Evaluation Results: Achieves specific loss and WER values on the evaluation set, indicating its effectiveness.

📚 Documentation

Model description

This model is a fine - tuned version of [facebook/wav2vec2 - xls - r - 1b](https://huggingface.co/facebook/wav2vec2 - xls - r - 1b) on the KRESNIK/ZEROTH_KOREAN - CLEAN dataset.

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: 7.5e - 05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
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: 2000
num_epochs: 50.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
4.603	0.72	500	4.6572	0.9985
2.6314	1.44	1000	2.0424	0.9256
2.2708	2.16	1500	0.9889	0.6989
2.1769	2.88	2000	0.8366	0.6312
2.1142	3.6	2500	0.7555	0.5998
2.0084	4.32	3000	0.7144	0.6003
1.9272	5.04	3500	0.6311	0.5461
1.8687	5.75	4000	0.6252	0.5430
1.8186	6.47	4500	0.5491	0.4988
1.7364	7.19	5000	0.5463	0.4959
1.6809	7.91	5500	0.4724	0.4484
1.641	8.63	6000	0.4679	0.4461
1.572	9.35	6500	0.4387	0.4236
1.5256	10.07	7000	0.3970	0.4003
1.5044	10.79	7500	0.3690	0.3893
1.4563	11.51	8000	0.3752	0.3875
1.394	12.23	8500	0.3386	0.3567
1.3641	12.95	9000	0.3290	0.3467
1.2878	13.67	9500	0.2893	0.3135
1.2602	14.39	10000	0.2723	0.3029
1.2302	15.11	10500	0.2603	0.2989
1.1865	15.83	11000	0.2440	0.2794
1.1491	16.55	11500	0.2500	0.2788
1.093	17.27	12000	0.2279	0.2629
1.0367	17.98	12500	0.2076	0.2443
0.9954	18.7	13000	0.1844	0.2259
0.99	19.42	13500	0.1794	0.2179
0.9385	20.14	14000	0.1765	0.2122
0.8952	20.86	14500	0.1706	0.1974
0.8841	21.58	15000	0.1791	0.1969
0.847	22.3	15500	0.1780	0.2060
0.8669	23.02	16000	0.1608	0.1862
0.8066	23.74	16500	0.1447	0.1626
0.7908	24.46	17000	0.1457	0.1655
0.7459	25.18	17500	0.1350	0.1445
0.7218	25.9	18000	0.1276	0.1421
0.703	26.62	18500	0.1177	0.1302
0.685	27.34	19000	0.1147	0.1305
0.6811	28.06	19500	0.1128	0.1244
0.6444	28.78	20000	0.1120	0.1213
0.6323	29.5	20500	0.1137	0.1166
0.5998	30.22	21000	0.1051	0.1107
0.5706	30.93	21500	0.1035	0.1037
0.5555	31.65	22000	0.1031	0.0927
0.5389	32.37	22500	0.0997	0.0900
0.5201	33.09	23000	0.0920	0.0912
0.5146	33.81	23500	0.0929	0.0947
0.515	34.53	24000	0.1000	0.0953
0.4743	35.25	24500	0.0922	0.0892
0.4707	35.97	25000	0.0852	0.0808
0.4456	36.69	25500	0.0855	0.0779
0.443	37.41	26000	0.0843	0.0738
0.4388	38.13	26500	0.0816	0.0699
0.4162	38.85	27000	0.0752	0.0645
0.3979	39.57	27500	0.0761	0.0621
0.3889	40.29	28000	0.0771	0.0625
0.3923	41.01	28500	0.0755	0.0598
0.3693	41.73	29000	0.0730	0.0578
0.3642	42.45	29500	0.0739	0.0598
0.3532	43.17	30000	0.0712	0.0553
0.3513	43.88	30500	0.0762	0.0516
0.3349	44.6	31000	0.0731	0.0504
0.3305	45.32	31500	0.0725	0.0507
0.3285	46.04	32000	0.0709	0.0489
0.3179	46.76	32500	0.0667	0.0467
0.3158	47.48	33000	0.0653	0.0494
0.3033	48.2	33500	0.0638	0.0456
0.3023	48.92	34000	0.0644	0.0464
0.2975	49.64	34500	0.0643	0.0455

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.2+cu102
Datasets 1.18.3.dev0
Tokenizers 0.11.0

📄 License

This model is licensed under the Apache 2.0 license.

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