Open-source Automatic Speech Recognition Model wav2vec2-large-voxrex-npsc

Wav2vec2 Large Voxrex Npsc

Developed by NbAiLab

An automatic speech recognition model fine-tuned on the NBAILAB/NPSC - 16K_MP3 dataset based on KBLab/wav2vec2-large-voxrex

Speech Recognition

Transformers

#Norwegian speech recognition #Low word error rate #Multi-scenario robustness

Downloads 37

Release Time : 3/2/2022

Model Overview

This model is an automatic speech recognition (ASR) model optimized for Norwegian speech recognition tasks, fine-tuned on the NPSC dataset, suitable for Norwegian speech-to-text tasks.

Model Features

Norwegian language optimization

Specially fine-tuned and optimized for Norwegian speech recognition tasks

Based on wav2vec2 architecture

Utilizes the powerful wav2vec2-large-voxrex base model

Robust speech processing

Capable of handling speech inputs under various speech qualities and environmental conditions

Model Capabilities

Norwegian speech recognition

Speech-to-text

16kHz audio processing

Use Cases

Speech transcription

Norwegian meeting minutes

Automatically transcribe Norwegian meeting recordings into text records

Achieves approximately 11% word error rate on the NPSC evaluation set

Voice assistant

Provide speech recognition capabilities for Norwegian voice assistants

🚀 wav2vec2-large-voxrex-npsc

This model is a fine - tuned version of KBLab/wav2vec2-large-voxrex on the NBAILAB/NPSC - 16K_MP3 dataset. It achieves the following results on the evaluation set:

Loss: nan
Wer: 1.0

🚀 Quick Start

This model is designed for automatic speech recognition. You can use it by leveraging the pre - trained weights and fine - tuning on your specific dataset.

✨ Features

Based on the pre - trained model KBLab/wav2vec2-large-voxrex.
Fine - tuned on the NBAILAB/NPSC - 16K_MP3 dataset.

📚 Documentation

Model description

This model is a fine - tuned version of KBLab/wav2vec2-large-voxrex on the NBAILAB/NPSC - 16K_MP3 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: 0.0001
train_batch_size: 16
eval_batch_size: 16
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: 2000
num_epochs: 15.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.9728	0.32	500	2.9449	1.0
2.5099	0.64	1000	1.8492	0.9910
0.7872	0.97	1500	0.4467	0.3774
0.5993	1.29	2000	0.3181	0.2819
0.5134	1.61	2500	0.2638	0.2401
0.4544	1.93	3000	0.2287	0.2091
0.4085	2.26	3500	0.2153	0.1918
0.3921	2.58	4000	0.2004	0.1804
0.4613	2.9	4500	0.1905	0.1732
0.3402	3.22	5000	0.1778	0.1659
0.3258	3.55	5500	0.1732	0.1571
0.3044	3.87	6000	0.1677	0.1497
0.2914	4.19	6500	0.1597	0.1420
0.278	4.51	7000	0.1574	0.1386
0.2858	4.84	7500	0.1552	0.1300
0.2585	5.16	8000	0.1523	0.1276
0.2827	5.48	8500	0.1448	0.1265
0.3365	5.8	9000	0.1411	0.1232
0.2488	6.13	9500	0.1456	0.1195
0.2406	6.45	10000	0.1414	0.1194
0.2488	6.77	10500	0.1393	0.1173
0.3084	7.09	11000	0.1379	0.1164
0.2365	7.41	11500	0.1387	0.1165
0.2217	7.74	12000	0.1381	0.1132
0.2381	8.06	12500	0.1360	0.1126
0.2329	8.38	13000	0.1357	0.1124
0.2103	8.7	13500	0.1335	0.1087
0.2366	9.03	14000	0.1388	0.1105
0.2289	9.35	14500	0.1383	0.1098
0.2486	9.67	15000	0.1386	0.1087
0.2772	9.99	15500	0.1598	0.1093
0.2728	10.32	16000	0.1814	0.1110
0.3437	10.64	16500	0.2505	0.1124
0.431	10.96	17000	0.2828	0.1143
0.3929	11.28	17500	0.2977	0.1149
0.4396	11.61	18000	0.3198	0.1170
0.59	11.93	18500	0.4158	0.1315
0.7813	12.25	19000	0.6123	0.2208
0.9345	12.57	19500	0.6815	0.2885
0.998	12.89	20000	0.7587	0.1991
1.0493	13.22	20500	0.7583	0.1996
1.438	13.54	21000	nan	1.0
0.0	13.86	21500	nan	1.0
0.0	14.18	22000	nan	1.0
0.0	14.51	22500	nan	1.0
0.0	14.83	23000	nan	1.0

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.2+cu113
Datasets 1.18.3.dev0
Tokenizers 0.11.0

📄 License

This model is licensed under the CC0 - 1.0 license.

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