wav2vec2-xls-r-300m-npsc-bokmaal Open-source Speech Recognition Model - Accurately Recognize Norwegian Written Language

Wav2vec2 Xls R 300m Npsc Bokmaal

Developed by NbAiLab

This is an automatic speech recognition model based on the XLS-R architecture, specifically trained for Norwegian written language (Bokmål) and performs excellently on the NPSC dataset.

Speech Recognition

Transformers

Open Source License:Apache-2.0 #Norwegian speech recognition #Low word error rate #Bokmål support

Downloads 23

Release Time : 3/2/2022

Model Overview

This model is an automatic speech recognition system trained on Facebook's wav2vec2-xls-r-300m architecture, specifically designed for speech-to-text tasks in Norwegian written language (Bokmål).

Model Features

Norwegian language optimization

Specifically trained and optimized for Norwegian written language (Bokmål)

High performance

Achieves a word error rate of 7.56% and a character error rate of 2.82% on the NPSC test set

Large-scale training

Trained using the NPSC dataset, which contains rich Norwegian speech data

Model Capabilities

Norwegian speech recognition

Speech-to-text

Audio content transcription

Use Cases

Speech transcription

Meeting minutes

Automatically transcribe Norwegian meeting recordings into text

Highly accurate transcription results

Media subtitle generation

Automatically generate subtitles for Norwegian video content

Enhances media content accessibility

Voice assistants

Norwegian voice command recognition

Used for understanding voice commands in Norwegian voice assistant systems

Improves voice interaction accuracy

🚀 wav2vec2-xls-r-300m-npsc-bokmaal

This model is designed for automatic speech recognition. It was trained from scratch on the None dataset and achieves good results on the evaluation set.

📚 Documentation

Model Information

Property	Details
Model Type	wav2vec2-xls-r-300m-npsc-bokmaal
Tags	generated_from_trainer, automatic-speech-recognition, NbAiLab/NPSC, robust-speech-event, false, nb-NO, hf-asr-leaderboard
Datasets	NbAiLab/NPSC
Language	nb-NO

Evaluation Results

This model achieves the following results on the NPSC dataset (16K_mp3_bokmaal):

Test (Bokmål) WER: 0.07556265455560153
Test (Bokmål) CER: 0.028191288775481386

On the evaluation set:

Loss: 0.1663
Wer: 0.0932

Training Procedure

Training Hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
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: 500
num_epochs: 15.0
mixed_precision_training: Native AMP

Training Results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0969	0.32	500	0.1773	0.1054
0.0929	0.64	1000	0.1672	0.1061
0.1018	0.97	1500	0.1770	0.1067
0.0871	1.29	2000	0.1832	0.1087
0.0908	1.61	2500	0.1830	0.1101
0.0975	1.93	3000	0.1848	0.1100
0.0936	2.26	3500	0.1853	0.1113
0.1025	2.58	4000	0.1958	0.1149
0.0989	2.9	4500	0.1776	0.1123
0.0946	3.22	5000	0.1825	0.1097
0.0859	3.55	5500	0.1864	0.1072
0.0867	3.87	6000	0.1886	0.1081
0.0783	4.19	6500	0.1883	0.1063
0.0804	4.51	7000	0.1831	0.1063
0.0797	4.84	7500	0.1884	0.1058
0.0705	5.16	8000	0.1802	0.1057
0.0795	5.48	8500	0.1854	0.1038
0.0711	5.8	9000	0.1766	0.1032
0.0973	6.13	9500	0.1663	0.1014
0.087	6.45	10000	0.1664	0.1014
0.0962	6.77	10500	0.1631	0.1009
0.0857	7.09	11000	0.1659	0.1002
0.0882	7.41	11500	0.1668	0.1007
0.0784	7.74	12000	0.1688	0.0996
0.0838	8.06	12500	0.1675	0.0984
0.0863	8.38	13000	0.1639	0.0979
0.0763	8.7	13500	0.1638	0.0980
0.0822	9.03	14000	0.1709	0.0972
0.0769	9.35	14500	0.1700	0.0965
0.0838	9.67	15000	0.1703	0.0974
0.0799	9.99	15500	0.1667	0.0957
0.0712	10.32	16000	0.1754	0.0960
0.0737	10.64	16500	0.1725	0.0968
0.0851	10.96	17000	0.1733	0.0958
0.076	11.28	17500	0.1682	0.0954
0.0712	11.61	18000	0.1713	0.0943
0.0745	11.93	18500	0.1662	0.0951
0.0864	12.25	19000	0.1692	0.0947
0.0937	12.57	19500	0.1624	0.0943
0.0915	12.89	20000	0.1678	0.0942
0.0926	13.22	20500	0.1641	0.0945
0.0912	13.54	21000	0.1665	0.0937
0.0917	13.86	21500	0.1648	0.0936
0.094	14.18	22000	0.1635	0.0935
0.0864	14.51	22500	0.1678	0.0934
0.0899	14.83	23000	0.1663	0.0932

Framework Versions

Transformers 4.17.0.dev0
Pytorch 1.10.2+cu113
Datasets 1.18.4.dev0
Tokenizers 0.11.0

📄 License

This model is released under the Apache-2.0 license.

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご