The open-source model of wav2vec2-xls-r-1b-npsc-bokmaal - Free implementation of automatic speech recognition for Norwegian Bokmål

Wav2vec2 Xls R 1b Npsc Bokmaal

Developed by NbAiLab

An automatic speech recognition model fine-tuned on the Norwegian written language (Bokmål) speech dataset based on the facebook/wav2vec2-xls-r-1b model

Speech Recognition

Transformers

Open Source License:Apache-2.0 #Norwegian Speech Recognition #High Precision WER #Bokmål Support

Downloads 23

Release Time : 3/2/2022

Model Overview

This is an automatic speech recognition model optimized for the Norwegian written language (Bokmål), fine-tuned on the NPSC dataset, with high recognition accuracy.

Model Features

High Accuracy

Achieves a word error rate of 7.9% and a character error rate of 2.97% on the NPSC test set

Large-scale Pretraining

Fine-tuned based on the 1-billion-parameter wav2vec2-xls-r-1b model

Norwegian Optimization

Speech recognition model specifically optimized for the Norwegian written language (Bokmål)

Model Capabilities

Norwegian written language speech recognition

Audio-to-text conversion

Speech content analysis

Use Cases

Speech Transcription

Meeting Minutes

Automatically transcribe Norwegian meeting recordings into text records

Highly accurate transcription results

Media Subtitle Generation

Automatically generate subtitles for Norwegian video content

Voice Assistants

Norwegian Voice Command Recognition

Speech recognition component for Norwegian voice assistant systems

🚀 wav2vec2-xls-r-1b-npsc

This model is a fine - tuned version of facebook/wav2vec2-xls-r-1b on the NbAiLab/NPSC (16K_mp3_bokmaal) dataset. It is designed for automatic speech recognition tasks and can achieve relatively low error rates on the evaluation set.

🚀 Quick Start

This model is ready - to - use for automatic speech recognition tasks. You can load it via the Hugging Face Transformers library.

📚 Documentation

Model Performance

It achieves the following results on the evaluation set:

Loss: 0.1598
WER: 0.0966

Model Details

Property	Details
Model Type	Fine - tuned version of facebook/wav2vec2-xls-r-1b
Training Data	NbAiLab/NPSC (16K_mp3_bokmaal)
Evaluation Metrics	- Test (Bokmål) WER: 0.07901700231893541 - Test (Bokmål) CER: 0.029734583252347752

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
0.8361	0.32	500	0.6304	0.4970
0.5703	0.64	1000	0.3195	0.2775
0.5451	0.97	1500	0.2700	0.2246
0.47	1.29	2000	0.2564	0.2329
0.4063	1.61	2500	0.2459	0.2099
0.374	1.93	3000	0.2175	0.1894
0.3297	2.26	3500	0.2036	0.1755
0.3145	2.58	4000	0.1957	0.1757
0.3989	2.9	4500	0.1923	0.1723
0.271	3.22	5000	0.1889	0.1649
0.2758	3.55	5500	0.1768	0.1588
0.2683	3.87	6000	0.1720	0.1534
0.2341	4.19	6500	0.1689	0.1471
0.2316	4.51	7000	0.1706	0.1405
0.2383	4.84	7500	0.1637	0.1426
0.2148	5.16	8000	0.1584	0.1347
0.2085	5.48	8500	0.1601	0.1387
0.2944	5.8	9000	0.1566	0.1294
0.1944	6.13	9500	0.1494	0.1271
0.1853	6.45	10000	0.1561	0.1247
0.235	6.77	10500	0.1461	0.1215
0.2286	7.09	11000	0.1447	0.1167
0.1781	7.41	11500	0.1502	0.1199
0.1714	7.74	12000	0.1425	0.1179
0.1725	8.06	12500	0.1427	0.1173
0.143	8.38	13000	0.1448	0.1142
0.154	8.7	13500	0.1392	0.1104
0.1447	9.03	14000	0.1404	0.1094
0.1471	9.35	14500	0.1404	0.1088
0.1479	9.67	15000	0.1414	0.1133
0.1607	9.99	15500	0.1458	0.1171
0.166	10.32	16000	0.1652	0.1264
0.188	10.64	16500	0.1713	0.1322
0.1461	10.96	17000	0.1423	0.1111
0.1289	11.28	17500	0.1388	0.1097
0.1273	11.61	18000	0.1438	0.1074
0.1317	11.93	18500	0.1312	0.1066
0.1448	12.25	19000	0.1446	0.1042
0.1424	12.57	19500	0.1386	0.1015
0.1392	12.89	20000	0.1379	0.1005
0.1408	13.22	20500	0.1408	0.0992
0.1239	13.54	21000	0.1338	0.0968
0.1244	13.86	21500	0.1335	0.0957
0.1254	14.18	22000	0.1382	0.0950
0.1597	14.51	22500	0.1544	0.0970
0.1566	14.83	23000	0.1589	0.0963

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 Apache 2.0 license.

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