XLS-R-300M-NPSC-4 Open-source Speech Recognition Model - Free and Accurate Norwegian Speech-to-Text Conversion

Xls R 300m Npsc 4

Developed by Rolv-Arild

This model is an automatic speech recognition model fine-tuned on the Norwegian Parliamentary Speech Corpus (NPSC) based on facebook/wav2vec2-xls-r-300m, supporting Norwegian speech-to-text tasks.

Speech Recognition

Transformers

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

Downloads 18

Release Time : 3/2/2022

Model Overview

This is an automatic speech recognition (ASR) model for Norwegian, fine-tuned on the 16K_MP3 dataset of the Norwegian Parliamentary Speech Corpus (NPSC), capable of converting Norwegian speech into text.

Model Features

High-accuracy Norwegian recognition

Achieved a word error rate (WER) of 16.97% on the NPSC evaluation set

Based on large-scale pre-trained model

Fine-tuned from facebook's wav2vec2-xls-r-300m model, with powerful speech feature extraction capabilities

Domain-specific optimization

Trained using Norwegian parliamentary speech data, suitable for formal Norwegian speech recognition

Model Capabilities

Norwegian speech recognition

16kHz audio processing

MP3 format support

Use Cases

Government & Parliament

Parliament meeting minutes

Automatically transcribe Norwegian parliamentary meeting content

Can replace manual transcription, improving efficiency

Education

Norwegian language learning aid

Help learners check Norwegian pronunciation accuracy

🚀 Fine-tuned Speech Recognition Model

This model is a fine - tuned version of the pre - trained model, which aims to enhance performance on speech recognition tasks. It fine - tunes facebook/wav2vec2-xls-r-300m on the NBAILAB/NPSC - 16K_MP3 dataset, achieving good results on the evaluation set.

🚀 Quick Start

This model is a fine - tuned version of facebook/wav2vec2-xls-r-300m on the NBAILAB/NPSC - 16K_MP3 dataset. It achieves the following results on the evaluation set:

Loss: 0.1957
Wer: 0.1697

🔧 Technical Details

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 7.5e - 05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 2000
num_epochs: 20.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
4.4527	0.28	250	4.0144	1.0
3.1828	0.56	500	3.1369	1.0
2.9927	0.85	750	3.0183	1.0
2.9591	1.13	1000	2.9991	1.0
2.8989	1.41	1250	2.9000	1.0000
2.4286	1.69	1500	1.7688	0.9550
1.6765	1.98	1750	0.6842	0.4855
1.4521	2.26	2000	0.5096	0.3736
1.3589	2.54	2250	0.4479	0.3335
1.3136	2.82	2500	0.4056	0.3123
1.2856	3.11	2750	0.3870	0.2987
1.2283	3.39	3000	0.3646	0.2828
1.2053	3.67	3250	0.3499	0.2748
1.2087	3.95	3500	0.3345	0.2603
1.2002	4.24	3750	0.3320	0.2523
1.1383	4.52	4000	0.3117	0.2439
1.1364	4.8	4250	0.3198	0.2383
1.158	5.08	4500	0.3071	0.2342
1.108	5.37	4750	0.3011	0.2314
1.1025	5.65	5000	0.2875	0.2289
1.0697	5.93	5250	0.2926	0.2256
1.0904	6.21	5500	0.2695	0.2245
1.0802	6.5	5750	0.2602	0.2189
1.0882	6.78	6000	0.2603	0.2168
1.0881	7.06	6250	0.2540	0.2293
1.0378	7.34	6500	0.2614	0.2193
1.0397	7.63	6750	0.2707	0.2104
1.0296	7.91	7000	0.2483	0.2119
1.0249	8.19	7250	0.2483	0.2047
1.013	8.47	7500	0.2487	0.2042
1.0064	8.76	7750	0.2456	0.2016
1.0668	9.04	8000	0.2397	0.1995
1.0129	9.32	8250	0.2374	0.1994
1.0164	9.6	8500	0.2206	0.1992
0.975	9.89	8750	0.2247	0.1973
0.9849	10.17	9000	0.2325	0.1953
0.9826	10.45	9250	0.2301	0.1934
0.9835	10.73	9500	0.2192	0.1942
0.9676	11.02	9750	0.2266	0.1913
0.9627	11.3	10000	0.2193	0.1921
0.976	11.58	10250	0.2309	0.1882
0.969	11.86	10500	0.2268	0.1886
0.9611	12.15	10750	0.2322	0.1863
0.9397	12.43	11000	0.2197	0.1844
0.9601	12.71	11250	0.2211	0.1871
0.9718	12.99	11500	0.2079	0.1898
0.9347	13.28	11750	0.2054	0.1843
0.9377	13.56	12000	0.2031	0.1842
0.934	13.84	12250	0.2059	0.1806
0.9295	14.12	12500	0.2122	0.1861
0.935	14.41	12750	0.2072	0.1787
0.9021	14.69	13000	0.2105	0.1781
0.9193	14.97	13250	0.2035	0.1786
0.9214	15.25	13500	0.2035	0.1766
0.9048	15.54	13750	0.1964	0.1758
0.9006	15.82	14000	0.1984	0.1757
0.9027	16.1	14250	0.2022	0.1743
0.9083	16.38	14500	0.1969	0.1744
0.9761	16.67	14750	0.1963	0.1728
0.9311	16.95	15000	0.1960	0.1737
0.886	17.23	15250	0.1929	0.1726
0.8969	17.51	15500	0.1928	0.1734
0.9084	17.8	15750	0.1937	0.1713
0.8795	18.08	16000	0.1978	0.1709
0.8883	18.36	16250	0.1956	0.1703
0.8901	18.64	16500	0.1933	0.1705
0.8922	18.93	16750	0.1962	0.1711
0.8765	19.21	17000	0.1962	0.1711
0.8992	19.49	17250	0.1965	0.1703
0.8778	19.77	17500	0.1957	0.1699

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.0+cu113
Datasets 1.18.1
Tokenizers 0.11.0

📄 License

This model is licensed under the Apache 2.0 license.

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