wav2vec2-large-xls-r-300m-kinyarwanda Open-source Model - Accurately Realize Kinyarwanda Speech Recognition

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Wav2vec2 Large Xls R 300m Kinyarwanda

Developed by peter2000

A Kinyarwanda speech recognition model fine-tuned on the common_voice dataset based on facebook/wav2vec2-xls-r-300m

Speech Recognition

Transformers

Open Source License:Apache-2.0 #Kinyarwanda speech recognition #Low-resource language model #XLS-R architecture

Downloads 13

Release Time : 5/27/2022

Model Overview

This is a speech recognition model optimized for Kinyarwanda, fine-tuned on the wav2vec2-xls-r-300m architecture, suitable for speech-to-text tasks

Model Features

Efficient Speech Recognition

Speech recognition capabilities optimized for Kinyarwanda

Based on Large-scale Pretraining

Fine-tuned from the facebook/wav2vec2-xls-r-300m pretrained model

Relatively Low Word Error Rate

Achieved a word error rate of 0.3246 on the evaluation set

Model Capabilities

Kinyarwanda speech recognition

Audio-to-text conversion

Use Cases

Speech Transcription

Kinyarwanda Speech Transcription

Convert Kinyarwanda speech content into text

Word error rate 0.3246

Voice Assistants

Kinyarwanda Voice Interaction

Provide speech recognition capabilities for Kinyarwanda voice assistants

🚀 wav2vec2-large-xls-r-300m-kinyarwanda

This model is a fine - tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. It offers enhanced performance for speech - related tasks in Kinyarwanda.

🚀 Quick Start

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

Loss: 0.3917
Wer: 0.3246

🔧 Technical Details

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 7e - 05
train_batch_size: 12
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 24
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 400
num_epochs: 8
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
9.0634	0.12	400	3.0554	1.0
2.8009	0.24	800	1.5927	0.9554
0.9022	0.36	1200	0.7328	0.6445
0.6213	0.48	1600	0.6138	0.5510
0.5299	0.6	2000	0.6072	0.5223
0.4999	0.72	2400	0.5449	0.4969
0.4731	0.84	2800	0.5261	0.4828
0.458	0.96	3200	0.5058	0.4607
0.4158	1.09	3600	0.4892	0.4463
0.4037	1.21	4000	0.4759	0.4429
0.4021	1.33	4400	0.4615	0.4330
0.3934	1.45	4800	0.4593	0.4315
0.3808	1.57	5200	0.4736	0.4344
0.3838	1.69	5600	0.4569	0.4249
0.3726	1.81	6000	0.4473	0.4140
0.3623	1.93	6400	0.4403	0.4097
0.3517	2.05	6800	0.4389	0.4061
0.333	2.17	7200	0.4383	0.4104
0.3354	2.29	7600	0.4360	0.3955
0.3257	2.41	8000	0.4226	0.3942
0.3275	2.53	8400	0.4206	0.4040
0.3262	2.65	8800	0.4172	0.3875
0.3206	2.77	9200	0.4209	0.3877
0.323	2.89	9600	0.4177	0.3825
0.3099	3.01	10000	0.4101	0.3691
0.3008	3.14	10400	0.4055	0.3709
0.2918	3.26	10800	0.4085	0.3800
0.292	3.38	11200	0.4089	0.3713
0.292	3.5	11600	0.4092	0.3730
0.2785	3.62	12000	0.4151	0.3687
0.2941	3.74	12400	0.4004	0.3639
0.2838	3.86	12800	0.4108	0.3703
0.2854	3.98	13200	0.3911	0.3596
0.2683	4.1	13600	0.3944	0.3575
0.2647	4.22	14000	0.3836	0.3538
0.2704	4.34	14400	0.4006	0.3540
0.2664	4.46	14800	0.3974	0.3553
0.2662	4.58	15200	0.3890	0.3470
0.2615	4.7	15600	0.3856	0.3507
0.2553	4.82	16000	0.3814	0.3497
0.2587	4.94	16400	0.3837	0.3440
0.2522	5.06	16800	0.3834	0.3486
0.2451	5.19	17200	0.3897	0.3414
0.2423	5.31	17600	0.3864	0.3481
0.2434	5.43	18000	0.3808	0.3416
0.2525	5.55	18400	0.3795	0.3408
0.2427	5.67	18800	0.3841	0.3411
0.2411	5.79	19200	0.3804	0.3366
0.2404	5.91	19600	0.3800	0.3328
0.2372	6.03	20000	0.3749	0.3335
0.2244	6.15	20400	0.3820	0.3327
0.2381	6.27	20800	0.3789	0.3325
0.2294	6.39	21200	0.3867	0.3298
0.2378	6.51	21600	0.3843	0.3281
0.2312	6.63	22000	0.3813	0.3277
0.2411	6.75	22400	0.3780	0.3268
0.2315	6.87	22800	0.3790	0.3280
0.241	6.99	23200	0.3776	0.3281
0.2313	7.11	23600	0.3929	0.3283
0.2423	7.24	24000	0.3905	0.3280
0.2337	7.36	24400	0.3979	0.3249
0.2368	7.48	24800	0.3980	0.3257
0.2409	7.6	25200	0.3937	0.3229
0.2416	7.72	25600	0.3867	0.3237
0.2364	7.84	26000	0.3912	0.3253
0.234	7.96	26400	0.3917	0.3246

Framework versions

Transformers 4.11.3
Pytorch 1.10.0+cu113
Datasets 1.18.3
Tokenizers 0.10.3

📄 License

This model is licensed under the Apache - 2.0 license.

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