wav2vec2-xls-r-300m-gn-cv8 Open-source Speech Recognition Model - Supports Automatic Speech Recognition for Guarani

Wav2vec2 Xls R 300m Gn Cv8

Developed by lgris

This is an automatic speech recognition (ASR) model fine-tuned on the Common Voice 8 dataset based on the facebook/wav2vec2-xls-r-300m model, supporting Guarani (gn).

Speech Recognition

Transformers

OtherOpen Source License:Apache-2.0 #Guarani speech recognition #Low-resource language model #WER optimization

Downloads 16

Release Time : 3/2/2022

Model Overview

This model is an automatic speech recognition system for Guarani (gn), fine-tuned based on the wav2vec2-xls-r-300m architecture, suitable for speech-to-text tasks.

Model Features

Multilingual support

Focused on Guarani speech recognition

Based on large-scale pre-trained model

Fine-tuned on facebook/wav2vec2-xls-r-300m

Open-source license

Uses Apache 2.0 license, allowing commercial use

Model Capabilities

Speech-to-text

Guarani speech recognition

Use Cases

Speech transcription

Guarani speech transcription

Convert Guarani speech to text

WER of 69.05 on Common Voice 8 test set

Voice assistance technology

Guarani voice assistant

Develop voice-controlled applications for Guarani users

🚀 wav2vec2-xls-r-300m-gn-cv8

This model is a fine - tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset, which is used for automatic speech recognition.

🚀 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: 0.9392
Wer: 0.7033

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
training_steps: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
20.0601	5.54	100	5.1622	1.0
3.7052	11.11	200	3.2869	1.0
3.3275	16.65	300	3.2162	1.0
3.2984	22.22	400	3.1638	1.0
3.1111	27.76	500	2.5541	1.0
2.238	33.32	600	1.2198	0.9616
1.5284	38.86	700	0.9571	0.8593
1.2735	44.43	800	0.8719	0.8363
1.1269	49.97	900	0.8334	0.7954
1.0427	55.54	1000	0.7700	0.7749
1.0152	61.11	1100	0.7747	0.7877
0.943	66.65	1200	0.7151	0.7442
0.9132	72.22	1300	0.7224	0.7289
0.8397	77.76	1400	0.7354	0.7059
0.8577	83.32	1500	0.7285	0.7263
0.7931	88.86	1600	0.7863	0.7084
0.7995	94.43	1700	0.7562	0.6880
0.799	99.97	1800	0.7905	0.7059
0.7373	105.54	1900	0.7791	0.7161
0.749	111.11	2000	0.8125	0.7161
0.6925	116.65	2100	0.7722	0.6905
0.7034	122.22	2200	0.8989	0.7136
0.6745	127.76	2300	0.8270	0.6982
0.6837	133.32	2400	0.8569	0.7161
0.6689	138.86	2500	0.8339	0.6982
0.6471	144.43	2600	0.8441	0.7110
0.615	149.97	2700	0.9038	0.7212
0.6477	155.54	2800	0.9089	0.7059
0.6047	161.11	2900	0.9149	0.7059
0.5613	166.65	3000	0.8582	0.7263
0.6017	172.22	3100	0.8787	0.7084
0.5546	177.76	3200	0.8753	0.6957
0.5747	183.32	3300	0.9167	0.7212
0.5535	188.86	3400	0.8448	0.6905
0.5331	194.43	3500	0.8644	0.7161
0.5428	199.97	3600	0.8730	0.7033
0.5219	205.54	3700	0.9047	0.6982
0.5158	211.11	3800	0.8706	0.7033
0.5107	216.65	3900	0.9139	0.7084
0.4903	222.22	4000	0.9456	0.7315
0.4772	227.76	4100	0.9475	0.7161
0.4713	233.32	4200	0.9237	0.7059
0.4743	238.86	4300	0.9305	0.6957
0.4705	244.43	4400	0.9561	0.7110
0.4908	249.97	4500	0.9389	0.7084
0.4717	255.54	4600	0.9234	0.6982
0.4462	261.11	4700	0.9323	0.6957
0.4556	266.65	4800	0.9432	0.7033
0.4691	272.22	4900	0.9389	0.7059
0.4601	277.76	5000	0.9392	0.7033

Framework versions

Transformers 4.16.0
Pytorch 1.10.0+cu111
Datasets 1.18.1
Tokenizers 0.11.0

📄 License

The model is licensed under the apache - 2.0 license.

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