Wav2vec2-xls-r-300m-ab-CV8 Open-source Model - Free Automatic Speech Recognition for Abkhaz

Wav2vec2 Xls R 300m Ab CV8

Developed by emre

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Common Voice dataset for Abkhaz automatic speech recognition tasks.

Speech Recognition

Transformers

OtherOpen Source License:Apache-2.0 #Abkhaz speech recognition #Low-resource language ASR #XLS-R architecture

Downloads 20

Release Time : 3/2/2022

Model Overview

This is an optimized automatic speech recognition (ASR) model for the Abkhaz language (ab), fine-tuned based on the wav2vec2-xls-r-300m architecture and trained on the Common Voice 8 dataset.

Model Features

Multilingual pre-training foundation

Based on facebook/wav2vec2-xls-r-300m, a powerful multilingual pre-trained model

Language-specific optimization

Specially fine-tuned for the Abkhaz language

Efficient training

Optimized training efficiency using mixed-precision training and gradient accumulation techniques

Model Capabilities

Abkhaz speech recognition

Speech-to-text

Use Cases

Speech transcription

Abkhaz speech transcription

Convert Abkhaz speech to text

Test WER 44.9

🚀 wav2vec2-xls-r-300m-ab-CV8

This model is a fine - tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. It can achieve high - quality automatic speech recognition, providing accurate speech - to - text conversion.

🚀 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: 0.2105
Wer: 0.5474

📚 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: 16
eval_batch_size: 8
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: 300
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
4.7729	0.63	500	3.0624	1.0021
2.7348	1.26	1000	1.0460	0.9815
1.2756	1.9	1500	0.4618	0.8309
1.0419	2.53	2000	0.3725	0.7449
0.9491	3.16	2500	0.3368	0.7345
0.9006	3.79	3000	0.3014	0.6936
0.8519	4.42	3500	0.2852	0.6767
0.8243	5.06	4000	0.2701	0.6504
0.7902	5.69	4500	0.2641	0.6221
0.7767	6.32	5000	0.2549	0.6192
0.7516	6.95	5500	0.2515	0.6179
0.737	7.59	6000	0.2408	0.5963
0.7217	8.22	6500	0.2429	0.6261
0.7101	8.85	7000	0.2366	0.5687
0.6922	9.48	7500	0.2277	0.5680
0.6866	10.11	8000	0.2242	0.5847
0.6703	10.75	8500	0.2222	0.5803
0.6649	11.38	9000	0.2247	0.5765
0.6513	12.01	9500	0.2182	0.5644
0.6369	12.64	10000	0.2128	0.5508
0.6425	13.27	10500	0.2132	0.5514
0.6399	13.91	11000	0.2116	0.5495
0.6208	14.54	11500	0.2105	0.5474

Framework versions

Transformers 4.11.3
Pytorch 1.10.0+cu111
Datasets 1.18.1
Tokenizers 0.10.3

📄 License

This model is released under the Apache 2.0 license.

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