This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset. It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets):

Loss: 0.137096
Wer: 0.196230

Model description

"facebook/wav2vec2-xls-r-300m" was finetuned.

Intended uses & limitations

More information needed

Training and evaluation data

Training data - Common voice Galician train.tsv, dev.tsv, invalidated.tsv, reported.tsv, and other.tsv Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0

Training procedure

For creating the training dataset, all possible datasets were appended and 90-10 split was used.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.000096
train_batch_size: 16
eval_batch_size: 16
seed: 13
gradient_accumulation_steps: 2
lr_scheduler_type: cosine_with_restarts
lr_scheduler_warmup_steps: 500
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Step	Training Loss	Validation Loss	Wer
500	5.038100	3.035432	1.000000
1000	2.180000	0.406300	0.557964
1500	0.331700	0.153797	0.262394
2000	0.171600	0.145268	0.235627
2500	0.125900	0.136622	0.228087
3000	0.105400	0.131650	0.224128
3500	0.087600	0.141032	0.217531
4000	0.078300	0.143675	0.214515
4500	0.070000	0.144607	0.208106
5000	0.061500	0.135259	0.202828
5500	0.055600	0.130638	0.203959
6000	0.050500	0.137416	0.202451
6500	0.046600	0.140379	0.200000
7000	0.040800	0.140179	0.200377
7500	0.041000	0.138089	0.196795
8000	0.038400	0.136927	0.197172

Framework versions

Transformers 4.16.0.dev0
Pytorch 1.10.0+cu102
Datasets 1.18.3
Tokenizers 0.10.3

Evaluation Commands

To evaluate on mozilla-foundation/common_voice_8_0 with split test

python eval.py --model_id Akashpb13/Galician_xlsr --dataset mozilla-foundation/common_voice_8_0 --config gl --split test