mms-1b-lug-eng – AI Model by jq | AlphaNeural AI

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jq/
mms-1b-lug-eng

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automatic-speech-recognition

generated_from_trainer

facebook/mms-1b-all

endpoints_compatible

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This model is a fine-tuned version of facebook/mms-1b-all on the generator dataset. It achieves the following results on the evaluation set:

Loss: 0.1619
Wer Lug: 0.161
Wer Eng: 0.096
Wer Mean: 0.129

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0003
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: 100
training_steps: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Lug	Wer Eng	Wer Mean
0.1876	0.1	500	0.1711	0.183	0.106	0.144
0.1971	0.2	1000	0.1702	0.172	0.106	0.139
0.1898	0.3	1500	0.1687	0.168	0.108	0.138
0.1903	0.4	2000	0.1686	0.165	0.103	0.134
0.1888	0.5	2500	0.1663	0.165	0.096	0.131
0.1908	1.1	3000	0.1637	0.16	0.095	0.127
0.1792	1.2	3500	0.1642	0.157	0.094	0.125
0.1963	1.3	4000	0.1625	0.158	0.095	0.127
0.184	1.4	4500	0.1623	0.158	0.094	0.126
0.1888	1.5	5000	0.1619	0.161	0.096	0.129

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2