metadata

library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
  - generated_from_trainer
datasets:
  - fleurs
metrics:
  - wer
model-index:
  - name: mms-1b-all-lg-CV-Fleurs_filtered-100hrs-v2
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: fleurs
          type: fleurs
          config: lg_ug
          split: test
          args: lg_ug
        metrics:
          - name: Wer
            type: wer
            value: 0.4090379008746356

mms-1b-all-lg-CV-Fleurs_filtered-100hrs-v2

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

Loss: 0.2933
Wer: 0.4090
Cer: 0.0749

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: 0.001
train_batch_size: 4
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 70
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.7083	1.0	7125	0.3181	0.4363	0.0781
0.2153	2.0	14250	0.3057	0.4327	0.0775
0.2092	3.0	21375	0.2982	0.4040	0.0738
0.207	4.0	28500	0.3020	0.4057	0.0740
0.2047	5.0	35625	0.3008	0.4136	0.0790
0.2025	6.0	42750	0.3010	0.4156	0.0763
0.1989	7.0	49875	0.3064	0.4101	0.0754
0.1989	8.0	57000	0.2903	0.4086	0.0751
0.1973	9.0	64125	0.2927	0.4	0.0737
0.1961	10.0	71250	0.2882	0.3986	0.0736
0.1952	11.0	78375	0.2895	0.4068	0.0741
0.1943	12.0	85500	0.2950	0.4096	0.0754
0.1933	13.0	92625	0.2945	0.4086	0.0749
0.1926	14.0	99750	0.2933	0.4004	0.0734
0.1912	15.0	106875	0.2925	0.4180	0.0755
0.1909	16.0	114000	0.2949	0.4149	0.0751
0.1902	17.0	121125	0.2888	0.4045	0.0740
0.189	18.0	128250	0.2856	0.4086	0.0744
0.1885	19.0	135375	0.2933	0.4125	0.0745
0.187	20.0	142500	0.2930	0.4115	0.0746
0.1877	21.0	149625	0.2886	0.4023	0.0737
0.1867	22.0	156750	0.2933	0.4009	0.0730
0.1863	23.0	163875	0.2893	0.4040	0.0738
0.1846	24.0	171000	0.2920	0.4146	0.0753
0.185	25.0	178125	0.2907	0.4017	0.0730
0.1836	26.0	185250	0.2939	0.3992	0.0730
0.1827	27.0	192375	0.2934	0.4144	0.0760
0.1827	28.0	199500	0.2962	0.4038	0.0736
0.1818	29.0	206625	0.2917	0.4063	0.0750
0.1818	30.0	213750	0.2933	0.4090	0.0749

Framework versions

Transformers 4.46.2
Pytorch 2.1.0+cu118
Datasets 3.1.0
Tokenizers 0.20.3