metadata

license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
  - generated_from_trainer
datasets:
  - nena_speech_1_0_test
metrics:
  - wer
model-index:
  - name: wav2vec2-large-mms-1b-barwar
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: nena_speech_1_0_test
          type: nena_speech_1_0_test
          config: barwar
          split: test
          args: barwar
        metrics:
          - name: Wer
            type: wer
            value: 1

wav2vec2-large-mms-1b-barwar

This model is a fine-tuned version of facebook/mms-1b-all on the nena_speech_1_0_test dataset. It achieves the following results on the evaluation set:

Loss: 3.4355
Wer: 1.0
Cer: 0.3295

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: 32
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
16.2134	0.15	25	17.6705	1.0	1.1562
6.1907	0.3	50	4.2262	0.9815	0.9986
3.6438	0.45	75	4.1625	1.0	0.7804
3.1627	0.6	100	4.1537	1.0	0.4727
2.2322	0.75	125	3.8028	1.0	0.4554
3.0705	0.9	150	3.3680	1.0	0.4352
2.9176	1.05	175	3.2934	1.0	0.4279
2.2255	1.2	200	3.6359	1.0	0.3926
2.4518	1.35	225	3.2249	1.0	0.3863
1.9254	1.5	250	3.7029	1.0	0.3875
2.7212	1.65	275	3.5201	1.0	0.3673
2.7976	1.8	300	3.3253	1.0	0.3986
2.0545	1.95	325	3.6138	1.0	0.3554
2.3335	2.1	350	3.5161	1.0	0.3554
2.0049	2.25	375	3.4727	1.0	0.3543
2.8896	2.4	400	3.2484	1.0	0.3535
1.9641	2.54	425	3.4330	1.0	0.3485
1.9649	2.69	450	3.8596	1.0	0.3444
2.0422	2.84	475	3.4291	1.0	0.3506
2.4093	2.99	500	3.3137	1.0	0.3434
1.8187	3.14	525	3.4423	1.0	0.3415
1.7495	3.29	550	3.5614	1.0	0.3431
2.0658	3.44	575	3.0324	1.0	0.3543
1.5128	3.59	600	3.6654	1.0	0.3452
1.7876	3.74	625	3.8747	1.0	0.3388
3.8652	3.89	650	2.9874	1.0	0.3387
2.8945	4.04	675	3.3015	1.0	0.3344
1.9763	4.19	700	3.1970	1.0	0.3389
2.0538	4.34	725	3.4811	1.0	0.3316
1.7723	4.49	750	3.6706	1.0	0.3305
2.0489	4.64	775	3.4281	1.0	0.3312
2.555	4.79	800	3.2610	1.0	0.3341
1.6591	4.94	825	3.4355	1.0	0.3295

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu121
Datasets 2.14.6
Tokenizers 0.14.1