metadata

license: cc-by-nc-4.0
tags:
  - generated_from_trainer
base_model: utter-project/mHuBERT-147
datasets:
  - common_voice_15_0
metrics:
  - wer
model-index:
  - name: mHuBERT-147-br
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: common_voice_15_0
          type: common_voice_15_0
          config: br
          split: None
          args: br
        metrics:
          - type: wer
            value: 53.76572908956329
            name: Wer

mHuBERT-147-br

This model is a fine-tuned version of utter-project/mHuBERT-147 on the common_voice_15_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.7650
Wer: 53.7657
Cer: 18.3841

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: 3.7e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 40
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Cer	Validation Loss	Wer
6.5746	2.18	1000	99.8848	3.8929	100.0
2.8591	4.36	2000	51.1549	1.8873	97.5296
1.4189	6.54	3000	27.4120	1.0985	77.2853
0.9787	8.71	4000	0.8995	71.3360	24.4590
0.803	10.89	5000	0.8429	67.1817	22.9902
0.718	13.07	6000	0.8035	63.8879	21.6750
0.6359	15.25	7000	0.7927	62.2502	21.1144
0.5832	17.43	8000	0.7508	60.3072	20.3406
0.555	19.61	9000	0.7509	58.7990	19.8568
0.5167	21.79	10000	0.7757	58.0218	19.7569
0.4917	23.97	11000	0.7588	56.9671	19.4574
0.4629	26.14	12000	0.7710	55.6255	19.0792
0.4454	28.32	13000	0.7546	55.0888	18.8257
0.4235	30.5	14000	0.7548	54.9963	18.7240
0.4135	32.68	15000	0.7689	54.6725	18.6222
0.411	34.86	16000	0.7619	54.4504	18.5320
0.3934	37.04	17000	0.7621	53.9323	18.4014
0.3912	39.22	18000	0.7650	53.7657	18.3841

Framework versions

Transformers 4.39.1
Pytorch 2.0.1+cu117
Datasets 2.18.0
Tokenizers 0.15.2