metadata

model-index:
  - name: mHuBERT-147-br
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_15_0
          type: common_voice_15_0
          config: br
          split: test
          args: br
        metrics:
          - name: WER
            type: wer
            value: 47
          - name: CER
            type: cer
            value: 16.7
language:
  - br
metrics:
  - wer
base_model: utter-project/mHuBERT-147
pipeline_tag: automatic-speech-recognition
datasets:
  - mozilla-foundation/common_voice_15_0

mHuBERT-147-br

This model is a fine-tuned version of utter-project/mHuBERT-147 on Mozilla Common Voice 15 Breton dataset and Roadennoù dataset. It achieves the following results on the validation set:

Loss: 0.7331
Wer: 50.09
Cer: 16.45

Model description

This model was trained to assess the performance of mHubert-147 for finetuning a Breton ASR model.

Intended uses & limitations

This model is a research model and shouldn't be used in production.

Training and evaluation data

90% of the Roadennoù dataset was used for training, the remaining 10% was used for validation in addition to MCV15-br validation dataset.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3.8e-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: 52
mixed_precision_training: Native AMP

Framework versions

Transformers 4.39.1
Pytorch 2.0.1+cu117
Datasets 2.18.0
Tokenizers 0.15.2