Badr Abdullah
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metadata
license: cc-by-nc-4.0
base_model: utter-project/mHuBERT-147
tags:
  - generated_from_trainer
datasets:
  - common_voice_17_0
metrics:
  - wer
model-index:
  - name: mHuBERT-147-upper-sorbian
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_17_0
          type: common_voice_17_0
          config: hsb
          split: validation
          args: hsb
        metrics:
          - name: Wer
            type: wer
            value: 1

Visualize in Weights & Biases

mHuBERT-147-upper-sorbian

This model is a fine-tuned version of utter-project/mHuBERT-147 on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

  • Loss: 3.2172
  • Wer: 1.0
  • Cer: 1.0

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: 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: 500
  • num_epochs: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
4.0738 3.9216 100 4.0797 1.0 1.0
3.223 7.8431 200 3.2273 1.0 1.0
3.1741 11.7647 300 3.2232 1.0 1.0
3.2292 15.6863 400 3.2237 1.0 1.0
3.2105 19.6078 500 3.2269 1.0 1.0
3.1911 23.5294 600 3.2202 1.0 1.0
3.2626 27.4510 700 3.2177 1.0 1.0
3.21 31.3725 800 3.2232 1.0 1.0
3.1871 35.2941 900 3.2211 1.0 1.0
3.2224 39.2157 1000 3.2249 1.0 1.0
3.2408 43.1373 1100 3.2215 1.0 1.0
3.2 47.0588 1200 3.2193 1.0 1.0
3.202 50.9804 1300 3.2181 1.0 1.0
3.2286 54.9020 1400 3.2190 1.0 1.0
3.1863 58.8235 1500 3.2187 1.0 1.0
3.1868 62.7451 1600 3.2174 1.0 1.0
3.226 66.6667 1700 3.2199 1.0 1.0
3.1944 70.5882 1800 3.2195 1.0 1.0
3.1997 74.5098 1900 3.2180 1.0 1.0
3.2184 78.4314 2000 3.2200 1.0 1.0
3.2252 82.3529 2100 3.2189 1.0 1.0
3.208 86.2745 2200 3.2176 1.0 1.0
3.2122 90.1961 2300 3.2170 1.0 1.0
3.2307 94.1176 2400 3.2169 1.0 1.0
3.1852 98.0392 2500 3.2172 1.0 1.0

Framework versions

  • Transformers 4.42.0.dev0
  • Pytorch 2.3.1+cu121
  • Datasets 2.19.2
  • Tokenizers 0.19.1