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--- |
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license: apache-2.0 |
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base_model: nutella-toast/wav2vec2-large-xls-r-ssw |
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tags: |
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- generated_from_trainer |
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datasets: |
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- ml-superb-subset |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-large-xls-r-ssw |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: ml-superb-subset |
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type: ml-superb-subset |
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config: ssw |
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split: dev |
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args: ssw |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.7320872274143302 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# wav2vec2-large-xls-r-ssw |
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This model is a fine-tuned version of [nutella-toast/wav2vec2-large-xls-r-ssw](https://huggingface.co/nutella-toast/wav2vec2-large-xls-r-ssw) on the ml-superb-subset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7327 |
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- Wer: 0.7321 |
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## Model description |
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Finetuned version of vanilla wav2vec2-large-xls-r for siSwati. For CS224S at Stanford University. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:------:|:----:|:---------------:|:------:| |
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| 0.5779 | 1.0471 | 100 | 0.7902 | 0.8785 | |
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| 0.5307 | 2.0942 | 200 | 0.8185 | 0.8660 | |
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| 0.4826 | 3.1414 | 300 | 0.8378 | 0.8692 | |
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| 0.4529 | 4.1885 | 400 | 0.8048 | 0.9097 | |
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| 0.5053 | 5.2356 | 500 | 0.9541 | 0.8910 | |
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| 0.4149 | 6.2827 | 600 | 0.7687 | 0.7913 | |
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| 0.3179 | 7.3298 | 700 | 0.7678 | 0.7850 | |
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| 0.2642 | 8.3770 | 800 | 0.7151 | 0.7321 | |
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| 0.2147 | 9.4241 | 900 | 0.7327 | 0.7321 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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