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--- |
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base_model: daila/wav2vec2-large-xls-r-300m-vi-colab |
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tags: |
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- generated_from_trainer |
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datasets: |
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- common_voice_16_1 |
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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-300m-vi-colab |
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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: common_voice_16_1 |
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type: common_voice_16_1 |
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config: vi |
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split: test |
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args: vi |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.5894672631150875 |
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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-300m-vi-colab |
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This model is a fine-tuned version of [daila/wav2vec2-large-xls-r-300m-vi-colab](https://huggingface.co/daila/wav2vec2-large-xls-r-300m-vi-colab) on the common_voice_16_1 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6432 |
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- Wer: 0.5895 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 16 |
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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: 32 |
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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: 30 |
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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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| 0.0916 | 4.52 | 400 | 1.5440 | 0.6357 | |
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| 0.1344 | 9.04 | 800 | 1.6043 | 0.6543 | |
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| 0.0926 | 13.56 | 1200 | 1.7226 | 0.6365 | |
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| 0.0703 | 18.08 | 1600 | 1.5989 | 0.6048 | |
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| 0.0557 | 22.6 | 2000 | 1.6714 | 0.6001 | |
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| 0.051 | 27.12 | 2400 | 1.6432 | 0.5895 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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