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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- audiofolder
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metrics:
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- wer
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model-index:
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- name: wav2vec2-xlsr-53-espeak-cv-ft-evn4-ntsema-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: audiofolder
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type: audiofolder
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config: default
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split: train
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args: default
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metrics:
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- name: Wer
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type: wer
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value: 0.9833333333333333
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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-xlsr-53-espeak-cv-ft-evn4-ntsema-colab
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This model is a fine-tuned version of [ntsema/wav2vec2-xlsr-53-espeak-cv-ft-sah2-ntsema-colab](https://huggingface.co/ntsema/wav2vec2-xlsr-53-espeak-cv-ft-sah2-ntsema-colab) on the audiofolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.0821
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- Wer: 0.9833
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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: 4
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 8
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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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| 4.3115 | 6.15 | 400 | 1.6416 | 0.9867 |
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| 0.9147 | 12.3 | 800 | 1.6538 | 0.9867 |
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| 0.5301 | 18.46 | 1200 | 1.8461 | 0.98 |
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| 0.2865 | 24.61 | 1600 | 2.0821 | 0.9833 |
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### Framework versions
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- Transformers 4.24.0
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- Pytorch 1.12.1+cu113
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- Datasets 2.6.1
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- Tokenizers 0.13.2
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