readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - uob_singlish
  model-index:
  - name: wav2vec2-xls-r-300m_Mrbrown_finetune1
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # wav2vec2-xls-r-300m_Mrbrown_finetune1
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the uob_singlish dataset.
  
  ## This time use self-made dataset(cut the audio of "https://www.youtube.com/watch?v=a2ZOTD3R7JI" into slices and write the corresponding transcript, totally 4 mins), don't know why the word-error-rate keep 1. But can know that much be the problem of dataset, because last time use the same pre-trained model and standard singlish corpus fine-tune get nice result. (can find it at:RuiqianLi/wav2vec2-large-xls-r-300m-singlish-colab)
  
  It achieves the following results on the evaluation set:
  - Loss: 3.0927
  - Wer: 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: 0.01
  - train_batch_size: 2
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 4
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---:|
  | 3.7943        | 20.0  | 200  | 3.0597          | 1.0 |
  | 2.9902        | 40.0  | 400  | 3.1604          | 1.0 |
  | 2.9696        | 60.0  | 600  | 3.1112          | 1.0 |
  | 2.8885        | 80.0  | 800  | 3.0234          | 1.0 |
  | 2.8154        | 100.0 | 1000 | 3.0927          | 1.0 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.10.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.10.3
  