readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - uob_singlish
  model-index:
  - name: malaya-speech_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. -->
  
  # malaya-speech_Mrbrown_finetune1
  
  This model is a fine-tuned version of [malay-huggingface/wav2vec2-xls-r-300m-mixed](https://huggingface.co/malay-huggingface/wav2vec2-xls-r-300m-mixed) 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), get really bad fine-tuning result, that may mean the training/fine-tuning dataset must be high quality/at least several hours? Or maybe is because the learning rate is set too high(0.01) ? Still searching for the important factors.
  
  It achieves the following results on the evaluation set:
  - Loss: 3.8458
  - Wer: 1.01
  
  ## 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  |
  |:-------------:|:-----:|:----:|:---------------:|:----:|
  | 0.3186        | 20.0  | 200  | 4.2225          | 1.13 |
  | 0.4911        | 40.0  | 400  | 4.0427          | 0.99 |
  | 0.9014        | 60.0  | 600  | 5.3285          | 1.04 |
  | 1.0955        | 80.0  | 800  | 3.6922          | 1.02 |
  | 0.7533        | 100.0 | 1000 | 3.8458          | 1.01 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.10.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.10.3
  