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malaya-speech_Mrbrown_finetune1
This model is a fine-tuned version of 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
