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Malaya-speech_fine-tune_realcase_22_Jun – AI Model by RuiqianLi | AlphaNeural AI
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Malaya-speech_fine-tune_realcase_22_Jun
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transformers
pytorch
tensorboard
wav2vec2
automatic-speech-recognition
generated_from_trainer
uob_singlish
endpoints_compatible
us
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Malaya-speech_fine-tune_realcase_22_Jun
This model is a fine-tuned version of
malay-huggingface/wav2vec2-xls-r-300m-mixed
on the uob_singlish dataset. It achieves the following results on the evaluation set:
Loss: 0.9569
Wer: 0.4062
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: 0.0005
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: 20
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.6913
20.0
100
0.9569
0.4062
Framework versions
Transformers 4.11.3
Pytorch 1.10.0+cu113
Datasets 1.18.3
Tokenizers 0.10.3
