This model is a fine-tuned version of
openai/whisper-medium
on the
Indonesian mozilla-foundation/common_voice_11_0, magic_data, titml and google/fleurs dataset. It achieves the following
results:
The following hyperparameters were used during training:
learning_rate: 1e-06
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 10000
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.0427
0.33
1000
0.0664
4.3807
0.042
0.66
2000
0.0658
3.9426
0.0265
0.99
3000
0.0657
3.8274
0.0211
1.32
4000
0.0679
3.8366
0.0212
1.66
5000
0.0682
3.8412
0.0206
1.99
6000
0.0683
3.8689
0.0166
2.32
7000
0.0711
3.9657
0.0095
2.65
8000
0.0717
3.9980
0.0122
2.98
9000
0.0714
3.9795
0.0049
3.31
10000
0.0720
3.9887
Evaluation
We evaluated the model using the test split of two datasets, the
Common Voice 11
and the
Google Fleurs
.
As Whisper can transcribe casing and punctuation, we also evaluate its performance using raw and normalized text.
(lowercase + removal of punctuations). The results are as follows:
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