whisper-medium-id-augmented – AI Model by Scrya | AlphaNeural AI

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Scrya/
whisper-medium-id-augmented

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automatic-speech-recognition

generated_from_trainer

indonesian-nlp/librivox-indonesia

mozilla-foundation/common_voice_11_0

endpoints_compatible

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Whisper Medium ID - FLEURS-CV-LBV - Augmented

This model is a fine-tuned version of openai/whisper-medium on the following datasets:

It achieves the following results on the evaluation set (Common Voice 11.0):

Loss: 0.2788
Wer: 7.6132
Cer: 2.3332

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

Training:

mozilla-foundation/common_voice_11_0 (train+validation)
google/fleurs (train+validation)
indonesian-nlp/librivox-indonesia (train)

Evaluation:

mozilla-foundation/common_voice_11_0 (test)
google/fleurs (test)
indonesian-nlp/librivox-indonesia (test)

Training procedure

Datasets were augmented on-the-fly using audiomentations via PitchShift, AddGaussianNoise and TimeStretch transformations at p=0.3.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 32
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	Cer
0.3002	1.9	1000	0.1659	8.1850	2.5333
0.0514	3.8	2000	0.1818	8.0559	2.5244
0.0145	5.7	3000	0.2150	7.8945	2.5281
0.0037	7.6	4000	0.2248	7.7100	2.3738
0.0016	9.51	5000	0.2402	7.6224	2.3591
0.0009	11.41	6000	0.2525	7.7654	2.3952
0.0005	13.31	7000	0.2609	7.5994	2.3487
0.0008	15.21	8000	0.2682	7.5855	2.3347
0.0002	17.11	9000	0.2756	7.6178	2.3288
0.0002	19.01	10000	0.2788	7.6132	2.3332

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.1+cu117
Datasets 2.7.1.dev0
Tokenizers 0.13.2