WHISPER-MEDIUM-LUGANDA-ASR-CV-14 – AI Model by dmusingu | AlphaNeural AI

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dmusingu/
WHISPER-MEDIUM-LUGANDA-ASR-CV-14

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automatic-speech-recognition

hf-asr-leaderboard

generated_from_trainer

mozilla-foundation/common_voice_14_0

openai/whisper-large

endpoints_compatible

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Whisper Large - Denis Musinguzi

This model is a fine-tuned version of openai/whisper-large on the Common Voice 14.0 dataset. It achieves the following results on the evaluation set:

Loss: 0.2966
Wer: 0.2467
Cer: 0.0700

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: 1e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
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	Cer	Validation Loss	Wer
0.6329	0.61	1600	0.0878	0.3515	0.3385
0.2241	1.22	3200	0.0589	0.3045	0.2517
0.1618	1.82	4800	0.0707	0.2801	0.2645
0.1109	2.43	6400	0.0774	0.2870	0.2580
0.0837	3.04	8000	0.0597	0.2900	0.2333
0.045	3.65	9600	0.2966	0.2467	0.0700

Framework versions

Transformers 4.38.1
Pytorch 2.2.1
Datasets 2.17.0
Tokenizers 0.15.2