whisper_large_v3_turbo_finetuned_en_id_v1 – AI Model by Willy030125 | AlphaNeural AI

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Willy030125/
whisper_large_v3_turbo_finetuned_en_id_v1

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automatic-speech-recognition

generated_from_trainer

Willy030125/librivox_filtered_id

Willy030125/ambient_noise_audio

Willy030125/STT_IndoSpeech_YT_Dataset

openai/whisper-large-v3-turbo

endpoints_compatible

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whisper_large_v3_turbo_finetuned_en_id_v1

This model is a fine-tuned version of openai/whisper-large-v3-turbo on the several datasets. It achieves the following results on the evaluation set:

eval_loss: 0.1362
eval_runtime: 263.9538
eval_samples_per_second: 5.906
eval_steps_per_second: 5.906
epoch: 3 (checkpoint model)
step: 687 (checkpoint model)

Training and Evaluation Data

Step	Epoch	Training Loss	Eval Loss	Grad Norm	Learning Rate	Eval Samples/s	Eval Steps/s	Eval Runtime (s)
229	1.00	0.3841	0.1563	2.216	9.77e-06	5.91	5.91	263.95
458	2.00	0.1163	0.1449	1.855	9.36e-06	5.91	5.91	263.95
687	3.00	0.0693	0.1363	1.538	8.96e-06	5.91	5.91	263.95
917	4.00	0.0408	0.1376	1.367	8.55e-06	5.89	5.89	264.59
1146	5.00	0.0229	0.1398	0.982	8.14e-06	5.90	5.90	264.43
1375	6.00	0.0126	0.1450	0.936	7.73e-06	5.91	5.91	263.95
1604	7.00	0.0082	0.1476	0.362	7.33e-06	5.91	5.91	263.95

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 8
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
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: 100
num_epochs: 25

Framework versions

Transformers 4.41.2
Pytorch 2.2.2+cu121
Datasets 2.18.0
Tokenizers 0.19.1