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UDA-LIDI/
openai-whisper-large-v3-fullFT-es_ecu911_V2martin_win30s_samples

Name: openai-whisper-large-v3-fullFT-es_ecu911_V2martin_win30s_samples
Availability: InStock

transformers

safetensors

whisper

automatic-speech-recognition

generated_from_trainer

openai/whisper-large-v3

finetune

apache-2.0

endpoints_compatible

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Whisper openai-whisper-large-v3

This model is a fine-tuned version of openai/whisper-large-v3 on the llamadas ecu911 dataset. It achieves the following results on the evaluation set:

Loss: 0.2501
Wer: 19.6292

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: 2e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.5057	1.0	422	0.3380	35.0250
0.2061	2.0	844	0.2458	32.0536
0.1136	3.0	1266	0.2270	26.1110
0.0685	4.0	1688	0.2281	17.5388
0.0444	5.0	2110	0.2248	18.6169
0.0301	6.0	2532	0.2470	18.6037
0.0234	7.0	2954	0.2420	18.1699
0.019	8.0	3376	0.2368	21.5751
0.0163	9.0	3798	0.2346	15.9216
0.0142	10.0	4220	0.2501	19.6292

Framework versions

Transformers 4.45.2
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.20.1

UDA-LIDI/openai-whisper-large-v3-fullFT-es_ecu911_V2martin_win30s_samples

Whisper openai-whisper-large-v3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

UDA-LIDI/
openai-whisper-large-v3-fullFT-es_ecu911_V2martin_win30s_samples