metadata
language:
- nl
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- procit006/STT_TTS_MozillaAndSTC_VoiceTextData_August27
metrics:
- wer
model-index:
- name: Whisper Small
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice + STC Aug 27 + Speechgen
type: procit006/STT_TTS_MozillaAndSTC_VoiceTextData_August27
args: 'config: nld'
metrics:
- name: Wer
type: wer
value: 1.3962338429236927
Whisper Small
This model is a fine-tuned version of openai/whisper-small on the Common Voice + STC Aug 27 + Speechgen dataset. It achieves the following results on the evaluation set:
- Loss: 0.0202
- Wer: 1.3962
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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 150
- training_steps: 3000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0892 | 0.2047 | 500 | 0.0897 | 6.6898 |
| 0.048 | 0.4093 | 1000 | 0.0480 | 3.4328 |
| 0.0379 | 0.6140 | 1500 | 0.0339 | 2.2033 |
| 0.0299 | 0.8187 | 2000 | 0.0269 | 2.2453 |
| 0.0074 | 1.0233 | 2500 | 0.0216 | 1.4842 |
| 0.0055 | 1.2280 | 3000 | 0.0202 | 1.3962 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1