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---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- honzapucalek/p6_moderate
metrics:
- wer
model-index:
- name: p6_moderate
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: honzapucalek/p6_moderate cs
type: honzapucalek/p6_moderate
config: cs
split: test
args: cs
metrics:
- name: Wer
type: wer
value: 0.2500697155605131
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# p6_moderate
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the honzapucalek/p6_moderate cs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8973
- Wer: 0.2501
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.0015 | 27.78 | 1000 | 0.7347 | 0.2520 |
| 0.0008 | 55.56 | 2000 | 0.7309 | 0.2523 |
| 0.0001 | 83.33 | 3000 | 0.8548 | 0.2531 |
| 0.0 | 111.11 | 4000 | 0.8869 | 0.2503 |
| 0.0 | 138.89 | 5000 | 0.8973 | 0.2501 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|