whisper-large-final / README.md
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---
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- generated_from_trainer
model-index:
- name: whisper-large-final
results: []
language:
- mn
---
<!-- 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. -->
# whisper-large-final
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0112
- eval_wer: 1.1712
- eval_runtime: 982.7637
- eval_samples_per_second: 1.892
- eval_steps_per_second: 0.237
- epoch: 6.4205
- step: 4000
## Model description
Step Training Loss Validation Loss Wer
500 0.431500 0.412413 48.265244
1000 0.244500 0.230148 29.284654
1500 0.134300 0.122366 16.588772
2000 0.055800 0.069241 10.551493
2500 0.045700 0.035967 4.860615
3000 0.027900 0.024117 3.425524
3500 0.011000 0.016053 1.770495
4000 0.004800 0.011227 1.171166
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 5000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.0
- Datasets 2.19.1
- Tokenizers 0.19.1