---
base_model: openai/whisper-medium
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: whisper-medium-finetuned
  results: []
---

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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/keviinkibe/huggingface/runs/yi3nfbu9)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/keviinkibe/huggingface/runs/yi3nfbu9)
# whisper-medium-finetuned

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.4751
- eval_wer: 89.3523
- eval_runtime: 1572.8468
- eval_samples_per_second: 0.159
- eval_steps_per_second: 0.02
- epoch: 9.012
- step: 300

## 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: 0.0001
- train_batch_size: 32
- 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: 20
- training_steps: 1000
- mixed_precision_training: Native AMP

### Framework versions

- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.2.2+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1