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---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-large
tags:
- generated_from_trainer
datasets:
- Jzuluaga/atcosim_corpus
metrics:
- wer
model-index:
- name: Whisper Large - Whisper with atcosim_corpus
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: The ATCOSIM Air Traffic Control Simulation Speech corpus is a speech database
of air traffic control (ATC) operator speech, provided by Graz University
of Technology (TUG) and Eurocontrol Experimental Centre (EEC)
type: Jzuluaga/atcosim_corpus
args: 'config: en, split: test'
metrics:
- name: Wer
type: wer
value: 0.9495627594735447
---
<!-- 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 - Whisper with atcosim_corpus
This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the The ATCOSIM Air Traffic Control Simulation Speech corpus is a speech database of air traffic control (ATC) operator speech, provided by Graz University of Technology (TUG) and Eurocontrol Experimental Centre (EEC) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0413
- Wer: 0.9496
## 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: 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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.012 | 2.0921 | 1000 | 0.0405 | 1.2543 |
| 0.0019 | 4.1841 | 2000 | 0.0372 | 1.0776 |
| 0.0001 | 6.2762 | 3000 | 0.0407 | 0.9716 |
| 0.0 | 8.3682 | 4000 | 0.0413 | 0.9496 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|