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---
language:
- en
license: apache-2.0
base_model: futureProofGlitch/whisper-small
tags:
- generated_from_trainer
datasets:
- speechcolab/gigaspeech
metrics:
- wer
model-index:
- name: FutureProofGlitch - Whisper Small - Version 2.0
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Gigaspeech
type: speechcolab/gigaspeech
config: xs
split: test
args: xs
metrics:
- name: Wer
type: wer
value: 16.45244089773603
---
<!-- 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. -->
# FutureProofGlitch - Whisper Small - Version 2.0
This model is a fine-tuned version of [futureProofGlitch/whisper-small](https://huggingface.co/futureProofGlitch/whisper-small) on the Gigaspeech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3078
- Wer Ortho: 28.4362
- Wer: 16.4524
## 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: 1.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.2267 | 0.5 | 500 | 0.3309 | 29.5720 | 18.0966 |
| 0.2035 | 0.99 | 1000 | 0.3078 | 28.4362 | 16.4524 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|