c_dialect / README.md
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---
base_model: openai/whisper-small
datasets:
- arrow
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: c_dialect
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: arrow
type: arrow
config: default
split: validation
args: default
metrics:
- type: wer
value: 4.318792583946361
name: Wer
---
<!-- 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. -->
# c_dialect
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the arrow dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0342
- Wer: 4.3188
## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 99
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.0301 | 1.4583 | 3500 | 0.0460 | 5.8606 |
| 0.0089 | 2.9167 | 7000 | 0.0342 | 4.3188 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1