transformers safetensors whisper automatic-speech-recognition generated_from_trainer base_model:openai/whisper-base license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: speechx-base-kannada
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# speechx-base-kannada

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1144
- Wer: 42.3786

## 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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.2594        | 0.1   | 1000  | 0.3520          | 65.6421 |
| 0.21          | 0.2   | 2000  | 0.3178          | 60.5516 |
| 0.1507        | 0.3   | 3000  | 0.3030          | 58.7877 |
| 0.1304        | 0.4   | 4000  | 0.1244          | 39.0290 |
| 0.1139        | 0.5   | 5000  | 0.1108          | 37.6041 |
| 0.1068        | 0.6   | 6000  | 0.1039          | 36.0586 |
| 0.1548        | 1.08  | 7000  | 0.1739          | 51.1577 |
| 0.1189        | 1.18  | 8000  | 0.1828          | 54.5993 |
| 0.1666        | 1.28  | 9000  | 0.1811          | 53.4042 |
| 0.1124        | 1.38  | 10000 | 0.1144          | 42.3786 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
