---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- wer
model-index:
- name: whisper-es-multicorpus-streaming-v3
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: generator
      type: generator
      config: default
      split: train
      args: default
    metrics:
    - type: wer
      value: 0.06276824034334764
      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. -->

# whisper-es-multicorpus-streaming-v3

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0816
- Wer: 0.0628

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.1346        | 0.2    | 200  | 0.1566          | 0.2221 |
| 0.117         | 0.4    | 400  | 0.1217          | 0.0790 |
| 0.0861        | 0.6    | 600  | 0.1079          | 0.0633 |
| 0.0419        | 1.1180 | 800  | 0.1172          | 0.0899 |
| 0.0467        | 1.318  | 1000 | 0.0816          | 0.0628 |


### Framework versions

- Transformers 4.57.2
- Pytorch 2.9.0+cu126
- Datasets 4.4.1
- Tokenizers 0.22.1