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wu-kiot
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whisper-small-fc-am

Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
whisper
Generated from Trainer
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whisper-small-fc-am
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---
library_name: transformers
license: apache-2.0
base_model: wu-kiot/whisper-small-am-fleurs
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: whisper-small-fc-am
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: am
      split: None
      args: am
    metrics:
    - name: Wer
      type: wer
      value: 62.73062730627307
---

<!-- 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-small-fc-am

This model is a fine-tuned version of [wu-kiot/whisper-small-am-fleurs](https://huggingface.co/wu-kiot/whisper-small-am-fleurs) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3756
- Wer: 62.7306

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 150
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2866        | 1.0   | 44   | 0.2855          | 63.9958 |
| 0.1582        | 2.0   | 88   | 0.2958          | 64.1539 |
| 0.0885        | 3.0   | 132  | 0.3311          | 67.4222 |
| 0.0793        | 4.0   | 176  | 0.3700          | 66.4207 |
| 0.0375        | 5.0   | 220  | 0.3756          | 62.7306 |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0