---
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-base-cs-cv11-train-stretch20-gain10-pitch20-gaussian20-lowpass10
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_11_0
      type: common_voice_11_0
      config: cs
      split: None
      args: cs
    metrics:
    - name: Wer
      type: wer
      value: 35.235937672671014
---

<!-- 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-base-cs-cv11-train-stretch20-gain10-pitch20-gaussian20-lowpass10

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3660
- Wer: 35.2359

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.5579        | 1.4440 | 1000 | 0.4514          | 43.2460 |
| 0.3787        | 2.8881 | 2000 | 0.3813          | 37.2012 |
| 0.2523        | 4.3321 | 3000 | 0.3686          | 35.5067 |
| 0.2408        | 5.7762 | 4000 | 0.3660          | 35.2359 |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1