transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer dataset:common_voice_11_0 base_model:openai/whisper-base license:apache-2.0 model-index endpoints_compatible region:us
---
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-timestetch02-gain01-pitch02-gaussian02-lowpass01-timemask50-freqmask50
  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: 42.66401443990128
---

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# whisper-base-cs-cv11-timestetch02-gain01-pitch02-gaussian02-lowpass01-timemask50-freqmask50

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: 0.4240
- Wer: 42.6640

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 1.5795        | 0.72  | 1000 | 0.5715          | 52.0039 |
| 1.3147        | 1.44  | 2000 | 0.4734          | 46.0014 |
| 1.1304        | 2.17  | 3000 | 0.4346          | 43.5205 |
| 1.1056        | 2.89  | 4000 | 0.4240          | 42.6640 |


### Framework versions

- Transformers 4.38.0
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
