transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer dataset:common_voice_11_0 base_model:openai/whisper-base model-index endpoints_compatible region:us
---
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-timemask020-freqmask020
  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: 31.65358971525399
---

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# whisper-base-cs-cv11-timestetch02-gain01-pitch02-gaussian02-lowpass01-timemask020-freqmask020

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: 0.3065
- Wer: 31.6536

## 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: 8000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.8775        | 0.72  | 1000 | 0.4722          | 45.7067 |
| 0.6983        | 1.44  | 2000 | 0.3854          | 38.8275 |
| 0.5251        | 2.17  | 3000 | 0.3505          | 35.8143 |
| 0.5001        | 2.89  | 4000 | 0.3291          | 33.8619 |
| 0.4956        | 3.61  | 5000 | 0.3192          | 33.0405 |
| 0.4256        | 4.33  | 6000 | 0.3110          | 32.4364 |
| 0.398         | 5.06  | 7000 | 0.3081          | 32.0588 |
| 0.3921        | 5.78  | 8000 | 0.3065          | 31.6536 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
