---
language:
- cs
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
base_model: openai/whisper-large-v2
model-index:
- name: Whisper Large-v2 Czech CV11 audio concatenation test
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: mozilla-foundation/common_voice_11_0
      type: mozilla-foundation/common_voice_11_0
      config: cs
      split: test
      args: cs
    metrics:
    - type: wer
      value: 8.37737794884072
      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 Large-v2 Czech CV11 audio concatenation test

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 cs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2563
- Wer: 8.3774

## Model description

First test of audio concatenation few short samples to one training sample together.

## 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: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.0022        | 24.39  | 1000 | 0.2181          | 8.7807 |
| 0.0002        | 48.77  | 2000 | 0.2563          | 8.3774 |
| 0.0001        | 73.17  | 3000 | 0.2756          | 8.4510 |
| 0.0001        | 97.55  | 4000 | 0.2871          | 8.4823 |
| 0.0001        | 121.94 | 5000 | 0.2913          | 8.4731 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2