---
language:
- lv
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
base_model: openai/whisper-large-v2
model-index:
- name: Whisper Large-v2 Latvian
  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: lv
      split: test
      args: lv
    metrics:
    - type: wer
      value: 17.77988614800759
      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 Latvian

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 lv dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2634
- Wer: 17.7799

## 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-06
- train_batch_size: 32
- eval_batch_size: 16
- 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: 100
- training_steps: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2124        | 3.02  | 200  | 0.2485          | 18.4061 |
| 0.0704        | 6.05  | 400  | 0.2634          | 17.7799 |
| 0.0379        | 10.01 | 600  | 0.3103          | 17.8178 |
| 0.0228        | 13.03 | 800  | 0.3555          | 18.4061 |
| 0.0139        | 16.06 | 1000 | 0.3733          | 18.4535 |


### Framework versions

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