transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer sr dataset:mozilla-foundation/common_voice_16_1 dataset:google/fleurs dataset:Sagicc/audio-lmb-ds dataset:classla/ParlaSpeech-RS base_model:openai/whisper-large-v3 license:apache-2.0 model-index endpoints_compatible region:us
---
language:
- sr
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
- google/fleurs
- Sagicc/audio-lmb-ds
- classla/ParlaSpeech-RS
metrics:
- wer
model-index:
- name: Whisper Large v2
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 16.1
      type: mozilla-foundation/common_voice_16_1
      config: sr
      split: test
      args: sr
    metrics:
    - name: Wer
      type: wer
      value: 0.06891082129009517
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper Large v2

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Common Voice 16.1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1401
- Wer Ortho: 0.1663
- Wer: 0.0689

## 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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 1500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.1691        | 0.03  | 500  | 0.1776          | 0.2060    | 0.0941 |
| 0.1538        | 0.05  | 1000 | 0.1459          | 0.1743    | 0.0730 |
| 0.1522        | 0.08  | 1500 | 0.1401          | 0.1663    | 0.0689 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.1