---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- multilingual_librispeech
metrics:
- wer
model-index:
- name: openai/whisper-large-v2
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: multilingual_librispeech
      type: multilingual_librispeech
      config: french
      split: test
      args: french
    metrics:
    - name: Wer
      type: wer
      value: 4.561620226935377
---

<!-- 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. -->

# openai/whisper-large-v2

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the multilingual_librispeech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0903
- Wer: 4.5616

## 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: 8
- 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1303        | 0.25  | 1000 | 0.1219          | 6.3618 |
| 0.0751        | 0.5   | 2000 | 0.1033          | 5.3905 |
| 0.0613        | 0.75  | 3000 | 0.0970          | 4.9193 |
| 0.0796        | 1.0   | 4000 | 0.0903          | 4.5616 |


### Framework versions

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