---
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- facebook/multilingual_librispeech
metrics:
- wer
model-index:
- name: Whisper largeV2 German MLS
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: facebook/multilingual_librispeech german
      type: facebook/multilingual_librispeech
      config: german
      split: test
      args: german
    metrics:
    - name: Wer
      type: wer
      value: 6.048320913895545
---

<!-- 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 largeV2 German MLS

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the facebook/multilingual_librispeech german dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1370
- Wer: 6.0483

## Model description


The model is fine-tuned for 4000 updates/steps on multilingual librispeech German train data.

- Zero-shot                        - 5.5 (MLS German test)
- Fine-tune MLS German train      - 6.04 (MLS German test)

Even after fine-tuning the model is doing slightly worse than the zero-shot.

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1755        | 0.25  | 1000 | 0.1844          | 7.7118 |
| 0.1185        | 0.5   | 2000 | 0.1636          | 7.0659 |
| 0.1081        | 0.75  | 3000 | 0.1396          | 6.0844 |
| 0.1222        | 1.0   | 4000 | 0.1370          | 6.0483 |


### Framework versions

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