transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-poor-data-german-colab12
  results: []
---

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# wav2vec2-xls-r-300m-poor-data-german-colab12

This model is a part of my bachelor thesis for built a towrds robuster ASR with Wav2Vec2.0 with german noise data. 
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the modified common voice dataset.
This model has been fine tuned with 10000 DP, 7500 for training and 2500 für test.
It achieves the following results on the evaluation set:
- Loss: 1.6421
- Wer: 0.9630

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 10
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 7
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 7.3484        | 1.0   | 132  | 3.1272          | 1.0    |
| 2.9727        | 2.0   | 264  | 2.9679          | 1.0    |
| 2.9202        | 3.0   | 396  | 3.2757          | 1.0    |
| 2.898         | 4.0   | 528  | 2.9306          | 1.0000 |
| 2.8612        | 5.0   | 660  | 2.8673          | 0.9983 |
| 2.5811        | 6.0   | 792  | 2.1479          | 0.9999 |
| 1.7869        | 7.0   | 924  | 1.6421          | 0.9630 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.13.3
