pytorch tensorboard wav2vec2 automatic-speech-recognition de dataset:mozilla-foundation/common_voice_9_0 transformers mozilla-foundation/common_voice_9_0 generated_from_trainer license:mit model-index
---
language:
- de
license: mit
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_9_0
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_9_0
model-index:
- name: wav2vec2-base-german-cv9
  results:   
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 6.1
      type: common_voice
      args: de
    metrics:
    - name: Test WER
      type: wer
      value: 10.565782902002716
    - name: Test CER
      type: cer
      value: 2.6226824852959657
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 6.1
      type: common_voice
      args: de
    metrics:
    - name: Test WER (+LM)
      type: wer
      value: 7.996088831362508
    - name: Test CER (+LM)
      type: cer
      value: 2.1515717711623326
---


# wav2vec2-base-german-cv9

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - DE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1742
- Wer: 0.1209

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Wer    |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 0.6827        | 1.0   | 3557   | 0.6695          | 0.6247 |
| 0.3992        | 2.0   | 7114   | 0.3738          | 0.3936 |
| 0.2611        | 3.0   | 10671  | 0.3011          | 0.3177 |
| 0.2536        | 4.0   | 14228  | 0.2672          | 0.2749 |
| 0.1943        | 5.0   | 17785  | 0.2487          | 0.2480 |
| 0.2004        | 6.0   | 21342  | 0.2246          | 0.2268 |
| 0.1605        | 7.0   | 24899  | 0.2176          | 0.2120 |
| 0.1579        | 8.0   | 28456  | 0.2046          | 0.2024 |
| 0.1668        | 9.0   | 32013  | 0.2027          | 0.1944 |
| 0.1338        | 10.0  | 35570  | 0.1968          | 0.1854 |
| 0.1478        | 11.0  | 39127  | 0.1963          | 0.1823 |
| 0.1177        | 12.0  | 42684  | 0.1956          | 0.1800 |
| 0.1245        | 13.0  | 46241  | 0.1889          | 0.1732 |
| 0.1124        | 14.0  | 49798  | 0.1868          | 0.1714 |
| 0.1112        | 15.0  | 53355  | 0.1805          | 0.1650 |
| 0.1209        | 16.0  | 56912  | 0.1860          | 0.1614 |
| 0.1002        | 17.0  | 60469  | 0.1828          | 0.1604 |
| 0.118         | 18.0  | 64026  | 0.1832          | 0.1580 |
| 0.0974        | 19.0  | 67583  | 0.1771          | 0.1555 |
| 0.1007        | 20.0  | 71140  | 0.1812          | 0.1532 |
| 0.0866        | 21.0  | 74697  | 0.1752          | 0.1504 |
| 0.0901        | 22.0  | 78254  | 0.1690          | 0.1477 |
| 0.0964        | 23.0  | 81811  | 0.1773          | 0.1489 |
| 0.085         | 24.0  | 85368  | 0.1776          | 0.1456 |
| 0.0945        | 25.0  | 88925  | 0.1786          | 0.1428 |
| 0.0804        | 26.0  | 92482  | 0.1737          | 0.1429 |
| 0.0832        | 27.0  | 96039  | 0.1789          | 0.1394 |
| 0.0683        | 28.0  | 99596  | 0.1741          | 0.1390 |
| 0.0761        | 29.0  | 103153 | 0.1688          | 0.1379 |
| 0.0833        | 30.0  | 106710 | 0.1726          | 0.1370 |
| 0.0753        | 31.0  | 110267 | 0.1774          | 0.1353 |
| 0.08          | 32.0  | 113824 | 0.1734          | 0.1344 |
| 0.0644        | 33.0  | 117381 | 0.1737          | 0.1334 |
| 0.0745        | 34.0  | 120938 | 0.1763          | 0.1335 |
| 0.0629        | 35.0  | 124495 | 0.1761          | 0.1311 |
| 0.0654        | 36.0  | 128052 | 0.1718          | 0.1302 |
| 0.0656        | 37.0  | 131609 | 0.1697          | 0.1301 |
| 0.0643        | 38.0  | 135166 | 0.1716          | 0.1279 |
| 0.0683        | 39.0  | 138723 | 0.1777          | 0.1279 |
| 0.0587        | 40.0  | 142280 | 0.1735          | 0.1271 |
| 0.0693        | 41.0  | 145837 | 0.1780          | 0.1260 |
| 0.0532        | 42.0  | 149394 | 0.1724          | 0.1245 |
| 0.0594        | 43.0  | 152951 | 0.1736          | 0.1250 |
| 0.0544        | 44.0  | 156508 | 0.1744          | 0.1238 |
| 0.0559        | 45.0  | 160065 | 0.1770          | 0.1232 |
| 0.0557        | 46.0  | 163622 | 0.1766          | 0.1231 |
| 0.0521        | 47.0  | 167179 | 0.1751          | 0.1220 |
| 0.0591        | 48.0  | 170736 | 0.1724          | 0.1217 |
| 0.0507        | 49.0  | 174293 | 0.1753          | 0.1212 |
| 0.0577        | 50.0  | 177850 | 0.1742          | 0.1209 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
