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:apache-2.0 model-index
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_9_0
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_9_0
model-index:
- name: wav2vec2-large-xlsr-53-german-cv9
  results:   
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 9
      type: mozilla-foundation/common_voice_9_0
      args: de
    metrics:
    - name: Test WER
      type: wer
      value: 9.480663281840769
    - name: Test CER
      type: cer
      value: 1.9167347943074394
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 9
      type: mozilla-foundation/common_voice_9_0
      args: de
    metrics:
    - name: Test WER (+LM)
      type: wer
      value: 7.49027762774117
    - name: Test CER  (+LM)
      type: cer
      value: 1.9167347943074394
  - 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: 8.122005951166668
    - name: Test CER
      type: cer
      value: 1.
  - 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: 6.1453182045203544
    - name: Test CER (+LM)
      type: cer
      value: 1.5247743373447677
---

# wav2vec2-large-xlsr-53-german-cv9

This model is a fine-tuned version of [./facebook/wav2vec2-large-xlsr-53](https://huggingface.co/./facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - DE dataset.

It achieves the following results on the test set:
- CER: 2.273015898213336
- Wer: 9.480663281840769

## 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 | Eval Wer|
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 0.4129        | 1.0   | 3557   | 0.3015          | 0.2499 |
| 0.2121        | 2.0   | 7114   | 0.1596          | 0.1567 |
| 0.1455        | 3.0   | 10671  | 0.1377          | 0.1354 |
| 0.1436        | 4.0   | 14228  | 0.1301          | 0.1282 |
| 0.1144        | 5.0   | 17785  | 0.1225          | 0.1245 |
| 0.1219        | 6.0   | 21342  | 0.1254          | 0.1208 |
| 0.104         | 7.0   | 24899  | 0.1198          | 0.1232 |
| 0.1016        | 8.0   | 28456  | 0.1149          | 0.1174 |
| 0.1093        | 9.0   | 32013  | 0.1186          | 0.1186 |
| 0.0858        | 10.0  | 35570  | 0.1182          | 0.1164 |
| 0.102         | 11.0  | 39127  | 0.1191          | 0.1186 |
| 0.0834        | 12.0  | 42684  | 0.1161          | 0.1096 |
| 0.0916        | 13.0  | 46241  | 0.1147          | 0.1107 |
| 0.0811        | 14.0  | 49798  | 0.1174          | 0.1136 |
| 0.0814        | 15.0  | 53355  | 0.1132          | 0.1114 |
| 0.0865        | 16.0  | 56912  | 0.1134          | 0.1097 |
| 0.0701        | 17.0  | 60469  | 0.1096          | 0.1054 |
| 0.0891        | 18.0  | 64026  | 0.1110          | 0.1076 |
| 0.071         | 19.0  | 67583  | 0.1141          | 0.1074 |
| 0.0726        | 20.0  | 71140  | 0.1094          | 0.1093 |
| 0.0647        | 21.0  | 74697  | 0.1088          | 0.1095 |
| 0.0643        | 22.0  | 78254  | 0.1105          | 0.1044 |
| 0.0764        | 23.0  | 81811  | 0.1072          | 0.1042 |
| 0.0605        | 24.0  | 85368  | 0.1095          | 0.1026 |
| 0.0722        | 25.0  | 88925  | 0.1144          | 0.1066 |
| 0.0597        | 26.0  | 92482  | 0.1087          | 0.1022 |
| 0.062         | 27.0  | 96039  | 0.1073          | 0.1027 |
| 0.0536        | 28.0  | 99596  | 0.1068          | 0.1027 |
| 0.0616        | 29.0  | 103153 | 0.1097          | 0.1037 |
| 0.0642        | 30.0  | 106710 | 0.1117          | 0.1020 |
| 0.0555        | 31.0  | 110267 | 0.1109          | 0.0990 |
| 0.0632        | 32.0  | 113824 | 0.1104          | 0.0977 |
| 0.0482        | 33.0  | 117381 | 0.1108          | 0.0958 |
| 0.0601        | 34.0  | 120938 | 0.1095          | 0.0957 |
| 0.0508        | 35.0  | 124495 | 0.1079          | 0.0973 |
| 0.0526        | 36.0  | 128052 | 0.1068          | 0.0967 |
| 0.0487        | 37.0  | 131609 | 0.1081          | 0.0966 |
| 0.0495        | 38.0  | 135166 | 0.1099          | 0.0956 |
| 0.0528        | 39.0  | 138723 | 0.1091          | 0.0923 |
| 0.0439        | 40.0  | 142280 | 0.1111          | 0.0928 |
| 0.0467        | 41.0  | 145837 | 0.1131          | 0.0943 |
| 0.0407        | 42.0  | 149394 | 0.1115          | 0.0944 |
| 0.046         | 43.0  | 152951 | 0.1106          | 0.0935 |
| 0.0447        | 44.0  | 156508 | 0.1083          | 0.0919 |
| 0.0434        | 45.0  | 160065 | 0.1093          | 0.0909 |
| 0.0472        | 46.0  | 163622 | 0.1092          | 0.0921 |
| 0.0414        | 47.0  | 167179 | 0.1106          | 0.0922 |
| 0.0501        | 48.0  | 170736 | 0.1094          | 0.0918 |
| 0.0388        | 49.0  | 174293 | 0.1099          | 0.0918 |
| 0.0428        | 50.0  | 177850 | 0.1103          | 0.0915 |


### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
