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---
license: apache-2.0
tags:
- automatic-speech-recognition
- multilingual_librispeech
- generated_from_trainer
datasets:
- multilingual_librispeech
model-index:
- name: wav2vec2-300m-mls-german-ft
results: []
---
<!-- 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. -->
# wav2vec2-300m-mls-german-ft
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MULTILINGUAL_LIBRISPEECH - GERMAN 10h dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2398
- Wer: 0.1520
## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 200.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 3.0132 | 7.25 | 500 | 2.9393 | 1.0 |
| 2.9241 | 14.49 | 1000 | 2.8734 | 1.0 |
| 1.0766 | 21.74 | 1500 | 0.2773 | 0.2488 |
| 0.8416 | 28.99 | 2000 | 0.2224 | 0.1990 |
| 0.8048 | 36.23 | 2500 | 0.2063 | 0.1792 |
| 0.7664 | 43.48 | 3000 | 0.2088 | 0.1748 |
| 0.6571 | 50.72 | 3500 | 0.2042 | 0.1668 |
| 0.7014 | 57.97 | 4000 | 0.2136 | 0.1649 |
| 0.6171 | 65.22 | 4500 | 0.2139 | 0.1641 |
| 0.6609 | 72.46 | 5000 | 0.2144 | 0.1621 |
| 0.6318 | 79.71 | 5500 | 0.2129 | 0.1600 |
| 0.6222 | 86.96 | 6000 | 0.2124 | 0.1582 |
| 0.608 | 94.2 | 6500 | 0.2255 | 0.1639 |
| 0.6099 | 101.45 | 7000 | 0.2265 | 0.1622 |
| 0.6069 | 108.7 | 7500 | 0.2246 | 0.1593 |
| 0.5929 | 115.94 | 8000 | 0.2323 | 0.1617 |
| 0.6218 | 123.19 | 8500 | 0.2287 | 0.1566 |
| 0.5751 | 130.43 | 9000 | 0.2275 | 0.1563 |
| 0.5181 | 137.68 | 9500 | 0.2316 | 0.1579 |
| 0.6306 | 144.93 | 10000 | 0.2372 | 0.1556 |
| 0.5874 | 152.17 | 10500 | 0.2362 | 0.1533 |
| 0.5546 | 159.42 | 11000 | 0.2342 | 0.1543 |
| 0.6294 | 166.67 | 11500 | 0.2381 | 0.1536 |
| 0.5989 | 173.91 | 12000 | 0.2360 | 0.1527 |
| 0.5697 | 181.16 | 12500 | 0.2399 | 0.1526 |
| 0.5379 | 188.41 | 13000 | 0.2375 | 0.1523 |
| 0.5022 | 195.65 | 13500 | 0.2395 | 0.1519 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0
- Datasets 1.15.2.dev0
- Tokenizers 0.10.3
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