pytorch tensorboard wav2vec2 automatic-speech-recognition dataset:common_voice transformers generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-japanese-colab
  results: []
---

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# wav2vec2-large-xls-r-300m-japanese-colab

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8060
- Wer: 0.1393

## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 2.44  | 100  | 3.7830          | 1.0    |
| No log        | 4.88  | 200  | 2.9520          | 0.9999 |
| No log        | 7.32  | 300  | 1.1938          | 0.2940 |
| 3.7558        | 9.76  | 400  | 0.7278          | 0.1977 |
| 3.7558        | 12.2  | 500  | 0.6456          | 0.1668 |
| 3.7558        | 14.63 | 600  | 0.6702          | 0.1530 |
| 3.7558        | 17.07 | 700  | 0.7131          | 0.1568 |
| 0.2503        | 19.51 | 800  | 0.7277          | 0.1488 |
| 0.2503        | 21.95 | 900  | 0.7558          | 0.1630 |
| 0.2503        | 24.39 | 1000 | 0.7611          | 0.1437 |
| 0.2503        | 26.83 | 1100 | 0.7501          | 0.1426 |
| 0.1316        | 29.27 | 1200 | 0.7635          | 0.1445 |
| 0.1316        | 31.71 | 1300 | 0.8348          | 0.1578 |
| 0.1316        | 34.15 | 1400 | 0.7285          | 0.1545 |
| 0.1316        | 36.59 | 1500 | 0.7949          | 0.1491 |
| 0.0974        | 39.02 | 1600 | 0.7706          | 0.1524 |
| 0.0974        | 41.46 | 1700 | 0.8180          | 0.1432 |
| 0.0974        | 43.9  | 1800 | 0.7718          | 0.1281 |
| 0.0974        | 46.34 | 1900 | 0.7915          | 0.1315 |
| 0.0731        | 48.78 | 2000 | 0.7905          | 0.1337 |
| 0.0731        | 51.22 | 2100 | 0.8401          | 0.1340 |
| 0.0731        | 53.66 | 2200 | 0.7810          | 0.1410 |
| 0.0731        | 56.1  | 2300 | 0.8034          | 0.1418 |
| 0.0569        | 58.54 | 2400 | 0.8219          | 0.1472 |
| 0.0569        | 60.98 | 2500 | 0.7661          | 0.1432 |
| 0.0569        | 63.41 | 2600 | 0.7989          | 0.1442 |
| 0.0569        | 65.85 | 2700 | 0.8212          | 0.1440 |
| 0.0456        | 68.29 | 2800 | 0.8029          | 0.1395 |
| 0.0456        | 70.73 | 2900 | 0.8113          | 0.1425 |
| 0.0456        | 73.17 | 3000 | 0.8298          | 0.1434 |
| 0.0456        | 75.61 | 3100 | 0.8131          | 0.1403 |
| 0.0343        | 78.05 | 3200 | 0.8313          | 0.1415 |
| 0.0343        | 80.49 | 3300 | 0.8395          | 0.1434 |
| 0.0343        | 82.93 | 3400 | 0.8048          | 0.1386 |
| 0.0343        | 85.37 | 3500 | 0.8126          | 0.1393 |
| 0.026         | 87.8  | 3600 | 0.7933          | 0.1378 |
| 0.026         | 90.24 | 3700 | 0.8317          | 0.1389 |
| 0.026         | 92.68 | 3800 | 0.8005          | 0.1378 |
| 0.026         | 95.12 | 3900 | 0.8059          | 0.1385 |
| 0.0204        | 97.56 | 4000 | 0.8071          | 0.1389 |
| 0.0204        | 100.0 | 4100 | 0.8060          | 0.1393 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
