transformers safetensors wav2vec2 automatic-speech-recognition generated_from_trainer base_model:jonatasgrosman/wav2vec2-large-xlsr-53-japanese license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
base_model: jonatasgrosman/wav2vec2-large-xlsr-53-japanese
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec-jdrt-RTSplit_and_SpeakerSplitModel-0107-5
  results: []
---

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# wav2vec-jdrt-RTSplit_and_SpeakerSplitModel-0107-5

This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-japanese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0032
- Wer: 0.1835
- Cer: 0.1557

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 35

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 14.5102       | 1.0   | 60   | 13.5418         | 1.0011 | 1.2966 |
| 6.2989        | 2.0   | 120  | 4.6126          | 1.0    | 0.8736 |
| 2.208         | 3.0   | 180  | 1.7112          | 0.9993 | 0.8160 |
| 1.098         | 4.0   | 240  | 0.8765          | 0.8410 | 0.5698 |
| 0.8201        | 5.0   | 300  | 0.6964          | 0.8221 | 0.5340 |
| 0.7499        | 6.0   | 360  | 0.6299          | 0.8217 | 0.5305 |
| 0.6753        | 7.0   | 420  | 0.5998          | 0.7691 | 0.4482 |
| 0.6003        | 8.0   | 480  | 0.5502          | 0.7394 | 0.4564 |
| 0.5732        | 9.0   | 540  | 0.5047          | 0.7098 | 0.3906 |
| 0.5404        | 10.0  | 600  | 0.4694          | 0.6679 | 0.3283 |
| 0.4889        | 11.0  | 660  | 0.3979          | 0.6379 | 0.3017 |
| 0.4401        | 12.0  | 720  | 0.3255          | 0.5849 | 0.2792 |
| 0.4295        | 13.0  | 780  | 0.2853          | 0.5044 | 0.2772 |
| 0.3216        | 14.0  | 840  | 0.2204          | 0.4511 | 0.2234 |
| 0.2583        | 15.0  | 900  | 0.1492          | 0.3929 | 0.2130 |
| 0.226         | 16.0  | 960  | 0.1007          | 0.2817 | 0.1690 |
| 0.2304        | 17.0  | 1020 | 0.0694          | 0.2439 | 0.1699 |
| 0.1487        | 18.0  | 1080 | 0.0471          | 0.2142 | 0.1758 |
| 0.1045        | 19.0  | 1140 | 0.0305          | 0.2168 | 0.1686 |
| 0.1104        | 20.0  | 1200 | 0.0256          | 0.2072 | 0.1625 |
| 0.094         | 21.0  | 1260 | 0.0226          | 0.2272 | 0.1760 |
| 0.0987        | 22.0  | 1320 | 0.0129          | 0.2013 | 0.1900 |
| 0.0753        | 23.0  | 1380 | 0.0110          | 0.2053 | 0.1786 |
| 0.0544        | 24.0  | 1440 | 0.0091          | 0.1909 | 0.1858 |
| 0.0684        | 25.0  | 1500 | 0.0083          | 0.1901 | 0.1728 |
| 0.0723        | 26.0  | 1560 | 0.0083          | 0.2027 | 0.1854 |
| 0.061         | 27.0  | 1620 | 0.0061          | 0.2020 | 0.1779 |
| 0.0635        | 28.0  | 1680 | 0.0059          | 0.1964 | 0.1818 |
| 0.0336        | 29.0  | 1740 | 0.0048          | 0.1887 | 0.1574 |
| 0.0455        | 30.0  | 1800 | 0.0036          | 0.1842 | 0.1694 |
| 0.0672        | 31.0  | 1860 | 0.0038          | 0.1838 | 0.1507 |
| 0.0315        | 32.0  | 1920 | 0.0033          | 0.1853 | 0.1555 |
| 0.0466        | 33.0  | 1980 | 0.0033          | 0.1827 | 0.1569 |
| 0.0491        | 34.0  | 2040 | 0.0035          | 0.1835 | 0.1556 |
| 0.0315        | 35.0  | 2100 | 0.0032          | 0.1835 | 0.1557 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
