
## Model description

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_8_0 - JA dataset.

### Benchmark WER result:
| | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0)
|---|---|---|
|without LM| 15.74 | 25.10 |
|with 4-grams LM| 15.37 | 16.09 |

### Benchmark CER result:
| | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0)
|---|---|---|
|without LM| 9.51 | 9.95 |
|with 4-grams LM| 6.91 | 7.15 |

## Evaluation

Please use the eval.py file to run the evaluation:

```python
python eval.py --model_id vutankiet2901/wav2vec2-large-xlsr-53-ja --dataset mozilla-foundation/common_voice_7_0 --config ja --split test --log_outputs
```

## 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: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 4.7776        | 4.73  | 1500  | 2.9540          | 0.9772 | 0.8489 |
| 1.9076        | 9.46  | 3000  | 0.7146          | 0.5371 | 0.2484 |
| 1.507         | 14.2  | 4500  | 0.5843          | 0.4689 | 0.2196 |
| 1.3742        | 18.93 | 6000  | 0.5286          | 0.4321 | 0.1988 |
| 1.2776        | 23.66 | 7500  | 0.5007          | 0.4056 | 0.1870 |
| 1.2003        | 28.39 | 9000  | 0.4676          | 0.3848 | 0.1802 |
| 1.1281        | 33.12 | 10500 | 0.4524          | 0.3694 | 0.1720 |
| 1.0657        | 37.85 | 12000 | 0.4449          | 0.3590 | 0.1681 |
| 1.0129        | 42.59 | 13500 | 0.4266          | 0.3423 | 0.1617 |
| 0.9691        | 47.32 | 15000 | 0.4214          | 0.3375 | 0.1587 |


### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0

{"language": ["ja"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common-voice", "hf-asr-leaderboard", "ja", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xlsr-53-ja", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice 7.0", "type": "mozilla-foundation/common_voice_8_0", "args": "ja"}, "metrics": [{"type": "wer", "value": 15.37, "name": "Test WER (with LM)"}, {"type": "cer", "value": 6.91, "name": "Test CER (with LM)"}, {"type": "wer", "value": 16.09, "name": "Test WER (with LM)"}, {"type": "cer", "value": 7.15, "name": "Test CER (with LM)"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "ja"}, "metrics": [{"type": "wer", "value": 37.96, "name": "Test WER (with LM)"}, {"type": "cer", "value": 21.11, "name": "Test CER (with LM)"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "ja"}, "metrics": [{"type": "cer", "value": 26.02, "name": "Test CER"}]}]}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition common-voice hf-asr-leaderboard ja robust-speech-event dataset:mozilla-foundation/common_voice_8_0 license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common-voice #hf-asr-leaderboard #ja #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 

Model description
-----------------


This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - JA dataset.


### Benchmark WER result:


COMMON VOICE 7.0: without LM, COMMON VOICE 8.0: 15.74
COMMON VOICE 7.0: with 4-grams LM, COMMON VOICE 8.0: 15.37


### Benchmark CER result:


COMMON VOICE 7.0: without LM, COMMON VOICE 8.0: 9.51
COMMON VOICE 7.0: with 4-grams LM, COMMON VOICE 8.0: 6.91


Evaluation
----------


Please use the URL file to run the evaluation:


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: 2000
* num\_epochs: 50.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0



### Benchmark WER result:


COMMON VOICE 7.0: without LM, COMMON VOICE 8.0: 15.74
COMMON VOICE 7.0: with 4-grams LM, COMMON VOICE 8.0: 15.37 ### Benchmark CER result:


COMMON VOICE 7.0: without LM, COMMON VOICE 8.0: 9.51
COMMON VOICE 7.0: with 4-grams LM, COMMON VOICE 8.0: 6.91


Evaluation
----------


Please use the URL file to run the evaluation:


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: 2000
* num\_epochs: 50.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common-voice #hf-asr-leaderboard #ja #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Benchmark WER result:


COMMON VOICE 7.0: without LM, COMMON VOICE 8.0: 15.74
COMMON VOICE 7.0: with 4-grams LM, COMMON VOICE 8.0: 15.37 ### Benchmark CER result:


COMMON VOICE 7.0: without LM, COMMON VOICE 8.0: 9.51
COMMON VOICE 7.0: with 4-grams LM, COMMON VOICE 8.0: 6.91


Evaluation
----------


Please use the URL file to run the evaluation:


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: 2000
* num\_epochs: 50.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common-voice #hf-asr-leaderboard #ja #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
### Benchmark WER result:


COMMON VOICE 7.0: without LM, COMMON VOICE 8.0: 15.74
COMMON VOICE 7.0: with 4-grams LM, COMMON VOICE 8.0: 15.37### Benchmark CER result:


COMMON VOICE 7.0: without LM, COMMON VOICE 8.0: 9.51
COMMON VOICE 7.0: with 4-grams LM, COMMON VOICE 8.0: 6.91


Evaluation
----------


Please use the URL file to run the evaluation:


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: 2000
* num\_epochs: 50.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0