
# 

This model is for transcribing audio into Hiragana, one format of Japanese language.

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the `mozilla-foundation/common_voice_8_0 dataset`. Note that the following results are achieved by:
- Modify `eval.py` to suit the use case.
- Since kanji and katakana shares the same sound as hiragana, we convert all texts to hiragana using [pykakasi](https://pykakasi.readthedocs.io) and tokenize them using [fugashi](https://github.com/polm/fugashi).

It achieves the following results on the evaluation set:
- Loss: 0.7751
- Cer: 0.2227

# Evaluation results (Running ./eval.py):

| Model    | Metric | Common-Voice-8/test | speech-recognition-community-v2/dev-data   |
|:--------:|:------:|:-------------------:|:------------------------------------------:|
| w/o LM   | WER    | 0.5964              | 0.5532                                     |
|          | CER    | 0.2944              | 0.2629                                     |
| w/  LM   | WER    | 0.5405              | 0.4877                                     |
|          | CER    | **0.2754**              | **0.2487**                                     |


## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 1000
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.4081        | 1.6   | 500   | 4.0983          | 1.0    |
| 3.303         | 3.19  | 1000  | 3.3563          | 1.0    |
| 3.1538        | 4.79  | 1500  | 3.2066          | 0.9239 |
| 2.1526        | 6.39  | 2000  | 1.1597          | 0.3355 |
| 1.8726        | 7.98  | 2500  | 0.9023          | 0.2505 |
| 1.7817        | 9.58  | 3000  | 0.8219          | 0.2334 |
| 1.7488        | 11.18 | 3500  | 0.7915          | 0.2222 |
| 1.7039        | 12.78 | 4000  | 0.7751          | 0.2227 |
| Stop & Train  |       |       |                 |        |
| 1.6571        | 15.97 | 5000  | 0.6788          | 0.1685 |
| 1.520400      | 19.16 | 6000  | 0.6095          | 0.1409 |
| 1.448200      | 22.35 | 7000  | 0.5843          | 0.1430 |
| 1.385400      | 25.54 | 8000  | 0.5699          | 0.1263 |
| 1.354200      | 28.73 | 9000  | 0.5686          | 0.1219 |
| 1.331500      | 31.92 | 10000 | 0.5502          | 0.1144 |
| 1.290800      | 35.11 | 11000 | 0.5371          | 0.1140 |
| Stop & Train  |       |       |                 |        |
| 1.235200      | 38.30 | 12000 | 0.5394          | 0.1106 |


### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0

{"language": ["ja"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "ja", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-300M - Japanese", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "ja"}, "metrics": [{"type": "wer", "value": 54.05, "name": "Test WER"}, {"type": "cer", "value": 27.54, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "ja"}, "metrics": [{"type": "wer", "value": 48.77, "name": "Validation WER"}, {"type": "cer", "value": 24.87, "name": "Validation CER"}]}, {"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": 27.36, "name": "Test CER"}]}]}]}
transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer hf-asr-leaderboard ja mozilla-foundation/common_voice_8_0 robust-speech-event dataset:mozilla-foundation/common_voice_8_0 doi:10.57967/hf/0124 license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #ja #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #doi-10.57967/hf/0124 #license-apache-2.0 #model-index #endpoints_compatible #region-us 


This model is for transcribing audio into Hiragana, one format of Japanese language.


This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the 'mozilla-foundation/common\_voice\_8\_0 dataset'. Note that the following results are achieved by:


* Modify 'URL' to suit the use case.
* Since kanji and katakana shares the same sound as hiragana, we convert all texts to hiragana using pykakasi and tokenize them using fugashi.


It achieves the following results on the evaluation set:


* Loss: 0.7751
* Cer: 0.2227


Evaluation results (Running ./URL):
===================================



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: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* 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: 1000
* training\_steps: 4000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* 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: 1000
* training\_steps: 4000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #ja #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #doi-10.57967/hf/0124 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* 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: 1000
* training\_steps: 4000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #ja #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #doi-10.57967/hf/0124 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* 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: 1000
* training\_steps: 4000
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0