README.md

---
language: ja
datasets:
- common_voice
metrics:
- wer
- cer
model-index:
- name: wav2vec2-xls-r-300m finetuned on Japanese Hiragana with no word boundaries by Hyungshin Ryu of SLPlab
  results:
  - task:
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice Japanese
      type: common_voice
      args: ja
    metrics:
       - name: Test WER
         type: wer
         value: 90.66
       - name: Test CER
         type: cer
         value: 19.35
---
# Wav2Vec2-XLS-R-300M-Japanese-Hiragana
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on Japanese Hiragana characters using the [Common Voice](https://huggingface.co/datasets/common_voice) and [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut).
The sentence outputs do not contain word boundaries. Audio inputs should be sampled at 16kHz.
## Usage
The model can be used directly as follows:

```python
!pip install mecab-python3
!pip install unidic-lite
!pip install pykakasi


import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset, load_metric
import pykakasi
import MeCab
import re


# load datasets, processor, and model
test_dataset = load_dataset("common_voice", "ja", split="test")
wer = load_metric("wer")
cer = load_metric("cer")
PTM = "slplab/wav2vec2-xls-r-300m-japanese-hiragana"
print("PTM:", PTM)
processor = Wav2Vec2Processor.from_pretrained(PTM)
model = Wav2Vec2ForCTC.from_pretrained(PTM)
device = "cuda"
model.to(device)


# preprocess datasets
wakati = MeCab.Tagger("-Owakati")
kakasi = pykakasi.kakasi()
chars_to_ignore_regex = "[、,。]"

def speech_file_to_array_fn_hiragana_nospace(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).strip()
    batch["sentence"] = ''.join([d['hira'] for d in kakasi.convert(batch["sentence"])])
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    resampler = torchaudio.transforms.Resample(sampling_rate, 16000)
    batch["speech"] = resampler(speech_array).squeeze()

    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn_hiragana_nospace)


#evaluate
def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16000, return_tensors="pt", padding=True)
    with torch.no_grad():
        logits = model(inputs.input_values.to(device)).logits
    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)
for i in range(10):
    print("="*20)
    print("Prd:", result[i]["pred_strings"])
    print("Ref:", result[i]["sentence"])
    
print("WER: {:2f}%".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
print("CER: {:2f}%".format(100 * cer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
| Original Text  | Prediction |
| ------------- | ------------- |
| この料理は家庭で作れます。 | このりょうりはかていでつくれます |
| 日本人は、決して、ユーモアと無縁な人種ではなかった。 | にっぽんじんはけしてゆうもあどむえんなじんしゅではなかった |
| 木村さんに電話を貸してもらいました。 | きむらさんにでんわおかしてもらいました |

## Test Results
**WER:** 90.66%,
**CER:** 19.35%
## Training
Trained on JSUT and train+valid set of Common Voice Japanese. Tested on test set of Common Voice Japanese.