README.md

---
dataset_info:
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: sentence
    dtype: string
  splits:
  - name: train
    num_bytes: 12264199958.656
    num_examples: 49504
  download_size: 11879936920
  dataset_size: 12264199958.656
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: apache-2.0
task_categories:
- automatic-speech-recognition
- translation
- text-to-speech
language:
- ja
size_categories:
- 10K<n<100K
---
common voice, google fleurs, JSUTv1.1, JAS_v2 (joujiboi/japanese-anime-speech-v2) processed for whisper. Not shuffled or normalized. 50% anime speech and 50% other. Other corpus's are fully represented.

if needed:

import neologdn
import MeCab
import re
from transformers.models.whisper.english_normalizer import BasicTextNormalizer

wakati = MeCab.Tagger("-Owakati")
special_characters = '[\,\、\。\．\「\」\…\？\・\!\-\;\:\"\“\%\‘\”\�]'


def norm_everything(batch): 
    batch["sentence"] = neologdn.normalize(batch["sentence"]).strip() 
    batch["sentence"] = normalizer(batch["sentence"]).strip() 
    batch["sentence"] = wakati.parse(batch["sentence"]).strip() 
    batch["sentence"] = re.sub(special_characters,'', batch["sentence"]).strip() 
    return batch 

ds = ds.map(norm_everything)