readme: ---
  language: ja
  datasets:
  - common_voice
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: wav2vec2-live-japanese
    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: 21.48%
         - name: Test CER
           type: cer
           value: 9.82%
  ---
  # wav2vec2-live-japanese
  https://github.com/ttop32/wav2vec2-live-japanese-translator    
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese hiragana using the 
  - [common_voice](https://huggingface.co/datasets/common_voice)     
  - [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut)     
  - [CSS10](https://github.com/Kyubyong/css10)     
  - [TEDxJP-10K](https://github.com/laboroai/TEDxJP-10K)     
  - [JVS](https://sites.google.com/site/shinnosuketakamichi/research-topics/jvs_corpus)
  - [JSSS](https://sites.google.com/site/shinnosuketakamichi/research-topics/jsss_corpus)
  ## Inference
  ```python
  #usage
  import torch
  import torchaudio
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  model = Wav2Vec2ForCTC.from_pretrained("ttop324/wav2vec2-live-japanese")
  processor = Wav2Vec2Processor.from_pretrained("ttop324/wav2vec2-live-japanese")
  test_dataset = load_dataset("common_voice", "ja", split="test")
  # Preprocessing the datasets.
  # We need to read the aduio files as arrays
  def speech_file_to_array_fn(batch):
      speech_array, sampling_rate = torchaudio.load(batch["path"])
      batch["speech"] = torchaudio.functional.resample(speech_array, sampling_rate, 16000)[0].numpy()    
      return batch
  test_dataset = test_dataset.map(speech_file_to_array_fn)
  inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
  with torch.no_grad():
  	logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
  predicted_ids = torch.argmax(logits, dim=-1)
  print("Prediction:", processor.batch_decode(predicted_ids))
  print("Reference:", test_dataset[:2]["sentence"])
  ```
  ## Evaluation
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import re
  import pykakasi 
  import MeCab
  wer = load_metric("wer")
  cer = load_metric("cer")
  model = Wav2Vec2ForCTC.from_pretrained("ttop324/wav2vec2-live-japanese").to("cuda")
  processor = Wav2Vec2Processor.from_pretrained("ttop324/wav2vec2-live-japanese")
  test_dataset = load_dataset("common_voice", "ja", split="test")
  chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\�‘、。．！，・―─~｢｣『』\\\\※\[\]\{\}「」〇？…]'
  wakati = MeCab.Tagger("-Owakati")
  kakasi = pykakasi.kakasi()
  kakasi.setMode("J","H")      # kanji to hiragana
  kakasi.setMode("K","H")      # katakana to hiragana
  conv = kakasi.getConverter()
  FULLWIDTH_TO_HALFWIDTH = str.maketrans(
      '　０１２３４５６７８９ａｂｃｄｅｆｇｈｉｊｋｌｍｎｏｐｑｒｓｔｕｖｗｘｙｚＡＢＣＤＥＦＧＨＩＪＫＬＭＮＯＰＱＲＳＴＵＶＷＸＹＺ！゛＃＄％＆（）＊＋、ー。／：；〈＝〉？＠［］＾＿‘｛｜｝～',
      ' 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&()*+,-./:;<=>?@[]^_`{|}~',
  )
  def fullwidth_to_halfwidth(s):
      return s.translate(FULLWIDTH_TO_HALFWIDTH)
  def preprocessData(batch):
      batch["sentence"] = fullwidth_to_halfwidth(batch["sentence"])
      batch["sentence"] = re.sub(chars_to_ignore_regex,' ', batch["sentence"]).lower()  #remove special char
      batch["sentence"] = wakati.parse(batch["sentence"])                              #add space
      batch["sentence"] = conv.do(batch["sentence"])                                   #covert to hiragana
      batch["sentence"] = " ".join(batch["sentence"].split())+" "                         #remove multiple space 
      
      speech_array, sampling_rate = torchaudio.load(batch["path"])
      batch["speech"] = torchaudio.functional.resample(speech_array, sampling_rate, 16000)[0].numpy()    
      return batch
  test_dataset = test_dataset.map(preprocessData)
  # Preprocessing the datasets.
  # We need to read the aduio files as arrays
  def evaluate(batch):
  	inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
  	with torch.no_grad():
  		logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).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)
  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"])))
  ```