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	---
	language: ja
	datasets:
	- common_voice
	- jsut
	metrics:
	- wer
	- cer
	tags:
	- audio
	- automatic-speech-recognition
	- speech
	- xlsr-fine-tuning-week
	license: apache-2.0
	model-index:
	- name: Japanese XLSR Wav2Vec2 Large 53
	results:
	- task:
	name: Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice ja
	type: common_voice
	args: ja
	metrics:
	- name: Test WER
	type: wer
	value: 51.72
	- name: Test CER
	type: cer
	value: 24.89
	---

	# Wav2Vec2-Large-XLSR-53-Japanese

	Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese using the [Common Voice](https://huggingface.co/datasets/common_voice), and JSUT dataset{s}.
	When using this model, make sure that your speech input is sampled at 16kHz.

	## Usage

	The model can be used directly (without a language model) as follows:

	```python
	import torch
	import torchaudio
	from datasets import load_dataset
	from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

	test_dataset = load_dataset("common_voice", "ja", split="test[:2%]")

	processor = Wav2Vec2Processor.from_pretrained("qqhann/w2v_hf_jsut_xlsr53")
	model = Wav2Vec2ForCTC.from_pretrained("qqhann/w2v_hf_jsut_xlsr53")

	resampler = torchaudio.transforms.Resample(48_000, 16_000)

	# 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"] = resampler(speech_array).squeeze().numpy()
	return batch

	test_dataset = test_dataset.map(speech_file_to_array_fn)
	inputs = processor(test_dataset["speech"][:2], 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["sentence"][:2])
	```

	## Evaluation

	The model can be evaluated as follows on the Japanese test data of Common Voice.

	```python
	!pip install torchaudio
	!pip install datasets transformers
	!pip install jiwer
	!pip install mecab-python3
	!pip install unidic-lite
	!python -m unidic download
	!pip install jaconv

	import torch
	import torchaudio
	from datasets import load_dataset, load_metric
	from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
	import re
	import MeCab
	from jaconv import kata2hira
	from typing import List

	# Japanese preprocessing
	tagger = MeCab.Tagger("-Owakati")
	chars_to_ignore_regex = '[\。\、\「\」\,\?\.\!\-\;\:\"\“\%\‘\”\�]'

	def text2kata(text):
	node = tagger.parseToNode(text)
	word_class = []
	while node:
	word = node.surface
	wclass = node.feature.split(',')
	if wclass[0] != u'BOS/EOS':
	if len(wclass) <= 6:
	word_class.append((word))
	elif wclass[6] == None:
	word_class.append((word))
	else:
	word_class.append((wclass[6]))
	node = node.next
	return ' '.join(word_class)

	def hiragana(text):
	return kata2hira(text2kata(text))

	test_dataset = load_dataset("common_voice", "ja", split="test")
	wer = load_metric("wer")
	resampler = torchaudio.transforms.Resample(48_000, 16_000) # JSUT is already 16kHz
	# resampler = torchaudio.transforms.Resample(16_000, 16_000) # JSUT is already 16kHz

	processor = Wav2Vec2Processor.from_pretrained("qqhann/w2v_hf_jsut_xlsr53")
	model = Wav2Vec2ForCTC.from_pretrained("qqhann/w2v_hf_jsut_xlsr53")
	model.to("cuda")


	# Preprocessing the datasets.
	# We need to read the aduio files as arrays
	def speech_file_to_array_fn(batch):
	batch["sentence"] = hiragana(batch["sentence"]).strip()
	batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
	speech_array, sampling_rate = torchaudio.load(batch["path"])
	batch["speech"] = resampler(speech_array).squeeze().numpy()
	return batch


	test_dataset = test_dataset.map(speech_file_to_array_fn)

	# 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)

	def cer_compute(predictions: List[str], references: List[str]):
	p = [" ".join(list(" " + pred.replace(" ", ""))).strip() for pred in predictions]
	r = [" ".join(list(" " + ref.replace(" ", ""))).strip() for ref in references]
	return wer.compute(predictions=p, references=r)

	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"])))
	```

	Test Result: 51.72 %

	## Training

	<!-- The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO: adapt to state all the datasets that were used for training. -->

	The privately collected JSUT Japanese dataset was used for training.

	<!-- The script used for training can be found [here](...) # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here. -->