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	---
	language: ja
	datasets:
	- common_voice
	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: { wer_result_on_test } #TODO (IMPORTANT): replace {wer_result_on_test} with the WER error rate you achieved on the common_voice test set. It should be in the format XX.XX (don't add the % sign here). Please remember to fill out this value after you evaluated your model, so that your model appears on the leaderboard. If you fill out this model card before evaluating your model, please remember to edit the model card afterward to fill in your value
	---

	# Wav2Vec2-Large-XLSR-53-{language} #TODO: replace language with your {language}, _e.g._ French

	Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on {language} using the [Common Voice](https://huggingface.co/datasets/common_voice), ... and ... dataset{s}. #TODO: replace {language} with your language, _e.g._ French and eventually add more datasets that were used and eventually remove common voice if model was not trained on common voice
	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/wav2vec2-large-xlsr-japanese-0325-1200")
	model = Wav2Vec2ForCTC.from_pretrained("qqhann/wav2vec2-large-xlsr-japanese-0325-1200")

	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 {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, _e.g._ French

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

	test_dataset = load_dataset("common_voice", "ja", split="test")
	wer = load_metric("wer")

	processor = Wav2Vec2Processor.from_pretrained("qqhann/wav2vec2-large-xlsr-japanese-0325-1200")
	model = Wav2Vec2ForCTC.from_pretrained("qqhann/wav2vec2-large-xlsr-japanese-0325-1200")
	model.to("cuda")

	chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]' # TODO: adapt this list to include all special characters you removed from the data
	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):
	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)

	print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
	```

	Test Result: XX.XX %

	<!-- # TODO: write output of print here. IMPORTANT: Please remember to also replace {wer_result_on_test} at the top of with this value here. tags. -->

	## 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 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. -->