add language tags to YAML, so this resource is more easily discovered (#2)

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	---
	language:
	- id
	- jv
	- sun
	datasets:
	- mozilla-foundation/common_voice_7_0
	- openslr
	- magic_data
	- titml
	metrics:
	- wer
	tags:
	- audio
	- automatic-speech-recognition
	- hf-asr-leaderboard
	- id
	- jv
	- robust-speech-event
	- speech
	- su
	license: apache-2.0
	model-index:
	- name: Wav2Vec2 Indonesian Javanese and Sundanese by Indonesian NLP
	results:
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice 6.1
	type: common_voice
	args: id
	metrics:
	- name: Test WER
	type: wer
	value: 4.056
	- name: Test CER
	type: cer
	value: 1.472
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice 7
	type: mozilla-foundation/common_voice_7_0
	args: id
	metrics:
	- name: Test WER
	type: wer
	value: 4.492
	- name: Test CER
	type: cer
	value: 1.577
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Robust Speech Event - Dev Data
	type: speech-recognition-community-v2/dev_data
	args: id
	metrics:
	- name: Test WER
	type: wer
	value: 48.94
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Robust Speech Event - Test Data
	type: speech-recognition-community-v2/eval_data
	args: id
	metrics:
	- name: Test WER
	type: wer
	value: 68.95
	---

	# Multilingual Speech Recognition for Indonesian Languages

	This is the model built for the project
	[Multilingual Speech Recognition for Indonesian Languages](https://github.com/indonesian-nlp/multilingual-asr).
	It is a fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
	model on the [Indonesian Common Voice dataset](https://huggingface.co/datasets/common_voice),
	[High-quality TTS data for Javanese - SLR41](https://huggingface.co/datasets/openslr), and
	[High-quality TTS data for Sundanese - SLR44](https://huggingface.co/datasets/openslr) datasets.

	We also provide a [live demo](https://huggingface.co/spaces/indonesian-nlp/multilingual-asr) to test the model.

	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", "id", split="test[:2%]")

	processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-indonesian-javanese-sundanese")
	model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-indonesian-javanese-sundanese")

	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[: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

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

	```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", "id", split="test")
	wer = load_metric("wer")

	processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-indonesian-javanese-sundanese")
	model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-indonesian-javanese-sundanese")
	model.to("cuda")

	chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\'\”\�]'

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

	# Preprocessing the datasets.
	# We need to read the audio 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 audio 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: 11.57 %

	## Training

	The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO

	The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
	(will be available soon)