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	---
	language: vi
	datasets:
	- vivos
	- common_voice
	metrics:
	- wer
	pipeline_tag: automatic-speech-recognition
	tags:
	- audio
	- speech
	- speechbrain
	- Transformer
	license: cc-by-nc-4.0
	widget:
	- example_title: Example 1
	src: https://huggingface.co/dragonSwing/wav2vec2-base-vn-270h/raw/main/example.mp3
	- example_title: Example 2
	src: https://huggingface.co/dragonSwing/wav2vec2-base-vn-270h/raw/main/example2.mp3
	model-index:
	- name: Wav2vec2 Base Vietnamese 270h
	results:
	- task:
	name: Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice vi
	type: common_voice
	args: vi
	metrics:
	- name: Test WER
	type: wer
	value: 9.66
	- task:
	name: Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice 7.0
	type: mozilla-foundation/common_voice_7_0
	args: vi
	metrics:
	- name: Test WER
	type: wer
	value: 5.57
	- task:
	name: Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice 8.0
	type: mozilla-foundation/common_voice_8_0
	args: vi
	metrics:
	- name: Test WER
	type: wer
	value: 5.76
	- task:
	name: Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: VIVOS
	type: vivos
	args: vi
	metrics:
	- name: Test WER
	type: wer
	value: 3.70
	---
	# Wav2Vec2-Base-Vietnamese-270h
	Fine-tuned Wav2Vec2 model on Vietnamese Speech Recognition task using about 270h labelled data combined from multiple datasets including [Common Voice](https://huggingface.co/datasets/common_voice), [VIVOS](https://huggingface.co/datasets/vivos), [VLSP2020](https://vlsp.org.vn/vlsp2020/eval/asr). The model was fine-tuned using SpeechBrain toolkit with a custom tokenizer. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io/).
	When using this model, make sure that your speech input is sampled at 16kHz.
	Please refer to [huggingface blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) or [speechbrain](https://github.com/speechbrain/speechbrain/tree/develop/recipes/CommonVoice/ASR/CTC) on how to fine-tune Wav2Vec2 model on a specific language.

	### Benchmark WER result:
	\| \| [VIVOS](https://huggingface.co/datasets/vivos) \| [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) \| [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) \|
	\|---\|---\|---\|---\|
	\|without LM\| 8.23 \| 12.15 \| 12.15 \|
	\|with 4-grams LM\| 3.70 \| 5.57 \| 5.76 \|

	The language model was trained using [OSCAR](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109) dataset on about 32GB of crawled text.

	### Install SpeechBrain
	To use this model, you should install speechbrain > 0.5.10

	### Usage
	The model can be used directly (without a language model) as follows:
	```python
	from speechbrain.pretrained import EncoderASR

	model = EncoderASR.from_hparams(source="dragonSwing/wav2vec2-base-vn-270h", savedir="pretrained_models/asr-wav2vec2-vi")
	model.transcribe_file('dragonSwing/wav2vec2-base-vn-270h/example.mp3')
	# Output: được hồ chí minh coi là một động lực lớn của sự phát triển đất nước
	```

	### Inference on GPU
	To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.

	### Evaluation
	The model can be evaluated as follows on the Vietnamese test data of Common Voice 8.0.
	```python
	import torch
	import torchaudio
	from datasets import load_dataset, load_metric, Audio
	from transformers import Wav2Vec2FeatureExtractor
	from speechbrain.pretrained import EncoderASR
	import re
	test_dataset = load_dataset("mozilla-foundation/common_voice_8_0", "vi", split="test", use_auth_token=True)
	test_dataset = test_dataset.cast_column("audio", Audio(sampling_rate=16_000))
	device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
	wer = load_metric("wer")
	extractor = Wav2Vec2FeatureExtractor.from_pretrained("dragonSwing/wav2vec2-base-vn-270h")
	model = EncoderASR.from_hparams(source="dragonSwing/wav2vec2-base-vn-270h", savedir="pretrained_models/asr-wav2vec2-vi", run_opts={'device': device})
	chars_to_ignore_regex = r'[,?.!\-;:"“%\'�]'
	# Preprocessing the datasets.
	# We need to read the audio files as arrays
	def speech_file_to_array_fn(batch):
	audio = batch["audio"]
	batch["target_text"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
	batch['speech'] = audio['array']
	return batch
	test_dataset = test_dataset.map(speech_file_to_array_fn)

	def evaluate(batch):
	# For padding inputs only
	inputs = extractor(
	batch['speech'],
	sampling_rate=16000,
	return_tensors="pt",
	padding=True,
	do_normalize=False
	).input_values
	input_lens = torch.ones(inputs.shape[0])
	pred_str, pred_tokens = model.transcribe_batch(inputs, input_lens)
	batch["pred_strings"] = pred_str

	return batch
	result = test_dataset.map(evaluate, batched=True, batch_size=1)
	print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["target_text"])))
	```
	Test Result: 12.155553%

	#### Citation
	```
	@misc{SB2021,
	author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
	title = {SpeechBrain},
	year = {2021},
	publisher = {GitHub},
	journal = {GitHub repository},
	howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
	}
	```

	#### About SpeechBrain
	SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.
	Website: [https://speechbrain.github.io](https://speechbrain.github.io/)
	GitHub: [https://github.com/speechbrain/speechbrain](https://github.com/speechbrain/speechbrain)