wav2vec2-large-xlsr-estonian / README.md

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	---
	language: et
	datasets:
	- common_voice
	tags:
	- audio
	- automatic-speech-recognition
	- speech
	- xlsr-fine-tuning-week
	license: apache-2.0
	widget:
	- label: Common Voice sample 1123
	src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-estonian/resolve/main/sample1123.flac
	- label: Common Voice sample 910
	src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-estonian/resolve/main/sample910.flac
	model-index:
	- name: XLSR Wav2Vec2 Estonian by Mehrdad Farahani
	results:
	- task:
	name: Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice et
	type: common_voice
	args: et
	metrics:
	- name: Test WER
	type: wer
	value: 33.93
	---


	# Wav2Vec2-Large-XLSR-53-Estonian

	Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Estonian using [Common Voice](https://huggingface.co/datasets/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:

	Requirements
	```bash
	# requirement packages
	!pip install git+https://github.com/huggingface/datasets.git
	!pip install git+https://github.com/huggingface/transformers.git
	!pip install torchaudio
	!pip install librosa
	!pip install jiwer
	```


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

	import numpy as np
	import re
	import string

	import IPython.display as ipd

	chars_to_ignore = [
	",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�",
	"#", "!", "?", "«", "»", "(", ")", "؛", ",", "?", ".", "!", "-", ";", ":", '"',
	"“", "%", "‘", "�", "–", "…", "_", "”", '“', '„'
	]
	chars_to_mapping = {
	"\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ",
	}

	def multiple_replace(text, chars_to_mapping):
	pattern = "\|".join(map(re.escape, chars_to_mapping.keys()))
	return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text))

	def remove_special_characters(text, chars_to_ignore_regex):
	text = re.sub(chars_to_ignore_regex, '', text).lower() + " "
	return text

	def normalizer(batch, chars_to_ignore, chars_to_mapping):
	chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]"""
	text = batch["sentence"].lower().strip()

	text = text.replace("\u0307", " ").strip()
	text = multiple_replace(text, chars_to_mapping)
	text = remove_special_characters(text, chars_to_ignore_regex)

	batch["sentence"] = text
	return batch


	def speech_file_to_array_fn(batch):
	speech_array, sampling_rate = torchaudio.load(batch["path"])
	speech_array = speech_array.squeeze().numpy()
	speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000)

	batch["speech"] = speech_array
	return batch


	def predict(batch):
	features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

	input_values = features.input_values.to(device)
	attention_mask = features.attention_mask.to(device)

	with torch.no_grad():
	logits = model(input_values, attention_mask=attention_mask).logits

	pred_ids = torch.argmax(logits, dim=-1)

	batch["predicted"] = processor.batch_decode(pred_ids)[0]
	return batch


	device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
	processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-estonian")
	model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-estonian").to(device)

	dataset = load_dataset("common_voice", "et", split="test[:1%]")
	dataset = dataset.map(
	normalizer,
	fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping},
	remove_columns=list(set(dataset.column_names) - set(['sentence', 'path']))
	)

	dataset = dataset.map(speech_file_to_array_fn)
	result = dataset.map(predict)

	max_items = np.random.randint(0, len(result), 10).tolist()
	for i in max_items:
	reference, predicted = result["sentence"][i], result["predicted"][i]
	print("reference:", reference)
	print("predicted:", predicted)
	print('---')
	```

	Output:
	```text
	reference: õhulossid lagunevad ning ees ootab maapind
	predicted: õhulassid lagunevad ning ees ootab maapind
	---
	reference: milliseks kiievisse pääsemise nimel võistlev muusik soome muusikamaastiku hetkeseisu hindab ning kas ta ka ennast sellel tulevikus tegutsemas näeb kuuled videost
	predicted: milliseks gievisse pääsemise nimel võitlev muusiks soome muusikama aastiku hetke seisu hindab ning kas ta ennast selle tulevikus tegutsemast näeb kuulad videost
	---
	reference: näiteks kui pool seina on tehtud tekib tunne et tahaks tegelikult natuke teistsugust ja hakkame otsast peale
	predicted: näiteks kui pool seine on tehtud tekib tunnetahaks tegelikult matuka teistsugust jahappanna otsast peane
	---
	reference: neuroesteetilised katsed näitavad et just nägude vaatlemine aktiveerib inimese aju esteetilist keskust
	predicted: neuroaisteetiliselt katsed näitaval et just nägude vaatlemine aptiveerid inimese aju est eedilist keskust
	---
	reference: paljud inimesed kindlasti kadestavad teid kuid ei julge samamoodi vabalt võtta
	predicted: paljud inimesed kindlasti kadestavadteid kuid ei julge sama moodi vabalt võtta
	---
	reference: parem on otsida pileteid inkognito veebi kaudu
	predicted: parem on otsida pileteid ning kognitu veebikaudu
	---
	reference: ja vot siin ma jäin vaikseks
	predicted: ja vat siisma ja invaikseks
	---
	reference: mida sa iseendale juubeli puhul soovid
	predicted: mida saise endale jubeli puhul soovid
	---
	reference: kuumuse ja kõrge temperatuuri tõttu kuivas tühjadel karjamaadel rohi mis muutus kergesti süttivaks
	predicted: kuumuse ja kõrge temperatuuri tõttu kuivast ühjadal karjamaadel rohi mis muutus kergesti süttivaks
	---
	reference: ilmselt on inimesi kelle jaoks on see hea lahendus
	predicted: ilmselt on inimesi kelle jaoks on see hea lahendus
	---
	```


	## Evaluation

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

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

	import numpy as np
	import re
	import string


	chars_to_ignore = [
	",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�",
	"#", "!", "?", "«", "»", "(", ")", "؛", ",", "?", ".", "!", "-", ";", ":", '"',
	"“", "%", "‘", "�", "–", "…", "_", "”", '“', '„'
	]
	chars_to_mapping = {
	"\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ",
	}

	def multiple_replace(text, chars_to_mapping):
	pattern = "\|".join(map(re.escape, chars_to_mapping.keys()))
	return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text))

	def remove_special_characters(text, chars_to_ignore_regex):
	text = re.sub(chars_to_ignore_regex, '', text).lower() + " "
	return text

	def normalizer(batch, chars_to_ignore, chars_to_mapping):
	chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]"""
	text = batch["sentence"].lower().strip()

	text = text.replace("\u0307", " ").strip()
	text = multiple_replace(text, chars_to_mapping)
	text = remove_special_characters(text, chars_to_ignore_regex)

	batch["sentence"] = text
	return batch


	def speech_file_to_array_fn(batch):
	speech_array, sampling_rate = torchaudio.load(batch["path"])
	speech_array = speech_array.squeeze().numpy()
	speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000)

	batch["speech"] = speech_array
	return batch


	def predict(batch):
	features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

	input_values = features.input_values.to(device)
	attention_mask = features.attention_mask.to(device)

	with torch.no_grad():
	logits = model(input_values, attention_mask=attention_mask).logits

	pred_ids = torch.argmax(logits, dim=-1)

	batch["predicted"] = processor.batch_decode(pred_ids)[0]
	return batch


	device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
	processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-estonian")
	model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-estonian").to(device)

	dataset = load_dataset("common_voice", "et", split="test")
	dataset = dataset.map(
	normalizer,
	fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping},
	remove_columns=list(set(dataset.column_names) - set(['sentence', 'path']))
	)

	dataset = dataset.map(speech_file_to_array_fn)
	result = dataset.map(predict)

	wer = load_metric("wer")

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

	Test Result:
	- WER: 33.93%


	## Training & Report
	The Common Voice `train`, `validation` datasets were used for training.

	You can see the training states [here](https://wandb.ai/m3hrdadfi/finetuned_wav2vec_xlsr_estonian/reports/Fine-Tuning-for-Wav2Vec2-Large-XLSR-53-Estonian--Vmlldzo1NjA1MTI?accessToken=k2b2g3a2i12m1sdwf13q8b226pplmmyw12joxo6vk38eb4djellfzmn9fp2725fw)

	The script used for training can be found [here](https://colab.research.google.com/github/m3hrdadfi/notebooks/blob/main/Fine_Tune_XLSR_Wav2Vec2_on_Estonian_ASR_with_%F0%9F%A4%97_Transformers_ipynb.ipynb)