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	---
	language: it
	datasets:
	- common_voice
	metrics:
	- wer
	tags:
	- audio
	- automatic-speech-recognition
	- speech
	- xlsr-fine-tuning-week
	license: apache-2.0
	model-index:
	- name: Wav2Vec2 Large 53 Italian by Gunjan Chhablani
	results:
	- task:
	name: Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice it
	type: common_voice
	args: it
	metrics:
	- name: Test WER
	type: wer
	value: 11.49
	---

	# Wav2Vec2-Large-XLSR-53-Italian
	Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Italian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
	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", "it", split="test[:2%]")

	processor = Wav2Vec2Processor.from_pretrained('gchhablani/wav2vec2-large-xlsr-it')
	model = Wav2Vec2ForCTC.from_pretrained('gchhablani/wav2vec2-large-xlsr-it')

	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 Portuguese test data of Common Voice.


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

	import unicodedata
	import jiwer

	def chunked_wer(targets, predictions, chunk_size=None):
	if chunk_size is None: return jiwer.wer(targets, predictions)
	start = 0
	end = chunk_size
	H, S, D, I = 0, 0, 0, 0
	while start < len(targets):
	chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end])
	H = H + chunk_metrics["hits"]
	S = S + chunk_metrics["substitutions"]
	D = D + chunk_metrics["deletions"]
	I = I + chunk_metrics["insertions"]
	start += chunk_size
	end += chunk_size
	return float(S + D + I) / float(H + S + D)

	allowed_characters = [
	" ",
	"'",
	'a',
	'b',
	'c',
	'd',
	'e',
	'f',
	'g',
	'h',
	'i',
	'j',
	'k',
	'l',
	'm',
	'n',
	'o',
	'p',
	'q',
	'r',
	's',
	't',
	'u',
	'v',
	'w',
	'x',
	'y',
	'z',
	'à',
	'á',
	'è',
	'é',
	'ì',
	'í',
	'ò',
	'ó',
	'ù',
	'ú',
	]

	def remove_accents(input_str):
	if input_str in allowed_characters:
	return input_str
	if input_str == 'ø':
	return 'o'
	elif input_str=='ß' or input_str =='ß':
	return 'b'
	elif input_str=='ё':
	return 'e'
	elif input_str=='đ':
	return 'd'
	nfkd_form = unicodedata.normalize('NFKD', input_str)
	only_ascii = nfkd_form.encode('ASCII', 'ignore').decode()

	if only_ascii is None or only_ascii=='':
	return input_str
	else:
	return only_ascii
	def fix_accents(sentence):
	new_sentence=''
	for char in sentence:
	new_sentence+=remove_accents(char)
	return new_sentence

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

	processor = Wav2Vec2Processor.from_pretrained('gchhablani/wav2vec2-large-xlsr-it')
	model = Wav2Vec2ForCTC.from_pretrained('gchhablani/wav2vec2-large-xlsr-it')
	model.to("cuda")

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

	chars_to_remove= [",", "?", ".", "!", "-", ";", ":", '""', "%", '"', "�",'ʿ','“','”','(','=','`','_','+','«','<','>','~','…','«','»','–','\[','\]','°','̇','´','ʾ','„','̇','̇','̇','¡'] # All extra characters

	chars_to_remove_regex = f'[{"".join(chars_to_remove)}]'

	# 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_remove_regex, '', batch["sentence"]).lower().replace('‘',"'").replace('ʻ',"'").replace('ʼ',"'").replace('’',"'").replace('ʹ',"''").replace('̇','')
	batch["sentence"] = fix_accents(batch["sentence"])
	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 * chunked_wer(predictions=result["pred_strings"], targets=result["sentence"],chunk_size=5000)))
	```

	Test Result: 11.49 %

	## Training

	The Common Voice `train` and `validation` datasets were used for training. The code can be found [here](https://github.com/gchhablani/wav2vec2-week/blob/main/fine-tune-xlsr-wav2vec2-on-italian-asr-with-transformers_final.ipynb).