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	---
	language: ga
	datasets:
	- common_voice
	metrics:
	- wer
	tags:
	- audio
	- automatic-speech-recognition
	- speech
	- xlsr-fine-tuning-week
	license: apache-2.0
	model-index:
	- name: XLSR Wav2Vec2 Irish by Jim O'Regan
	results:
	- task:
	name: Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice ga-IE
	type: common_voice
	args: ga-IE
	metrics:
	- name: Test WER
	type: wer
	value: 47.4
	---
	# Wav2Vec2-Large-XLSR-Irish
	Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
	on the [Irish Common Voice dataset](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:
	```python
	import torch
	import torchaudio
	from datasets import load_dataset
	from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
	test_dataset = load_dataset("common_voice", "ga-IE", split="test[:2%]")
	processor = Wav2Vec2Processor.from_pretrained("jimregan/wav2vec2-large-xlsr-irish-basic")
	model = Wav2Vec2ForCTC.from_pretrained("jimregan/wav2vec2-large-xlsr-irish-basic")
	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):
	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 Irish 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", "ga-IE", split="test")
	wer = load_metric("wer")
	processor = Wav2Vec2Processor.from_pretrained("jimregan/wav2vec2-large-xlsr-irish-basic")
	model = Wav2Vec2ForCTC.from_pretrained("jimregan/wav2vec2-large-xlsr-irish-basic")
	model.to("cuda")
	# So, tolower() for Irish is a bit complicated: tAthar -> t-athair
	# toupper() is non-deterministic :)
	def is_upper_vowel(letter):
	if letter in ['A', 'E', 'I', 'O', 'U', 'Á', 'É', 'Í', 'Ó', 'Ú']:
	return True
	else:
	return False
	def irish_lower(word):
	if len(word) > 1 and word[0] in ['n', 't'] and is_upper_vowel(word[1]):
	return word[0] + '-' + word[1:].lower()
	else:
	return word.lower()
	def irish_lower_sentence(sentence):
	return " ".join([irish_lower(w) for w in sentence.split(" ")])
	chars_to_ignore_regex = '[,\?\.\!\;\:\"\“\%\‘\”\*]'
	def remove_special_characters(sentence):
	tmp = re.sub('’ ', ' ', sentence)
	tmp = re.sub("’$", '', tmp)
	tmp = re.sub('’', '\'', tmp)
	tmp = re.sub(chars_to_ignore_regex, '', tmp)
	sentence = irish_lower_sentence(tmp) + ' '
	return sentence
	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"] = remove_special_characters(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 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: 43.7 %

	## Training

	The Common Voice `train` and `validation` datasets were used for training.

	The script used for training can be found [here](https://github.com/jimregan/wav2vec2-sprint/blob/main/irish/fine-tune-xlsr-wav2vec2-on-irish-asr-with-transformers.ipynb)