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	---
	language: eu
	datasets:
	- common_voice
	metrics:
	- wer
	tags:
	- audio
	- automatic-speech-recognition
	- speech
	- xlsr-fine-tuning-week
	license: apache-2.0
	model-index:
	- name: XLSR Wav2Vec2 Large 53 Basque by pcuenq
	results:
	- task:
	name: Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice eu
	type: common_voice
	args: eu
	metrics:
	- name: Test WER
	type: wer
	value: 15.34
	---

	# Wav2Vec2-Large-XLSR-53-EU

	Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Basque 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", "eu", split="test[:2%]")

	processor = Wav2Vec2Processor.from_pretrained("pcuenq/wav2vec2-large-xlsr-53-eu")
	model = Wav2Vec2ForCTC.from_pretrained("pcuenq/wav2vec2-large-xlsr-53-eu")

	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 Basque 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", "eu", split="test")
	wer = load_metric("wer")

	model_name = "pcuenq/wav2vec2-large-xlsr-53-eu"

	processor = Wav2Vec2Processor.from_pretrained(model_name)
	model = Wav2Vec2ForCTC.from_pretrained(model_name)
	model.to("cuda")

	## Text pre-processing

	chars_to_ignore_regex = '[\,\¿\?\.\¡\!\-\;\:\"\“\%\‘\”\\…\’\ː\'\‹\›\`\´\®\—\→]'
	chars_to_ignore_pattern = re.compile(chars_to_ignore_regex)

	def remove_special_characters(batch):
	batch["sentence"] = chars_to_ignore_pattern.sub('', batch["sentence"]).lower() + " "
	return batch

	## Audio pre-processing

	import librosa
	def speech_file_to_array_fn(batch):
	speech_array, sample_rate = torchaudio.load(batch["path"])
	batch["speech"] = librosa.resample(speech_array.squeeze().numpy(), sample_rate, 16_000)
	return batch

	# Text transformation and audio resampling
	def cv_prepare(batch):
	batch = remove_special_characters(batch)
	batch = speech_file_to_array_fn(batch)
	return batch

	# Number of CPUs or None
	num_proc = 16
	test_dataset = test_dataset.map(cv_prepare, remove_columns=['path'], num_proc=num_proc)

	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)

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

	Test Result: 15.34 %

	## Training

	The Common Voice `train` and `validation` datasets were used for training. Training was performed for 22 + 20 epochs with the following parameters:

	- Batch size 16, 2 gradient accumulation steps.
	- Learning rate: 2.5e-4
	- Activation dropout: 0.05
	- Attention dropout: 0.1
	- Hidden dropout: 0.05
	- Feature proj. dropout: 0.05
	- Mask time probability: 0.08
	- Layer dropout: 0.05