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	---
	language: es
	datasets:
	- common_voice
	metrics:
	- wer
	- cer
	tags:
	- audio
	- automatic-speech-recognition
	- speech
	- xlsr-fine-tuning-week
	license: apache-2.0
	model-index:
	- name: XLSR Wav2Vec2 Spanish by Jonatas Grosman
	results:
	- task:
	name: Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice es
	type: common_voice
	args: es
	metrics:
	- name: Test WER
	type: wer
	value: 8.81
	- name: Test CER
	type: cer
	value: 2.70
	---

	# Wav2Vec2-Large-XLSR-53-Spanish

	Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Spanish using the [Common Voice](https://huggingface.co/datasets/common_voice).
	When using this model, make sure that your speech input is sampled at 16kHz.

	The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint

	## Usage

	The model can be used directly (without a language model) as follows:

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

	LANG_ID = "es"
	MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-spanish"
	SAMPLES = 10

	test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")

	processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
	model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)

	# Preprocessing the datasets.
	# We need to read the audio files as arrays
	def speech_file_to_array_fn(batch):
	speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
	batch["speech"] = speech_array
	batch["sentence"] = batch["sentence"].upper()
	return batch

	test_dataset = test_dataset.map(speech_file_to_array_fn)
	inputs = processor(test_dataset["speech"], 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)
	predicted_sentences = processor.batch_decode(predicted_ids)

	for i, predicted_sentence in enumerate(predicted_sentences):
	print("-" * 100)
	print("Reference:", test_dataset[i]["sentence"])
	print("Prediction:", predicted_sentence)
	```

	\| Reference \| Prediction \|
	\| ------------- \| ------------- \|
	\| HABITA EN AGUAS POCO PROFUNDAS Y ROCOSAS. \| HABITAN AGUAS POCO PROFUNDAS Y ROCOSAS \|
	\| OPERA PRINCIPALMENTE VUELOS DE CABOTAJE Y REGIONALES DE CARGA. \| OPERA PRINCIPALMENTE VUELO DE CARBOTAJES Y REGIONALES DE CARGAN \|
	\| PARA VISITAR CONTACTAR PRIMERO CON LA DIRECCIÓN. \| PARA VISITAR CONTACTAR PRIMERO CON LA DIRECCIÓN \|
	\| TRES \| TRES \|
	\| REALIZÓ LOS ESTUDIOS PRIMARIOS EN FRANCIA, PARA CONTINUAR LUEGO EN ESPAÑA. \| REALIZÓ LOS ESTUDIOS PRIMARIOS EN FRANCIA PARA CONTINUAR LUEGO EN ESPAÑA \|
	\| EN LOS AÑOS QUE SIGUIERON, ESTE TRABAJO ESPARTA PRODUJO DOCENAS DE BUENOS JUGADORES. \| EN LOS AÑOS QUE SIGUIERON ESTE TRABAJO ESPARTA PRODUJO DOCENA DE BUENOS JUGADORES \|
	\| SE ESTÁ TRATANDO DE RECUPERAR SU CULTIVO EN LAS ISLAS CANARIAS. \| SE ESTÓ TRATANDO DE RECUPERAR SU CULTIVO EN LAS ISLAS CANARIAS \|
	\| SÍ \| SÍ \|
	\| "FUE ""SACADA"" DE LA SERIE EN EL EPISODIO ""LEAD"", EN QUE ALEXANDRA CABOT REGRESÓ." \| FUE SACADA DE LA SERIE EN EL EPISODIO LEED EN QUE ALEXANDRA KAOT REGRESÓ \|
	\| SE UBICAN ESPECÍFICAMENTE EN EL VALLE DE MOKA, EN LA PROVINCIA DE BIOKO SUR. \| SE UBICAN ESPECÍFICAMENTE EN EL VALLE DE MOCA EN LA PROVINCIA DE PÍOCOSUR \|

	## Evaluation

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

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

	LANG_ID = "es"
	MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-spanish"
	DEVICE = "cuda"

	CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
	"؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
	"{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
	"、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
	"『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"]

	test_dataset = load_dataset("common_voice", LANG_ID, split="test")

	wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
	cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py

	chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"

	processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
	model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
	model.to(DEVICE)

	# Preprocessing the datasets.
	# We need to read the audio files as arrays
	def speech_file_to_array_fn(batch):
	with warnings.catch_warnings():
	warnings.simplefilter("ignore")
	speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
	batch["speech"] = speech_array
	batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper()
	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(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).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)

	predictions = [x.upper() for x in result["pred_strings"]]
	references = [x.upper() for x in result["sentence"]]

	print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
	print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
	```

	Test Result:

	In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-04-22). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used.

	\| Model \| WER \| CER \|
	\| ------------- \| ------------- \| ------------- \|
	\| jonatasgrosman/wav2vec2-large-xlsr-53-spanish \| 8.81% \| 2.70% \|
	\| pcuenq/wav2vec2-large-xlsr-53-es \| 10.55% \| 3.20% \|
	\| facebook/wav2vec2-large-xlsr-53-spanish \| 16.99% \| 5.40% \|
	\| mrm8488/wav2vec2-large-xlsr-53-spanish \| 19.20% \| 5.96% \|