Fixing widget error and bibtex format

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	---
	language: is
	datasets:
	- language-and-voice-lab/samromur_asr
	- language-and-voice-lab/samromur_children
	- language-and-voice-lab/malromur_asr
	- language-and-voice-lab/althingi_asr
	tags:
	- audio
	- automatic-speech-recognition
	- icelandic
	- xlrs-53-icelandic
	- iceland
	- reykjavik
	- samromur
	license: cc-by-4.0
	model-index:
	- name: wav2vec2-large-xlsr-53-icelandic-ep10-1000h
	results:
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Samrómur (Test)
	type: language-and-voice-lab/samromur_asr
	split: test
	args:
	language: is
	metrics:
	- name: WER
	type: wer
	value: 9.847
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Samrómur (Dev)
	type: language-and-voice-lab/samromur_asr
	split: validation
	args:
	language: is
	metrics:
	- name: WER
	type: wer
	value: 8.736
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Samrómur Children (Test)
	type: language-and-voice-lab/samromur_children
	split: test
	args:
	language: is
	metrics:
	- name: WER
	type: wer
	value: 9.391
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Samrómur Children (Dev)
	type: language-and-voice-lab/samromur_children
	split: validation
	args:
	language: is
	metrics:
	- name: WER
	type: wer
	value: 6.055
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Malrómur (Test)
	type: language-and-voice-lab/malromur_asr
	split: test
	args:
	language: is
	metrics:
	- name: WER
	type: wer
	value: 5.643
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Malrómur (Dev)
	type: language-and-voice-lab/malromur_asr
	split: validation
	args:
	language: is
	metrics:
	- name: WER
	type: wer
	value: 6.156
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Althingi (Test)
	type: language-and-voice-lab/althingi_asr
	split: test
	args:
	language: is
	metrics:
	- name: WER
	type: wer
	value: 11.437
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Althingi (Dev)
	type: language-and-voice-lab/althingi_asr
	split: validation
	args:
	language: is
	metrics:
	- name: WER
	type: wer
	value: 11.093
	---
	# wav2vec2-large-xlsr-53-icelandic-ep10-1000h

	The "wav2vec2-large-xlsr-53-icelandic-ep10-1000h" is an acoustic model suitable for Automatic Speech Recognition in Icelandic. It is the result of fine-tuning the model "facebook/wav2vec2-large-xlsr-53" for 10 epochs with around 1000 hours of Icelandic data developed by the [Language and Voice Laboratory](https://huggingface.co/language-and-voice-lab). Most of the data is available at public repositories such as [LDC](https://www.ldc.upenn.edu/), [OpenSLR](https://openslr.org/) or [Clarin.is](https://clarin.is/)

	The specific list of corpora used to fine-tune the model is:

	- [Samrómur 21.05 (114h34m)](http://www.openslr.org/112/)
	- [Samrómur Children (127h25m)](https://catalog.ldc.upenn.edu/LDC2022S11)
	- [Malrómur (119hh03m)](https://clarin.is/en/resources/malromur/)
	- [Althingi Parliamentary Speech (514h29m)](https://catalog.ldc.upenn.edu/LDC2021S01)
	- L2-Speakers Data (125h55m) Unpublished material

	The fine-tuning process was performed during December (2022) in the servers of the Language and Voice Laboratory (https://lvl.ru.is/) at Reykjavík University (Iceland) by Carlos Daniel Hernández Mena.

	# Evaluation
	```python
	import torch
	from transformers import Wav2Vec2Processor
	from transformers import Wav2Vec2ForCTC

	#Load the processor and model.
	MODEL_NAME="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h"
	processor = Wav2Vec2Processor.from_pretrained(MODEL_NAME)
	model = Wav2Vec2ForCTC.from_pretrained(MODEL_NAME)

	#Load the dataset
	from datasets import load_dataset, load_metric, Audio
	ds=load_dataset("language-and-voice-lab/samromur_children", split="test")

	#Downsample to 16kHz
	ds = ds.cast_column("audio", Audio(sampling_rate=16_000))

	#Process the dataset
	def prepare_dataset(batch):
	audio = batch["audio"]
	#Batched output is "un-batched" to ensure mapping is correct
	batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0]
	with processor.as_target_processor():
	batch["labels"] = processor(batch["normalized_text"]).input_ids
	return batch
	ds = ds.map(prepare_dataset, remove_columns=ds.column_names,num_proc=1)

	#Define the evaluation metric
	import numpy as np
	wer_metric = load_metric("wer")
	def compute_metrics(pred):
	pred_logits = pred.predictions
	pred_ids = np.argmax(pred_logits, axis=-1)
	pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id
	pred_str = processor.batch_decode(pred_ids)
	#We do not want to group tokens when computing the metrics
	label_str = processor.batch_decode(pred.label_ids, group_tokens=False)
	wer = wer_metric.compute(predictions=pred_str, references=label_str)
	return {"wer": wer}

	#Do the evaluation (with batch_size=1)
	model = model.to(torch.device("cuda"))
	def map_to_result(batch):
	with torch.no_grad():
	input_values = torch.tensor(batch["input_values"], device="cuda").unsqueeze(0)
	logits = model(input_values).logits
	pred_ids = torch.argmax(logits, dim=-1)
	batch["pred_str"] = processor.batch_decode(pred_ids)[0]
	batch["sentence"] = processor.decode(batch["labels"], group_tokens=False)
	return batch
	results = ds.map(map_to_result,remove_columns=ds.column_names)

	#Compute the overall WER now.
	print("Test WER: {:.3f}".format(wer_metric.compute(predictions=results["pred_str"], references=results["sentence"])))
	```
	Test Result: 0.094

	# BibTeX entry and citation info
	When publishing results based on these models please refer to:
	```bibtex
	@misc{mena2022xlrs53icelandic,
	title={Acoustic Model in Icelandic: wav2vec2-large-xlsr-53-icelandic-ep10-1000h.},
	author={Hernandez Mena, Carlos Daniel},
	url={https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h},
	year={2022}
	}
	```

	# Acknowledgements

	Special thanks to Jón Guðnason, head of the Language and Voice Lab for providing computational power to make this model possible. We also want to thank to the "Language Technology Programme for Icelandic 2019-2023" which is managed and coordinated by Almannarómur, and it is funded by the Icelandic Ministry of Education, Science and Culture.