Fixing bibtex format

29953ad 7 months ago

No virus

8.4 kB

	---
	language: es
	datasets:
	- ciempiess/ciempiess_light
	- ciempiess/ciempiess_balance
	- ciempiess/ciempiess_fem
	- common_voice
	- hub4ne_es_LDC98S74
	- callhome_es_LDC96S35
	tags:
	- audio
	- automatic-speech-recognition
	- spanish
	- xlrs-53-spanish
	- ciempiess
	- cimpiess-unam
	license: cc-by-4.0
	model-index:
	- name: wav2vec2-large-xlsr-53-spanish-ep5-944h
	results:
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Mozilla Common Voice 10.0 (Test)
	type: mozilla-foundation/common_voice_10_0
	split: test
	args:
	language: es
	metrics:
	- name: WER
	type: wer
	value: 9.20
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Mozilla Common Voice 10.0 (Dev)
	type: mozilla-foundation/common_voice_10_0
	split: validation
	args:
	language: es
	metrics:
	- name: WER
	type: wer
	value: 8.02
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: CIEMPIESS-TEST
	type: ciempiess/ciempiess_test
	split: test
	args:
	language: es
	metrics:
	- name: WER
	type: wer
	value: 11.17
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: 1997 Spanish Broadcast News Speech (HUB4-NE)
	type: HUB4NE_LDC98S74
	split: test
	args:
	language: es
	metrics:
	- name: WER
	type: wer
	value: 7.48
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: CALLHOME Spanish Speech (Test)
	type: callhome_LDC96S35
	split: test
	args:
	language: es
	metrics:
	- name: WER
	type: wer
	value: 39.12
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: CALLHOME Spanish Speech (Dev)
	type: callhome_LDC96S35
	split: validation
	args:
	language: es
	metrics:
	- name: WER
	type: wer
	value: 40.39
	---

	# wav2vec2-large-xlsr-53-spanish-ep5-944h
	Paper: [The state of end-to-end systems for Mexican Spanish speech recognition](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/viewFile/6485/3892)

	The "wav2vec2-large-xlsr-53-spanish-ep5-944h" is an acoustic model suitable for Automatic Speech Recognition in Spanish. It is the result of fine-tuning the model "facebook/wav2vec2-large-xlsr-53" for 5 epochs with around 944 hours of Spanish data gathered or developed by the [CIEMPIESS-UNAM Project](https://huggingface.co/ciempiess) since 2012. Most of the data is available at the the CIEMPIESS-UNAM Project homepage http://www.ciempiess.org/. The rest can be found in public repositories such as [LDC](https://www.ldc.upenn.edu/) or [OpenSLR](https://openslr.org/)

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

	- [CIEMPIESS-LIGHT (18h25m)](https://catalog.ldc.upenn.edu/LDC2017S23)
	- [CIEMPIESS-BALANCE (18h20m)](https://catalog.ldc.upenn.edu/LDC2018S11)
	- [CIEMPIESS-FEM (13h54m)](https://catalog.ldc.upenn.edu/LDC2019S07)
	- [CHM150 (1h38m)](https://catalog.ldc.upenn.edu/LDC2016S04)
	- [TEDX_SPANISH (24h29m)](https://openslr.org/67/)
	- [LIBRIVOX_SPANISH (73h01m)](https://catalog.ldc.upenn.edu/LDC2020S01)
	- [WIKIPEDIA_SPANISH (25h37m)](https://catalog.ldc.upenn.edu/LDC2021S07)
	- [VOXFORGE_SPANISH (49h42m)](http://www.voxforge.org/es)
	- [MOZILLA COMMON VOICE 10.0 (320h22m)](https://commonvoice.mozilla.org/es)
	- [HEROICO (16h33m)](https://catalog.ldc.upenn.edu/LDC2006S37)
	- [LATINO-40 (6h48m)](https://catalog.ldc.upenn.edu/LDC95S28)
	- [CALLHOME_SPANISH (13h22m)](https://catalog.ldc.upenn.edu/LDC96S35)
	- [HUB4NE_SPANISH (31h41m)](https://catalog.ldc.upenn.edu/LDC98S74)
	- [FISHER_SPANISH (127h22m)](https://catalog.ldc.upenn.edu/LDC2010S01)
	- [Chilean Spanish speech data set (7h08m)](https://openslr.org/71/)
	- [Colombian Spanish speech data set (7h34m)](https://openslr.org/72/)
	- [Peruvian Spanish speech data set (9h13m)](https://openslr.org/73/)
	- [Argentinian Spanish speech data set (8h01m)](https://openslr.org/61/)
	- [Puerto Rico Spanish speech data set (1h00m)](https://openslr.org/74/)
	- [MediaSpeech Spanish (10h00m)](https://openslr.org/108/)
	- [DIMEX100-LIGHT (6h09m)](https://turing.iimas.unam.mx/~luis/DIME/CORPUS-DIMEX.html)
	- [DIMEX100-NIÑOS (08h09m)](https://turing.iimas.unam.mx/~luis/DIME/CORPUS-DIMEX.html)
	- [GOLEM-UNIVERSUM (00h10m)](https://turing.iimas.unam.mx/~luis/DIME/CORPUS-DIMEX.html)
	- [GLISSANDO (6h40m)](https://glissando.labfon.uned.es/es)
	- TELE_con_CIENCIA (28h16m) Unplished Material
	- UNSHAREABLE MATERIAL (118h22m) Not available for sharing

	The fine-tuning process was performed during November (2022) in the servers of the Language and Voice Lab (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-spanish-ep5-944h"
	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("ciempiess/ciempiess_test", 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: 0.112
	# BibTeX entry and citation info
	When publishing results based on these models please refer to:
	```bibtex
	@misc{mena2022xlrs53spanish,
	title={Acoustic Model in Spanish: wav2vec2-large-xlsr-53-spanish-ep5-944h.},
	author={Hernandez Mena, Carlos Daniel},
	url={https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-spanish-ep5-944h},
	year={2022}
	}
	```
	# Acknowledgements

	The author wants to thank to the social service program ["Desarrollo de Tecnologías del Habla"](http://profesores.fi-b.unam.mx/carlos_mena/servicio.html) at the [Facultad de Ingeniería (FI)](https://www.ingenieria.unam.mx/) of the [Universidad Nacional Autónoma de México (UNAM)](https://www.unam.mx/). He also thanks to the social service students for all the hard work.

	Special thanks to Jón Guðnason, head of the Language and Voice Lab for providing computational power to make this model possible. The author also thanks 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.