stt_es_quartznet15x5_ft_ep53_944h

Paper: The state of end-to-end systems for Mexican Spanish speech recognition

NOTE! This model was trained with the NeMo version: nemo-toolkit==1.10.0

The "stt_es_quartznet15x5_ft_ep53_944h" is an acoustic model created with NeMo which is suitable for Automatic Speech Recognition in Spanish.

It is the result of fine-tuning the model "stt_es_quartznet15x5.nemo" with around 944 hours of Spanish data gathered or developed by the CIEMPIESS-UNAM Project 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 or OpenSLR

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

• CIEMPIESS-LIGHT (18h25m)
• CIEMPIESS-BALANCE (18h20m)
• CIEMPIESS-FEM (13h54m)
• CHM150 (1h38m)
• TEDX_SPANISH (24h29m)
• LIBRIVOX_SPANISH (73h01m)
• WIKIPEDIA_SPANISH (25h37m)
• VOXFORGE_SPANISH (49h42m)
• MOZILLA COMMON VOICE 10.0 (320h22m)
• HEROICO (16h33m)
• LATINO-40 (6h48m)
• CALLHOME_SPANISH (13h22m)
• HUB4NE_SPANISH (31h41m)
• FISHER_SPANISH (127h22m)
• Chilean Spanish speech data set (7h08m)
• Colombian Spanish speech data set (7h34m)
• Peruvian Spanish speech data set (9h13m)
• Argentinian Spanish speech data set (8h01m)
• Puerto Rico Spanish speech data set (1h00m)
• MediaSpeech Spanish (10h00m)
• DIMEX100-LIGHT (6h09m)
• DIMEX100-NIÑOS (08h09m)
• GOLEM-UNIVERSUM (00h10m)
• GLISSANDO (6h40m)
• TELE_con_CIENCIA (28h16m) Unplished Material
• UNSHAREABLE MATERIAL (118h22m) Not available for sharing

The fine-tuning process was perform during October (2022) in the servers of the Language and Voice Laboratory at Reykjavík University (Iceland) by Carlos Daniel Hernández Mena.

@misc{mena2022quartznet15x5spanish,
      title={Acoustic Model in Spanish: stt\_es\_quartznet15x5\_ft\_ep53\_944h.}, 
      author={Hernandez Mena, Carlos Daniel},
      url={https://huggingface.co/carlosdanielhernandezmena/stt_es_quartznet15x5_ft_ep53_944h},
      year={2022}
}

Acknowledgements

The author wants to thank to the social service program "Desarrollo de Tecnologías del Habla" at the Facultad de Ingeniería (FI) of the Universidad Nacional Autónoma de México (UNAM). 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.