readme: ---
  language:
  - es
  library_name: nemo
  datasets:
  - common_voice
  - ciempiess_test
  - hub4ne_es_LDC98S74
  - callhome_es_LDC96S35
  thumbnail: null
  tags:
  - automatic-speech-recognition
  - speech
  - audio
  - CTC
  - pytorch
  - NeMo
  - QuartzNet
  - QuartzNet15x5
  - spanish
  license: cc-by-4.0
  widget:
  model-index:
  - name: stt_es_quartznet15x5_ft_ep53_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: 17.99 
    - 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: 15.97   
    - 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: 19.48
    - 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: 14.48
    - 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: 55.43
    - 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: 56.34  
  ---
  
  # stt_es_quartznet15x5_ft_ep53_944h
  
  **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"](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_es_quartznet15x5) 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 perform during October (2022) in the servers of the [Language and Voice Laboratory](https://lvl.ru.is/) at Reykjavík University (Iceland) by Carlos Daniel Hernández Men