
## Info

This Wav2Vec2 model was first pretrained on 500 hours Kalmyk TV recordings and 1000 hours Mongolian speech recognition dataset. After that, the model was finetuned on a 300 hours [Kalmyk synthetic STT dataset](https://github.com/tugstugi/mongolian-nlp#datasets) created by a voice conversion model.
* 50% WER on a private test set created from Kalmyk TV recordnings
* on clean voice recordings, the model should have much lower WER
* voice conversion info
  * 300 hours [Kalmyk synthetic STT dataset](https://github.com/tugstugi/mongolian-nlp#datasets)
  * The source voice is a Kalmyk female voice TTS
  * Target voices are from the VCTK dataset
  * example data: https://twitter.com/tugstugi/status/1409111296897912835
  * each WAV has a different text created from Kalmyk books

{"language": "xal", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition"]}
transformers pytorch wav2vec2 automatic-speech-recognition speech audio xal license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #audio #xal #license-apache-2.0 #endpoints_compatible #has_space #region-us 


## Info

This Wav2Vec2 model was first pretrained on 500 hours Kalmyk TV recordings and 1000 hours Mongolian speech recognition dataset. After that, the model was finetuned on a 300 hours Kalmyk synthetic STT dataset created by a voice conversion model.
* 50% WER on a private test set created from Kalmyk TV recordnings
* on clean voice recordings, the model should have much lower WER
* voice conversion info
  * 300 hours Kalmyk synthetic STT dataset
  * The source voice is a Kalmyk female voice TTS
  * Target voices are from the VCTK dataset
  * example data: URL
  * each WAV has a different text created from Kalmyk books

## Info

This Wav2Vec2 model was first pretrained on 500 hours Kalmyk TV recordings and 1000 hours Mongolian speech recognition dataset. After that, the model was finetuned on a 300 hours Kalmyk synthetic STT dataset created by a voice conversion model.
* 50% WER on a private test set created from Kalmyk TV recordnings
* on clean voice recordings, the model should have much lower WER
* voice conversion info
  * 300 hours Kalmyk synthetic STT dataset
  * The source voice is a Kalmyk female voice TTS
  * Target voices are from the VCTK dataset
  * example data: URL
  * each WAV has a different text created from Kalmyk books
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #audio #xal #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 ## Info

This Wav2Vec2 model was first pretrained on 500 hours Kalmyk TV recordings and 1000 hours Mongolian speech recognition dataset. After that, the model was finetuned on a 300 hours Kalmyk synthetic STT dataset created by a voice conversion model.
* 50% WER on a private test set created from Kalmyk TV recordnings
* on clean voice recordings, the model should have much lower WER
* voice conversion info
  * 300 hours Kalmyk synthetic STT dataset
  * The source voice is a Kalmyk female voice TTS
  * Target voices are from the VCTK dataset
  * example data: URL
  * each WAV has a different text created from Kalmyk books
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #audio #xal #license-apache-2.0 #endpoints_compatible #has_space #region-us 
## Info

This Wav2Vec2 model was first pretrained on 500 hours Kalmyk TV recordings and 1000 hours Mongolian speech recognition dataset. After that, the model was finetuned on a 300 hours Kalmyk synthetic STT dataset created by a voice conversion model.
* 50% WER on a private test set created from Kalmyk TV recordnings
* on clean voice recordings, the model should have much lower WER
* voice conversion info
  * 300 hours Kalmyk synthetic STT dataset
  * The source voice is a Kalmyk female voice TTS
  * Target voices are from the VCTK dataset
  * example data: URL
  * each WAV has a different text created from Kalmyk books