
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk

⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk

This model was trained using the base model https://huggingface.co/fav-kky/wav2vec2-base-cs-80k-ClTRUS (pre-trained from 80 thousand hours of Czech speech)

This model has apostrophes and hyphens.

Metrics:

| Dataset | CER | WER |
|-|-|-|
| CV7 (no LM) |  0.0978 | 0.4191 |
| CV7 (with LM) | 0.0418 | 0.13 |
| CV10 (no LM) | 0.0946 | 0.412 |
| CV10 (with LM) | 0.0328 | 0.0981 |

{"language": ["uk"], "license": "cc-by-nc-sa-4.0", "datasets": ["mozilla-foundation/common_voice_10_0"]}
transformers pytorch wav2vec2 automatic-speech-recognition uk dataset:mozilla-foundation/common_voice_10_0 license:cc-by-nc-sa-4.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #uk #dataset-mozilla-foundation/common_voice_10_0 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us 

🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech\_recognition\_uk


⭐ See other Ukrainian models - URL


This model was trained using the base model URL (pre-trained from 80 thousand hours of Czech speech)


This model has apostrophes and hyphens.


Metrics:


Dataset: CV7 (no LM), CER: 0.0978, WER: 0.4191
Dataset: CV7 (with LM), CER: 0.0418, WER: 0.13
Dataset: CV10 (no LM), CER: 0.0946, WER: 0.412
Dataset: CV10 (with LM), CER: 0.0328, WER: 0.0981



TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #uk #dataset-mozilla-foundation/common_voice_10_0 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #uk #dataset-mozilla-foundation/common_voice_10_0 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us 
