
# OpenAI whisper на Македонски јазик

Loss: 0.54
WER: 54

## Fine tuning dataset

Common Voice Corpus 11.0

## Initial observations

Although using an old dataset from 2022, the results show great performance compared to Wav2Vec 2.0
{"language": ["mk"], "license": "mit", "tags": ["ASR", "whisper"], "datasets": ["mozilla-foundation/common_voice_11_0"], "pipeline_tag": "automatic-speech-recognition"}
transformers pytorch tensorboard whisper automatic-speech-recognition ASR mk dataset:mozilla-foundation/common_voice_11_0 license:mit endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #ASR #mk #dataset-mozilla-foundation/common_voice_11_0 #license-mit #endpoints_compatible #has_space #region-us 


# OpenAI whisper на Македонски јазик

Loss: 0.54
WER: 54

## Fine tuning dataset

Common Voice Corpus 11.0

## Initial observations

Although using an old dataset from 2022, the results show great performance compared to Wav2Vec 2.0
# OpenAI whisper на Македонски јазик

Loss: 0.54
WER: 54 ## Fine tuning dataset

Common Voice Corpus 11.0 ## Initial observations

Although using an old dataset from 2022, the results show great performance compared to Wav2Vec 2.0
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #ASR #mk #dataset-mozilla-foundation/common_voice_11_0 #license-mit #endpoints_compatible #has_space #region-us 
 # OpenAI whisper на Македонски јазик

Loss: 0.54
WER: 54 ## Fine tuning dataset

Common Voice Corpus 11.0 ## Initial observations

Although using an old dataset from 2022, the results show great performance compared to Wav2Vec 2.0
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #ASR #mk #dataset-mozilla-foundation/common_voice_11_0 #license-mit #endpoints_compatible #has_space #region-us 
# OpenAI whisper на Македонски јазик

Loss: 0.54
WER: 54## Fine tuning dataset

Common Voice Corpus 11.0## Initial observations

Although using an old dataset from 2022, the results show great performance compared to Wav2Vec 2.0