This is a Whisper-large-v2 model
openai/whisper-large-v2 finetuned on around 1200 hours of diverse Estonian data.
This is a general-purpose Estonian ASR model trained in the Lab of Language Technology at TalTech.
This model is intended for general-purpose speech recognition, such as broadcast conversations, interviews, talks, etc.
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Convert the HF model to CT2 format:
ct2-transformers-converter --model TalTechNLP/whisper-large-et --output_dir whisper-large-et.ct2 --copy_files tokenizer.json --quantization float16
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Decode:
whisper-ctranslate2 --model_directory whisper-large-et.ct2 --task transcribe --language et --beam_size 5 some_file.mp3
Since this model was trained on mostly broadcast speech and texts from the web, it might have problems correctly decoding the following:
Finetuned using Espnet, and then comverted to transformers format using
this script.
Finetuning procedure is similar to
this model.
Finetuning was done for 3 epochs, with model averaging at the end of training.
Update: 2023-10-03 version of the model is trained on long segments (like the original Whisper model) and
is therefore especially well suited to be used e.g. with
faster-whisper to
transcribe long speech recordings "end-to-end" (i.e., without any prior segmentation).
WER results below are obtained using greedy decoding (i.e., beam size 1).
