Further train a fine tuned wav2vec model
Hi everyone,I wonder what is the best approach to fine tune wav2vec model to different domains, on low resource language. for example, if I have 100 hours of data, recorded in professional studio with high quality equipment, and 10 hours of low quality data. Now assume my use case is the low quality samples.First I want to train wav2vec on my language with the high quality large dataset, and then fine tune on the smaller(which is my use case)? or should I train only on the smaller? use all the data and give more weight to the low quality data(e.g duplicate samples with minor random noises)?Is this even possible to fine tune a pre-trained model twice? (i.e fine tuning fine-tuned model)I saw this post:Further Train a fine-tuned Modell?ModelsHi Guys, 
how would i fruther train a already fine-tuned modell on new data? 
Iwant to (further) train a wav2vec2 Modell which was trained on CommonVoice on a new Dataset. Whould i have to train the modell from Scratch and use both datasets or could i just “finetune” the already fine tuned modell on new data? I tried multiple Learning-Rates and i just destroy the Modell (accuracy-wise) or the values dont change at all if i further train the modellbut there is no answer…Thanks
2022-09-25T08:29:01Z
since you linked my post - i couldnt fine tune the wav2vec model. i used a diffrent model instead(silero speech2text)but i couldnt improve the results, i was just able to match the results after further finetuningso i had 0 success
2022-09-25T09:01:52Z
Thanks.I assume it’s the same as separating the language model from the acoustic model? i.e. if I want train one AM and use 2 different LM - I have to train wav2vec again? (or using LM as separated layer above the AM)