The issue here is for most radio systems you end up with about 3 kHz of effective audio bandwidth (sampling). Most ASR/STT models are trained on at least 16 kHz audio.
Did you try a telephony oriented model like aspire or similar? They’re trained on sort-of 8 kHz audio and might work better.
I tried something similar for my SDR feeds and gave up because it’s just too challenging and niche - the sampling, the jargon, the 10 codes, the background noise, static on analog systems/drop outs on digital systems, rate of speech, etc all contribute to very challenges issues for an ML model.
Did you try a telephony oriented model like aspire or similar? They’re trained on sort-of 8 kHz audio and might work better.
I tried something similar for my SDR feeds and gave up because it’s just too challenging and niche - the sampling, the jargon, the 10 codes, the background noise, static on analog systems/drop outs on digital systems, rate of speech, etc all contribute to very challenges issues for an ML model.