What model "large" is uses? On faster-whisper I see only -v1, -v2 and -v3.

[gety9]

Title.

Speechlib has 4 settings for large (large, large-v1, large-v2, large-v3)

Faster whisper only 3? (large-v1, large-v2, large-v3; as shown on their HF account)

Also which setting model do you recommend?

[NavodPeiris]

I think large and large-v1 are the same. For me, medium was accurate enough and was significantly faster than large models. It may not be as accurate as large models but if you want a decent and fast transcription choose "medium".

some tests I did:

These metrics are from Google Colab tests.
These metrics do not take into account model download times.
These metrics are done without quantization enabled.
(quantization will make this even faster)

metrics for faster-whisper "tiny" model:
    on gpu:
        audio name: obama_zach.wav
        duration: 6 min 36 s
        diarization time: 24s
        speaker recognition time: 10s
        transcription time: 64s


metrics for faster-whisper "small" model:
    on gpu:
        audio name: obama_zach.wav
        duration: 6 min 36 s
        diarization time: 24s
        speaker recognition time: 10s
        transcription time: 95s


metrics for faster-whisper "medium" model:
    on gpu:
        audio name: obama_zach.wav
        duration: 6 min 36 s
        diarization time: 24s
        speaker recognition time: 10s
        transcription time: 193s


metrics for faster-whisper "large" model:
    on gpu:
        audio name: obama_zach.wav
        duration: 6 min 36 s
        diarization time: 24s
        speaker recognition time: 10s
        transcription time: 343s