word-level timestamps? about pywhispercpp

[abdeladim-s]

Hi @antiboredom,
You are welcome :) 'Glad you found it useful.

To achieve word-level timestamps, you will need to enable token_timestamps and set max_len to 1, like the following:

from pywhispercpp.model import Model

model = Model('base.en', n_threads=6)
words = model.transcribe('file.mp3', token_timestamps=True, max_len=1)
for word in words:
    print(word.text)

[antiboredom]

Thank you! not sure why I was having trouble sorting that out myself!!

One more thing, and I'm not sure if this is just a whisper thing or related to your project, but I'm seeing one longer word being broken up. In my test case, "Enormous" is becoming "En", "orm", "ous". Any ideas why that might be happening?

[abdeladim-s]

it's a bit tricky to figure it out, as it is not an exact word-level timestamp per say, in fact you can set the max_len to whatever number of chars you want, so when you set max_len to 1, every token will be in its own line, and it will give similar results to a word-level timestamps.

And I think this is the problem with your test case, it seems like "Enormous" is tokenized into 3 tokens, and you get every token by its own. Although, I've never get such a case!

Can you try for example to change the max_len to 8 for example ?

[antiboredom]

Interesting! When I try max_len set to 8, I get "Enorm" and "ous", and then occasionally multiple words like "and if" appearing on the same line... I have also tried faster-whisper which does work as expected for word-level timestamps, but is significantly slower than your implementation...

[abdeladim-s]

You still get two separate words from "Enormous" even after max_len set to 8, interesting test case!
Could you please share the audio file with me, I would like to test it on my end ?

Yes Faster-whisper is great and should give you good results and it should be as fast as well, at least when I test it a while ago! But I didn't compare the performance of the two implementations to be honest.