-
Notifications
You must be signed in to change notification settings - Fork 112
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Transcription of 1 minute file takes 20 - 40 seconds #113
Comments
Try the running the following command instead
Please let me know if that improves the inference speed or not. |
fedirz
pushed a commit
that referenced
this issue
Oct 12, 2024
fedirz
pushed a commit
that referenced
this issue
Oct 12, 2024
I believe this is happening because the CPU version in docker cannot utilize more than one thread. I even tried overriding the config via ENV, and it's still the same :( docker run \
-p 8000:8000 \
--cpus=10 \
-e WHISPER__INFERENCE_DEVICE=auto \
-e WHISPER__CPU_THREADS=10 \
-e OMP_NUM_THREADS=10 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--name faster-whisper-server fedirz/faster-whisper-server:latest-cpu |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Hello,
I am using the faster-whisper-server on a Mac M1 with the following start command:
docker run --publish 8000:8000 --volume ~/.cache/huggingface:/root/.cache/huggingface fedirz/faster-whisper-server:latest-cpu
And I'm receiving no performance improvements over the original Open AI Model with the following command:
curl http://localhost:8000/v1/audio/transcriptions -F "file=@test.wav" -F "stream=false" -F "language=en" -F "model=Systran/faster-whisper-small"
This is a 1 minute file which takes between 20s - 40s to transcribe depending on the model size.
To transcribe the same audio file using the Systran faster-whisper directly it takes around a 1-3s.
I'm really unsure why this would be the case. Can anyone shed some light onto what may be causing this?
Many thanks
Martin
The text was updated successfully, but these errors were encountered: