Comments (2)
joshuachris2001
commented on June 8, 2024
bump, as it would be interesting.
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frostyfrog
commented on June 8, 2024
I was interested in this as well and gave it a shot. Following the install instructions for Google Coral USB Accelerator, I setup a python virtual env for version 3.9.12. Once I verified that the Coral was working and it could identify birds, the next step was getting whisper-jax up and running. Doing so involves the following steps:
- Installing
jax[tpu] - Follow directions to setup
whisper-jaxas an endpoint - modify
app/app.pyto useopenai/whisper-base.enoropenai/whisper-tiny.en. Anything larger uses a ton of swap space (I got up to 12 GB of swap in one of my tests)
Fortunately, even with the smaller models, it appears to work. Though it can take quite a while to do transcriptions with the single USB Accelerator TPU (2 and a half hours for an hours worth of audio)
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Related Issues (20)
- How to add millisecond for the timestamp?
- I have downloaded the flax_model, where can I call it?
- why whisper-jax did not use my GPU? HOT 3
- Rust impl
- Unsuccessful deployment HOT 1
- Slower than openai whisper with my gpu HOT 2
- I want to use whisper-at models HOT 1
- Has translate be integrated into transcribe? It returns English but expect Chinese. HOT 3
- Slow post processing HOT 1
- unable to run TPU using current kaggle environment HOT 1
- Large Model causing performance degradation?
- Shape Error when running on GPU HOT 2
- HuggingFace space erroring more often than usual HOT 1
- Transcription issues.
- Punctuation mark
- Confidence score and average log probability on Whisper-JAX
- whisper-large-v3 (in demo code) VS whisper-large-v2 (in kaggle notebook)
- Add wrapper for wyoming API
- Kernel always restarting when JIT compiling the forward call on MacBook Pro M3 Max
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