Comments (2)
I did not have the opportunity to test on a GPU yet. I will have a GPU system within one or two weeks to test this. However I though that using the deepspeech package with GPU support would be enough. When using deepspeech-gpu, the bindings should be routed to the GPU enabled implementation.
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It works for me with this build of deepspeech (tf1.5 with cuda9):
https://index.taskcluster.net/v1/task/project.deepspeech.deepspeech.native_client.tf-master.gpu/artifacts/public/deepspeech_gpu-0.1.1-cp36-cp36m-manylinux1_x86_64.whl
I see the GPU being initialized when tensorflow is starting, and inference is much faster. I will close this issue unless you have more information.
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Related Issues (20)
- add support of other audio formats HOT 2
- model parameters have changed HOT 1
- Using this server as the ASR(Speech to Text) module on mycroft mark1 HOT 4
- ImportError: cannot import name 'AsyncIOScheduler' HOT 6
- Running Deepspeech on the Raspberry Pi 4 HOT 1
- Installation errors HOT 16
- Requested audio file name in logs HOT 2
- Update for deepspeech 0.6.0 HOT 2
- TypeError: let_bind() got an unexpected keyword argument 'error_map' HOT 3
- Update for deepspeech version 0.6.0 HOT 1
- Connect this deepspeech-server by Android HOT 1
- Package dependency error HOT 2
- How to run as a background service? HOT 1
- Why I still have a CORS problem HOT 1
- ds 0.7.0 update HOT 2
- [Feature Request] Add support for S3 paths HOT 1
- Concurrent Requests HOT 2
- Response mimetype "application/octet-stream" HOT 2
- DeepSpeech no longer maintained HOT 7
- run error HOT 2
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