The following optimization strategies can be used to address the lack of video memory:
- Chunking audio::
Splicing results after cutting long audio into 15-20 second segments (e.g. with Librosa library) and inputting them into the model separately - Adjusting batch parameters::
existdecode_default.yamlset up inbatch_size: 1and enablestreaming: truestreaming - Enable Mixing Accuracy::
Adding parameters when loading a model--fp16Reduces graphics memory footprint by approximately 40% - Hardware Optimization::
1. Free up unused video memory:torch.cuda.empty_cache()
2. Setting environment variables:export PYTORCH_CUDA_ALLOC_CONF=garbage_collection_threshold:0.6
Real-world tests have shown that these methods allow the 12GB video memory GPU to process audio stably for more than an hour.
This answer comes from the articleOpusLM_7B_Anneal: an efficient unified model for speech recognition and synthesisThe
May not be reproduced without permission:AI productivity tools " How to optimize the speech recognition performance of OpusLM_7B_Anneal on low graphics memory GPUs?
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