Three ways to solve the problem of insufficient CUDA video memory
CSM Voice Cloning relies on the GPU for model inference, which can cause interruptions when the local graphics card runs low on video memory. The following is a step-by-step solution:
- Method 1: Shorten the audio sample
Clips incoming audio samples to 30 seconds - 1 minute, significantly reducing the graphics memory footprint. It is recommended to use tools such as Audacity to capture the clearest part of the pronunciation. - Method 2: Switch to run in the cloud
Use cloud GPUs through the Modal platform:- Install the Modal client:
pip install modal - Configure the account:
modal token new - Run the cloud script:
modal run modal_voice_cloning.py
- Install the Modal client:
- Method 3: Adjustment of model parameters
Modify the max_seq_len parameter in models.py to lower it to 2048 or 1024, noting that this may affect the quality of long audio generation.
This answer comes from the articleCSM Voice Cloning: Fast Voice Cloning with the CSM-1BThe
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