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Cache pipeline by model+device only #499

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Merged
satra merged 1 commit into from
Apr 30, 2026
Merged

Cache pipeline by model+device only #499

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6 changes: 3 additions & 3 deletions src/senselab/audio/tasks/classification/huggingface.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,16 +44,16 @@ def _get_hf_audio_classification_pipeline(
device, _ = _select_device_and_dtype(
user_preference=device, compatible_devices=[DeviceType.CUDA, DeviceType.CPU]
)
key = f"{model.path_or_uri}-{model.revision}-{top_k}-{function_to_apply}-{device.value}"
# Cache by model identity + device only. top_k, function_to_apply,
# and batch_size are call-time parameters passed at pipe() invocation.
key = f"{model.path_or_uri}-{model.revision}-{device.value}"
Comment on lines +47 to +49

@gemini-code-assist gemini-code-assist Bot Apr 30, 2026

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high

The comment states that batch_size is a call-time parameter passed at pipe() invocation. However, in classify_audios_with_transformers (line 149), batch_size is not actually passed to the pipeline call, meaning the batch_size argument is currently ignored. Additionally, since top_k, function_to_apply, and batch_size are no longer used to construct the cache key or initialize the pipeline, they are now unused parameters in this method. Consider removing them from the signature of _get_hf_audio_classification_pipeline and updating the caller at line 108.

if key not in cls._pipelines:
cls._pipelines[key] = cast(
Pipeline,
pipeline( # type: ignore[call-overload]
task="audio-classification",
model=model.path_or_uri,
revision=model.revision,
top_k=top_k,
function_to_apply=function_to_apply,
device=device.value,
),
)
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