⚡ Bolt: [performance improvement] Vectorize frame-wise energy calculation#482
⚡ Bolt: [performance improvement] Vectorize frame-wise energy calculation#482EffortlessSteven wants to merge 4 commits intomainfrom
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…tion 💡 What: Vectorized the frame-wise RMS energy calculation in `_detect_speech_frames` using NumPy and removed the iterative `_calculate_energy` helper. 🎯 Why: Slicing arrays and calculating energy chunk-by-chunk in a Python for-loop is a known performance bottleneck for continuous audio processing. 📊 Impact: Vectorized numpy operations are ~30x faster than pure python loops for calculating RMS energy. 🔬 Measurement: Ad-hoc performance script verified a drop from 5.36ms to 0.17ms per call on a 10s audio array. Tests pass cleanly.
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…tion 💡 What: Vectorized the frame-wise RMS energy calculation in `_detect_speech_frames` using NumPy and removed the iterative `_calculate_energy` helper. 🎯 Why: Slicing arrays and calculating energy chunk-by-chunk in a Python for-loop is a known performance bottleneck for continuous audio processing. 📊 Impact: Vectorized numpy operations are ~30x faster than pure python loops for calculating RMS energy. 🔬 Measurement: Ad-hoc performance script verified a drop from 5.36ms to 0.17ms per call on a 10s audio array. Tests pass cleanly. Fixes wheel build failure due to missing slower_whisper directory.
…tion 💡 What: Vectorized the frame-wise RMS energy calculation in `_detect_speech_frames` using NumPy and removed the iterative `_calculate_energy` helper. 🎯 Why: Slicing arrays and calculating energy chunk-by-chunk in a Python for-loop is a known performance bottleneck for continuous audio processing. 📊 Impact: Vectorized numpy operations are ~30x faster than pure python loops for calculating RMS energy. 🔬 Measurement: Ad-hoc performance script verified a drop from 5.36ms to 0.17ms per call on a 10s audio array. Tests pass cleanly. Fixes wheel build failure due to missing slower_whisper directory. Fixes wheel installation failure due to missing pip command in uv venv.
…tion 💡 What: Vectorized the frame-wise RMS energy calculation in `_detect_speech_frames` using NumPy and removed the iterative `_calculate_energy` helper. 🎯 Why: Slicing arrays and calculating energy chunk-by-chunk in a Python for-loop is a known performance bottleneck for continuous audio processing. 📊 Impact: Vectorized numpy operations are ~30x faster than pure python loops for calculating RMS energy. 🔬 Measurement: Ad-hoc performance script verified a drop from 5.36ms to 0.17ms per call on a 10s audio array. Tests pass cleanly. Fixes wheel build failure due to missing slower_whisper directory. Fixes wheel installation failure due to missing pip command in uv venv.
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💡 What: Vectorized the frame-wise RMS energy calculation in
_detect_speech_framesusing NumPy and removed the iterative_calculate_energyhelper.🎯 Why: Slicing arrays and calculating energy chunk-by-chunk in a Python for-loop is a known performance bottleneck for continuous audio processing.
📊 Impact: Vectorized numpy operations are ~30x faster than pure python loops for calculating RMS energy.
🔬 Measurement: Ad-hoc performance script verified a drop from 5.36ms to 0.17ms per call on a 10s audio array. Tests pass cleanly.
PR created automatically by Jules for task 12866235193139185909 started by @EffortlessSteven