⚡️ Speed up method AsyncTranscriptions.create by 6% - #19
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⚡️ Speed up method AsyncTranscriptions.create by 6%#19codeflash-ai[bot] wants to merge 1 commit into
AsyncTranscriptions.create by 6%#19codeflash-ai[bot] wants to merge 1 commit into
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The optimized code achieves a 5% runtime improvement and 0.6% throughput increase through several targeted optimizations:
**Key Optimizations:**
1. **Session Context Management (APIRequestor.arequest):**
- Replaced manual `ctx.__aenter__()` and `ctx.__aexit__()` calls with `async with AioHTTPSession()` context manager
- This eliminates redundant context management overhead and ensures proper resource cleanup
- Line profiler shows the session setup time reduced from 4.35ms to part of the 6.89ms context entry
2. **File Type Checking Optimization (AsyncTranscriptions.create):**
- Pre-computed `file_is_str = isinstance(file, str)` and `file_is_path = isinstance(file, Path)` to cache type checks
- Avoided double `Path(file)` conversion by using `file if file_is_path else Path(file)`
- Reduced redundant `isinstance()` calls in the file handling logic
3. **Parameter Processing Efficiency:**
- Pre-computed `param_format` using `getattr(response_format, "value", response_format)` to avoid repeated attribute lookups
- Simplified boolean conversion from `str(value).lower()` to direct `"true" if value else "false"`
- Streamlined the timestamp granularities handling with cached attribute access
4. **File Cleanup Simplification:**
- Replaced nested dictionary checks with direct `files_data.get("file")` for cleaner file object retrieval
- Maintained the same safety checks but with fewer operations
**Performance Impact:**
The optimizations particularly benefit scenarios with frequent API calls and file operations. The 5% runtime improvement comes primarily from reduced context management overhead and fewer redundant type checks. The throughput improvement (651 vs 647 ops/second) indicates better resource utilization, especially valuable for batch transcription workloads where these micro-optimizations compound across many requests.
These changes are most effective for high-frequency transcription scenarios where the reduced per-operation overhead accumulates to meaningful performance gains.
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📄 6% (0.06x) speedup for
AsyncTranscriptions.createinsrc/together/resources/audio/transcriptions.py⏱️ Runtime :
445 microseconds→420 microseconds(best of5runs)📝 Explanation and details
The optimized code achieves a 5% runtime improvement and 0.6% throughput increase through several targeted optimizations:
Key Optimizations:
Session Context Management (APIRequestor.arequest):
ctx.__aenter__()andctx.__aexit__()calls withasync with AioHTTPSession()context managerFile Type Checking Optimization (AsyncTranscriptions.create):
file_is_str = isinstance(file, str)andfile_is_path = isinstance(file, Path)to cache type checksPath(file)conversion by usingfile if file_is_path else Path(file)isinstance()calls in the file handling logicParameter Processing Efficiency:
param_formatusinggetattr(response_format, "value", response_format)to avoid repeated attribute lookupsstr(value).lower()to direct"true" if value else "false"File Cleanup Simplification:
files_data.get("file")for cleaner file object retrievalPerformance Impact:
The optimizations particularly benefit scenarios with frequent API calls and file operations. The 5% runtime improvement comes primarily from reduced context management overhead and fewer redundant type checks. The throughput improvement (651 vs 647 ops/second) indicates better resource utilization, especially valuable for batch transcription workloads where these micro-optimizations compound across many requests.
These changes are most effective for high-frequency transcription scenarios where the reduced per-operation overhead accumulates to meaningful performance gains.
✅ Correctness verification report:
🌀 Generated Regression Tests and Runtime
To edit these changes
git checkout codeflash/optimize-AsyncTranscriptions.create-mh00j2toand push.