Parallelize chunked Parakeet batch transcription#507
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hamzaq2000 wants to merge 1 commit intoFluidInference:mainfrom
Open
Parallelize chunked Parakeet batch transcription#507hamzaq2000 wants to merge 1 commit intoFluidInference:mainfrom
hamzaq2000 wants to merge 1 commit intoFluidInference:mainfrom
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Why is this change needed?
This PR speeds up Parakeet batch transcription for long audio by ~2.2-2.8x, by parallelizing the existing stateless chunked path. It doesn't change the streaming/live transcription path.
It adds a configurable
parallelChunkConcurrencysetting toASRConfig, letsAsrManagercreate worker clones from already-loadedAsrModels, and updatesChunkProcessorto send independent chunks across that worker pool before merging the results with the existing merge logic.The important part is that the decoding behavior for each chunk stays the same. The patch is really about scheduling chunk work in parallel so the runtime can keep more hardware busy and improve throughput on longer files.
Validation
Benchmarked on Apple M3, using 16 KHz 16-bit mono wav file downloaded from this video (~1 hour duration), with 5 runs each for current upstream vs. PR branch.
I compared the resulting transcripts and word timings before and after this change for v2, v3, and
tdt-ctc-110m, and found no differences. So based on this one test file at least, the optimization appears safe.Peak memory footprint was measured with macOS
/usr/bin/time -lp. While it does increase, the measured increase is modest relative to the speedup, so I think it's reasonable to keepparallelChunkConcurrencyset to4by default rather than make it opt-in.parallelChunkConcurrencyOptimal ValueA default value of
4for the chunk parallelism was chosen becuase values higher than it yielded little to no extra speedup and values less than it still left speed on the table; on the two devices I tested on, at least, which were iPhone SE 3 and M3 MacBook Air.AI Disclosure
OpenAI Codex was used to write the code for this patch.