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[aoti-et] Add an ASR runner and an Whisper example to showcase how to use it #15486
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/15486
Note: Links to docs will display an error until the docs builds have been completed. ❗ 1 Active SEVsThere are 1 currently active SEVs. If your PR is affected, please view them below: ❌ 1 New Failure, 17 Pending, 1 Unrelated FailureAs of commit 23b7c15 with merge base cc72b35 ( NEW FAILURE - The following job has failed:
FLAKY - The following job failed but was likely due to flakiness present on trunk:
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LGTM on my side. I will let @jackzhxng give the stamp about asr_runner.
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overall LGTM
use it **Key Changes:** * Create new ASR runner extension in `extension/asr/runner/` with reusable runner components (runner.h/cpp) * Update CMake configuration files to support ASR runner builds (executorch-config.cmake, default.cmake, llm.cmake) * Add new Whisper model example in `examples/models/whisper/` with CMake build, README, and main.cpp runner * Bump optimum-executorch commit pin for Whisper support * Update CUDA CI workflow for testing This change enables automatic speech recognition (ASR) capabilities in ExecuTorch with Whisper as the first supported model, following a similar pattern to the existing LLM runner infrastructure.
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Great work @larryliu0820
A few comments before landing
| #include <executorch/runtime/core/result.h> | ||
| #include <pytorch/tokenizers/tokenizer.h> | ||
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| namespace executorch::extension::asr { |
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Once this lands, we can renew this PR and add language bindings
| if(EXECUTORCH_BUILD_METAL) | ||
| list(APPEND _link_libraries metal_backend) | ||
| executorch_target_link_options_shared_lib(metal_backend) | ||
| endif() |
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Is metal supported?
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Thanks
Key Changes:
extension/asr/runner/with reusable runner components (runner.h/cpp)examples/models/whisper/with CMake build, README, and main.cpp runnerThis change enables automatic speech recognition (ASR) capabilities in ExecuTorch with Whisper as the first supported model, following a similar pattern to the existing LLM runner infrastructure.