- 05 Apr, 2022 1 commit
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Zhaoheng Ni authored
Summary: The multi-processing works well on MFCC features. However, it sometimes makes the script hang when dumping HuBERT features. Change it to for-loop resolves the issue. Pull Request resolved: https://github.com/pytorch/audio/pull/2311 Reviewed By: mthrok Differential Revision: D35393813 Pulled By: nateanl fbshipit-source-id: afdc14557a1102b20ecd5fafba0964a913250a11
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- 04 Apr, 2022 2 commits
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Caroline Chen authored
Summary: update example ASR pipeline to use the recently added pretrained LM API for decoding Pull Request resolved: https://github.com/pytorch/audio/pull/2317 Reviewed By: mthrok Differential Revision: D35361354 Pulled By: carolineechen fbshipit-source-id: cac7cf55bd9f86417f319191c1405819fe2a7b46
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Zhaoheng Ni authored
Summary: Some arguments in `ArgumentParser` are not used in the `lexicon_decoder`. Fix them to use the ones in the parser. Pull Request resolved: https://github.com/pytorch/audio/pull/2315 Reviewed By: carolineechen Differential Revision: D35357678 Pulled By: nateanl fbshipit-source-id: 4e70418cf03708b82bc158cafd9999a80ad08f92
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- 01 Apr, 2022 1 commit
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Zhaoheng Ni authored
Summary: When checkpoint is on GPU device and preprocessing is on CPU, the script will throw an exception error. Fix it to load the model state dictionary into CPU by default. Pull Request resolved: https://github.com/pytorch/audio/pull/2310 Reviewed By: mthrok Differential Revision: D35316903 Pulled By: nateanl fbshipit-source-id: d3e7183400ba133240aa6d205f5c671a421a9fed
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- 25 Mar, 2022 1 commit
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Caroline Chen authored
Summary: add function to download pretrained files for LibriSpeech 3-gram/4-gram KenLM, tests, and updated tutorial Pull Request resolved: https://github.com/pytorch/audio/pull/2275 Reviewed By: mthrok Differential Revision: D35115418 Pulled By: carolineechen fbshipit-source-id: 83ff22380fce9c753bb4a7b7e3d89aa66c2831c0
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- 24 Mar, 2022 2 commits
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Caroline Chen authored
Summary: rendered: - [tutorial](https://output.circle-artifacts.com/output/job/e7fb5a23-87cf-4dd5-b4a8-8b4f91e20eb4/artifacts/0/docs/tutorials/asr_inference_with_ctc_decoder_tutorial.html) - [docs](https://output.circle-artifacts.com/output/job/e7fb5a23-87cf-4dd5-b4a8-8b4f91e20eb4/artifacts/0/docs/prototype.ctc_decoder.html) Pull Request resolved: https://github.com/pytorch/audio/pull/2278 Reviewed By: mthrok Differential Revision: D35097734 Pulled By: carolineechen fbshipit-source-id: 1e5d5fff0b7740757cca358cf3ea44c6488fcd5c
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moto authored
Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2288 Reviewed By: hwangjeff Differential Revision: D35099492 Pulled By: mthrok fbshipit-source-id: 955c5e617469009ae2600d2764d601d794ee916f
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- 22 Mar, 2022 1 commit
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Hagen Wierstorf authored
Summary: The calculation of the SNR in tha data augmentation examples seems to be wrong to me:  If we start from the definition of the signal-to-noise ratio using the root mean square value we get: ``` SNR = 20 log10 ( rms(scale * speech) / rms(noise) ) ``` this can be transformed to ``` scale = 10^(SNR/20) rms(noise) / rms(speech) ``` In the example not `rms` is used but `lambda x: x.norm(p=2)`, but as we have the same length of the speech and noise signal, we have ``` rms(noise) / rms(speech) = noise.norm(p=2) / speech.norm(p=2) ``` this would lead us to: ``` 10^(SNR/20) = e^(SNR / 10) ``` which is not true. Hence I changed `e^(SNR / 10)` to `10^(SNR/20)`. For the proposed SNR values of 20 dB, 10 dB, 3 dB the value of the scale would change from 7.39, 2.72, 1.35 to 10.0, 3.16, 1.41. Pull Request resolved: https://github.com/pytorch/audio/pull/2285 Reviewed By: nateanl Differential Revision: D35047737 Pulled By: mthrok fbshipit-source-id: ac24c8fd48ef06b4b611e35163084644330a3ef3
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- 17 Mar, 2022 1 commit
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moto authored
Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2281 Reviewed By: carolineechen Differential Revision: D34939494 Pulled By: mthrok fbshipit-source-id: e97100b95a8e3d3e28805d8fab43b66120c2254d
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- 10 Mar, 2022 1 commit
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moto authored
Summary: Follo-up on post-commit review from https://github.com/pytorch/audio/issues/2202 Pull Request resolved: https://github.com/pytorch/audio/pull/2270 Reviewed By: hwangjeff Differential Revision: D34793460 Pulled By: mthrok fbshipit-source-id: 039ddeca015fc77b89c571820b7ef2b0857f5723
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- 08 Mar, 2022 1 commit
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Zhaoheng Ni authored
Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2143 Reviewed By: carolineechen Differential Revision: D34722238 Pulled By: nateanl fbshipit-source-id: 72809c9db91c94d8e853c80ed8522eeffe5ff136
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- 26 Feb, 2022 1 commit
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moto authored
Summary: This commit adds tutorial for device ASR, and update API for device streaming. The changes for the interface are 1. Add `timeout` and `backoff` parameters to `process_packet` and `stream` methods. 2. Move `fill_buffer` method to private. When dealing with device stream, there are situations where the device buffer is not ready and the system returns `EAGAIN`. In such case, the previous implementation of `process_packet` method raised an exception in Python layer , but for device ASR, this is inefficient. A better approach is to retry within C++ layer in blocking manner. The new `timeout` parameter serves this purpose. Pull Request resolved: https://github.com/pytorch/audio/pull/2202 Reviewed By: nateanl Differential Revision: D34475829 Pulled By: mthrok fbshipit-source-id: bb6d0b125d800f87d189db40815af06fbd4cab59
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- 24 Feb, 2022 1 commit
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Caroline Chen authored
Summary: fix a style check failure from internal diff Pull Request resolved: https://github.com/pytorch/audio/pull/2258 Reviewed By: nateanl Differential Revision: D34459526 Pulled By: carolineechen fbshipit-source-id: d0e6782b5689c3bf63214a4ec6a75dd757678e0d
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- 23 Feb, 2022 1 commit
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Binh Tang authored
Summary: We proactively remove references to the deprecated DDP accelerator to prepare for the breaking changes following the release of PyTorch Lighting 1.6 (see T112240890). Differential Revision: D34295318 fbshipit-source-id: 7b2245ca9c7c2900f510722b33af8d8eeda49919
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- 17 Feb, 2022 2 commits
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Zhaoheng Ni authored
Summary: - Refactor the current `LibriSpeechRNNTModule`'s unit test. - Add unit tests for `TEDLIUM3RNNTModule` and `MuSTCRNNTModule` - Replace the lambda with partial in `TEDLIUM3RNNTModule` to pass the lightning unit test. Pull Request resolved: https://github.com/pytorch/audio/pull/2240 Reviewed By: mthrok Differential Revision: D34285195 Pulled By: nateanl fbshipit-source-id: 4f20749c85ddd25cbb0eafc1733c64212542338f
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moto authored
Summary: https://554729-90321822-gh.circle-artifacts.com/0/docs/tutorials/online_asr_tutorial.html 1. Add figure to explain the caching 2. Fix the initialization of stream iterator Pull Request resolved: https://github.com/pytorch/audio/pull/2226 Reviewed By: carolineechen Differential Revision: D34265971 Pulled By: mthrok fbshipit-source-id: 243301e74c4040f4b8cd111b363e70da60e5dae4
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- 16 Feb, 2022 6 commits
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Zhaoheng Ni authored
Summary: This PR adds ``EMFORMER_RNNT_BASE_MUSTC`` support in `pipeline_demo.py`. The bundle is trained on MuST-C release 2.0 dataset. The model preserves the casing and punctuations in the transcript. Here is a screen recording of how it works in streaming and non-streaming modes: https://user-images.githubusercontent.com/8653221/154356521-fe84bdc1-fb0c-41bd-8729-9edbb3224a07.mov Pull Request resolved: https://github.com/pytorch/audio/pull/2248 Reviewed By: hwangjeff Differential Revision: D34282598 Pulled By: nateanl fbshipit-source-id: 42ed7e2623031dfebd176ef0c6bfd70da3c897d4
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Zhaoheng Ni authored
Summary: - Use dictionary to select the `RNNTBundle` and the corresponding dataset. - Use the dictionary's keys as choices in ArgumentParser Pull Request resolved: https://github.com/pytorch/audio/pull/2239 Reviewed By: mthrok Differential Revision: D34267070 Pulled By: nateanl fbshipit-source-id: 99c7942d5c7c1518694e1ae02a55a7decd87c220
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Zhaoheng Ni authored
Summary: - Add docstring to `eval.py` and `pipeline_demo.py` under `emformer_rnnt` directory. - Refactor logger and ArgumentParser Pull Request resolved: https://github.com/pytorch/audio/pull/2238 Reviewed By: mthrok Differential Revision: D34267059 Pulled By: nateanl fbshipit-source-id: 4b8d3d183ee7bc0ad71ce305cab87bfa90208b2e
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Caroline Chen authored
Summary: LM in example script was unintentionally changed to None when adding no LM support previously. this changes it back and is consistent with the WERs listed in the readme Pull Request resolved: https://github.com/pytorch/audio/pull/2235 Reviewed By: nateanl Differential Revision: D34273042 Pulled By: carolineechen fbshipit-source-id: 824b1ce18195e39dc534b2ec9c5312bbe3bb1812
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Zhaoheng Ni authored
Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2237 Reviewed By: mthrok Differential Revision: D34267000 Pulled By: nateanl fbshipit-source-id: 4c264aea6cf3fba5d8728d5fe60f9f471815852d
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Zhaoheng Ni authored
Summary: Replace underscore with dash in ArgumentParser's arguments. Pull Request resolved: https://github.com/pytorch/audio/pull/2236 Reviewed By: mthrok Differential Revision: D34266977 Pulled By: nateanl fbshipit-source-id: ceacac12c04016a8dbf2a1a7d6bbcf65d4d53d21
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- 15 Feb, 2022 1 commit
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moto authored
Summary: Updating the context cacher so that fetched audio chunk is used for inference immediately. https://github.com/pytorch/audio/pull/2202#discussion_r802838174 Pull Request resolved: https://github.com/pytorch/audio/pull/2213 Reviewed By: hwangjeff Differential Revision: D34235230 Pulled By: mthrok fbshipit-source-id: 6e4aee7cca34ca81e40c0cb13497182f20f7f04e
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- 11 Feb, 2022 5 commits
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nateanl authored
Summary: - Add a MUSTC dataset under examples - Add a lightning module for MuST-C dataset - Refactor `train.py`, `eval.py`, and `global_stats.py` scripts Pull Request resolved: https://github.com/pytorch/audio/pull/2219 Reviewed By: hwangjeff Differential Revision: D34180466 Pulled By: nateanl fbshipit-source-id: 9fc74ce7527da1a81dd0738e124428f9d516d164
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hwangjeff authored
Summary: Adds SentencePiece model training script for LibriSpeech Emformer RNN-T example recipe; updates readme with references. Pull Request resolved: https://github.com/pytorch/audio/pull/2218 Reviewed By: nateanl Differential Revision: D34177295 Pulled By: hwangjeff fbshipit-source-id: 9f32805af792fb8c6f834f2812e20104177a6c43
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nateanl authored
Summary: We refactored the demo script that can apply RNNT decoding using both `torchaudio.pipelines.EMFORMER_RNNT_BASE_LIBRISPEECH` and `torchaudio.prototype.pipelines.EMFORMER_RNNT_BASE_TEDLIUM3` in both streaming and non-streaming mode. (The first hypothesis prediction is streaming and the second one is non-streaming). We convert each token id sequence to word pieces and then manually join the word pieces. This allows us to preserve leading whitespaces on output strings and therefore account for word breaks and continuations across token processor invocations, which is particularly useful when performing streaming ASR. https://user-images.githubusercontent.com/8653221/153627956-f0806f18-3c1c-44df-ac07-ec2def58a0cf.mov Pull Request resolved: https://github.com/pytorch/audio/pull/2203 Reviewed By: carolineechen Differential Revision: D34006388 Pulled By: nateanl fbshipit-source-id: 3d31173ee10cdab8a2f5802570e22b50fcce5632
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hwangjeff authored
Summary: Adds unit tests for Emformer RNN-T LibriSpeech recipe. Also makes changes to recipe to resolve errors with pickling lambda functions in Windows. Pull Request resolved: https://github.com/pytorch/audio/pull/2216 Reviewed By: nateanl Differential Revision: D34171480 Pulled By: hwangjeff fbshipit-source-id: 5fcebb457051f3041766324863728411180f5e1e
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hwangjeff authored
Summary: - Removes 100-batch truncation in TEDLIUM3 recipe. - Reinstates `train_spm.py` for TEDLIUM3. Pull Request resolved: https://github.com/pytorch/audio/pull/2217 Reviewed By: nateanl Differential Revision: D34171525 Pulled By: hwangjeff fbshipit-source-id: 54698e5e1b094c26c28eec9b8b1722223077876c
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- 10 Feb, 2022 1 commit
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hwangjeff authored
Summary: Consolidates LibriSpeech and TED-LIUM Release 3 Emformer RNN-T training recipes in a single directory. Pull Request resolved: https://github.com/pytorch/audio/pull/2212 Reviewed By: mthrok Differential Revision: D34120104 Pulled By: hwangjeff fbshipit-source-id: 29c6e27195d5998f76d67c35b718110e73529456
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- 09 Feb, 2022 1 commit
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hwangjeff authored
Summary: Yesterday's release of librosa 0.9.0 made args keyword-only and changed default padding from "reflect" to "zero" for some functions. This PR adjusts callsites in our tutorials and tests accordingly. Pull Request resolved: https://github.com/pytorch/audio/pull/2208 Reviewed By: mthrok Differential Revision: D34099793 Pulled By: hwangjeff fbshipit-source-id: 4e2642cdda8aae6d0a928befaf1bbb3873d229bc
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- 04 Feb, 2022 1 commit
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Zhaoheng Ni authored
Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2177 Reviewed By: hwangjeff Differential Revision: D33893052 Pulled By: nateanl fbshipit-source-id: 00ff011eb96662b162c0327196a9564721e9c8f7
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- 03 Feb, 2022 3 commits
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Zhaoheng Ni authored
Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2199 Reviewed By: hwangjeff Differential Revision: D33979923 Pulled By: nateanl fbshipit-source-id: 566ba1944dd3511fee740ac17fea2dcb0e5810fa
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Zhaoheng Ni authored
Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2195 Reviewed By: hwangjeff Differential Revision: D33950179 Pulled By: nateanl fbshipit-source-id: 5fcfa4f433fffdcbb3b8e97f7c90fb8f723a30a2
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moto authored
Summary: * tutorial for streaming API https://541810-90321822-gh.circle-artifacts.com/0/docs/tutorials/streaming_api_tutorial.html * tutorial for online speech recognition with Emformer RNN-T https://541810-90321822-gh.circle-artifacts.com/0/docs/tutorials/online_asr_tutorial.html Pull Request resolved: https://github.com/pytorch/audio/pull/2193 Reviewed By: hwangjeff Differential Revision: D33971312 Pulled By: mthrok fbshipit-source-id: f9b69114255f15eaf4463ca85b3efb0ba321a95f
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- 02 Feb, 2022 2 commits
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Caroline Chen authored
Summary: resulting tutorial: https://538358-90321822-gh.circle-artifacts.com/0/docs/tutorials/asr_inference_with_ctc_decoder_tutorial.html - add visualization for timestep alignments - modify section organization for decoder construction Pull Request resolved: https://github.com/pytorch/audio/pull/2188 Reviewed By: mthrok Differential Revision: D33954937 Pulled By: carolineechen fbshipit-source-id: 8f397229d74c994b8793a30623e1de4c19ebd401
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hwangjeff authored
Summary: Rather than apply SentencePiece's `decode` to directly convert each hypothesis's token id sequence to an output string, we convert each token id sequence to word pieces and then manually join the word pieces ourselves. This allows us to preserve leading whitespaces on output strings and therefore account for word breaks and continuations across token processor invocations, which is particularly useful when performing streaming ASR. https://user-images.githubusercontent.com/8345689/152093668-11fb775a-bf7b-4b1d-9516-9f8d5a9b6683.mov Versus the previous behavior visualized in https://github.com/pytorch/audio/issues/2093, the scheme here properly constructs words comprising multiple pieces. Pull Request resolved: https://github.com/pytorch/audio/pull/2192 Reviewed By: mthrok Differential Revision: D33936622 Pulled By: hwangjeff fbshipit-source-id: e550980c7d4cac9e982315508f793a6b816752e9
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- 01 Feb, 2022 3 commits
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hwangjeff authored
Summary: Missed a couple of spots in https://github.com/pytorch/audio/issues/2187. Pull Request resolved: https://github.com/pytorch/audio/pull/2189 Reviewed By: carolineechen, nateanl, mthrok Differential Revision: D33926342 Pulled By: hwangjeff fbshipit-source-id: e1324c0fe8f9be90ad3143d19cd61c3d53f02b06
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hwangjeff authored
Summary: Moves ASR features out of `torchaudio.prototype`. Specifically, merges contents of `torchaudio.prototype.models` into `torchaudio.models` and contents of `torchaudio.prototype.pipelines` into `torchaudio.pipelines` and updates refs, tests, and docs accordingly. Pull Request resolved: https://github.com/pytorch/audio/pull/2187 Reviewed By: nateanl, mthrok Differential Revision: D33918092 Pulled By: hwangjeff fbshipit-source-id: f003f289a7e5d7d43f85b7c270b58bdf2ed6344c
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hwangjeff authored
Summary: Adds script for generating global feature statistics along with new feature statistics json for LibriSpeech RNN-T training recipe. Pull Request resolved: https://github.com/pytorch/audio/pull/2183 Reviewed By: mthrok Differential Revision: D33902377 Pulled By: hwangjeff fbshipit-source-id: ec347a685ae67aefc485084aac6ed2efd653250f
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- 31 Jan, 2022 1 commit
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moto authored
Summary: Changing the URL of tutorial assets to `download.pytorch.org` which is more appropriate for user facing materials. Pull Request resolved: https://github.com/pytorch/audio/pull/2182 Reviewed By: nateanl Differential Revision: D33887839 Pulled By: mthrok fbshipit-source-id: 30569672e8caf30aae5476036dfdadc8ebd436bf
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