Andros Tjandra

USA, United States
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Summary

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Andros Tjandra is a Senior Research Scientist at Meta AI with eight years of experience driving advances in speech processing, NLP, and machine learning. A PhD from Nara Institute of Science and Technology supported by a MEXT scholarship, he has contributed research and engineering across Google Brain, Facebook AI, RIKEN, and Meta’s FAIR, publishing on disentangled speech representations and transformer-based ASR. He blends deep research with practical engineering—contributing code to the widely used fairseq toolkit (including wav2vec/xlsr audio inference scripts and MMS ASR fixes) and creating a Colab tutorial to make ASR tooling more accessible. Based in New York, he repeatedly bridges academic rigor and production-ready systems, with a track record of turning complex speech models into reproducible tools.
code8 years of coding experience
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Github Skills (12)

pytorch10
machine-learning10
audio-processing10
python10
fairseq10
natural-language-processing9
asr9
automatic-speech-recognition9
speech-recognition9
nlp9
google-colab8
google-colaboratory8

Programming languages (1)

Python

Github contributions (5)

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facebookresearch/fairseq

Oct 2022 - Oct 2022

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Role in this project:
userML Engineer
Contributions:2 reviews, 2 commits, 10 PRs in 1 day
Contributions summary:Andros contributed to the `facebookresearch/fairseq` repository by adding scripts for generating and evaluating embeddings and logits for a speech classification task. These scripts utilize the fairseq framework, specifically within the wav2vec and xlsr submodules, indicating work related to audio processing and machine learning model inference. Additionally, the user fixed an issue in the MMS ASR inference script by reordering the decoded output, indicating involvement in Automatic Speech Recognition (ASR) tasks. The user also created a Colab tutorial for MMS ASR inference.
aipythonsequence-to-sequencepytorchartificial-intelligence
Unified automatic quality assessment for speech, music, and sound.
Contributions:1 review, 7 PRs, 16 pushes in 5 months
speech
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