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View All on GitHubFacebook AI Research Sequence-to-Sequence Toolkit written in Python.
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
AI Summary: The task involves guiding a user on fine-tuning the Facebook AI Research Sequence-to-Sequence Toolkit (Fairseq) NLLB model to improve its support for the Punjabi language, considering its Gurmukhi script and dialectal variations. This includes advising on data requirements (parallel Punjabi-English datasets), leveraging existing seed datasets, and identifying relevant tutorials or resources.
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
AI Summary: The task is to implement a testing mechanism for the FairSeq speech-to-text (STT) model by generating speech from input text, converting the speech back to text using STT, and comparing the original and resulting texts. This involves integrating a STT system with the existing TTS capabilities of FairSeq and developing a metric to quantify the accuracy of the round-trip text conversion.
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.