pytorch cross-modal Transformer using two features: MFCC from audio signal (1-channel); BERT last layer fine-tuned by multi-sentiment dataset. Requirements.
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STEP1: Convert input audio to text using Google ASR API; STEP2: Extract MFCC feature from input audio; STEP3: Conduct MLM on KoBERT through colloquial ...
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cross-modal model between audio(MFCC) and text(KoBERT) - audiotext-transformer/datasets.py at master · Donghwa-KIM/audiotext-transformer.
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cross-modal model between audio(MFCC) and text(KoBERT) - audiotext-transformer/utils.py at master · Donghwa-KIM/audiotext-transformer.
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This project has not set up a SECURITY.md file yet. There aren't any published security advisories. Footer. © 2024 GitHub, Inc. Footer navigation. Terms ...
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Data Scientist. Donghwa-KIM has 80 repositories available. Follow their code on GitHub.
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The modelStudio package automates the explanatory analysis of machine learning predictive models. It generates advanced interactive model explanations in ...
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Illustration of AST. This repository contains the official implementation (in PyTorch) of the Self-Supervised Audio Spectrogram Transformer (SSAST) proposed in ...
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