https://github.com/Berkeley-Speech-Group/sylber
공식 코드Sylber: Syllabic Embedding Representation of Speech from Raw Audio
Papers with Code 아카이브
Speech Tokenization
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Speech tokenization is the task of representing speech signals as a sequence of discrete units. Such representations can be later used for various downstream tasks including automatic speech recognition, text-to-speech, etc. Such representation serves as the basis of Speech Language Models.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Magic-Ma-tech/Audio-Jailbreak-Attacks
공식 코드Audio Jailbreak Attacks: Exposing Vulnerabilities in SpeechGPT in a White-Box Framework
https://github.com/alanbaade/SyllableLM
공식 코드SyllableLM: Learning Coarse Semantic Units for Speech Language Models
https://github.com/anupsingh15/BEST-STD
공식 코드BEST-STD: Bidirectional Mamba-Enhanced Speech Tokenization for Spoken Term Detection
https://github.com/apple/dmel
공식 코드https://github.com/mct10/repcodec
공식 코드RepCodec: A Speech Representation Codec for Speech Tokenization
https://github.com/mtkresearch/taste-spokenlm
공식 코드TASTE: Text-Aligned Speech Tokenization and Embedding for Spoken Language Modeling
https://github.com/mubtasimahasan/dm-codec
공식 코드DM-Codec: Distilling Multimodal Representations for Speech Tokenization
https://github.com/ryota-komatsu/speaker_disentangled_hubert
공식 코드Self-Supervised Syllable Discovery Based on Speaker-Disentangled HuBERT
https://github.com/slp-rl/salmon
TASTE: Text-Aligned Speech Tokenization and Embedding for Spoken Language Modeling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.