https://github.com/keonlee9420/DiffSinger
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
Papers with Code 아카이브
Audio-Visual Speech Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Audio-visual speech recognition is the task of transcribing a paired audio and visual stream into text.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/keonlee9420/Expressive-FastSpeech2
https://github.com/keonlee9420/FastPitchFormant
https://github.com/keonlee9420/PortaSpeech
https://github.com/keonlee9420/StyleSpeech
https://github.com/keonlee9420/VAENAR-TTS
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search
https://github.com/keonlee9420/WaveGrad2
https://github.com/keonlee9420/evaluate-zero-shot-tts
DiTTo-TTS: Diffusion Transformers for Scalable Text-to-Speech without Domain-Specific Factors
https://github.com/kh-kim/simple-nmt
https://github.com/kingridda/voice-cloning-AI
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
https://github.com/kingstarcraft/speech-to-text-wavenet2
https://github.com/kinimod23/ATS_Project
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
https://github.com/komyeongjin/specdiff-gan
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/ksw0306/ClariNet
ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech
https://github.com/ksw0306/WaveVAE
https://github.com/ktho22/vctts
Emotional Voice Conversion using Multitask Learning with Text-to-speech
https://github.com/kuleshov/audio-super-res
https://github.com/kunzhou9646/mixed_emotions
https://github.com/kyutai-labs/hibiki
Moshi: a speech-text foundation model for real-time dialogue
https://github.com/kyutai-labs/moshivis
Moshi: a speech-text foundation model for real-time dialogue
https://github.com/lakahaga/dc-comix-tts
https://github.com/lifeiteng/naturalspeech3_facodec
NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models
https://github.com/lifeiteng/vall-e
Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
https://github.com/liguigui/speech-to-text-wavenet
https://github.com/liyaSileshi/amharic-sentiment-analysis
https://github.com/ljuvela/GELP
GELP: GAN-Excited Linear Prediction for Speech Synthesis from Mel-spectrogram
https://github.com/lordmartian/deep_avsr
https://github.com/lturing/Tools
Hierarchical Generative Modeling for Controllable Speech Synthesis
https://github.com/lucidrains/audiolm-pytorch
https://github.com/lucidrains/naturalspeech2-pytorch
NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers
https://github.com/lucidrains/vector-quantize-pytorch
https://github.com/lucidrains/voicebox-pytorch
Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale
https://github.com/lumaku/ctc-segmentation
https://github.com/m-toman/tacorn
https://github.com/majidAdibian77/ResGrad
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/majidAdibian77/persian-SV2TTS
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.