https://github.com/rodsveiga/rbm_flows_ising
An exact mapping between the Variational Renormalization Group and Deep Learning
Papers with Code 아카이브
Speech Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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Speech Recognition is the task of converting spoken language into text. It involves recognizing the words spoken in an audio recording and transcribing them into a written format. The goal is to accurately transcribe the speech in real-time or from recorded audio, taking into account factors such as accents, speaking speed, and background noise.
<span style="color:grey; opacity: 0.6">( Image credit: SpecAugment )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/roedoejet/fastspeech2
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/roedoejet/fastspeech2_acl2022_reproducibility
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/rongjiehuang/transpeech
GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech
https://github.com/rosinality/imputer-pytorch
Imputer: Sequence Modelling via Imputation and Dynamic Programming
https://github.com/rosinality/melgan-pytorch
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
https://github.com/roudimit/AVLnet
AVLnet: Learning Audio-Visual Language Representations from Instructional Videos
https://github.com/royjames/room-impulse-responses
https://github.com/rufaelfekadu/seq2seq-vc
Pretraining Techniques for Sequence-to-Sequence Voice Conversion
https://github.com/rvoleti89/icaasp-2019-word-subs
Investigating the Effects of Word Substitution Errors on Sentence Embeddings
https://github.com/rwth-i6/returnn-experiments/tree/master/2019-librispeech-system
RWTH ASR Systems for LibriSpeech: Hybrid vs Attention -- w/o Data Augmentation
https://github.com/s-omranpour/Pytorch-Speech-Recognition
State-of-the-Art Speech Recognition Using Multi-Stream Self-Attention With Dilated 1D Convolutions
https://github.com/s3nh/pytorch-tacotron2
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
https://github.com/s3prl/End-to-end-ASR-Pytorch
https://github.com/sadhusamik/fdlp_spectrogram
Radically Old Way of Computing Spectra: Applications in End-to-End ASR
https://github.com/salesforce/DAPC
Representation Learning for Sequence Data with Deep Autoencoding Predictive Components
https://github.com/sam2125/translatotron
Direct speech-to-speech translation with a sequence-to-sequence model
https://github.com/samuellundberg/NeuralArchitectureSearch
https://github.com/sanchit-gandhi/whisper-jax
https://github.com/sangHa0411/ImageNet
https://github.com/sanjanamallya/speech_to_text
https://github.com/sankalp2610/Speech_Command_Recognition
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/santi-pdp/segan_pytorch
Whispered-to-voiced Alaryngeal Speech Conversion with Generative Adversarial Networks
https://github.com/sanyuan-chen/unispeech
UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware Pre-Training
https://github.com/saraalemadi/DroneAudioDataset
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/satishjasthi/Densenet_smplified
https://github.com/scottjiao/Gradient-Compression-Methods
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding
https://github.com/scpark20/universal-music-translation
https://github.com/seasonyc/densenet
https://github.com/sebastian-hofstaetter/neural-ranking-drmm
https://github.com/sebastianGehrmann/tensorflow-statereader
https://github.com/securefederatedai/openfl
Communication-Efficient Learning of Deep Networks from Decentralized Data
https://github.com/seominseok0429/label-smoothing-visualization-pytorch
https://github.com/seongho608/ringformer
Conformer: Convolution-augmented Transformer for Speech Recognition
https://github.com/sephiroce/srf
Sequential Routing Framework: Fully Capsule Network-based Speech Recognition
https://github.com/sergeyrachev/neuon
3D Convolutional Neural Networks for Cross Audio-Visual Matching Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.