The Preprocessing and Training That HierSpeech++ Went Through
#texttospeech #speechsynthesizer #hierspeech #wav2vec #melspectogram #acousticrepresentation #semanticrepresentation #adamwoptimizer
https://hackernoon.com/the-preprocessing-and-training-that-hierspeech-went-through
#texttospeech #speechsynthesizer #hierspeech #wav2vec #melspectogram #acousticrepresentation #semanticrepresentation #adamwoptimizer
https://hackernoon.com/the-preprocessing-and-training-that-hierspeech-went-through
Hackernoon
The Preprocessing and Training That HierSpeech++ Went Through
We trained HierSpeech++ with a batch size of 160 for 1,000k steps on eight NVIDIA A6000 GPUs.
Speech Synthesis Tasks We Had to Complete: Voice Conversion and Text-to-Speech
#speechsynthesis #texttospeech #voiceconversion #speechsynthesizer #heirarchicalsynthesizer #yapptalgorithm #speechsr #koreauniversity
https://hackernoon.com/speech-synthesis-tasks-we-had-to-complete-voice-conversion-and-text-to-speech
#speechsynthesis #texttospeech #voiceconversion #speechsynthesizer #heirarchicalsynthesizer #yapptalgorithm #speechsr #koreauniversity
https://hackernoon.com/speech-synthesis-tasks-we-had-to-complete-voice-conversion-and-text-to-speech
Hackernoon
Speech Synthesis Tasks We Had to Complete: Voice Conversion and Text-to-Speech
For voice conversion, we first extract the semantic representation by MMS from the audio at 16 kHz, and F0 using the YAPPT algorithm.
How We Used a Speech Super-Resolution to Train Our Model
#texttospeech #speechsynthesizer #speechsuperresolution #bigvgan #speechwaveform #sourcefilterencoder #twotemporalencoder #wavenet
https://hackernoon.com/how-we-used-a-speech-super-resolution-to-train-our-model
#texttospeech #speechsynthesizer #speechsuperresolution #bigvgan #speechwaveform #sourcefilterencoder #twotemporalencoder #wavenet
https://hackernoon.com/how-we-used-a-speech-super-resolution-to-train-our-model
Hackernoon
How We Used a Speech Super-Resolution to Train Our Model
In this stage, we simply upsample a low-resolution speech waveform to a high-resolution speech waveform from 16 kHz to 48 kHz as illustrated in Fig 5.
The Backbone Speech Synthesizer for HierSpeech++
#hierspeech #speechsynthesizer #hiervst #acousticencoder #multipathsemanticencoder #autoencoder #waveformgeneration #vits
https://hackernoon.com/the-backbone-speech-synthesizer-for-hierspeech
#hierspeech #speechsynthesizer #hiervst #acousticencoder #multipathsemanticencoder #autoencoder #waveformgeneration #vits
https://hackernoon.com/the-backbone-speech-synthesizer-for-hierspeech
Hackernoon
The Backbone Speech Synthesizer for HierSpeech++
We propose a hierarchical speech synthesizer as the backbone speech synthesizer for HierSpeech++
Introducing Hierspeech++: A Human-Level Zeroshot Speech Synthesis Model
#hierspeech #speechsynthesizer #zershotspeechsynthesismodel #speechsr #ttssystems #neuralaudiocodec #melspectogram #crosslingualspeechsynthesis
https://hackernoon.com/introducing-hierspeech-a-human-level-zeroshot-speech-synthesis-model
#hierspeech #speechsynthesizer #zershotspeechsynthesismodel #speechsr #ttssystems #neuralaudiocodec #melspectogram #crosslingualspeechsynthesis
https://hackernoon.com/introducing-hierspeech-a-human-level-zeroshot-speech-synthesis-model
Hackernoon
Introducing Hierspeech++: A Human-Level Zeroshot Speech Synthesis Model
In this study, we propose HierSpeech++, a human-level zeroshot speech synthesis model in terms of naturalness and voice similarity.
The 7 Objective Metrics We Conducted for the Reconstruction and Resynthesis Tasks
#speechsynthesizer #texttospeech #resynthesis #syntheticspeech #voxceleb2 #mospredictionmodel #speakerencoder #koreauniversity
https://hackernoon.com/the-7-objective-metrics-we-conducted-for-the-reconstruction-and-resynthesis-tasks
#speechsynthesizer #texttospeech #resynthesis #syntheticspeech #voxceleb2 #mospredictionmodel #speakerencoder #koreauniversity
https://hackernoon.com/the-7-objective-metrics-we-conducted-for-the-reconstruction-and-resynthesis-tasks
Hackernoon
The 7 Objective Metrics We Conducted for the Reconstruction and Resynthesis Tasks
For VC, we used two subjective metrics: naturalness mean opinion score (nMOS) and voice similarity MOS (sMOS) with a CI of 95%
How We Used the LibriTTS Dataset to Train the Hierarchical Speech Synthesizer
#speechsynthesizer #aihub #multispeakerspeechsynthesis #vctkdataset #hierspeech #speechsuperresolution #koreauniversity #libritts
https://hackernoon.com/how-we-used-the-libritts-dataset-to-train-the-hierarchical-speech-synthesizer
#speechsynthesizer #aihub #multispeakerspeechsynthesis #vctkdataset #hierspeech #speechsuperresolution #koreauniversity #libritts
https://hackernoon.com/how-we-used-the-libritts-dataset-to-train-the-hierarchical-speech-synthesizer
Hackernoon
How We Used the LibriTTS Dataset to Train the Hierarchical Speech Synthesizer
We utilized LibriTTS dataset [90] to train the hierarchical speech synthesizer.
A Deeper Look at Speech Super-Resolution
#texttospeech #speechsuperresolution #speechsr #speechsynthesizer #speechsynthesismodel #opensourcedatabase #vctkdataset #dtwbaseddiscriminators
https://hackernoon.com/a-deeper-look-at-speech-super-resolution
#texttospeech #speechsuperresolution #speechsr #speechsynthesizer #speechsynthesismodel #opensourcedatabase #vctkdataset #dtwbaseddiscriminators
https://hackernoon.com/a-deeper-look-at-speech-super-resolution
Hackernoon
A Deeper Look at Speech Super-Resolution
We introduced SpeechSR for a simple and efficient speech super-resolution for real-world practical application