WaveGlow
E200567
autoregressive-free vocoder
deep learning model
flow-based generative model
neural network model
speech synthesis system
text-to-speech model
WaveGlow is a flow-based generative neural network model for fast, high-quality text-to-speech audio synthesis.
All labels observed (2)
| Label | Occurrences |
|---|---|
| WaveGlow canonical | 2 |
| WaveGlow: A Flow-based Generative Network for Speech Synthesis | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T1793249 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: WaveGlow Context triple: [WaveNet, ledTo, WaveGlow]
-
A.
WaveNet
WaveNet is a deep generative neural network architecture for raw audio that produces highly natural-sounding speech and other audio signals.
-
B.
Audion
Audion is an early triode vacuum tube invented by Lee de Forest that enabled the amplification of electrical signals and was crucial to the development of radio and electronics.
-
C.
Versoix
Versoix is a Swiss municipality on the shores of Lake Geneva, known as a residential suburb of Geneva with lakeside promenades and a mix of urban and natural landscapes.
-
D.
Soundwave
Soundwave is a Decepticon from the Transformers franchise, known for his cold, calculating loyalty to Megatron and his ability to deploy smaller cassette-like minions for espionage and combat.
-
E.
Gorgophone
Gorgophone is a figure in Greek mythology, traditionally known as a daughter of Perseus and Andromeda and noted as one of the first women to remarry after being widowed.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: WaveGlow Target entity description: WaveGlow is a flow-based generative neural network model for fast, high-quality text-to-speech audio synthesis.
-
A.
WaveNet
WaveNet is a deep generative neural network architecture for raw audio that produces highly natural-sounding speech and other audio signals.
-
B.
Audion
Audion is an early triode vacuum tube invented by Lee de Forest that enabled the amplification of electrical signals and was crucial to the development of radio and electronics.
-
C.
Versoix
Versoix is a Swiss municipality on the shores of Lake Geneva, known as a residential suburb of Geneva with lakeside promenades and a mix of urban and natural landscapes.
-
D.
Soundwave
Soundwave is a Decepticon from the Transformers franchise, known for his cold, calculating loyalty to Megatron and his ability to deploy smaller cassette-like minions for espionage and combat.
-
E.
Gorgophone
Gorgophone is a figure in Greek mythology, traditionally known as a daughter of Perseus and Andromeda and noted as one of the first women to remarry after being widowed.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
autoregressive-free vocoder
ⓘ
deep learning model ⓘ flow-based generative model ⓘ neural network model ⓘ speech synthesis system ⓘ text-to-speech model ⓘ |
| advantageOverAutoregressiveModels |
lower inference latency
ⓘ
parallel sampling ⓘ |
| architectureComponent |
series of flow steps
ⓘ
upsampling network for conditioning ⓘ |
| audioQuality | near state-of-the-art at time of publication ⓘ |
| basedOn |
Glow
ⓘ
WaveNet ⓘ |
| codeRepository | GitHub ⓘ |
| comparedWith |
ClariNet
ⓘ
Parallel WaveNet ⓘ WaveNet ⓘ |
| designedTo |
enable real-time TTS
ⓘ
replace autoregressive vocoders ⓘ |
| developedBy |
NVIDIA
ⓘ
linked to:
NVIDIA Corporation
|
| distributionAssumption | simple prior distribution on latent space ⓘ |
| domain |
audio generation
ⓘ
speech processing ⓘ |
| framework | PyTorch ⓘ |
| input | mel-spectrograms ⓘ |
| introducedAt | 2018 ⓘ |
| language | Python ⓘ |
| output | time-domain audio waveform ⓘ |
| paperTitle |
WaveGlow: A Flow-based Generative Network for Speech Synthesis
ⓘ
linked to:
WaveGlow
|
| probabilityModel | exact likelihood model ⓘ |
| property |
fast parallel audio generation
ⓘ
fully convolutional architecture ⓘ high-quality speech synthesis ⓘ single-network architecture ⓘ |
| publisher |
NVIDIA
ⓘ
linked to:
NVIDIA Corporation
|
| releasedAs | open source ⓘ |
| supports | GPU acceleration ⓘ |
| task |
neural vocoding
ⓘ
text-to-speech synthesis ⓘ |
| trainingDataType | paired text and speech corpora ⓘ |
| trainingObjective |
log-likelihood maximization
ⓘ
maximum likelihood ⓘ |
| usedFor |
neural TTS systems
ⓘ
speech synthesis research ⓘ voice assistants ⓘ |
| uses |
affine coupling layers
ⓘ
invertible 1x1 convolutions ⓘ normalizing flows ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: WaveGlow Description of subject: WaveGlow is a flow-based generative neural network model for fast, high-quality text-to-speech audio synthesis.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: WaveGlow