Comments (5)
Hi,
I had similar questions as you.
The TKR is explained here: #27
The models labeled "FunCodec"/"M1" are actually time-domain models and similar to Encodec (the differences are explained here: #5).
As far as I understand, only the models labeled as "Mag,Angle"/"Mag,Phase" correspond to the FreqCodec (i.e. frequency domain) architecture.
from funcodec.
From the first two tables, extrapolating to the third table, is there an error, why should the scores of 4.29 and 3.86, in the time domain of M1, not be in the frequency domain of M2 M3?
Because I think the first two tables are frequency domain scores, so the third table with the same score should also be frequency domain.
from funcodec.
Hi, I had similar questions as you.
The TKR is explained here: #27 The models labeled "FunCodec"/"M1" are actually time-domain models and similar to Encodec (the differences are explained here: #5). As far as I understand, only the models labeled as "Mag,Angle"/"Mag,Phase" correspond to the FreqCodec (i.e. frequency domain) architecture.
Hi @lbehringer
Thank you for your reply to help me understand this article
As far as I know:
- the tag in Figure 2 in the article: "The overall architecture of the
FunCodec
models." should be changed to ".... of theFreqCodec
models" - This article proposes a new model in the time domain,
Funcodec
(with some modifications on soundstream and Encodec), and then a new model in the frequency domain,FreqCodec
(with some modifications on FunCodec, such as adding domain transformations), right?
from funcodec.
Hi, I had similar questions as you.
The TKR is explained here: #27 The models labeled "FunCodec"/"M1" are actually time-domain models and similar to Encodec (the differences are explained here: #5). As far as I understand, only the models labeled as "Mag,Angle"/"Mag,Phase" correspond to the FreqCodec (i.e. frequency domain) architecture.
Thanks for your helpful response.
from funcodec.
Hi, I had similar questions as you.
The TKR is explained here: #27 The models labeled "FunCodec"/"M1" are actually time-domain models and similar to Encodec (the differences are explained here: #5). As far as I understand, only the models labeled as "Mag,Angle"/"Mag,Phase" correspond to the FreqCodec (i.e. frequency domain) architecture.Hi @lbehringer Thank you for your reply to help me understand this article As far as I know:
- the tag in Figure 2 in the article: "The overall architecture of the
FunCodec
models." should be changed to ".... of theFreqCodec
models"- This article proposes a new model in the time domain,
Funcodec
(with some modifications on soundstream and Encodec), and then a new model in the frequency domain,FreqCodec
(with some modifications on FunCodec, such as adding domain transformations), right?
The response of @lbehringer is really helpful, and your understanding is almost right. The Figure 2 is used to offer a universal framework of both time-domain and frequency-domain models in FunCodec, therefore the title is "The overall architecture of the FunCodec
models."
For item 2, your understanding is right.
from funcodec.
Related Issues (20)
- TypeError: 'NoneType' object is not callable HOT 4
- [bug] encoding阶段生成的codec.txt, 无法直接读取? HOT 1
- Discriminator loss? HOT 5
- LauraTTS模型的训练花了多长时间? HOT 5
- 如何仅用funcodec的4层或者8层量化器进行推理 HOT 2
- zipfile.BadZipFile: File is not a zip file HOT 1
- run.sh: 34: utils/parse_options.sh: Syntax error: Bad for loop variable HOT 1
- How to check progress? HOT 1
- 运行run.sh在stage 4时报错 HOT 2
- Stage 3 HOT 2
- Feature to resume training after stopping? HOT 1
- NET/Plugin : dlerror=libnccl-net.so: cannot open shared object file: No such file or directory No plugin found (libnccl-net.so)
- Out of memory when train on large dataset (librilight)
- Stage 1 can only be run on one gpu card 0 HOT 1
- Model sometimes stops training due to nan inputs HOT 2
- Should the audio be normed to [-1, 1]? HOT 1
- Encountering errors when training LauraGPT with codec model configured to num_quantizers=16, codebook_size=512 HOT 1
- 多机多卡
- 流式推理
- audio_codec-encodec-zh_en-general-16k-nq32ds640-pytorch模型,Huggingface和Modelscope下载的参数不同
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from funcodec.