FFTNet

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A PyTorch implementation of the FFTNet: a Real-Time Speaker-Dependent Neural Vocoder

FFTNet 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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# FFTNet

A PyTorch implementation of the [FFTNet: a Real-Time Speaker-Dependent Neural Vocoder](http://gfx.cs.princeton.edu/pubs/Jin_2018_FAR/)

## Quick Start:
1. Install requirements:
  ```
  pip install -r requirements.txt
  ```
2. Download dataset:
  ```
  wget http://festvox.org/cmu_arctic/cmu_arctic/packed/cmu_us_slt_arctic-0.95-release.tar.bz2
  tar xf cmu_us_slt_arctic-0.95-release.tar.bz2
  ```
3. Extract features:
  ```
  python preprocess.py
  ```
4. Training with default hyperparams:
  ```
  python train.py
  ```
5. Synthesize from model:
    ```
    python generate.py --checkpoint=/path/to/model --lc_file=/path/to/local_conditon
    ```

## TODO:
- [ ] Test and modify conditonal sampling.
- [ ] Fast generation. (Without fast generation, it generates about 230 samples per second with a free TITAN Xp.)
- [ ] Post-synthesis denoising.

## Notes:
  * This is not offical implementation, some details are different from the paper.
  * Now the generated speech from this repo is not so good as wavenet vocoder.
  * Work in progress.

FFTNet

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