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Towards Achieving Robust Universal Neural Vocoding

A PyTorch implementation of Towards Achieving Robust Universal Neural Vocoding. Audio samples can be found here. Colab demo can be found here. Accompanying Tacotron implementation can be found here

Architecture of the vocoder.
Fig 1:Architecture of the vocoder.

Quick Start

Ensure you have Python 3.6 and PyTorch 1.7 or greater installed. Then install the package with:

pip install univoc

Example Usage

Open In Colab

importtorchimportsoundfileassffromunivocimportVocoder# download pretrained weights (and optionally move to GPU)vocoder=Vocoder.from_pretrained(
"https://github.com/bshall/UniversalVocoding/releases/download/v0.2/univoc-ljspeech-7mtpaq.pt"
).cuda()
# load log-Mel spectrogram from file or from tts (see https://github.com/bshall/Tacotron for example)mel= ...
# generate waveformwithtorch.no_grad():
wav, sr=vocoder.generate(mel)
# save outputsf.write("path/to/save.wav", wav, sr)

Train from Scratch

  1. Clone the repo:
git clone https://github.com/bshall/UniversalVocoding
cd ./UniversalVocoding
  1. Install requirements:
pip install -r requirements.txt
  1. Download and extract the LJ-Speech dataset:
wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
tar -xvjf LJSpeech-1.1.tar.bz2
  1. Download the train split here and extract it in the root directory of the repo.
  2. Extract Mel spectrograms and preprocess audio:
python preprocess.py in_dir=path/to/LJSpeech-1.1 out_dir=datasets/LJSpeech-1.1
  1. Train the model:
python train.py checkpoint_dir=ljspeech dataset_dir=datasets/LJSpeech-1.1

Pretrained Models

Pretrained weights for the 10-bit LJ-Speech model are available here.

Notable Differences from the Paper

  1. Trained on 16kHz audio from a single speaker. For an older version trained on 102 different speakers form the ZeroSpeech 2019: TTS without T English dataset click here.
  2. Uses an embedding layer instead of one-hot encoding.

Acknowlegements

About

A PyTorch implementation of "Robust Universal Neural Vocoding"

bshall.github.io/UniversalVocoding/

Topics

neural-vocoderpytorchspeech-synthesiswavernn

Resources

Readme
MIT license
Activity

Stars

238 stars

Watchers

13 watching

Forks

40 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

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Skip to content
BranchesTags
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Open In Colab
Towards Achieving Robust Universal Neural Vocoding
A PyTorch implementation of Towards Achieving Robust Universal Neural Vocoding.
Audio samples can be found here. Colab demo can be found here. Accompanying Tacotron implementation can be found here
Architecture of the vocoder.
Fig 1:Architecture of the vocoder.
Quick Start
Ensure you have Python 3.6 and PyTorch 1.7 or greater installed. Then install the package with:
pip install univoc

Example Usage
Open In Colab
importtorchimportsoundfileassffromunivocimportVocoder# download pretrained weights (and optionally move to GPU)vocoder=Vocoder.from_pretrained(
"https://github.com/bshall/UniversalVocoding/releases/download/v0.2/univoc-ljspeech-7mtpaq.pt"
).cuda()
# load log-Mel spectrogram from file or from tts (see https://github.com/bshall/Tacotron for example)mel= ...
# generate waveformwithtorch.no_grad():
wav, sr=vocoder.generate(mel)
# save outputsf.write("path/to/save.wav", wav, sr)
Train from Scratch
  1. Clone the repo:
git clone https://github.com/bshall/UniversalVocoding
cd ./UniversalVocoding

  1. Install requirements:
pip install -r requirements.txt

  1. Download and extract the LJ-Speech dataset:
wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
tar -xvjf LJSpeech-1.1.tar.bz2

  1. Download the train split here and extract it in the root directory of the repo.
  2. Extract Mel spectrograms and preprocess audio:
python preprocess.py in_dir=path/to/LJSpeech-1.1 out_dir=datasets/LJSpeech-1.1

  1. Train the model:
python train.py checkpoint_dir=ljspeech dataset_dir=datasets/LJSpeech-1.1

Pretrained Models
Pretrained weights for the 10-bit LJ-Speech model are available here.
Notable Differences from the Paper
  1. Trained on 16kHz audio from a single speaker. For an older version trained on 102 different speakers form the ZeroSpeech 2019: TTS without T English dataset click here.
  2. Uses an embedding layer instead of one-hot encoding.
Acknowlegements
About
A PyTorch implementation of "Robust Universal Neural Vocoding"
bshall.github.io/UniversalVocoding/
Topics
neural-vocoderpytorchspeech-synthesiswavernn
Resources
Readme
MIT license
Activity
Stars
238 stars
Watchers
13 watching
Forks
40 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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Skip to content
BranchesTags
Repository files navigation
Open In Colab
Towards Achieving Robust Universal Neural Vocoding
A PyTorch implementation of Towards Achieving Robust Universal Neural Vocoding.
Audio samples can be found here. Colab demo can be found here. Accompanying Tacotron implementation can be found here
Architecture of the vocoder.
Fig 1:Architecture of the vocoder.
Quick Start
Ensure you have Python 3.6 and PyTorch 1.7 or greater installed. Then install the package with:
pip install univoc

Example Usage
Open In Colab
importtorchimportsoundfileassffromunivocimportVocoder# download pretrained weights (and optionally move to GPU)vocoder=Vocoder.from_pretrained(
"https://github.com/bshall/UniversalVocoding/releases/download/v0.2/univoc-ljspeech-7mtpaq.pt"
).cuda()
# load log-Mel spectrogram from file or from tts (see https://github.com/bshall/Tacotron for example)mel= ...
# generate waveformwithtorch.no_grad():
wav, sr=vocoder.generate(mel)
# save outputsf.write("path/to/save.wav", wav, sr)
Train from Scratch
  1. Clone the repo:
git clone https://github.com/bshall/UniversalVocoding
cd ./UniversalVocoding

  1. Install requirements:
pip install -r requirements.txt

  1. Download and extract the LJ-Speech dataset:
wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
tar -xvjf LJSpeech-1.1.tar.bz2

  1. Download the train split here and extract it in the root directory of the repo.
  2. Extract Mel spectrograms and preprocess audio:
python preprocess.py in_dir=path/to/LJSpeech-1.1 out_dir=datasets/LJSpeech-1.1

  1. Train the model:
python train.py checkpoint_dir=ljspeech dataset_dir=datasets/LJSpeech-1.1

Pretrained Models
Pretrained weights for the 10-bit LJ-Speech model are available here.
Notable Differences from the Paper
  1. Trained on 16kHz audio from a single speaker. For an older version trained on 102 different speakers form the ZeroSpeech 2019: TTS without T English dataset click here.
  2. Uses an embedding layer instead of one-hot encoding.
Acknowlegements
About
A PyTorch implementation of "Robust Universal Neural Vocoding"
bshall.github.io/UniversalVocoding/
Topics
neural-vocoderpytorchspeech-synthesiswavernn
Resources
Readme
MIT license
Activity
Stars
238 stars
Watchers
13 watching
Forks
40 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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Open In Colab
Towards Achieving Robust Universal Neural Vocoding
A PyTorch implementation of Towards Achieving Robust Universal Neural Vocoding.
Audio samples can be found here. Colab demo can be found here. Accompanying Tacotron implementation can be found here
Architecture of the vocoder.
Fig 1:Architecture of the vocoder.
Quick Start
Ensure you have Python 3.6 and PyTorch 1.7 or greater installed. Then install the package with:
pip install univoc

Example Usage
Open In Colab
importtorchimportsoundfileassffromunivocimportVocoder# download pretrained weights (and optionally move to GPU)vocoder=Vocoder.from_pretrained(
"https://github.com/bshall/UniversalVocoding/releases/download/v0.2/univoc-ljspeech-7mtpaq.pt"
).cuda()
# load log-Mel spectrogram from file or from tts (see https://github.com/bshall/Tacotron for example)mel= ...
# generate waveformwithtorch.no_grad():
wav, sr=vocoder.generate(mel)
# save outputsf.write("path/to/save.wav", wav, sr)
Train from Scratch
  1. Clone the repo:
git clone https://github.com/bshall/UniversalVocoding
cd ./UniversalVocoding

  1. Install requirements:
pip install -r requirements.txt

  1. Download and extract the LJ-Speech dataset:
wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
tar -xvjf LJSpeech-1.1.tar.bz2

  1. Download the train split here and extract it in the root directory of the repo.
  2. Extract Mel spectrograms and preprocess audio:
python preprocess.py in_dir=path/to/LJSpeech-1.1 out_dir=datasets/LJSpeech-1.1

  1. Train the model:
python train.py checkpoint_dir=ljspeech dataset_dir=datasets/LJSpeech-1.1

Pretrained Models
Pretrained weights for the 10-bit LJ-Speech model are available here.
Notable Differences from the Paper
  1. Trained on 16kHz audio from a single speaker. For an older version trained on 102 different speakers form the ZeroSpeech 2019: TTS without T English dataset click here.
  2. Uses an embedding layer instead of one-hot encoding.
Acknowlegements
About
A PyTorch implementation of "Robust Universal Neural Vocoding"
bshall.github.io/UniversalVocoding/
Topics
neural-vocoderpytorchspeech-synthesiswavernn
Resources
Readme
MIT license
Activity
Stars
238 stars
Watchers
13 watching
Forks
40 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
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})();
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var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Open In Colab
Towards Achieving Robust Universal Neural Vocoding
A PyTorch implementation of Towards Achieving Robust Universal Neural Vocoding.
Audio samples can be found here. Colab demo can be found here. Accompanying Tacotron implementation can be found here
Architecture of the vocoder.
Fig 1:Architecture of the vocoder.
Quick Start
Ensure you have Python 3.6 and PyTorch 1.7 or greater installed. Then install the package with:
pip install univoc

Example Usage
Open In Colab
importtorchimportsoundfileassffromunivocimportVocoder# download pretrained weights (and optionally move to GPU)vocoder=Vocoder.from_pretrained(
"https://github.com/bshall/UniversalVocoding/releases/download/v0.2/univoc-ljspeech-7mtpaq.pt"
).cuda()
# load log-Mel spectrogram from file or from tts (see https://github.com/bshall/Tacotron for example)mel= ...
# generate waveformwithtorch.no_grad():
wav, sr=vocoder.generate(mel)
# save outputsf.write("path/to/save.wav", wav, sr)
Train from Scratch
  1. Clone the repo:
git clone https://github.com/bshall/UniversalVocoding
cd ./UniversalVocoding

  1. Install requirements:
pip install -r requirements.txt

  1. Download and extract the LJ-Speech dataset:
wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
tar -xvjf LJSpeech-1.1.tar.bz2

  1. Download the train split here and extract it in the root directory of the repo.
  2. Extract Mel spectrograms and preprocess audio:
python preprocess.py in_dir=path/to/LJSpeech-1.1 out_dir=datasets/LJSpeech-1.1

  1. Train the model:
python train.py checkpoint_dir=ljspeech dataset_dir=datasets/LJSpeech-1.1

Pretrained Models
Pretrained weights for the 10-bit LJ-Speech model are available here.
Notable Differences from the Paper
  1. Trained on 16kHz audio from a single speaker. For an older version trained on 102 different speakers form the ZeroSpeech 2019: TTS without T English dataset click here.
  2. Uses an embedding layer instead of one-hot encoding.
Acknowlegements
About
A PyTorch implementation of "Robust Universal Neural Vocoding"
bshall.github.io/UniversalVocoding/
Topics
neural-vocoderpytorchspeech-synthesiswavernn
Resources
Readme
MIT license
Activity
Stars
238 stars
Watchers
13 watching
Forks
40 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
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var __m = "*";
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Skip to content
BranchesTags
Repository files navigation
Open In Colab
Towards Achieving Robust Universal Neural Vocoding
A PyTorch implementation of Towards Achieving Robust Universal Neural Vocoding.
Audio samples can be found here. Colab demo can be found here. Accompanying Tacotron implementation can be found here
Architecture of the vocoder.
Fig 1:Architecture of the vocoder.
Quick Start
Ensure you have Python 3.6 and PyTorch 1.7 or greater installed. Then install the package with:
pip install univoc

Example Usage
Open In Colab
importtorchimportsoundfileassffromunivocimportVocoder# download pretrained weights (and optionally move to GPU)vocoder=Vocoder.from_pretrained(
"https://github.com/bshall/UniversalVocoding/releases/download/v0.2/univoc-ljspeech-7mtpaq.pt"
).cuda()
# load log-Mel spectrogram from file or from tts (see https://github.com/bshall/Tacotron for example)mel= ...
# generate waveformwithtorch.no_grad():
wav, sr=vocoder.generate(mel)
# save outputsf.write("path/to/save.wav", wav, sr)
Train from Scratch
  1. Clone the repo:
git clone https://github.com/bshall/UniversalVocoding
cd ./UniversalVocoding

  1. Install requirements:
pip install -r requirements.txt

  1. Download and extract the LJ-Speech dataset:
wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
tar -xvjf LJSpeech-1.1.tar.bz2

  1. Download the train split here and extract it in the root directory of the repo.
  2. Extract Mel spectrograms and preprocess audio:
python preprocess.py in_dir=path/to/LJSpeech-1.1 out_dir=datasets/LJSpeech-1.1

  1. Train the model:
python train.py checkpoint_dir=ljspeech dataset_dir=datasets/LJSpeech-1.1

Pretrained Models
Pretrained weights for the 10-bit LJ-Speech model are available here.
Notable Differences from the Paper
  1. Trained on 16kHz audio from a single speaker. For an older version trained on 102 different speakers form the ZeroSpeech 2019: TTS without T English dataset click here.
  2. Uses an embedding layer instead of one-hot encoding.
Acknowlegements
About
A PyTorch implementation of "Robust Universal Neural Vocoding"
bshall.github.io/UniversalVocoding/
Topics
neural-vocoderpytorchspeech-synthesiswavernn
Resources
Readme
MIT license
Activity
Stars
238 stars
Watchers
13 watching
Forks
40 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
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Skip to content
BranchesTags
Repository files navigation
Open In Colab
Towards Achieving Robust Universal Neural Vocoding
A PyTorch implementation of Towards Achieving Robust Universal Neural Vocoding.
Audio samples can be found here. Colab demo can be found here. Accompanying Tacotron implementation can be found here
Architecture of the vocoder.
Fig 1:Architecture of the vocoder.
Quick Start
Ensure you have Python 3.6 and PyTorch 1.7 or greater installed. Then install the package with:
pip install univoc

Example Usage
Open In Colab
importtorchimportsoundfileassffromunivocimportVocoder# download pretrained weights (and optionally move to GPU)vocoder=Vocoder.from_pretrained(
"https://github.com/bshall/UniversalVocoding/releases/download/v0.2/univoc-ljspeech-7mtpaq.pt"
).cuda()
# load log-Mel spectrogram from file or from tts (see https://github.com/bshall/Tacotron for example)mel= ...
# generate waveformwithtorch.no_grad():
wav, sr=vocoder.generate(mel)
# save outputsf.write("path/to/save.wav", wav, sr)
Train from Scratch
  1. Clone the repo:
git clone https://github.com/bshall/UniversalVocoding
cd ./UniversalVocoding

  1. Install requirements:
pip install -r requirements.txt

  1. Download and extract the LJ-Speech dataset:
wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
tar -xvjf LJSpeech-1.1.tar.bz2

  1. Download the train split here and extract it in the root directory of the repo.
  2. Extract Mel spectrograms and preprocess audio:
python preprocess.py in_dir=path/to/LJSpeech-1.1 out_dir=datasets/LJSpeech-1.1

  1. Train the model:
python train.py checkpoint_dir=ljspeech dataset_dir=datasets/LJSpeech-1.1

Pretrained Models
Pretrained weights for the 10-bit LJ-Speech model are available here.
Notable Differences from the Paper
  1. Trained on 16kHz audio from a single speaker. For an older version trained on 102 different speakers form the ZeroSpeech 2019: TTS without T English dataset click here.
  2. Uses an embedding layer instead of one-hot encoding.
Acknowlegements
About
A PyTorch implementation of "Robust Universal Neural Vocoding"
bshall.github.io/UniversalVocoding/
Topics
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Open In Colab
Towards Achieving Robust Universal Neural Vocoding
A PyTorch implementation of Towards Achieving Robust Universal Neural Vocoding.
Audio samples can be found here. Colab demo can be found here. Accompanying Tacotron implementation can be found here
Architecture of the vocoder.
Fig 1:Architecture of the vocoder.
Quick Start
Ensure you have Python 3.6 and PyTorch 1.7 or greater installed. Then install the package with:
pip install univoc

Example Usage
Open In Colab
importtorchimportsoundfileassffromunivocimportVocoder# download pretrained weights (and optionally move to GPU)vocoder=Vocoder.from_pretrained(
"https://github.com/bshall/UniversalVocoding/releases/download/v0.2/univoc-ljspeech-7mtpaq.pt"
).cuda()
# load log-Mel spectrogram from file or from tts (see https://github.com/bshall/Tacotron for example)mel= ...
# generate waveformwithtorch.no_grad():
wav, sr=vocoder.generate(mel)
# save outputsf.write("path/to/save.wav", wav, sr)
Train from Scratch
  1. Clone the repo:
git clone https://github.com/bshall/UniversalVocoding
cd ./UniversalVocoding

  1. Install requirements:
pip install -r requirements.txt

  1. Download and extract the LJ-Speech dataset:
wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
tar -xvjf LJSpeech-1.1.tar.bz2

  1. Download the train split here and extract it in the root directory of the repo.
  2. Extract Mel spectrograms and preprocess audio:
python preprocess.py in_dir=path/to/LJSpeech-1.1 out_dir=datasets/LJSpeech-1.1

  1. Train the model:
python train.py checkpoint_dir=ljspeech dataset_dir=datasets/LJSpeech-1.1

Pretrained Models
Pretrained weights for the 10-bit LJ-Speech model are available here.
Notable Differences from the Paper
  1. Trained on 16kHz audio from a single speaker. For an older version trained on 102 different speakers form the ZeroSpeech 2019: TTS without T English dataset click here.
  2. Uses an embedding layer instead of one-hot encoding.
Acknowlegements
About
A PyTorch implementation of "Robust Universal Neural Vocoding"
bshall.github.io/UniversalVocoding/
Topics
neural-vocoderpytorchspeech-synthesiswavernn
Resources
Readme
MIT license
Activity
Stars
238 stars
Watchers
13 watching
Forks
40 forks
Report repository
Releases
Packages
Used by
Contributors
Languages