PyTorch Implementation of NCSOFT's FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis

Last update: Jan 02, 2023

Overview

FastPitchFormant - PyTorch Implementation

PyTorch Implementation of FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis.

Quickstart

Dependencies

You can install the Python dependencies with

pip3 install -r requirements.txt

Inference

You have to download the pretrained models and put them in output/ckpt/LJSpeech/.

For English single-speaker TTS, run

python3 synthesize.py --text "YOUR_DESIRED_TEXT" --restore_step 1000000 --mode single -p config/LJSpeech/preprocess.yaml -m config/LJSpeech/model.yaml -t config/LJSpeech/train.yaml

The generated utterances will be put in output/result/.

Batch Inference

Batch inference is also supported, try

python3 synthesize.py --source preprocessed_data/LJSpeech/val.txt --restore_step 1000000 --mode batch -p config/LJSpeech/preprocess.yaml -m config/LJSpeech/model.yaml -t config/LJSpeech/train.yaml

to synthesize all utterances in preprocessed_data/LJSpeech/val.txt

Controllability

The pitch/speaking rate of the synthesized utterances can be controlled by specifying the desired pitch/energy/duration ratios. For example, one can increase the speaking rate by 20 % and decrease the pitch by 20 % by

python3 synthesize.py --text "YOUR_DESIRED_TEXT" --restore_step 1000000 --mode single -p config/LJSpeech/preprocess.yaml -m config/LJSpeech/model.yaml -t config/LJSpeech/train.yaml --duration_control 0.8 --pitch_control 0.8

Training

Datasets

The supported datasets are

LJSpeech: a single-speaker English dataset consists of 13100 short audio clips of a female speaker reading passages from 7 non-fiction books, approximately 24 hours in total.

Preprocessing

First, run

python3 prepare_align.py config/LJSpeech/preprocess.yaml

for some preparations.

As described in the paper, Montreal Forced Aligner (MFA) is used to obtain the alignments between the utterances and the phoneme sequences. Alignments for the LJSpeech datasets are provided here. You have to unzip the files in preprocessed_data/LJSpeech/TextGrid/.

After that, run the preprocessing script by

python3 preprocess.py config/LJSpeech/preprocess.yaml

Alternately, you can align the corpus by yourself. Download the official MFA package and run

./montreal-forced-aligner/bin/mfa_align raw_data/LJSpeech/ lexicon/librispeech-lexicon.txt english preprocessed_data/LJSpeech

./montreal-forced-aligner/bin/mfa_train_and_align raw_data/LJSpeech/ lexicon/librispeech-lexicon.txt preprocessed_data/LJSpeech

to align the corpus and then run the preprocessing script.

python3 preprocess.py config/LJSpeech/preprocess.yaml

Training

Train your model with

python3 train.py -p config/LJSpeech/preprocess.yaml -m config/LJSpeech/model.yaml -t config/LJSpeech/train.yaml

TensorBoard

Use

tensorboard --logdir output/log/LJSpeech

to serve TensorBoard on your localhost.

Implementation Issues

Use HiFi-GAN instead of VocGAN for vocoding.

Citation

@misc{lee2021fastpitchformant,
  author = {Lee, Keon},
  title = {FastPitchFormant},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/keonlee9420/FastPitchFormant}}
}

References

PyTorch implementation of Tacotron speech synthesis model.

tacotron_pytorch PyTorch implementation of Tacotron speech synthesis model. Inspired from keithito/tacotron. Currently not as much good speech quality

279 Dec 9, 2022

PyTorch implementation of Lip to Speech Synthesis with Visual Context Attentional GAN (NeurIPS2021)

Lip to Speech Synthesis with Visual Context Attentional GAN This repository contains the PyTorch implementation of the following paper: Lip to Speech

6 Nov 2, 2022

Implementation of Diverse Semantic Image Synthesis via Probability Distribution Modeling

Diverse Semantic Image Synthesis via Probability Distribution Modeling (CVPR 2021) Paper Zhentao Tan, Menglei Chai, Dongdong Chen, Jing Liao, Qi Chu,

45 Nov 17, 2022

A Flow-based Generative Network for Speech Synthesis

WaveGlow: a Flow-based Generative Network for Speech Synthesis Ryan Prenger, Rafael Valle, and Bryan Catanzaro In our recent paper, we propose WaveGlo

2k Dec 26, 2022

Official implementation of the paper: "LDNet: Unified Listener Dependent Modeling in MOS Prediction for Synthetic Speech"

LDNet Author: Wen-Chin Huang (Nagoya University) Email: [email protected] This is the official implementation of the paper "LDNet

40 Nov 20, 2022

TalkNet 2: Non-Autoregressive Depth-Wise Separable Convolutional Model for Speech Synthesis with Explicit Pitch and Duration Prediction.

TalkNet 2 [WIP] TalkNet 2: Non-Autoregressive Depth-Wise Separable Convolutional Model for Speech Synthesis with Explicit Pitch and Duration Predictio

69 Dec 17, 2022

HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis

HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis Jungil Kong, Jaehyeon Kim, Jaekyoung Bae In our paper, we p

31 Dec 8, 2022

BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis

Bilateral Denoising Diffusion Models (BDDMs) This is the official PyTorch implementation of the following paper: BDDM: BILATERAL DENOISING DIFFUSION M

172 Dec 23, 2022

This program uses trial auth token of Azure Cognitive Services to do speech synthesis for you.

🗣️ aspeak A simple text-to-speech client using azure TTS API(trial). 😆 TL;DR: This program uses trial auth token of Azure Cognitive Services to do s

359 Jan 5, 2023

Comments

Error when duration_control is <1

I can set any value above 1 (ie. '--duration_control 1.9') to slow down the speaking rate, but can't do the opposite, anything below 1 (ie. 0.9) will throw this error message:

C:\FastPitchFormant>python synthesize.py --text "testing" --restore_step 600000 --mode single -p config/LJSpeech/preprocess.yaml -m config/LJSpeech/model.yaml -t config/LJSpeech/train.yaml --duration_control 0.9 --pitch_control 1
Removing weight norm...
Raw Text Sequence: testing
Phoneme Sequence: {T EH1 S T IH0 NG}
Traceback (most recent call last):
  File "synthesize.py", line 207, in <module>
    synthesize(model, args.restore_step, configs, vocoder, batchs, control_values)
  File "synthesize.py", line 95, in synthesize
    output = model(
  File "C:\ProgramData\Anaconda3\envs\pttf2cu111py38\lib\site-packages\torch\nn\modules\module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\FastPitchFormant\model\FastPitchFormant.py", line 89, in forward
    formant_hidden = self.formant_generator(h, mel_masks)
  File "C:\ProgramData\Anaconda3\envs\pttf2cu111py38\lib\site-packages\torch\nn\modules\module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\FastPitchFormant\model\modules.py", line 329, in forward
    output, enc_slf_attn = enc_layer(
  File "C:\ProgramData\Anaconda3\envs\pttf2cu111py38\lib\site-packages\torch\nn\modules\module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\FastPitchFormant\model\blocks.py", line 109, in forward
    enc_output, enc_slf_attn = self.slf_attn(
  File "C:\ProgramData\Anaconda3\envs\pttf2cu111py38\lib\site-packages\torch\nn\modules\module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\FastPitchFormant\model\blocks.py", line 162, in forward
    output, attn = self.attention(q, k, v, mask=mask)
  File "C:\ProgramData\Anaconda3\envs\pttf2cu111py38\lib\site-packages\torch\nn\modules\module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\FastPitchFormant\model\blocks.py", line 189, in forward
    attn = attn.masked_fill(mask, -np.inf)
RuntimeError: The size of tensor a (32) must match the size of tensor b (34) at non-singleton dimension 2

opened by MaxGodTier 1

Releases(v1.0.0)

v1.0.0(Aug 3, 2021)

First Official Release
Source code(tar.gz)
Source code(zip)
v0.1.0(Jul 17, 2021)

Source code(tar.gz)
Source code(zip)

Owner

Keon Lee

GitHub Repository

A Jinja extension (compatible with Flask and other frameworks) to compile and/or compress your assets.

94 Nov 21, 2022

RDA: Robust Domain Adaptation via Fourier Adversarial Attacking

RDA: Robust Domain Adaptation via Fourier Adversarial Attacking Updates 08/2021: check out our domain adaptation for video segmentation paper Domain A

17 Nov 30, 2022

This repository provides the code for MedViLL(Medical Vision Language Learner).

MedViLL This repository provides the code for MedViLL(Medical Vision Language Learner). Our proposed architecture MedViLL is a single BERT-based model

39 Jan 05, 2023

50-days-of-Statistics-for-Data-Science - This repository consist of a 50-day program

50-days-of-Statistics-for-Data-Science - This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.

22 Dec 09, 2022

Distributed DataLoader For Pytorch Based On Ray

Dpex——用户无感知分布式数据预处理组件一、前言随着GPU与CPU的算力差距越来越大以及模型训练时的预处理Pipeline变得越来越复杂，CPU部分的数据预处理已经逐渐成为了模型训练的瓶颈所在，这导致单机的GPU配置的提升并不能带来期望的线性加速。预处理性能瓶颈的本质在于每个GPU能够使用的C

23 Nov 02, 2022

Turning SymPy expressions into PyTorch modules.

sympytorch A micro-library as a convenience for turning SymPy expressions into PyTorch Modules. All SymPy floats become trainable parameters. All SymP

89 Dec 13, 2022

pip install python-office

🍬 python for office 👉 http://www.python4office.cn/ 👈 🌎 English Documentation 📚 简介 Python-office 是一个 Python 自动化办公第三方库，能解决大部分自动化办公的问题。而且每个功能只需一行代码，

272 Dec 29, 2022

Pointer networks Tensorflow2

Pointer networks Tensorflow2 原文：https://arxiv.org/abs/1506.03134 仅供参考与学习，内含代码备注环境 tensorflow==2.6.0 tqdm matplotlib numpy 《pointer networks》阅读笔记应用场景

7 Oct 27, 2022

Sign Language is detected in realtime using video sequences. Our approach involves MediaPipe Holistic for keypoints extraction and LSTM Model for prediction.

RealTime Sign Language Detection using Action Recognition Approach Real-Time Sign Language is commonly predicted using models whose architecture consi

15 Aug 20, 2022

PyTorch Implementation of NCSOFT's FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis

Related tags

Overview

FastPitchFormant - PyTorch Implementation

Quickstart

Dependencies

Inference

Batch Inference

Controllability

Training

Datasets

Preprocessing

Training

TensorBoard

Implementation Issues

Citation

References

You might also like...

PyTorch implementation of Tacotron speech synthesis model.

PyTorch implementation of Lip to Speech Synthesis with Visual Context Attentional GAN (NeurIPS2021)

Implementation of Diverse Semantic Image Synthesis via Probability Distribution Modeling

A Flow-based Generative Network for Speech Synthesis

Official implementation of the paper: "LDNet: Unified Listener Dependent Modeling in MOS Prediction for Synthetic Speech"

TalkNet 2: Non-Autoregressive Depth-Wise Separable Convolutional Model for Speech Synthesis with Explicit Pitch and Duration Prediction.

HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis

BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis

This program uses trial auth token of Azure Cognitive Services to do speech synthesis for you.

Comments

Error when duration_control is <1

Releases(v1.0.0)

v1.0.0(Aug 3, 2021)

v0.1.0(Jul 17, 2021)

Owner

Keon Lee

A Jinja extension (compatible with Flask and other frameworks) to compile and/or compress your assets.

RDA: Robust Domain Adaptation via Fourier Adversarial Attacking

This repository provides the code for MedViLL(Medical Vision Language Learner).

50-days-of-Statistics-for-Data-Science - This repository consist of a 50-day program

Distributed DataLoader For Pytorch Based On Ray

Turning SymPy expressions into PyTorch modules.

pip install python-office

Pointer networks Tensorflow2

Sign Language is detected in realtime using video sequences. Our approach involves MediaPipe Holistic for keypoints extraction and LSTM Model for prediction.

SCNet: Learning Semantic Correspondence

Bachelor's Thesis in Computer Science: Privacy-Preserving Federated Learning Applied to Decentralized Data

A python script to convert images to animated sus among us crewmate twerk jifs as seen on r/196

Explaining Hyperparameter Optimization via PDPs

MGFN: Multi-Graph Fusion Networks for Urban Region Embedding was accepted by IJCAI-2022.

BBB streaming without Xorg and Pulseaudio and Chromium and other nonsense (heavily WIP)

NCVX (NonConVeX): A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning.

TensorFlow tutorials and best practices.

The implementation of the CVPR2021 paper "Structure-Aware Face Clustering on a Large-Scale Graph with 10^7 Nodes"

The code is an implementation of Feedback Convolutional Neural Network for Visual Localization and Segmentation.

Toolchain to build Yoshi's Island from source code