Pytorch Implementation of DiffSinger: Diffusion Acoustic Model for Singing Voice Synthesis (TTS Extension)

Related tags

Deep Learningtext-to-speechpytorchttsspeech-synthesisenglishdiffusionsinging-voicediffusion-modelsneural-ttsnon-autoregressivefastspeechddpmdiffsinger
Overview

DiffSinger - PyTorch Implementation

PyTorch implementation of DiffSinger: Diffusion Acoustic Model for Singing Voice Synthesis (TTS Extension).

Status (2021.06.03)

  • Naive Version of DiffSinger
  • Shallow Diffusion Mechanism: Training boundary predictor by leveraging pre-trained auxiliary decoder + Training denoiser using k as a maximum time step

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 160000 --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 160000 --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/volume/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 volume by 20 % by

python3 synthesize.py --text "YOUR_DESIRED_TEXT" --restore_step 160000 --mode single -p config/LJSpeech/preprocess.yaml -m config/LJSpeech/model.yaml -t config/LJSpeech/train.yaml --duration_control 0.8 --energy_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.
  • (will be added more)

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 from ming024's FastSpeech2. 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

or

./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. The loss curves, synthesized mel-spectrograms, and audios are shown.

Implementation Issues

  1. Pitch extractor comparison (on LJ001-0006.wav)

    pyworld is used to extract f0 (fundamental frequency) as pitch information in this implementation. Empirically, however, I found that all three methods were equally acceptable for clean datasets (e.g., LJSpeech) as above figures. Note that pysptk would work better for noisy datasets (as described in STYLER).

  2. Stack two layers of FFTBlock for the lyrics encoder (text encoder).

  3. (Naive version) The number of learnable parameters is 34.337M, which is larger than the original paper (26.744M). The diffusion module takes a significant portion of whole parameters.

  4. I did not remove the energy prediction of FastSpeech2 since it is not critical to the model training or performance (as described in LightSpeech). It should be easily removed without any performance degradation.

  5. Use HiFi-GAN instead of Parallel WaveGAN (PWG) for vocoding.

Citation

@misc{lee2021diffsinger,
  author = {Lee, Keon},
  title = {DiffSinger},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/keonlee9420/DiffSinger}}
}

References

You might also like...
This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEECH" submitted to ICASSP 2022

CPC_DeepCluster This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEEC

LEAP Lab 2 Sep 15, 2022
Pytorch Implementation of Google's Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling
Pytorch Implementation of Google's Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling

Parallel Tacotron2 Pytorch Implementation of Google's Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling

Keon Lee 170 Dec 27, 2022
PyTorch implementation of the method described in the paper VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop.
PyTorch implementation of the method described in the paper VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop.

VoiceLoop PyTorch implementation of the method described in the paper VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop. VoiceLoop is a n

Meta Archive 873 Dec 15, 2022
This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

OpenAI 3k Dec 26, 2022
Codebase for Diffusion Models Beat GANS on Image Synthesis.

Codebase for Diffusion Models Beat GANS on Image Synthesis.

Katherine Crowson 128 Dec 2, 2022
High-Resolution Image Synthesis with Latent Diffusion Models
High-Resolution Image Synthesis with Latent Diffusion Models

Latent Diffusion Models Requirements A suitable conda environment named ldm can be created and activated with: conda env create -f environment.yaml co

CompVis Heidelberg 5.6k Jan 4, 2023
BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis
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

null 172 Dec 23, 2022
Code release for paper: The Boombox: Visual Reconstruction from Acoustic Vibrations
Code release for paper: The Boombox: Visual Reconstruction from Acoustic Vibrations

The Boombox: Visual Reconstruction from Acoustic Vibrations Boyuan Chen, Mia Chiquier, Hod Lipson, Carl Vondrick Columbia University Project Website |

Boyuan Chen 12 Nov 30, 2022
Multistream CNN for Robust Acoustic Modeling
Multistream CNN for Robust Acoustic Modeling

Multistream Convolutional Neural Network (CNN) A multistream CNN is a novel neural network architecture for robust acoustic modeling in speech recogni

ASAPP Research 37 Sep 21, 2022
Comments
  • Training Error

    Training Error

    In this case, , i ran the scripts python3 train.py -p config/vietnam/preprocess.yaml -m config/vietnam/model.yaml -t config/vietnam/train.yaml File "train.py", line 199, in main(args, configs) File "train.py", line 85, in main losses = Loss(batch, output) File "/home/thanhdo/envs/diffsinger_env/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/home/thanhdo/Documents/DiffSinger/model/loss.py", line 69, in forward log_duration_targets = log_duration_targets.masked_select(src_masks) RuntimeError: The size of tensor a (39) must match the size of tensor b (136) at non-singleton dimension 1

    Screen Shot 2021-06-23 at 3 56 10 PM

    opened by thanhdo99 8
  • diffusion_projection in ResidualBlock

    diffusion_projection in ResidualBlock

    Your implementation has diffusion_projection for every residual block similar to DiffWave, but this is inconsistent with the paper as the original architecture directly adds E_t (output of the step embedding module) to the input before the first convolution layer. Is there a reason behind this change?

    opened by tebin 1
Releases(v0.1.0)
Owner
Keon Lee
Conversational AI | Expressive Speech Synthesis | Open-domain Dialog | Empathic Computing | NLP | Disentangled Representation | Generative Models | HCI
Keon Lee
GitHub Repository
Repositorio oficial del curso IIC2233 Programación Avanzada 🚀✨

IIC2233 - Programación Avanzada Evaluación Las evaluaciones serán efectuadas por medio de actividades prácticas en clases y tareas. Se calculará la no

IIC2233 @ UC 47 Sep 06, 2022
A simple baseline for 3d human pose estimation in tensorflow. Presented at ICCV 17.

3d-pose-baseline This is the code for the paper Julieta Martinez, Rayat Hossain, Javier Romero, James J. Little. A simple yet effective baseline for 3

Julieta Martinez 1.3k Jan 03, 2023
Texture mapping with variational auto-encoders

vae-textures This is an experiment with using variational autoencoders (VAEs) to perform mesh parameterization. This was also my first project using J

Alex Nichol 41 May 24, 2022
Short and long time series classification using convolutional neural networks

time-series-classification Short and long time series classification via convolutional neural networks In this project, we present a novel framework f

35 Oct 22, 2022
A pure PyTorch batched computation implementation of "CIF: Continuous Integrate-and-Fire for End-to-End Speech Recognition"

A pure PyTorch batched computation implementation of "CIF: Continuous Integrate-and-Fire for End-to-End Speech Recognition"

張致強 14 Dec 02, 2022
The official PyTorch code for NeurIPS 2021 ML4AD Paper, "Does Thermal data make the detection systems more reliable?"

MultiModal-Collaborative (MMC) Learning Framework for integrating RGB and Thermal spectral modalities This is the official code for NeurIPS 2021 Machi

NeurAI 12 Nov 02, 2022
Code for the paper "PortraitNet: Real-time portrait segmentation network for mobile device" @ CAD&Graphics2019

PortraitNet Code for the paper "PortraitNet: Real-time portrait segmentation network for mobile device". @ CAD&Graphics 2019 Introduction We propose a

265 Dec 01, 2022
Real-time object detection on Android using the YOLO network with TensorFlow

TensorFlow YOLO object detection on Android Source project android-yolo is the first implementation of YOLO for TensorFlow on an Android device. It is

Nataniel Ruiz 624 Jan 03, 2023
Event sourced bank - A wide-and-shallow example using the Python event sourcing library

Event Sourced Bank A "wide but shallow" example of using the Python event sourci

3 Mar 09, 2022
Election Exit Poll Prediction and U.S.A Presidential Speech Analysis using Machine Learning

Machine_Learning Election Exit Poll Prediction and U.S.A Presidential Speech Analysis using Machine Learning This project is based on 2 case-studies:

Avnika Mehta 1 Jan 27, 2022
This is an easy python software which allows to sort images with faces by gender and after by age.

Gender-age Classifier This is an easy python software which allows to sort images with faces by gender and after by age. Usage First install Deepface

Claudio Ciccarone 6 Sep 17, 2022
TGS Salt Identification Challenge

TGS Salt Identification Challenge This is an open solution to the TGS Salt Identification Challenge. Note Unfortunately, we can no longer provide supp

neptune.ai 123 Nov 04, 2022
Human segmentation models, training/inference code, and trained weights, implemented in PyTorch

Human-Segmentation-PyTorch Human segmentation models, training/inference code, and trained weights, implemented in PyTorch. Supported networks UNet: b

Thuy Ng 474 Dec 19, 2022
WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose

WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose Yijun Zhou and James Gregson - BMVC2020 Abstract: We present an end-to-end head-pos

368 Dec 26, 2022
[NeurIPS '21] Adversarial Attacks on Graph Classification via Bayesian Optimisation (GRABNEL)

Adversarial Attacks on Graph Classification via Bayesian Optimisation @ NeurIPS 2021 This repository contains the official implementation of GRABNEL,

Xingchen Wan 12 Dec 23, 2022
Keqing Chatbot With Python

KeqingChatbot A public running instance can be found on telegram as @keqingchat_bot. Requirements Python 3.8 or higher. A bot token. Local Deploy git

Rikka-Chan 2 Jan 16, 2022
Code for the paper "Functional Regularization for Reinforcement Learning via Learned Fourier Features"

Reinforcement Learning with Learned Fourier Features State-space Soft Actor-Critic Experiments Move to the state-SAC-LFF repository. cd state-SAC-LFF

Alex Li 10 Nov 11, 2022
Clustergram - Visualization and diagnostics for cluster analysis in Python

Clustergram Visualization and diagnostics for cluster analysis Clustergram is a diagram proposed by Matthias Schonlau in his paper The clustergram: A

Martin Fleischmann 96 Dec 26, 2022
A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items

A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items This repository co

Taimur Hassan 3 Mar 16, 2022
Simple implementation of OpenAI CLIP model in PyTorch.

It was in January of 2021 that OpenAI announced two new models: DALL-E and CLIP, both multi-modality models connecting texts and images in some way. In this article we are going to implement CLIP mod

Moein Shariatnia 226 Jan 05, 2023