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Fastspeech 2

UnOfficial PyTorch implementation of FastSpeech 2: Fast and High-Quality End-to-End Text to Speech. This repo uses the FastSpeech implementation of Espnet as a base. In this implementation I tried to replicate the exact paper details but still some modification required for better model, this repo open for any suggestion and improvement. This repo uses Nvidia's tacotron 2 preprocessing for audio pre-processing and MelGAN as vocoder.

Demo :

Requirements :

All code written in Python 3.6.2 .

Install Pytorch

Before installing pytorch please check your Cuda version by running following command : nvcc --version

pip install torch torchvision

In this repo I have used Pytorch 1.6.0 for torch.bucketize feature which is not present in previous versions of PyTorch.

Installing other requirements :

pip install -r requirements.txt

To use Tensorboard install tensorboard version 1.14.0 seperatly with supported tensorflow (1.14.0)

For Preprocessing :

filelists folder contains MFA (Motreal Force aligner) processed LJSpeech dataset files so you don't need to align text with audio (for extract duration) for LJSpeech dataset. For other dataset follow instruction here. For other pre-processing run following command :

python .\nvidia_preprocessing.py -d path_of_wavs

For finding the min and max of F0 and Energy

python .\compute_statistics.py

Update the following in hparams.py by min and max of F0 and Energy

p_min = Min F0/pitch
p_max = Max F0
e_min = Min energy
e_max = Max energy

For training

 python train_fastspeech.py --outdir etc -c configs/default.yaml -n "name"

For inference

Currently only phonemes based Synthesis supported.

python .\inference.py -c .\configs\default.yaml -p .\checkpoints\first_1\ts_version2_fastspeech_fe9a2c7_7k_steps.pyt --out output --text "ModuleList can be indexed like a regular Python list but modules it contains are properly registered."

For TorchScript Export

python export_torchscript.py -c configs/default.yaml -n fastspeech_scrip --outdir etc

Checkpoint and samples:

Checkpoint find here
For samples check sample folder.

Tensorboard

Training : Tensorboard Validation :

Note

Coding of this repo is roughly done just to re-produce the paper and experimentation purpose. Needed a code cleanup and opyimization for better use.
Currently this repo produces good quality audio but still it is in WIP, many improvement needed.
Loss curve for F0 is quite high.
I am using raw F0 and energy for train a model, but we can also use normalize F0 and energy for stable training.
Using Postnet for better audio quality.
For more complete and end to end Voice cloning or Text to Speech (TTS) toolbox ⚡ please visit Deepsync Technologies.