SlopTTS is an experimental text-to-speech system designed around a custom FSQ-based speech codec and flow-based generation pipeline. The repository contains training and inference code for speech synthesis, codec training, duration modeling, speaker conditioning, and latent flow modeling.
The project is intended for research and experimentation in neural speech synthesis and speech representation learning.
Features
Custom FSQ speech codec pipeline
Flow-based latent generation
Speaker-aware synthesis components
Duration prediction modules
End-to-end training scripts
Inference utilities for speech generation
Modular architecture for experimentation and research
Repository Structure
text
1Configs/ Configuration files
2Data/ Dataset-related resources
3Modules/ Model components
45train_codec_hybrid_temporal.py Codec training
6train_codec_mel_speaker.py Mel + speaker codec training
7train_codec_speaker.py Speaker codec training
89train_duration_predictor_context.py
10 Duration prediction training
1112train_fsq_flow_convnext.py Flow model training
1314train_predictors_speaker_flow_context_temporal.py
15 Predictor training
1617infer_sloptts.py Main TTS inference
18infer_fsq_flow.py Flow inference
1920models.py Core model definitions
21models_speaker.py Speaker modules
22models_mel_speaker.py Mel-speaker models
2324losses.py Training losses
25optimizers.py Optimizer utilities
26utils.py Helper functions
Project Overview
SlopTTS follows a multi-stage speech generation pipeline. Text is processed into intermediate representations, which are transformed through duration and contextual prediction modules. A custom FSQ-based codec is used to represent speech efficiently, while flow-based models generate coherent latent representations for speech reconstruction.
The repository separates codec training, predictor training, and speech generation into independent stages, allowing each component to be improved or replaced individually.
Training
Training is organized into multiple stages:
Codec training
Speaker-aware representation learning
Duration prediction
Flow model training
Predictor training
End-to-end synthesis evaluation
Individual training scripts are provided for each stage.
Example:
python train_codec_hybrid_temporal.py
or
python train_fsq_flow_convnext.py
Configuration files can be adjusted according to dataset size, hardware resources, and training objectives.
Inference
To generate speech:
python infer_sloptts.py
Additional flow-based inference utilities are available:
python infer_fsq_flow.py
Refer to the configuration files for model paths and runtime settings.
Requirements
Recommended:
Python 3.10+
PyTorch
CUDA-capable GPU
NumPy
SciPy
Additional dependencies may be listed in the project environment configuration.
Current Status
This repository is actively developed and may contain experimental components. Interfaces, training procedures, and model architectures can change between versions.
Intended Use
SlopTTS is intended for:
Speech synthesis research
Codec-based TTS experiments
Representation learning research
Multilingual speech generation studies
Custom voice and speaker modeling research
Disclaimer
This project is provided for research and educational purposes. Generated speech quality depends on the training data, configuration, and model checkpoints used.
