Create README.md

README.md

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+---
+language:
+- en
+- pl
+- ar
+- es
+- hi
+- de
+tags:
+- TTS
+---
+# SlopTTS
+
+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
+Configs/                          Configuration files
+Data/                             Dataset-related resources
+Modules/                          Model components
+
+train_codec_hybrid_temporal.py    Codec training
+train_codec_mel_speaker.py        Mel + speaker codec training
+train_codec_speaker.py            Speaker codec training
+
+train_duration_predictor_context.py
+                                  Duration prediction training
+
+train_fsq_flow_convnext.py        Flow model training
+
+train_predictors_speaker_flow_context_temporal.py
+                                  Predictor training
+
+infer_sloptts.py                  Main TTS inference
+infer_fsq_flow.py                 Flow inference
+
+models.py                         Core model definitions
+models_speaker.py                 Speaker modules
+models_mel_speaker.py             Mel-speaker models
+
+losses.py                         Training losses
+optimizers.py                     Optimizer utilities
+utils.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:
+
+1. Codec training
+2. Speaker-aware representation learning
+3. Duration prediction
+4. Flow model training
+5. Predictor training
+6. End-to-end synthesis evaluation
+
+Individual training scripts are provided for each stage.
+
+Example:
+
+```bash
+python train_codec_hybrid_temporal.py
+```
+
+or
+
+```bash
+python train_fsq_flow_convnext.py
+```
+
+Configuration files can be adjusted according to dataset size, hardware resources, and training objectives.
+
+## Inference
+
+To generate speech:
+
+```bash
+python infer_sloptts.py
+```
+
+Additional flow-based inference utilities are available:
+
+```bash
+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.