TTS

This project is a deep learning text-to-speech toolkit used for training and deploying neural speech synthesis models. It provides a comprehensive framework for converting written text into spoken audio, utilizing neural vocoders to transform synthesized spectrograms into high-fidelity audio waveforms.

The toolkit includes a voice cloning system that replicates specific human voices by extracting speaker embeddings from short audio samples. It also supports multi-speaker audio synthesis, allowing the generation of speech across different vocal identities using specialized model architectures.

The system covers the full speech synthesis pipeline, including tools for speech dataset curation, custom model training with performance tracking, and a command-line interface for audio generation. For network access, it provides a self-hosted HTTP server to deploy speech synthesis models as an API.

Features

Neural Text-to-Speech Engines - Offers a comprehensive deep learning toolkit for training and deploying neural text-to-speech engines.

Text-to-Speech - Provides a comprehensive framework for synthesizing high-fidelity human speech from text input using deep learning.

Custom Model Training - Provides a framework for developing and training custom speech synthesis models with performance tracking.

Phonetic Text Analysis - Implements neural processing to transform written text into linguistic representations before acoustic feature generation.

Speech Model Fine-Tuning - Includes a framework for fine-tuning and training custom speech models with integrated logging.

Multi-Speaker Synthesis - Utilizes specialized model weights and speaker IDs to support diverse vocal identities within a single network.

Speaker Embeddings - Extracts speaker embeddings from audio samples to condition the synthesis model on specific vocal characteristics.

Voice Cloning - Replicates specific human voices by extracting speaker embeddings from short audio samples.

Neural Vocoders - Includes neural vocoders that transform synthesized spectrograms into high-fidelity time-domain audio waveforms.

Web-Based Model Deployment - Provides a self-hosted HTTP server to deploy speech synthesis models as a network-accessible API.

Curation Utilities - Includes tools for preparing and cleaning text-to-speech datasets to ensure high quality for model training.

Dataset Curation Tools - Provides tools to prepare and clean text-to-speech datasets to ensure high quality for model training.

Synthesis API Endpoints - Runs pre-trained synthesis models as an HTTP server to provide audio generation over a network.

Self-Hosted Synthesis Servers - Provides a self-hosted HTTP server to deploy speech synthesis models as an API.

Voice Identity Conversions - Enables matching the vocal characteristics of source audio files to target speaker identities.

CLI Speech Generators - Ships a command-line interface for generating audio files from text using pre-trained speech models.

Acoustic Model Pipelines - Implements a two-stage pipeline that renders text into spectrograms before passing them to a vocoder.

Generative Media Tools - Deep learning toolkit for text-to-speech.

Acoustic User Interface - Deep learning toolkit for text-to-speech generation.

Media and Communication - Deep learning toolkit for text-to-speech.

Text To Speech - Deep learning toolkit for research and production speech synthesis.

coqui-aiTTS

Features

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