Coqui TTS

Converts text input to natural-sounding speech across 1100+ languages using a modular TTS pipeline that chains text processing, acoustic modeling, and vocoding stages. The system uses a unified BaseTTS class hierarchy supporting multiple model architectures (VITS, Tacotron, Glow-TTS, FastPitch) with language-specific text processors that handle phoneme conversion, grapheme normalization, and sentence segmentation before feeding spectrograms to neural vocoders for waveform generation.

Enables synthesis of speech in a target speaker's voice by encoding reference audio samples through a speaker encoder network that extracts speaker embeddings, which are then injected into the TTS model's decoder during inference. The system supports both speaker-conditional models (VITS, Tacotron2) that accept speaker embeddings as conditioning input and fine-tuning of speaker encoders on custom speaker datasets to improve voice similarity for out-of-distribution speakers.

Enables synthesis of speech from multiple speakers using speaker-conditional TTS models (VITS, Tacotron2) that accept speaker embeddings or speaker IDs as conditioning input during inference. The system supports both discrete speaker IDs (for models trained on multi-speaker datasets) and continuous speaker embeddings (from speaker encoders), allowing users to generate speech in any speaker's voice by providing either a speaker ID or reference audio; the Synthesizer class handles speaker embedding extraction and injection transparently.

Supports streaming synthesis where audio is generated and returned in chunks rather than waiting for the entire synthesis to complete, enabling real-time TTS applications. The system processes text in sentence-length chunks, generates spectrograms incrementally, and streams audio chunks to the client as they become available; this reduces latency for long-form synthesis and enables interactive applications like voice assistants that need to start playing audio before synthesis completes.

Converts text to phoneme sequences using language-specific phoneme inventories and grapheme-to-phoneme (G2P) conversion rules. The system supports multiple phoneme sets (IPA, language-specific phoneme sets) and uses rule-based or neural G2P models to convert text to phonemes. Phoneme sequences are then used as input to TTS models instead of raw text, improving pronunciation accuracy.

Provides a pluggable model architecture system where users select from multiple TTS model families (VITS, Tacotron, Glow-TTS, FastPitch, FastSpeech) through a configuration-driven approach. Each architecture inherits from BaseTTS and is instantiated via a config object (e.g., VitsConfig, Tacotron2Config) that specifies hyperparameters, layer counts, and training objectives; the ModelManager loads pre-trained weights and configs from a .models.json catalog, and the Synthesizer transparently handles architecture-specific inference logic.

Supports training TTS models on custom datasets through a modular training system that loads pre-trained model checkpoints and continues training on user-provided audio/text pairs. The training pipeline includes data loading via PyTorch DataLoaders with custom samplers, loss computation specific to each model architecture, gradient-based optimization, and checkpoint management; users can fine-tune entire models or specific components (e.g., speaker encoder only) by selectively freezing layers and adjusting learning rates.

Normalizes and converts input text to phoneme sequences using language-specific text processors that handle grapheme-to-phoneme conversion, number/date expansion, abbreviation resolution, and sentence segmentation. The system maintains a registry of language-specific processors (e.g., EnglishProcessor, Mandarin Processor) that inherit from a BaseProcessor class and apply rules like converting '123' to 'one hundred twenty-three' and splitting long text into sentences to prevent acoustic artifacts from long sequences.

Converts acoustic spectrograms (mel-spectrograms or linear spectrograms) generated by TTS models into raw audio waveforms using neural vocoder models (HiFi-GAN, Glow-TTS vocoder, WaveGlow). The vocoder inference pipeline loads pre-trained vocoder checkpoints, applies spectral normalization/denormalization to match training conditions, and runs the vocoder network to produce high-quality audio; the system supports multiple vocoder architectures and automatically selects compatible vocoders for each TTS model.

Maintains a .models.json catalog of pre-trained TTS and vocoder models with metadata (architecture, language, dataset, download URL) and provides a ModelManager class that lists available models, downloads them on-demand from remote repositories, caches them locally, and automatically loads model configurations and weights. Users specify models via strings like 'tts_models/en/ljspeech/vits' which are resolved to download URLs and cached in ~/.local/share/tts_models/ for offline reuse.

Provides a tts command-line tool (implemented in TTS/bin/synthesize.py) that enables text-to-speech synthesis, model listing, and model downloading without writing Python code. The CLI supports reading text from files or stdin, specifying model/speaker/language via flags, and writing output to audio files; it also includes subcommands for listing available models, downloading models, and running a TTS server for HTTP-based synthesis.

Provides a training pipeline for speaker encoder networks that learn to extract speaker embeddings from audio samples, enabling zero-shot speaker adaptation. The training system loads speaker datasets, computes speaker embeddings via the encoder, applies speaker-specific loss functions (e.g., speaker verification losses), and optimizes the encoder to produce discriminative speaker representations that generalize to unseen speakers. Users can fine-tune pre-trained speaker encoders on custom speaker datasets to improve voice cloning quality.

Supports inference-time optimizations including model quantization (converting float32 weights to int8 or float16) and layer pruning to reduce model size and latency. The system provides utilities for converting pre-trained models to quantized formats compatible with PyTorch's quantization API, enabling faster inference on CPU and edge devices; users can trade off audio quality for speed by selecting quantized model variants.