coqui-ai/tts

Coqui TTS is a deep learning toolkit that converts written text into spoken audio, the technology behind voice assistants and audiobook narration. The problem it addresses is that building a high-quality text-to-speech system from scratch requires significant AI research expertise; Coqui TTS packages up many of the best published research models and makes them usable with a few lines of Python code. You can use it to generate realistic speech in over 1,100 languages using pre-trained models, or train and fine-tune models on your own voice data. The library implements a pipeline with two main stages: first, a spectrogram model converts text into an intermediate representation called a mel-spectrogram (a visual map of the frequency content of the audio over time), and then a vocoder model converts that spectrogram into actual waveform audio. The toolkit includes implementations of many well-known academic model architectures such as Tacotron2, Glow-TTS, VITS, and XTTS, as well as vocoders like MelGAN and HiFiGAN. A key feature called multi-speaker TTS allows a single model to produce speech in different voices, and voice cloning lets you generate speech that sounds like a specific person given a short audio sample. The XTTS model mentioned in the README supports streaming output with low latency, making it viable for real-time applications. You would use Coqui TTS when building any application that needs to speak, accessibility tools, interactive voice responses, virtual assistants, language learning apps, or content creation pipelines. The entire toolkit is written in Python and uses PyTorch as its deep learning runtime. Models are available through pip and can run on a CPU or GPU, with GPU strongly recommended for fast inference and training.