--- id: tts version: "0.22.0" license: MPL-2.0 license_treatment: copyleft maintenance: dormant --- # TTS — Deep learning for Text to Speech by Coqui. License: copyleft · Maintenance: dormant · Downloads: 108.1K/mo ## What it is and what it does TTS is a PyTorch-based library for neural text-to-speech synthesis that provides pretrained models and architectures including Tacotron2, Glow-TTS, VITS, XTTS, Bark, and Tortoise. It handles the full pipeline from text input to audio output, supporting both inference with released models and training custom models on new datasets. The library depends on a substantial stack of 39 runtime packages: torch, torchaudio, scipy, librosa, scikit-learn, and language-specific tools including jieba, g2pkk, bangla, jamo, hangul-romanize, gruut, and pysbd. It includes vocoder models (MelGAN, HiFiGAN, ParallelWaveGAN) to convert spectrograms to waveforms, speaker encoders for multi-speaker synthesis, and utilities for dataset curation. Installation is straightforward via pip, though the dependency footprint and model downloads make it a medium-friction package. Use it for: - Generate speech from text in applications requiring voice output, using pretrained models without training. - Train custom TTS models on proprietary voice datasets for domain-specific or branded voice synthesis. - Implement voice cloning by fine-tuning existing models with speaker-specific audio samples. - Build multilingual speech synthesis pipelines with language-specific models and tools. - Integrate TTS into web services or applications via the library's Flask-based server for synthesis. ## Worth the install? AI-flagged interpretation of the facts above — verify before relying. TTS is a deep learning library for text-to-speech synthesis that generates spoken audio from text using pretrained models across multiple languages, with support for model training and fine-tuning. Yes, with conditions. Install if you need neural TTS synthesis and can accept the MPL-2.0 copyleft constraint and substantial dependency footprint. The library has recent commits and is in active use, but has not released since 2023-12-12. Avoid if you require active maintenance guarantees or proprietary licensing flexibility. ## Install pip install tts uv add tts poetry add tts ## Installing TTS Before you install: Medium install friction due to 39 runtime dependencies including torch, torchaudio, scipy, and librosa. The project is dormant with last commit on 2024-08-16. Prebuilt wheels available for Python 3.9–3.11 on Linux x86_64 reduce friction for standard environments. License in practice: Licensed under MPL-2.0, a copyleft license requiring derivative works to be distributed under the same license and disclosing source code modifications. This affects proprietary deployments. Quickstart: pip install TTS from TTS.api import TTS tts = TTS(model_name="tts_models/en/ljspeech/glow-tts", gpu=False) tts.tts_to_file(text="Hello world", file_path="output.wav") Requires Python >=3.9.0, <3.12. Large model downloads on first use; GPU recommended for inference speed but CPU inference supported. Verify before relying: - Whether dormant status indicates active maintenance or abandonment despite recent commits. - Performance characteristics and inference latency for different model architectures. - Memory requirements for different pretrained models during inference and training. - Actual number of supported languages and models available in the pretrained collection. ## Package facts - License: MPL-2.0 (copyleft) - Python support: capped_below_current - Install friction: medium - Maintenance: dormant - Downloads: 108.1K/month (top 15,000 on PyPI) - Known vulnerabilities: none known ## Tags text to speech synthesis, neural TTS models, voice generation library, multilingual speech synthesis, TTS model training, speech synthesis deep learning, voice cloning TTS, speech-synthesis, deep-learning, voice-generation [View on SkillFed](https://skillfed.io/packages/tts) · [View on PyPI](https://pypi.org/project/tts/)