音声
PaddleSpeech
Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text…
★ 12.7k スターApache-2.0
ガイドを見る →vitsは、jaywalnut310が公開しているオープンソースの音声プロジェクトです（GitHub: jaywalnut310/vits）。GitHubスターは7,893、フォークは1,378で、MITライセンスの下で公開されています。GitHubでの最終更新は2023年12月6日です。
VITS — the original end-to-end text-to-speech model: a conditional VAE with adversarial learning, in PyTorch.
Decide whether you will train on LJ Speech, multi-speaker VCTK, or your own dataset (which needs the preprocessing/g2p step first).
Clone jaywalnut310/vits from GitHub (or use the install path in the run steps below) and skim its README for the parts relevant to your goal.
Clone the repo, install its Python requirements, and build the Cython monotonic alignment search extension as the README's prerequisites describe.
Run the provided inference example with a pretrained setup to hear output, then start a short training run on one dataset.
A single conditional VAE with adversarial learning generates speech from text end to end, the approach the VITS paper introduced.
Includes the alignment search component (with a Cython build) that aligns text and speech during training.
Training examples cover LJ Speech (single speaker) and VCTK (multi-speaker with speaker IDs).
A preprocess script converts your file lists to phonemes (g2p), with pre-processed phonemes already provided for LJ Speech and VCTK.
The README provides concrete training commands per dataset and an inference example to synthesise speech.
The canonical implementation researchers cite and fork as the base for later TTS work.
Get the source from the official GitHub repository for vits.
git clone https://github.com/jaywalnut310/vits.git
cd vitsAfter cloning and installing requirements, build the Cython monotonic alignment search per the README prerequisites.
# cd monotonic_align && python setup.py build_ext --inplace — see README pre-requisitesPreprocess your file lists, then run the training example for your dataset (LJ Speech or VCTK) from the README.
python preprocess.py --text_index 1 --filelists filelists/ljs_audio_text_train_filelist.txt # then run the dataset's training example — see READMEjaywalnut310 and co-authors, through the jaywalnut310/vits repository, with an online demo page linked from the repo.
Yes — the project is open source under the MIT license as recorded for this listing. You can use, modify and distribute it under the MIT terms.
No API key or paid service is involved — it is local PyTorch training and inference; you need a GPU and training data for good results.
A hosted API gives instant voices with no training, billed per character. VITS gives you the model itself to train on your own data and run locally — full control and no per-use fees, but you do the training work.
Clone the repository (or use the documented installer), follow the install and configure steps on this page, and run the smallest real task first — run the provided inference example with a pretrained setup to hear output, then start a short training run on one dataset.
Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text…
Human-level text-to-speech through style diffusion and adversarial training with speech language models.
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A generative speech model built for daily dialogue — natural, conversational text-to-speech
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LibreAIカタログに掲載された音声プロジェクトの中で最もスターの多いもの — vitsが合わなかった場合に検討したい、現実的な候補リストです。
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Robust Speech Recognition via Large-Scale Weak Supervision
1 min voice data can also be used to train a good TTS model! (few shot voice cloning)
Clone a voice from 5 seconds of audio and generate arbitrary speech in real time
Port of OpenAI's Whisper model in C/C++
カタログの中でvitsとスター数が近い音声プロジェクト — 前後の棚もそのまま見て回れます。
EmotiVoice 😊: a Multi-Voice and Prompt-Controlled TTS Engine
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