enhuiz/vall-e

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2976 stars · 399 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

An unofficial PyTorch implementation of the VALL-E text-to-speech audio language model, built on the EnCodec tokenizer. It provides training scripts for AR and NAR models plus a CLI for synthesizing speech from text with a reference audio sample.

Use cases

• train a VALL-E text-to-speech model on custom audio data
• clone a voice from a short reference wav clip
• synthesize speech from text using an audio language model
• experiment with AR and NAR transformer models for TTS
• quantize audio with EnCodec for neural TTS training

When to choose

• you want to train or fine-tune VALL-E from scratch on your own data
• you need an open-source PyTorch codebase for audio-LM TTS research
• you have a DeepSpeed-compatible GPU setup and want to experiment

When to avoid

• you need a ready-to-use pretrained model with polished demos
• you want production TTS without training infrastructure
• you cannot meet DeepSpeed GPU and CUDA/ROCm compiler requirements
• you need permissively licensed audio decoding (EnCodec is CC-BY-NC 4.0)

Facets

library · maturity experimental

tts machine-learning deep-learning llm-training audio-processing cli speech-processing machine-learning deep-learning python cli vall-e text-to-speech audio-language-model pytorch encodec voice-cloning unofficial-implementation audio linux gpu

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