index-tts-lora: High-Quality Speech Synthesis with LoRA Fine-tuning

index-tts-lora: High-Quality Speech Synthesis with LoRA Fine-tuning

Summary

index-tts-lora offers a robust solution for high-quality speech synthesis, leveraging LoRA fine-tuning on the index-tts framework. It significantly enhances prosody and naturalness for both single and multi-speaker voices. This project provides practical methods for training and inference, making advanced voice synthesis more accessible.

Repository Info

Updated on March 23, 2026
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Tags

PythonSpeech SynthesisTTSLoRAFine-tuningAIMachine LearningAudio Processing

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Introduction

The index-tts-lora project, built upon Bilibili's index-tts, provides a powerful solution for enhancing speech synthesis. It focuses on applying LoRA (Low-Rank Adaptation) fine-tuning to achieve superior prosody and naturalness in generated audio. This repository supports both single-speaker and multi-speaker setups, making it versatile for various voice synthesis applications.

Installation and Usage

To get started with index-tts-lora, follow these steps for audio processing, training, and inference.

1. Audio token and speaker condition extraction

First, extract audio tokens and speaker conditions from your audio list.

# Extract tokens and speaker conditions
python tools/extract_codec.py --audio_list ${audio_list} --extract_condition

# audio_list format: audio_path + transcript, separated by \t
/path/to/audio.wav ?????????????????????????????

After extraction, processed files and speaker_info.json will be generated under the finetune_data/processed_data/ directory.

2. Training

Initiate the training process using the provided script.

python train.py

3. Inference

Once trained, you can perform inference to generate speech.

python indextts/infer.py

Fine-tuning Results and Examples

The project demonstrates impressive fine-tuning results using Chinese audio data from Kai Shu Tells Stories. With approximately 30 minutes of audio and 270 audio clips, index-tts-lora shows significant improvements in speech quality. The dataset was split into 244 training samples and 26 validation samples.

Here are some speech synthesis examples:

Text Audio
??????????????????????????????????????????????? kaishu_cn_1.wav
?????????????????????????????????????????????? kaishu_cn_2.wav
??Java????????M??????????????????Java Script?????????????? kaishu_cn_en_mix_1.wav
?? financial report ??????????????? revenue performance ? expenditure trends? kaishu_cn_en_mix_2.wav
???????????????????????????????????????????????????? kaishu_raokouling.wav
A thin man lies against the side of the street with his shirt and a shoe off and bags nearby. kaishu_en_1.wav
As research continued, the protective effect of fluoride against dental decay was demonstrated. kaishu_en_2.wav

Model Evaluation

Model Evaluation Image

Why Use index-tts-lora?

Developers and researchers looking to achieve high-quality, natural-sounding speech synthesis will find index-tts-lora particularly useful. Its LoRA fine-tuning approach allows for efficient adaptation to specific voices, enhancing prosody and overall naturalness with relatively small datasets. The support for both single and multi-speaker scenarios makes it a flexible tool for diverse TTS projects.

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