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

This repository profile is provided by osrepos.com, an open source repository discovery platform.

Source: osrepos.com
Repository profile: https://osrepos.com/repo/asr-pub-index-tts-lora
Generated for open source discovery and AI-assisted research.

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.

GitHub: https://github.com/asr-pub/index-tts-lora
OSRepos URL: https://osrepos.com/repo/asr-pub-index-tts-lora

## 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.

## Topics

- Python
- Speech Synthesis
- TTS
- LoRA
- Fine-tuning
- AI
- Machine Learning
- Audio Processing

## Repository Information

Last analyzed by OSRepos: Mon Mar 23 2026 08:37:04 GMT+0000 (Western European Standard Time)
Detail views: 4
GitHub clicks: 2

## Safety Notice

OSRepos shares public repositories for knowledge and discovery only. Review source code, dependencies, licenses, and security implications before running or installing anything.

## Content

## Introduction
The `index-tts-lora` project, built upon Bilibili's [index-tts](https://github.com/index-tts/index-tts "index-tts" target="_blank"), 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.

shell
# 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.

shell
python train.py


### 3. Inference
Once trained, you can perform inference to generate speech.

shell
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](https://github.com/user-attachments/files/22354649/kaishu_cn_1.wav "kaishu_cn_1.wav" target="_blank") |
| ?????????????????????????????????????????????? | [kaishu_cn_2.wav](https://github.com/user-attachments/files/22354652/kaishu_cn_2.wav "kaishu_cn_2.wav" target="_blank") |
| ??Java????????M??????????????????Java Script?????????????? | [kaishu_cn_en_mix_1.wav](https://github.com/user-attachments/files/22354654/kaishu_cn_en_mix_1.wav "kaishu_cn_en_mix_1.wav" target="_blank") |
| ?? financial report ??????????????? revenue performance ? expenditure trends? | [kaishu_cn_en_mix_2.wav](https://github.com/user-attachments/files/22354656/kaishu_cn_en_mix_2.wav "kaishu_cn_en_mix_2.wav" target="_blank") |
| ???????????????????????????????????????????????????? | [kaishu_raokouling.wav](https://github.com/user-attachments/files/22354658/kaishu_raokouling.wav "kaishu_raokouling.wav" target="_blank") |
| A thin man lies against the side of the street with his shirt and a shoe off and bags nearby. | [kaishu_en_1.wav](https://github.com/user-attachments/files/22354659/kaishu_en_1.wav "kaishu_en_1.wav" target="_blank") |
| As research continued, the protective effect of fluoride against dental decay was demonstrated. | [kaishu_en_2.wav](https://github.com/user-attachments/files/22354661/kaishu_en_2.wav "kaishu_en_2.wav" target="_blank") |

### Model Evaluation
![Model Evaluation Image](https://github.com/user-attachments/assets/fb86938d-95d9-4b10-9588-2de1e43b51d1 "Model Evaluation" target="_blank")

## 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.

## Links
*   **GitHub Repository:** [https://github.com/asr-pub/index-tts-lora](https://github.com/asr-pub/index-tts-lora "index-tts-lora GitHub" target="_blank")
*   **Original index-tts project:** [https://github.com/index-tts/index-tts](https://github.com/index-tts/index-tts "index-tts GitHub" target="_blank")
*   **finetune-index-tts:** [https://github.com/yrom/finetune-index-tts](https://github.com/yrom/finetune-index-tts "finetune-index-tts GitHub" target="_blank")