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![GitHub Repo stars](https://img.shields.io/github/stars/FunAudioLLM/InspireMusic) Please support our community by starring it 感谢大家支持 [**Highlights**](#highlights) | [**Introduction**](#introduction) | [**Installation**](#installation) | [**Quick Start**](#quick-start) | [**Tutorial**](https://github.com/FunAudioLLM/InspireMusic#tutorial) | [**Models**](#model-zoo) | [**Contact**](#contact) --- ## Highlights **InspireMusic** focuses on music generation, song generation, and audio generation. - A unified toolkit designed for music, song, and audio generation. - Music generation tasks with high audio quality. - Long-form music generation. ## Introduction > [!Note] > This repo contains the algorithm infrastructure and some simple examples. Currently only support English text prompts. > [!Tip] > To preview the performance, please refer to [InspireMusic Demo Page](https://funaudiollm.github.io/inspiremusic). InspireMusic is a unified music, song, and audio generation framework through the audio tokenization integrated with autoregressive transformer and flow-matching based model. The original motive of this toolkit is to empower the common users to innovate soundscapes and enhance euphony in research through music, song, and audio crafting. The toolkit provides both training and inference codes for AI-based generative models that create high-quality music. Featuring a unified framework, InspireMusic incorporates audio tokenizers with autoregressive transformer and super-resolution flow-matching modeling, allowing for the controllable generation of music, song, and audio with both text and audio prompts. The toolkit currently supports music generation, will support song generation, audio generation in the future. ## InspireMusic

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Figure 1: An overview of the InspireMusic framework. We introduce InspireMusic, a unified framework for music, song, audio generation capable of producing high-quality long-form audio. InspireMusic consists of the following three key components. Audio Tokenizers convert the raw audio waveform into discrete audio tokens that can be efficiently processed and trained by the autoregressive transformer model. Audio waveform of lower sampling rate has converted to discrete tokens via a high bitrate compression audio tokenizer[1]. Autoregressive Transformer model is based on Qwen2.5[2] as the backbone model and is trained using a next-token prediction approach on both text and audio tokens, enabling it to generate coherent and contextually relevant token sequences. The audio and text tokens are the inputs of an autoregressive model with the next token prediction to generate tokens. Super-Resolution Flow-Matching Model based on flow modeling method, maps the generated tokens to latent features with high-resolution fine-grained acoustic details[3] obtained from a higher sampling rate of audio to ensure the acoustic information flow connected with high fidelity through models. A vocoder then generates the final audio waveform from these enhanced latent features. InspireMusic supports a range of tasks including text-to-music, music continuation, music reconstruction, and music super-resolution.

## Installation ### Clone - Clone the repo ``` sh git clone --recursive https://github.com/FunAudioLLM/InspireMusic.git # If you failed to clone submodule due to network failures, please run the following command until success cd InspireMusic git submodule update --recursive # or you can download the third_party repo Matcha-TTS manually cd third_party && git clone https://github.com/shivammehta25/Matcha-TTS.git ``` ### Install from Source InspireMusic requires Python>=3.8, PyTorch>=2.0.1, flash attention==2.6.2/2.6.3, CUDA>=11.2. You can install the dependencies with the following commands: - Install Conda: please see https://docs.conda.io/en/latest/miniconda.html - Create Conda env: ``` shell conda create -n inspiremusic python=3.8 conda activate inspiremusic cd InspireMusic # pynini is required by WeTextProcessing, use conda to install it as it can be executed on all platforms. conda install -y -c conda-forge pynini==2.1.5 pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com # install flash attention to speedup training pip install flash-attn --no-build-isolation ``` - Install within the package: ```shell cd InspireMusic # You can run to install the packages python setup.py install pip install flash-attn --no-build-isolation ``` We also recommend having `sox` or `ffmpeg` installed, either through your system or Anaconda: ```shell # # Install sox # ubuntu sudo apt-get install sox libsox-dev # centos sudo yum install sox sox-devel # Install ffmpeg # ubuntu sudo apt-get install ffmpeg # centos sudo yum install ffmpeg ``` ### Use Docker Run the following command to build a docker image from Dockerfile provided. ```shell docker build -t inspiremusic . ``` Run the following command to start the docker container in interactive mode. ```shell docker run -ti --gpus all -v .:/workspace/InspireMusic inspiremusic ``` ### Use Docker Compose Run the following command to build a docker compose environment and docker image from the docker-compose.yml file. ```shell docker compose up -d --build ``` Run the following command to attach to the docker container in interactive mode. ```shell docker exec -ti inspire-music bash ``` ### Quick Start Here is a quick example inference script for music generation. ``` shell cd InspireMusic mkdir -p pretrained_models # Download models # ModelScope git clone https://www.modelscope.cn/iic/InspireMusic-1.5B-Long.git pretrained_models/InspireMusic-1.5B-Long # HuggingFace git clone https://huggingface.co/FunAudioLLM/InspireMusic-1.5B-Long.git pretrained_models/InspireMusic-1.5B-Long cd examples/music_generation # run a quick inference example sh infer_1.5b_long.sh ``` Here is a quick start running script to run music generation task including data preparation pipeline, model training, inference. ``` shell cd InspireMusic/examples/music_generation/ sh run.sh ``` ### One-line Inference #### Text-to-music Task One-line Shell script for text-to-music task. ``` shell cd examples/music_generation # with flow matching, use one-line command to get a quick try python -m inspiremusic.cli.inference # custom the config like the following one-line command python -m inspiremusic.cli.inference --task text-to-music -m "InspireMusic-1.5B-Long" -g 0 -t "Experience soothing and sensual instrumental jazz with a touch of Bossa Nova, perfect for a relaxing restaurant or spa ambiance." -c intro -s 0.0 -e 30.0 -r "exp/inspiremusic" -o output -f wav # without flow matching, use one-line command to get a quick try python -m inspiremusic.cli.inference --task text-to-music -g 0 -t "Experience soothing and sensual instrumental jazz with a touch of Bossa Nova, perfect for a relaxing restaurant or spa ambiance." --fast True ``` Alternatively, you can run the inference with just a few lines of Python code. ```python from inspiremusic.cli.inference import InspireMusic from inspiremusic.cli.inference import env_variables if __name__ == "__main__": env_variables() model = InspireMusic(model_name = "InspireMusic-Base") model.inference("text-to-music", "Experience soothing and sensual instrumental jazz with a touch of Bossa Nova, perfect for a relaxing restaurant or spa ambiance.") ``` #### Music Continuation Task One-line Shell script for music continuation task. ```shell cd examples/music_generation # with flow matching python -m inspiremusic.cli.inference --task continuation -g 0 -a audio_prompt.wav # without flow matching python -m inspiremusic.cli.inference --task continuation -g 0 -a audio_prompt.wav --fast True ``` Alternatively, you can run the inference with just a few lines of Python code. ```python from inspiremusic.cli.inference import InspireMusic from inspiremusic.cli.inference import env_variables if __name__ == "__main__": env_variables() model = InspireMusic(model_name = "InspireMusic-Base") # just use audio prompt model.inference("continuation", None, "audio_prompt.wav") # use both text prompt and audio prompt model.inference("continuation", "Continue to generate jazz music.", "audio_prompt.wav") ``` ## Models ### Download Models You may download our pretrained InspireMusic models for music generation. ```shell # use git to download models，please make sure git lfs is installed. mkdir -p pretrained_models git clone https://www.modelscope.cn/iic/InspireMusic.git pretrained_models/InspireMusic ``` ### Available Models Currently, we open source the music generation models support 24KHz mono and 48KHz stereo audio. The table below presents the links to the ModelScope and Huggingface model hub. | Model name | Model Links | Remarks | |--------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------| | InspireMusic-Base-24kHz | [![model](https://img.shields.io/badge/ModelScope-Model-green.svg)](https://modelscope.cn/models/iic/InspireMusic-Base-24kHz/summary) [![model](https://img.shields.io/badge/HuggingFace-Model-green.svg)](https://huggingface.co/FunAudioLLM/InspireMusic-Base-24kHz) | Pre-trained Music Generation Model, 24kHz mono, 30s | | InspireMusic-Base | [![model](https://img.shields.io/badge/ModelScope-Model-green.svg)](https://modelscope.cn/models/iic/InspireMusic/summary) [![model](https://img.shields.io/badge/HuggingFace-Model-green.svg)](https://huggingface.co/FunAudioLLM/InspireMusic-Base) | Pre-trained Music Generation Model, 48kHz, 30s | | InspireMusic-1.5B-24kHz | [![model](https://img.shields.io/badge/ModelScope-Model-green.svg)](https://modelscope.cn/models/iic/InspireMusic-1.5B-24kHz/summary) [![model](https://img.shields.io/badge/HuggingFace-Model-green.svg)](https://huggingface.co/FunAudioLLM/InspireMusic-1.5B-24kHz) | Pre-trained Music Generation 1.5B Model, 24kHz mono, 30s | | InspireMusic-1.5B | [![model](https://img.shields.io/badge/ModelScope-Model-green.svg)](https://modelscope.cn/models/iic/InspireMusic-1.5B/summary) [![model](https://img.shields.io/badge/HuggingFace-Model-green.svg)](https://huggingface.co/FunAudioLLM/InspireMusic-1.5B) | Pre-trained Music Generation 1.5B Model, 48kHz, 30s | | InspireMusic-1.5B-Long | [![model](https://img.shields.io/badge/ModelScope-Model-green.svg)](https://modelscope.cn/models/iic/InspireMusic-1.5B-Long/summary) [![model](https://img.shields.io/badge/HuggingFace-Model-green.svg)](https://huggingface.co/FunAudioLLM/InspireMusic-1.5B-Long) | Pre-trained Music Generation 1.5B Model, 48kHz, support long-form music generation up to several minutes | | InspireSong-1.5B | [![model](https://img.shields.io/badge/ModelScope-Model-lightgrey.svg)]() [![model](https://img.shields.io/badge/HuggingFace-Model-lightgrey.svg)]() | Pre-trained Song Generation 1.5B Model, 48kHz stereo | | InspireAudio-1.5B | [![model](https://img.shields.io/badge/ModelScope-Model-lightgrey.svg)]() [![model](https://img.shields.io/badge/HuggingFace-Model-lightgrey.svg)]() | Pre-trained Audio Generation 1.5B Model, 48kHz stereo | | Wavtokenizer[[1]](https://openreview.net/forum?id=yBlVlS2Fd9) (75Hz) | [![model](https://img.shields.io/badge/ModelScope-Model-green.svg)](https://modelscope.cn/models/iic/InspireMusic-1.5B-Long/file/view/master?fileName=wavtokenizer%252Fmodel.pt) [![model](https://img.shields.io/badge/HuggingFace-Model-green.svg)](https://huggingface.co/FunAudioLLM/InspireMusic-1.5B-Long/tree/main/wavtokenizer) | An extreme low bitrate audio tokenizer for music with one codebook at 24kHz audio. | | Music_tokenizer (75Hz) | [![model](https://img.shields.io/badge/ModelScope-Model-green.svg)](https://modelscope.cn/models/iic/InspireMusic-1.5B-24kHz/file/view/master?fileName=music_tokenizer%252Fmodel.pt) [![model](https://img.shields.io/badge/HuggingFace-Model-green.svg)](https://huggingface.co/FunAudioLLM/InspireMusic-1.5B-24kHz/tree/main/music_tokenizer) | A music tokenizer based on HifiCodec[3] at 24kHz audio. | | Music_tokenizer (150Hz) | [![model](https://img.shields.io/badge/ModelScope-Model-green.svg)](https://modelscope.cn/models/iic/InspireMusic-1.5B-Long/file/view/master?fileName=music_tokenizer%252Fmodel.pt) [![model](https://img.shields.io/badge/HuggingFace-Model-green.svg)](https://huggingface.co/FunAudioLLM/InspireMusic-1.5B-Long/tree/main/music_tokenizer) | A music tokenizer based on HifiCodec[3] at 48kHz audio. | ## Basic Usage At the moment, InspireMusic contains the training and inference codes for [music generation](https://github.com/FunAudioLLM/InspireMusic/tree/main/examples/music_generation). ### Training Here is an example to train LLM model, support BF16/FP16 training. ```shell torchrun --nnodes=1 --nproc_per_node=8 \ --rdzv_id=1024 --rdzv_backend="c10d" --rdzv_endpoint="localhost:0" \ inspiremusic/bin/train.py \ --train_engine "torch_ddp" \ --config conf/inspiremusic.yaml \ --train_data data/train.data.list \ --cv_data data/dev.data.list \ --model llm \ --model_dir `pwd`/exp/music_generation/llm/ \ --tensorboard_dir `pwd`/tensorboard/music_generation/llm/ \ --ddp.dist_backend "nccl" \ --num_workers 8 \ --prefetch 100 \ --pin_memory \ --deepspeed_config ./conf/ds_stage2.json \ --deepspeed.save_states model+optimizer \ --fp16 ``` Here is an example code to train flow matching model, does not support FP16 training. ```shell torchrun --nnodes=1 --nproc_per_node=8 \ --rdzv_id=1024 --rdzv_backend="c10d" --rdzv_endpoint="localhost:0" \ inspiremusic/bin/train.py \ --train_engine "torch_ddp" \ --config conf/inspiremusic.yaml \ --train_data data/train.data.list \ --cv_data data/dev.data.list \ --model flow \ --model_dir `pwd`/exp/music_generation/flow/ \ --tensorboard_dir `pwd`/tensorboard/music_generation/flow/ \ --ddp.dist_backend "nccl" \ --num_workers 8 \ --prefetch 100 \ --pin_memory \ --deepspeed_config ./conf/ds_stage2.json \ --deepspeed.save_states model+optimizer ``` ### Inference Here is an example script to quickly do model inference. ```shell cd InspireMusic/examples/music_generation/ sh infer.sh ``` Here is an example code to run inference with normal mode, i.e., with flow matching model for text-to-music and music continuation tasks. ```shell pretrained_model_dir = "pretrained_models/InspireMusic/" for task in 'text-to-music' 'continuation'; do python inspiremusic/bin/inference.py --task $task \ --gpu 0 \ --config conf/inspiremusic.yaml \ --prompt_data data/test/parquet/data.list \ --flow_model $pretrained_model_dir/flow.pt \ --llm_model $pretrained_model_dir/llm.pt \ --music_tokenizer $pretrained_model_dir/music_tokenizer \ --wavtokenizer $pretrained_model_dir/wavtokenizer \ --result_dir `pwd`/exp/inspiremusic/${task}_test \ --chorus verse done ``` Here is an example code to run inference with fast mode, i.e., without flow matching model for text-to-music and music continuation tasks. ```shell pretrained_model_dir = "pretrained_models/InspireMusic/" for task in 'text-to-music' 'continuation'; do python inspiremusic/bin/inference.py --task $task \ --gpu 0 \ --config conf/inspiremusic.yaml \ --prompt_data data/test/parquet/data.list \ --flow_model $pretrained_model_dir/flow.pt \ --llm_model $pretrained_model_dir/llm.pt \ --music_tokenizer $pretrained_model_dir/music_tokenizer \ --wavtokenizer $pretrained_model_dir/wavtokenizer \ --result_dir `pwd`/exp/inspiremusic/${task}_test \ --chorus verse \ --fast done ``` ### Hardware requirements Previous test on H800 GPU, InspireMusic could generate 30 seconds audio with real-time factor (RTF) around 1.6~1.8. For normal mode, we recommend using hardware with at least 24GB of GPU memory for better experience. For fast mode, 12GB GPU memory is enough. ## Citation ```bibtex @misc{InspireMusic2025, title={InspireMusic: Integrating Super Resolution and Large Language Model for High-Fidelity Long-Form Music Generation}, author={Chong Zhang and Yukun Ma and Qian Chen and Wen Wang and Shengkui Zhao and Zexu Pan and Hao Wang and Chongjia Ni and Trung Hieu Nguyen and Kun Zhou and Yidi Jiang and Chaohong Tan and Zhifu Gao and Zhihao Du and Bin Ma}, year={2025}, eprint={2503.00084}, archivePrefix={arXiv}, primaryClass={cs.SD}, url={https://arxiv.org/abs/2503.00084}, } ``` --- ## Disclaimer The content provided above is for research purposes only and is intended to demonstrate technical capabilities. Some examples are sourced from the internet. If any content infringes on your rights, please contact us to request its removal.