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[submodule "third_party/Matcha-TTS"]
path = third_party/Matcha-TTS
url = https://github.com/shivammehta25/Matcha-TTS.git
\ No newline at end of file
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# InspireMusic
支持音乐、歌曲及音频的生成，为用户提供多样化选择。
## 论文
`无`
## 模型结构
InspireMusic基于Qwen模型初始化的自回归Transformer模型预测音频token。
<div align=center>
<img src="./doc/structure.png"/>
</div>
## 算法原理
通过具有高压缩比的WavTokenizer将输入的连续音频特征转换成离散音频token，然后利用基于Qwen模型初始化的自回归Transformer模型预测音频token，再由CFM扩散模型重建音频的潜层特征，最终通过Vocoder输出高质量的音频波形。
<div align=center>
<img src="./doc/algorithm.png"/>
</div>
## 环境配置
```
mv InspireMusic_pytorch InspireMusic # 去框架名后缀
```
### Docker（方法一）
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.3.0-py3.10-dtk24.04.3-ubuntu20.04
# <your IMAGE ID>为以上拉取的docker的镜像ID替换，本镜像为：b272aae8ec72
docker run -it --shm-size=64G -v $PWD/InspireMusic:/home/InspireMusic -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name music <your IMAGE ID> bash
cd /home/InspireMusic
pip install -r requirements.txt
```
### Dockerfile（方法二）
```
cd /home/InspireMusic/docker
docker build --no-cache -t InspireMusic:latest .
docker run --shm-size=64G --name music -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video -v $PWD/../../InspireMusic:/home/InspireMusic -it music bash
# 若遇到Dockerfile启动的方式安装环境需要长时间等待，可注释掉里面的pip安装，启动容器后再安装python库：pip install -r requirements.txt。
```
### Anaconda（方法三）
1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装：
- https://developer.hpccube.com/tool/
```
DTK驱动:dtk24.04.3
python:python3.10
torch:2.3.0
torchvision:0.18.1
torchaudio:2.1.2
triton:2.1.0
vllm:0.6.2
flash-attn:2.6.1
deepspeed:0.14.2
apex:1.3.0
xformers:0.0.25
transformers:4.48.0
```
`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应。`
2、其它非特殊库参照requirements.txt安装
```
cd /home/InspireMusic
pip install -r requirements.txt
```
## 数据集
`无`
## 训练
`无`
本项目的训练需一定的乐理基础，一般人难以训练出较好的效果，感兴趣的用户请参考源项目的[`README_origin`](./README_origin.md)训练。
## 推理
### 单机单卡
```
# 预训练权重放入：/home/InspireMusic/pretrained_models/
cd /home/InspireMusic/examples/music_generation
python -m inspiremusic.cli.inference # 或 sh test.sh
```
项目当前处在初期研发时期，源项目仍存在一些bug和效果问题，逐渐完善中。
更多资料可参考源项目的[`README_origin`](./README_origin.md)
## result
`输入: `
```
prompt(默认): "Experience soothing and sensual instrumental jazz with a touch of Bossa Nova, perfect for a relaxing restaurant or spa ambiance."
```
`输出:`
```
/home/InspireMusic/examples/music_generation/exp/inspiremusic/output_audio.wav
```
### 精度
DCU与GPU精度一致，推理框架：pytorch。
## 应用场景
### 算法类别
`音乐生成`
### 热点应用行业
`广媒,影视,动漫,医疗,家居,教育`
## 预训练权重
预训练权重快速下载中心：[SCNet AIModels](http://113.200.138.88:18080/aimodels) ，项目中的预训练权重可从快速下载通道下载：[InspireMusic-1.5B-Long](http://113.200.138.88:18080/aimodels/funaudiollm/InspireMusic-1.5B-Long.git)
Hugging Face下载地址为：[InspireMusic-1.5B-Long](https://huggingface.co/FunAudioLLM/InspireMusic-1.5B-Long)
## 源码仓库及问题反馈
- http://developer.sourcefind.cn/codes/modelzoo/InspireMusic_pytorch.git
## 参考资料
- https://github.com/FunAudioLLM/InspireMusic.git
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# Copyright (c) 2024 Alibaba Inc (authors: Chong Zhang)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
os.system('nvidia-smi')
os.system('apt update -y && apt-get install -y apt-utils && apt install -y unzip')
os.environ['PYTHONPATH'] = 'third_party/Matcha-TTS'
os.system('mkdir pretrained_models && cd pretrained_models && git clone https://huggingface.co/FunAudioLLM/InspireMusic-Base.git &&git clone https://huggingface.co/FunAudioLLM/InspireMusic-1.5B-Long.git &&git clone https://huggingface.co/FunAudioLLM/InspireMusic-1.5B.git &&git clone https://huggingface.co/FunAudioLLM/InspireMusic-1.5B-24kHz.git &&git clone https://huggingface.co/FunAudioLLM/InspireMusic-Base-24kHz.git && for i in InspireMusic-Base InspireMusic-Base-24kHz InspireMusic-1.5B InspireMusic-1.5B-24kHz InspireMusic-1.5B-Long; do sed -i -e "s/\.\.\/\.\.\///g" ${i}/inspiremusic.yaml; done && cd ..')
import sys
import torch
print(torch.backends.cudnn.version())
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append('{}/third_party/Matcha-TTS'.format(ROOT_DIR))
import spaces
import gradio as gr
from inspiremusic.cli.inference import InspireMusicUnified, set_env_variables
import torchaudio
import datetime
import hashlib
import importlib
MODELS = ["InspireMusic-1.5B-Long", "InspireMusic-1.5B", "InspireMusic-Base", "InspireMusic-1.5B-24kHz", "InspireMusic-Base-24kHz"]
AUDIO_PROMPT_DIR = "demo/audio_prompts"
OUTPUT_AUDIO_DIR = "demo/outputs"
DEMO_TEXT_PROMPTS = ["Jazz music with drum beats.",
"A captivating classical piano performance, this piece exudes a dynamic and intense atmosphere, showcasing intricate and expressive instrumental artistry.",
"A soothing instrumental piece blending elements of light music and pop, featuring a gentle guitar rendition. The overall feel is serene and reflective, likely instrumental with no vocals.",
"The instrumental rock piece features dynamic oscillations and wave-like progressions, creating an immersive and energetic atmosphere. The music is purely instrumental, with no vocals, and it blends elements of rock and post-rock for a powerful and evocative experience.",
"The classical instrumental piece exudes a haunting and evocative atmosphere, characterized by its intricate guitar work and profound emotional depth.",
"Experience a dynamic blend of instrumental electronic music with futuristic house vibes, featuring energetic beats and a captivating rhythm. The tracks are likely instrumental, focusing on the immersive soundscapes rather than vocal performances."]
def generate_filename():
hash_object = hashlib.sha256(str(int(datetime.datetime.now().timestamp())).encode())
hash_string = hash_object.hexdigest()
return hash_string
def get_args(
task, text="", audio=None, model_name="InspireMusic-Base",
chorus="intro",
output_sample_rate=48000, max_generate_audio_seconds=30.0, time_start = 0.0, time_end=30.0, trim=False):
if "24kHz" in model_name:
output_sample_rate = 24000
if output_sample_rate == 24000:
fast = True
else:
fast = False
# This function constructs the arguments required for InspireMusic
args = {
"task" : task,
"text" : text,
"audio_prompt" : audio,
"model_name" : model_name,
"chorus" : chorus,
"fast" : fast,
"fade_out" : True,
"trim" : trim,
"output_sample_rate" : output_sample_rate,
"min_generate_audio_seconds": 10.0,
"max_generate_audio_seconds": max_generate_audio_seconds,
"max_audio_prompt_length": 5.0,
"model_dir" : os.path.join("pretrained_models",
model_name),
"result_dir" : OUTPUT_AUDIO_DIR,
"output_fn" : generate_filename(),
"format" : "wav",
"time_start" : time_start,
"time_end": time_end,
"fade_out_duration": 1.0,
}
if args["time_start"] is None:
args["time_start"] = 0.0
args["time_end"] = args["time_start"] + args["max_generate_audio_seconds"]
print(args)
return args
def trim_audio(audio_file, cut_seconds=5):
audio, sr = torchaudio.load(audio_file)
num_samples = cut_seconds * sr
cutted_audio = audio[:, :num_samples]
output_path = os.path.join(AUDIO_PROMPT_DIR, "audio_prompt_" + generate_filename() + ".wav")
torchaudio.save(output_path, cutted_audio, sr)
return output_path
@spaces.GPU()
def music_generation(args):
set_env_variables()
model = InspireMusicUnified(
model_name=args["model_name"],
model_dir=args["model_dir"],
min_generate_audio_seconds=args["min_generate_audio_seconds"],
max_generate_audio_seconds=args["max_generate_audio_seconds"],
sample_rate=24000,
output_sample_rate=args["output_sample_rate"],
load_jit=True,
load_onnx=False,
fast=args["fast"],
result_dir=args["result_dir"])
output_path = model.inference(
task=args["task"],
text=args["text"],
audio_prompt=args["audio_prompt"],
chorus=args["chorus"],
time_start=args["time_start"],
time_end=args["time_end"],
output_fn=args["output_fn"],
max_audio_prompt_length=args["max_audio_prompt_length"],
fade_out_duration=args["fade_out_duration"],
output_format=args["format"],
fade_out_mode=args["fade_out"],
trim=args["trim"])
return output_path
def demo_inspiremusic_t2m(text, model_name, chorus,
output_sample_rate, max_generate_audio_seconds):
args = get_args(
task='text-to-music', text=text, audio=None,
model_name=model_name, chorus=chorus,
output_sample_rate=output_sample_rate,
max_generate_audio_seconds=max_generate_audio_seconds)
return music_generation(args)
def demo_inspiremusic_con(text, audio, model_name, chorus,
output_sample_rate, max_generate_audio_seconds):
args = get_args(
task='continuation', text=text, audio=trim_audio(audio, cut_seconds=5),
model_name=model_name, chorus=chorus,
output_sample_rate=output_sample_rate,
max_generate_audio_seconds=max_generate_audio_seconds)
return music_generation(args)
def main():
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("""
# InspireMusic
- Support music generation tasks with long-form and high audio quality, sampling rates up to 48kHz.
- Github: https://github.com/FunAudioLLM/InspireMusic/
- Available music generation models: [InspireMusic-1.5B-Long](https://huggingface.co/FunAudioLLM/InspireMusic-1.5B-Long), [InspireMusic-1.5B](https://huggingface.co/FunAudioLLM/InspireMusic-1.5B), [InspireMusic-Base](https://huggingface.co/FunAudioLLM/InspireMusic-Base), [InspireMusic-1.5B-24kHz](https://huggingface.co/FunAudioLLM/InspireMusic-1.5B-24kHz), [InspireMusic-Base-24kHz](https://huggingface.co/FunAudioLLM/InspireMusic-Base-24kHz). Both on Huggingface and ModelScope.
- Currently only support English text prompts.
- This page is for demo purpose, if you want to generate long-form audio, e.g., 5mins, please try to deploy locally. Thank you for your support.
""")
with gr.Row(equal_height=True):
model_name = gr.Dropdown(
MODELS, label="Select Model Name",
value="InspireMusic-1.5B-Long")
chorus = gr.Dropdown(["intro", "verse", "chorus", "outro"],
label="Chorus Mode", value="intro")
output_sample_rate = gr.Dropdown([48000, 24000],
label="Output Audio Sample Rate (Hz)",
value=48000)
max_generate_audio_seconds = gr.Slider(10, 300,
label="Generate Audio Length (s)",
value=30)
with gr.Row(equal_height=True):
text_input = gr.Textbox(label="Input Text (For Text-to-Music Task)",
value="Experience soothing and sensual instrumental jazz with a touch of Bossa Nova, perfect for a relaxing restaurant or spa ambiance.")
audio_input = gr.Audio(
label="Input Audio Prompt (For Music Continuation Task)",
type="filepath")
music_output = gr.Audio(label="Generated Music", type="filepath", autoplay=True, show_download_button = True)
with gr.Row():
button = gr.Button("Start Text-to-Music Task")
button.click(demo_inspiremusic_t2m,
inputs=[text_input, model_name,
chorus,
output_sample_rate,
max_generate_audio_seconds],
outputs=music_output)
generate_button = gr.Button("Start Music Continuation Task")
generate_button.click(demo_inspiremusic_con,
inputs=[text_input, audio_input, model_name,
chorus,
output_sample_rate,
max_generate_audio_seconds],
outputs=music_output)
t2m_examples = gr.Examples(examples=DEMO_TEXT_PROMPTS, inputs=[text_input])
demo.launch()
if __name__ == '__main__':
os.makedirs(AUDIO_PROMPT_DIR, exist_ok=True)
os.makedirs(OUTPUT_AUDIO_DIR, exist_ok=True)
main()
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FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.3.0-py3.10-dtk24.04.3-ubuntu20.04
ENV DEBIAN_FRONTEND=noninteractive
# RUN yum update && yum install -y git cmake wget build-essential
# RUN source /opt/dtk-24.04.3/env.sh
# # 安装pip相关依赖
COPY requirements.txt requirements.txt
RUN pip3 install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
docker/requirements.txt 0 → 100644
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# --extra-index-url https://download.pytorch.org/whl/cu118
conformer==0.3.2
# deepspeed==0.14.2; sys_platform == 'linux'
diffusers==0.27.2
gdown==5.1.0
gradio==4.32.2
grpcio==1.57.0
grpcio-tools==1.57.0
hydra-core==1.3.2
HyperPyYAML==1.2.2
inflect==7.3.1
librosa==0.10.2
lightning==2.2.4
matplotlib==3.7.5
modelscope==1.15.0
networkx==3.1
omegaconf==2.3.0
onnx==1.17.0
# onnxruntime-gpu==1.16.0; sys_platform == 'linux'
onnxruntime==1.16.0; sys_platform == 'darwin' or sys_platform == 'windows'
openai-whisper==20231117
protobuf==4.25
pydantic==2.7.0
rich==13.7.1
soundfile==0.12.1
tensorboard==2.14.0
# torch==2.0.1
# torchaudio==2.0.2
uvicorn==0.30.0
wget==3.2
fastapi==0.111.0
fastapi-cli==0.0.4
WeTextProcessing==1.0.3
transformers
accelerate
huggingface-hub==0.25.2
julius
# https://github.com/Dao-AILab/flash-attention/releases/download/v2.6.3/flash_attn-2.6.3+cu118torch2.0cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
docker_start.sh 0 → 100644
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docker run -it --shm-size=64G -v $PWD/InspireMusic:/home/InspireMusic -v /public/DL_DATA/AI:/home/AI -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name music b272aae8ec72 bash
# python -m torch.utils.collect_env
examples/music_generation/conf/ds_stage2.json 0 → 100644
View file @ 0112b0f0
{
"train_micro_batch_size_per_gpu": 1,
"gradient_accumulation_steps": 1,
"steps_per_print": 100,
"gradient_clipping": 5,
"fp16": {
"enabled": false,
"auto_cast": false,
"loss_scale": 0,
"initial_scale_power": 16,
"loss_scale_window": 256,
"hysteresis": 2,
"consecutive_hysteresis": false,
"min_loss_scale": 1
},
"bf16": {
"enabled": false
},
"zero_force_ds_cpu_optimizer": false,
"zero_optimization": {
"stage": 2,
"offload_optimizer": {
"device": "none",
"pin_memory": true
},
"allgather_partitions": true,
"allgather_bucket_size": 5e8,
"overlap_comm": false,
"reduce_scatter": true,
"reduce_bucket_size": 5e8,
"contiguous_gradients" : true
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": 0.001,
"weight_decay": 0.0001,
"torch_adam": true,
"adam_w_mode": true
}
}
}
\ No newline at end of file
examples/music_generation/conf/inspiremusic.fromscratch.yaml 0 → 100644
View file @ 0112b0f0
# set random seed, so that you may reproduce your result.
__set_seed1: !apply:random.seed [1024]
__set_seed2: !apply:numpy.random.seed [1024]
__set_seed3: !apply:torch.manual_seed [1024]
__set_seed4: !apply:torch.cuda.manual_seed_all [1024]
# fixed params
sample_rate: 24000
text_encoder_input_size: 512
llm_input_size: 896
llm_output_size: 896
basemodel_path: '../../pretrained_models/InspireMusic-Base/'
generator_path: '../../pretrained_models/InspireMusic-Base/music_tokenizer'
# model params
# for all class/function included in this repo, we use !<name> or !<new> for intialization, so that user may find all corresponding class/function according to one single yaml.
# for system/third_party class/function, we do not require this.
llm: !new:inspiremusic.llm.llm.LLM
text_encoder_input_size: !ref <text_encoder_input_size>
llm_input_size: !ref <llm_input_size>
llm_output_size: !ref <llm_output_size>
audio_token_size: 4096
length_normalized_loss: True
lsm_weight: 0
text_encoder_conf:
name: "none"
llm: !new:inspiremusic.transformer.qwen_encoder.QwenEmbeddingEncoder
input_size: !ref <text_encoder_input_size>
pretrain_path: !ref <basemodel_path>
sampling: !name:inspiremusic.utils.common.ras_sampling
top_p: 0.8
top_k: 50
win_size: 10
tau_r: 0.1
train_cfg_ratio: 0.2
infer_cfg_ratio: 7.0
flow: !new:inspiremusic.flow.flow.MaskedDiff
input_size: 256
output_size: 80
output_type: 'mel'
vocab_size: 4096
input_frame_rate: 75
only_mask_loss: True
encoder: !new:inspiremusic.transformer.encoder.ConformerEncoder
output_size: 512
attention_heads: 4
linear_units: 1024
num_blocks: 3
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
normalize_before: True
input_layer: 'linear'
pos_enc_layer_type: 'rel_pos_espnet'
selfattention_layer_type: 'rel_selfattn'
input_size: 256
use_cnn_module: False
macaron_style: False
length_regulator: !new:inspiremusic.flow.length_regulator.InterpolateRegulator
channels: 512
sampling_ratios: [1, 1, 1, 1]
decoder: !new:inspiremusic.flow.flow_matching.ConditionalCFM
in_channels: 240
cfm_params: !new:omegaconf.DictConfig
content:
sigma_min: 1e-06
solver: 'euler'
t_scheduler: 'cosine'
training_cfg_rate: 0.2
inference_cfg_rate: 0.7
reg_loss_type: 'l1'
estimator: !new:inspiremusic.flow.decoder.ConditionalDecoder
in_channels: 1024
out_channels: 512
channels: [256, 256]
dropout: 0.0
attention_head_dim: 64
n_blocks: 4
num_mid_blocks: 8
num_heads: 8
act_fn: 'gelu'
generator_model_dir: !ref <generator_path>
hift: !new:inspiremusic.hifigan.generator.HiFTGenerator
in_channels: 80
base_channels: 512
nb_harmonics: 8
sampling_rate: !ref <sample_rate>
nsf_alpha: 0.1
nsf_sigma: 0.003
nsf_voiced_threshold: 10
upsample_rates: [8, 8]
upsample_kernel_sizes: [16, 16]
istft_params:
n_fft: 16
hop_len: 4
resblock_kernel_sizes: [3, 7, 11]
resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
source_resblock_kernel_sizes: [7, 11]
source_resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5]]
lrelu_slope: 0.1
audio_limit: 0.99
f0_predictor: !new:inspiremusic.hifigan.f0_predictor.ConvRNNF0Predictor
num_class: 1
in_channels: 80
cond_channels: 512
wavtokenizer: !new:inspiremusic.hifigan.generator.HiFTGenerator
# processor functions
parquet_opener: !name:inspiremusic.dataset.processor.parquet_opener
get_tokenizer: !name:inspiremusic.text.tokenizer.get_tokenizer
tokenizer_path: !ref <basemodel_path>
tokenizer_name: "qwen-2.0"
allowed_special: 'all'
tokenize: !name:inspiremusic.dataset.processor.tokenize
get_tokenizer: !ref <get_tokenizer>
allowed_special: !ref <allowed_special>
filter: !name:inspiremusic.dataset.processor.filter
max_length: 28000
min_length: 0
token_max_length: 200
token_min_length: 1
resample: !name:inspiremusic.dataset.processor.resample
resample_rate: !ref <sample_rate>
feat_extractor: !name:matcha.utils.audio.mel_spectrogram
n_fft: 1024
num_mels: 128
sampling_rate: !ref <sample_rate>
hop_size: 256
win_size: 1024
fmin: 0
fmax: 24000
center: False
compute_fbank: !name:inspiremusic.dataset.processor.compute_fbank
feat_extractor: !ref <feat_extractor>
parse_embedding: !name:inspiremusic.dataset.processor.parse_embedding
normalize: True
shuffle: !name:inspiremusic.dataset.processor.shuffle
shuffle_size: 1000
sort: !name:inspiremusic.dataset.processor.sort
sort_size: 500 # sort_size should be less than shuffle_size
batch: !name:inspiremusic.dataset.processor.batch
batch_type: 'dynamic'
max_frames_in_batch: 12000
padding: !name:inspiremusic.dataset.processor.padding
# dataset processor pipeline
data_pipeline: [
!ref <parquet_opener>,
!ref <tokenize>,
!ref <shuffle>,
!ref <sort>,
!ref <filter>,
!ref <batch>,
!ref <padding>,
]
# train conf
train_conf:
optim: adam
optim_conf:
lr: 0.001 # change to 0.001 if you want to train flow from scratch
scheduler: warmuplr
scheduler_conf:
warmup_steps: 5000
max_epoch: 200
grad_clip: 5
accum_grad: 2
log_interval: 100
save_per_step: 1000
examples/music_generation/conf/inspiremusic.yaml 0 → 100644
View file @ 0112b0f0
# set random seed, so that you may reproduce your result.
__set_seed1: !apply:random.seed [1024]
__set_seed2: !apply:numpy.random.seed [1024]
__set_seed3: !apply:torch.manual_seed [1024]
__set_seed4: !apply:torch.cuda.manual_seed_all [1024]
# fixed params
sample_rate: 24000
target_sample_rate: 48000
text_encoder_input_size: 512
llm_input_size: 896
llm_output_size: 896
basemodel_path: '../../pretrained_models/InspireMusic-Base/'
generator_path: '../../pretrained_models/InspireMusic-Base/music_tokenizer'
# model params
# for all class/function included in this repo, we use !<name> or !<new> for intialization, so that user may find all corresponding class/function according to one single yaml.
# for system/third_party class/function, we do not require this.
llm: !new:inspiremusic.llm.llm.LLM
text_encoder_input_size: !ref <text_encoder_input_size>
llm_input_size: !ref <llm_input_size>
llm_output_size: !ref <llm_output_size>
audio_token_size: 4096
length_normalized_loss: True
lsm_weight: 0
text_encoder_conf:
name: "none"
llm: !new:inspiremusic.transformer.qwen_encoder.QwenEmbeddingEncoder
input_size: !ref <text_encoder_input_size>
pretrain_path: !ref <basemodel_path>
sampling: !name:inspiremusic.utils.common.topk_sampling
top_k: 350
train_cfg_ratio: 0.2
infer_cfg_ratio: 3.0
flow: !new:inspiremusic.flow.flow.MaskedDiff
input_size: 256
output_size: 80
output_type: 'mel'
vocab_size: 4096
input_frame_rate: 75
only_mask_loss: True
encoder: !new:inspiremusic.transformer.encoder.ConformerEncoder
output_size: 512
attention_heads: 4
linear_units: 1024
num_blocks: 3
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
normalize_before: True
input_layer: 'linear'
pos_enc_layer_type: 'rel_pos_espnet'
selfattention_layer_type: 'rel_selfattn'
input_size: 256
use_cnn_module: False
macaron_style: False
length_regulator: !new:inspiremusic.flow.length_regulator.InterpolateRegulator
channels: 512
sampling_ratios: [1, 1, 1, 1]
decoder: !new:inspiremusic.flow.flow_matching.ConditionalCFM
in_channels: 240
cfm_params: !new:omegaconf.DictConfig
content:
sigma_min: 1e-06
solver: 'euler'
t_scheduler: 'cosine'
training_cfg_rate: 0.2
inference_cfg_rate: 0.7
reg_loss_type: 'l1'
estimator: !new:inspiremusic.flow.decoder.ConditionalDecoder
in_channels: 1024
out_channels: 512
channels: [256, 256]
dropout: 0.0
attention_head_dim: 64
n_blocks: 4
num_mid_blocks: 8
num_heads: 8
act_fn: 'gelu'
generator_model_dir: !ref <generator_path>
hift: !new:inspiremusic.hifigan.generator.HiFTGenerator
in_channels: 80
base_channels: 512
nb_harmonics: 8
sampling_rate: !ref <sample_rate>
nsf_alpha: 0.1
nsf_sigma: 0.003
nsf_voiced_threshold: 10
upsample_rates: [8, 8]
upsample_kernel_sizes: [16, 16]
istft_params:
n_fft: 16
hop_len: 4
resblock_kernel_sizes: [3, 7, 11]
resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
source_resblock_kernel_sizes: [7, 11]
source_resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5]]
lrelu_slope: 0.1
audio_limit: 0.99
f0_predictor: !new:inspiremusic.hifigan.f0_predictor.ConvRNNF0Predictor
num_class: 1
in_channels: 80
cond_channels: 512
wavtokenizer: !new:inspiremusic.hifigan.generator.HiFTGenerator
# processor functions
parquet_opener: !name:inspiremusic.dataset.processor.parquet_opener
get_tokenizer: !name:inspiremusic.text.tokenizer.get_tokenizer
tokenizer_path: !ref <basemodel_path>
tokenizer_name: "qwen-2.0"
allowed_special: 'all'
tokenize: !name:inspiremusic.dataset.processor.tokenize
get_tokenizer: !ref <get_tokenizer>
allowed_special: !ref <allowed_special>
filter: !name:inspiremusic.dataset.processor.filter
max_length: 20000
min_length: 1
token_max_length: 200
token_min_length: 1
max_acoustic_length: 20000
min_acoustic_length: 1800
mode: 'train_flow'
resample: !name:inspiremusic.dataset.processor.resample
resample_rate: !ref <sample_rate>
feat_extractor: !name:matcha.utils.audio.mel_spectrogram
n_fft: 1024
num_mels: 128
sampling_rate: !ref <sample_rate>
hop_size: 256
win_size: 1024
fmin: 0
fmax: 24000
center: False
compute_fbank: !name:inspiremusic.dataset.processor.compute_fbank
feat_extractor: !ref <feat_extractor>
parse_embedding: !name:inspiremusic.dataset.processor.parse_embedding
normalize: True
shuffle: !name:inspiremusic.dataset.processor.shuffle
shuffle_size: 1000
sort: !name:inspiremusic.dataset.processor.sort
sort_size: 500 # sort_size should be less than shuffle_size
batch: !name:inspiremusic.dataset.processor.batch
batch_type: 'dynamic'
max_frames_in_batch: 15500 # llm 12000
# batch_type: 'static'
# batch_size: 2 # llm 12000
padding: !name:inspiremusic.dataset.processor.padding
mode: 'train'
# dataset processor pipeline
data_pipeline: [
!ref <parquet_opener>,
!ref <tokenize>,
!ref <shuffle>,
!ref <sort>,
!ref <filter>,
!ref <batch>,
!ref <padding>,
]
# train conf
train_conf:
optim: adam
optim_conf:
lr: 0.0001 # change to 0.001 if you want to train flow from scratch
scheduler: warmuplr
scheduler_conf:
warmup_steps: 500
max_epoch: 200
grad_clip: 5
accum_grad: 2
log_interval: 100
save_per_step: 500
examples/music_generation/conf/inspiremusic_1.5b.yaml 0 → 100644
View file @ 0112b0f0
# set random seed, so that you may reproduce your result.
__set_seed1: !apply:random.seed [1024]
__set_seed2: !apply:numpy.random.seed [1024]
__set_seed3: !apply:torch.manual_seed [1024]
__set_seed4: !apply:torch.cuda.manual_seed_all [1024]
# fixed params
sample_rate: 24000
text_encoder_input_size: 512
llm_input_size: 1536
llm_output_size: 1536
basemodel_path: '../../pretrained_models/InspireMusic-1.5B/'
generator_path: '../../pretrained_models/InspireMusic-1.5B/music_tokenizer'
# model params
# for all class/function included in this repo, we use !<name> or !<new> for intialization, so that user may find all corresponding class/function according to one single yaml.
# for system/third_party class/function, we do not require this.
llm: !new:inspiremusic.llm.llm.LLM
text_encoder_input_size: !ref <text_encoder_input_size>
llm_input_size: !ref <llm_input_size>
llm_output_size: !ref <llm_output_size>
audio_token_size: 4096
length_normalized_loss: True
lsm_weight: 0
text_encoder_conf:
name: "none"
llm: !new:inspiremusic.transformer.qwen_encoder.QwenEmbeddingEncoder
input_size: !ref <text_encoder_input_size>
pretrain_path: !ref <basemodel_path>
sampling: !name:inspiremusic.utils.common.topk_sampling
top_k: 350
train_cfg_ratio: 0.2
infer_cfg_ratio: 3.0
flow: !new:inspiremusic.flow.flow.MaskedDiff
input_size: 256
output_size: 80
output_type: 'mel'
vocab_size: 4096
input_frame_rate: 75
only_mask_loss: True
encoder: !new:inspiremusic.transformer.encoder.ConformerEncoder
output_size: 512
attention_heads: 4
linear_units: 1024
num_blocks: 3
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
normalize_before: True
input_layer: 'linear'
pos_enc_layer_type: 'rel_pos_espnet'
selfattention_layer_type: 'rel_selfattn'
input_size: 256
use_cnn_module: False
macaron_style: False
length_regulator: !new:inspiremusic.flow.length_regulator.InterpolateRegulator
channels: 512
sampling_ratios: [1, 1, 1, 1]
decoder: !new:inspiremusic.flow.flow_matching.ConditionalCFM
in_channels: 240
cfm_params: !new:omegaconf.DictConfig
content:
sigma_min: 1e-06
solver: 'euler'
t_scheduler: 'cosine'
training_cfg_rate: 0.2
inference_cfg_rate: 0.7
reg_loss_type: 'l1'
estimator: !new:inspiremusic.flow.decoder.ConditionalDecoder
in_channels: 1024
out_channels: 512
channels: [256, 256]
dropout: 0.0
attention_head_dim: 64
n_blocks: 4
num_mid_blocks: 8
num_heads: 8
act_fn: 'gelu'
generator_model_dir: !ref <generator_path>
hift: !new:inspiremusic.hifigan.generator.HiFTGenerator
in_channels: 80
base_channels: 512
nb_harmonics: 8
sampling_rate: !ref <sample_rate>
nsf_alpha: 0.1
nsf_sigma: 0.003
nsf_voiced_threshold: 10
upsample_rates: [8, 8]
upsample_kernel_sizes: [16, 16]
istft_params:
n_fft: 16
hop_len: 4
resblock_kernel_sizes: [3, 7, 11]
resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
source_resblock_kernel_sizes: [7, 11]
source_resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5]]
lrelu_slope: 0.1
audio_limit: 0.99
f0_predictor: !new:inspiremusic.hifigan.f0_predictor.ConvRNNF0Predictor
num_class: 1
in_channels: 80
cond_channels: 512
wavtokenizer: !new:inspiremusic.hifigan.generator.HiFTGenerator
# processor functions
parquet_opener: !name:inspiremusic.dataset.processor.parquet_opener
get_tokenizer: !name:inspiremusic.text.tokenizer.get_tokenizer
tokenizer_path: !ref <basemodel_path>
tokenizer_name: "qwen-2.5"
allowed_special: 'all'
tokenize: !name:inspiremusic.dataset.processor.tokenize
get_tokenizer: !ref <get_tokenizer>
allowed_special: !ref <allowed_special>
filter: !name:inspiremusic.dataset.processor.filter
max_length: 28000
min_length: 0
token_max_length: 200
token_min_length: 1
resample: !name:inspiremusic.dataset.processor.resample
resample_rate: !ref <sample_rate>
feat_extractor: !name:matcha.utils.audio.mel_spectrogram
n_fft: 1024
num_mels: 128
sampling_rate: !ref <sample_rate>
hop_size: 256
win_size: 1024
fmin: 0
fmax: 24000
center: False
compute_fbank: !name:inspiremusic.dataset.processor.compute_fbank
feat_extractor: !ref <feat_extractor>
parse_embedding: !name:inspiremusic.dataset.processor.parse_embedding
normalize: True
shuffle: !name:inspiremusic.dataset.processor.shuffle
shuffle_size: 1000
sort: !name:inspiremusic.dataset.processor.sort
sort_size: 500 # sort_size should be less than shuffle_size
batch: !name:inspiremusic.dataset.processor.batch
batch_type: 'dynamic'
max_frames_in_batch: 10000 # llm 12000
padding: !name:inspiremusic.dataset.processor.padding
# dataset processor pipeline
data_pipeline: [
!ref <parquet_opener>,
!ref <tokenize>,
!ref <shuffle>,
!ref <sort>,
!ref <filter>,
!ref <batch>,
!ref <padding>,
]
# train conf
train_conf:
optim: adam
optim_conf:
lr: 0.0001 # change to 0.001 if you want to train flow from scratch
scheduler: warmuplr
scheduler_conf:
warmup_steps: 5000
max_epoch: 200
grad_clip: 5
accum_grad: 2
log_interval: 100
save_per_step: 500
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