Skip to content

Latest commit

 

History

History
212 lines (162 loc) · 6.34 KB

README.md

File metadata and controls

212 lines (162 loc) · 6.34 KB

Amphion Diffusion-based Vocoder Recipe

Supported Model Architectures

Diffusion-based Vocoders utilize the diffusion process for audio generation, as illustrated below:



Until now, Amphion Diffusion-based Vocoder has supported the following models and training strategies.

You can use any vocoder architecture with any dataset you want. There are four steps in total:

  1. Data preparation
  2. Feature extraction
  3. Training
  4. Inference

NOTE: You need to run every command of this recipe in the Amphion root path:

cd Amphion

1. Data Preparation

You can train the vocoder with any datasets. Amphion's supported open-source datasets are detailed here.

Configuration

Specify the dataset path in exp_config_base.json. Note that you can change the dataset list to use your preferred datasets.

"dataset": [
    "csd",
    "kising",
    "m4singer",
    "nus48e",
    "opencpop",
    "opensinger",
    "opera",
    "pjs",
    "popbutfy",
    "popcs",
    "ljspeech",
    "vctk",
    "libritts",
],
"dataset_path": {
    // TODO: Fill in your dataset path
    "csd": "[dataset path]",
    "kising": "[dataset path]",
    "m4singer": "[dataset path]",
    "nus48e": "[dataset path]",
    "opencpop": "[dataset path]",
    "opensinger": "[dataset path]",
    "opera": "[dataset path]",
    "pjs": "[dataset path]",
    "popbutfy": "[dataset path]",
    "popcs": "[dataset path]",
    "ljspeech": "[dataset path]",
    "vctk": "[dataset path]",
    "libritts": "[dataset path]",
},

2. Feature Extraction

The needed features are speficied in the individual vocoder direction so it doesn't require any modification.

Configuration

Specify the dataset path and the output path for saving the processed data and the training model in exp_config_base.json:

    // TODO: Fill in the output log path. The default value is "Amphion/ckpts/vocoder"
    "log_dir": "ckpts/vocoder",
    "preprocess": {
        // TODO: Fill in the output data path. The default value is "Amphion/data"
        "processed_dir": "data",
        ...
    },

Run

Run the run.sh as the preproces stage (set --stage 1).

sh egs/vocoder/diffusion/{vocoder_name}/run.sh --stage 1

NOTE: The CUDA_VISIBLE_DEVICES is set as "0" in default. You can change it when running run.sh by specifying such as --gpu "1".

3. Training

Configuration

We provide the default hyparameters in the exp_config_base.json. They can work on single NVIDIA-24g GPU. You can adjust them based on you GPU machines.

"train": {
    "batch_size": 32,
    "max_epoch": 1000000,
    "save_checkpoint_stride": [20],
    "adamw": {
        "lr": 2.0e-4,
        "adam_b1": 0.8,
        "adam_b2": 0.99
    },
    "exponential_lr": {
        "lr_decay": 0.999
    },
}

Run

Run the run.sh as the training stage (set --stage 2). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in Amphion/ckpts/vocoder/[YourExptName].

sh egs/vocoder/diffusion/{vocoder_name}/run.sh --stage 2 --name [YourExptName]

NOTE: The CUDA_VISIBLE_DEVICES is set as "0" in default. You can change it when running run.sh by specifying such as --gpu "0,1,2,3".

If you want to resume or finetune from a pretrained model, run:

sh egs/vocoder/diffusion/{vocoder_name}/run.sh --stage 2 \
	--name [YourExptName] \
	--resume_type ["resume" for resuming training and "finetune" for loading parameters only] \
	--checkpoint Amphion/ckpts/vocoder/[YourExptName]/checkpoint \

NOTE: For multi-gpu training, the main_process_port is set as 29500 in default. You can change it when running run.sh by specifying such as --main_process_port 29501.

4. Inference

Run

Run the run.sh as the training stage (set --stage 3), we provide three different inference modes, including infer_from_dataset, infer_from_feature, and infer_from_audio.

sh egs/vocoder/diffusion/{vocoder_name}/run.sh --stage 3 \
	--infer_mode [Your chosen inference mode] \
	--infer_datasets [Datasets you want to inference, needed when infer_from_dataset] \
	--infer_feature_dir [Your path to your predicted acoustic features, needed when infer_from_feature] \
	--infer_audio_dir [Your path to your audio files, needed when infer_form_audio] \
	--infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \
	--infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \

a. Inference from Dataset

Run the run.sh with specified datasets, here is an example.

sh egs/vocoder/diffusion/{vocoder_name}/run.sh --stage 3 \
	--infer_mode infer_from_dataset \
	--infer_datasets "libritts vctk ljspeech" \
	--infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \
	--infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \

b. Inference from Features

If you want to inference from your generated acoustic features, you should first prepare your acoustic features into the following structure:

 ┣ {infer_feature_dir}
 ┃ ┣ mels
 ┃ ┃ ┣ sample1.npy
 ┃ ┃ ┣ sample2.npy

Then run the run.sh with specificed folder direction, here is an example.

sh egs/vocoder/diffusion/{vocoder_name}/run.sh --stage 3 \
	--infer_mode infer_from_feature \
	--infer_feature_dir [Your path to your predicted acoustic features] \
	--infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \
	--infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \

c. Inference from Audios

If you want to inference from audios for quick analysis synthesis, you should first prepare your audios into the following structure:

 ┣ audios
 ┃ ┣ sample1.wav
 ┃ ┣ sample2.wav

Then run the run.sh with specificed folder direction, here is an example.

sh egs/vocoder/diffusion/{vocoder_name}/run.sh --stage 3 \
	--infer_mode infer_from_audio \
	--infer_audio_dir [Your path to your audio files] \
	--infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \
	--infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \