fxencoder-plusplus 0.1.5

Fx-Encoder++ for audio effects representation

Fx-Encoder++

Convert audio effects from your music into encoded representations suitable for audio effects processing and analysis tasks.

Paper HugginFace

About Fx-Encoder++

We adopt the codebase of CLAP for this project.

An audio effects representation learning based on SimCLR.

Architecture

Usage

Installation

pip install fxencoder_plusplus

Usage

Notice: The input to Fx-Encoder++ should be stereo

Initialize Models

from fxencoder_plusplus import load_model 

# Load default base model (auto-downloads if needed)
DEVICE = 'cuda'
model = load_model(
    'default',
    device=DEVICE,
)

Extract audio effects representations from mixture tracks or stem tracks, where a single representation encodes the overall audio effects style of the entire input.

import torch 
import librosa 
audio_path = librosa.example('trumpet')
wav, sr = librosa.load(audio_path, sr=44100, mono=False)
wav = torch.from_numpy(wav).unsqueeze(0).unsqueeze(0).repeat(1, 2, 1).to(DEVICE) # [1, 2, seq_len]

fx_emb = model.get_fx_embedding(wav)
print(fx_emb.shape) # [1, embed_dim], [1, 128]

## if you want to get the embedding before projection, then 
fx_emb = model.get_fx_embedding(wav, normalized=False)
print(fx_emb.shape) # [1, embed_dim], [1, 2048]

Extract instrument-specific audio effects representations from mixture tracks. For example, extract the audio effects representation of just the vocals within a full mix.

1. Audio Reference:

import torchaudio 
import julius 
mixture_path = "/path/to/mixture.wav"
mixture, sr = torchaudio.load(mixture_path, num_frames=441000)
mixture = mixture.unsqueeze(0).to(DEVICE) # [1, channel, seq_len]

query_path = "/path/to/inst.wav"
query, sr = torchaudio.load(query_path, frame_offset=441000, num_frames=441000)
query = query.unsqueeze(0).to(DEVICE) # [1, channel, seq_len]
query = julius.resample_frac(query, int(44100), int(48000))

_, fx_emb = model.get_fx_embedding_by_audio_query(mixture, query)
print(fx_emb.shape) # [1, embed_dim], [1, 128]

2. Text Reference:

import torchaudio 
mixture_path = "/path/to/mixture.wav"
mixture, sr = torchaudio.load(mixture_path, num_frames=441000)
mixture = mixture.unsqueeze(0).to(DEVICE) # [1, channel, seq_len]

query = "the sound of vocals"

_, fx_emb = model.get_fx_embedding_by_text_query(mixture, query)
print(fx_emb.shape) # [1, embed_dim], [1, 128]

Training

Env

1. Create environment with conda

conda create --name fxenc python=3.10.14

2. Install

pip install -r requirements.txt 

Prepare Fx-Normalized Dataset

Because the dataset has copyright restriction, unfortunatly we cannot directly share preprocessed datasets.

0. Download MUSDB, MoisesDB
1. Please check FxNorm-automix for preparing audio effects normalized dataset

Run

bash scripts/train_proposed.sh 

Evaluation

We develop a retrieval-based evaluation pipeline (Using MUSDB dataset as the example)

0. Check FxNorm-automix for preparing audio effects normalized dataset
1. Synthesize evaluation dataset: check build_musdb.py
2. Run retrieval-based evaluation: check eval_retrieval.py

LICENSE

This library is released under the CC BY-NC 4.0 license. Please refer to the LICENSE file for more details.