app.py

import gradio as gr
import numpy as np
import torch
import yaml
import json
import pyloudnorm as pyln
from hydra.utils import instantiate
from random import normalvariate
from soxr import resample
from functools import partial

from modules.utils import chain_functions, vec2statedict, get_chunks
from modules.fx import clip_delay_eq_Q


space_md = """
# Random Vocal Effects Generator

This is a demo of the paper [DiffVox: A Differentiable Model for Capturing and Analysing Professional Effects Distributions](https://arxiv.org/abs/2504.14735), accepted at DAFx 2025.
In this demo, you can upload a raw vocal audio file (in mono) and apply random effects to make it sound better!

The effects consist of series of EQ, compressor, delay, and reverb.
The generator is a PCA model derived from 365 vocal effects presets fitted with the same effects chain.
This interface allows you to control the first 10 principal components (PCs) of the generator, randomise them, and render the audio.
For the rest of the PCs, you can choose to randomise them or set them to zero.

To give you some idea, we emperically found that the first PC controls the amount of reverb and the second PC controls the amount of brightness.
Note that adding these PCs together does not necessarily mean that their effects are additive in the final audio.
We found sometimes the effects of least important PCs are more perceptible.
Try to play around with the sliders and buttons and see what you can come up with!

Currently only a portion of PCs are tweakable, but in the future we will add more controls and visualisation tools.
For example:
- Exposing all the PCs
- Directly controlling the parameters of the effects
- Visualising the PCA space
- Visualising the frequency responses/dynamic curves of the effects
- Exporting the effects settings as JSON files
"""

SLIDER_MAX = 3
SLIDER_MIN = -3
NUMBER_OF_PCS = 10
TEMPERATURE = 0.7
CONFIG_PATH = "presets/rt_config.yaml"
PCA_PARAM_FILE = "presets/internal/gaussian.npz"
INFO_PATH = "presets/internal/info.json"


with open(CONFIG_PATH) as fp:
    fx_config = yaml.safe_load(fp)["model"]

fx = instantiate(fx_config)
fx.eval()

pca_params = np.load(PCA_PARAM_FILE)
mean = pca_params["mean"]
cov = pca_params["cov"]
eigvals, eigvecs = np.linalg.eigh(cov)
eigvals = np.flip(eigvals, axis=0)[:75]
eigvecs = np.flip(eigvecs, axis=1)[:, :75]
U = eigvecs * np.sqrt(eigvals)
U = torch.from_numpy(U).float()
mean = torch.from_numpy(mean).float()


with open(INFO_PATH) as f:
    info = json.load(f)

param_keys = info["params_keys"]
original_shapes = list(
    map(lambda lst: lst if len(lst) else [1], info["params_original_shapes"])
)

*vec2dict_args, _ = get_chunks(param_keys, original_shapes)
vec2dict_args = [param_keys, original_shapes] + vec2dict_args
vec2dict = partial(
    vec2statedict,
    **dict(
        zip(
            [
                "keys",
                "original_shapes",
                "selected_chunks",
                "position",
                "U_matrix_shape",
            ],
            vec2dict_args,
        )
    ),
)


meter = pyln.Meter(44100)


@torch.no_grad()
def inference(audio, randomise_rest, *pcs):
    sr, y = audio
    if sr != 44100:
        y = resample(y, sr, 44100)
    if y.dtype.kind != "f":
        y = y / 32768.0

    if y.ndim == 1:
        y = y[:, None]
    loudness = meter.integrated_loudness(y)
    y = pyln.normalize.loudness(y, loudness, -18.0)

    y = torch.from_numpy(y).float().T.unsqueeze(0)
    if y.shape[1] != 1:
        y = y.mean(dim=1, keepdim=True)

    M = eigvals.shape[0]
    z = torch.cat(
        [
            torch.tensor([float(x) for x in pcs]),
            (
                torch.randn(M - len(pcs)) * TEMPERATURE
                if randomise_rest
                else torch.zeros(M - len(pcs))
            ),
        ]
    )
    x = U @ z + mean

    fx.load_state_dict(vec2dict(x), strict=False)
    fx.apply(partial(clip_delay_eq_Q, Q=0.707))

    rendered = fx(y).squeeze(0).T.numpy()
    if np.max(np.abs(rendered)) > 1:
        rendered = rendered / np.max(np.abs(rendered))
    return (44100, (rendered * 32768).astype(np.int16))


def get_important_pcs(n=10, **kwargs):
    sliders = [
        gr.Slider(minimum=SLIDER_MIN, maximum=SLIDER_MAX, label=f"PC {i}", **kwargs)
        for i in range(1, n + 1)
    ]
    return sliders


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            gr.Markdown(
                space_md,
            )
            audio_input = gr.Audio(type="numpy", sources="upload", label="Input Audio")
            with gr.Row():
                random_button = gr.Button(
                    f"Randomise the first {NUMBER_OF_PCS} PCs",
                    elem_id="randomise-button",
                )
                reset_button = gr.Button(
                    "Reset",
                    elem_id="reset-button",
                )
                render_button = gr.Button(
                    "Run", elem_id="render-button", variant="primary"
                )
            random_rest_checkbox = gr.Checkbox(
                label=f"Randomise PCs > {NUMBER_OF_PCS} (default to zeros)",
                value=False,
                elem_id="randomise-checkbox",
            )
            sliders = get_important_pcs(NUMBER_OF_PCS, value=0)
        with gr.Column():
            audio_output = gr.Audio(
                type="numpy", label="Output Audio", interactive=False
            )

    render_button.click(
        inference,
        inputs=[
            audio_input,
            random_rest_checkbox,
        ]
        + sliders,
        outputs=audio_output,
    )

    random_button.click(
        lambda *xs: [
            chain_functions(
                partial(max, SLIDER_MIN),
                partial(min, SLIDER_MAX),
            )(normalvariate(0, 1))
            for _ in range(len(xs))
        ],
        inputs=sliders,
        outputs=sliders,
    )
    reset_button.click(
        lambda *xs: [0 for _ in range(len(xs))],
        inputs=sliders,
        outputs=sliders,
    )

demo.launch()