app/infer.py

from glob import glob
import os
from typing import Tuple
from demucs.separate import main as demucs
import gradio as gr
import numpy as np
import soundfile as sf
from configs.config import Config
from infer.modules.vc.modules import VC
from zero import zero
from model import device


@zero(duration=120)
def infer(
    exp_dir: str, original_audio: str, f0add: int, index_rate: float, protect: float
) -> Tuple[int, np.ndarray]:
    model = os.path.join(exp_dir, "model.pth")
    if not os.path.exists(model):
        raise gr.Error("Model not found")

    index = glob(f"{exp_dir}/added_*.index")
    if index:
        index = index[0]
    else:
        index = None

    base = os.path.basename(original_audio)
    base = os.path.splitext(base)[0]
    demucs(
        ["--two-stems", "vocals", "-d", str(device), "-n", "htdemucs", original_audio]
    )
    out = os.path.join("separated", "htdemucs", base, "vocals.wav")

    cfg = Config()
    vc = VC(cfg)
    vc.get_vc(model)
    _, wav_opt = vc.vc_single(
        0,
        out,
        f0add,
        None,
        "rmvpe",
        index,
        None,
        index_rate,
        3,  # this only has effect when f0_method is "harvest"
        0,
        1,
        protect,
    )

    sr = wav_opt[0]
    data = wav_opt[1]

    return sr, data


def merge(exp_dir: str, original_audio: str, vocal: Tuple[int, np.ndarray]) -> str:
    base = os.path.basename(original_audio)
    base = os.path.splitext(base)[0]
    music = os.path.join("separated", "htdemucs", base, "no_vocals.wav")

    tmp = os.path.join(exp_dir, "tmp.wav")
    sf.write(tmp, vocal[1], vocal[0])

    os.system(
        f"ffmpeg -i {music} -i {tmp} -filter_complex '[1]volume=2[a];[0][a]amix=inputs=2:duration=first:dropout_transition=2' -ac 2 -y {tmp}.merged.mp3"
    )

    return f"{tmp}.merged.mp3"


class InferenceTab:
    def __init__(self):
        pass

    def ui(self):
        gr.Markdown("# Inference")
        gr.Markdown(
            "After trained model is pruned, you can use it to infer on new music. \n"
            "Upload the original audio and adjust the F0 add value to generate the inferred audio."
        )

        with gr.Row():
            self.original_audio = gr.Audio(
                label="Upload original audio",
                type="filepath",
                show_download_button=True,
            )

            with gr.Column():
                self.f0add = gr.Slider(
                    label="F0 +/-",
                    minimum=-16,
                    maximum=16,
                    step=1,
                    value=0,
                )
                self.index_rate = gr.Slider(
                    label="Index rate",
                    minimum=-0,
                    maximum=1,
                    step=0.01,
                    value=0.5,
                )
                self.protect = gr.Slider(
                    label="Protect",
                    minimum=0,
                    maximum=1,
                    step=0.01,
                    value=0.33,
                )

            self.infer_btn = gr.Button(value="Infer", variant="primary")
        with gr.Row():
            self.infer_output = gr.Audio(
                label="Inferred audio", show_download_button=True, format="mp3"
            )
        with gr.Row():
            self.merge_output = gr.Audio(
                label="Merged audio", show_download_button=True, format="mp3"
            )

    def build(self, exp_dir: gr.Textbox):
        self.infer_btn.click(
            fn=infer,
            inputs=[
                exp_dir,
                self.original_audio,
                self.f0add,
                self.index_rate,
                self.protect,
            ],
            outputs=[self.infer_output],
        ).success(
            fn=merge,
            inputs=[exp_dir, self.original_audio, self.infer_output],
            outputs=[self.merge_output],
        )