app.py

import math
import tempfile
from pathlib import Path

import yaml
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio as ta
import soundfile as sf
import gradio as gr
from tqdm import tqdm
from typing import Union, Tuple, Optional
from torch import Tensor

from pyharp import build_endpoint, ModelCard


# ─────────────────────────────────────────────
# UNet Utilities
# ─────────────────────────────────────────────

class UNetUtils:
    def __init__(self, F=None, T=None, n_fft=4096, win_length=None,
                 hop_length=None, center=True, device='cpu'):
        self.n_fft = n_fft
        self.win_length = n_fft if win_length is None else win_length
        self.hop_length = self.win_length // 4 if hop_length is None else hop_length
        self.hann_window = torch.hann_window(self.win_length, periodic=True).to(device)
        self.center = center
        self.device = device
        self.F = F
        self.T = T

    def fold_unet_inputs(self, x):
        time_dim = x.size(-1)
        pad_len = math.ceil(time_dim / self.T) * self.T - time_dim
        padded = F.pad(x, (0, pad_len))
        if time_dim < self.T:
            return padded
        return torch.cat(torch.split(padded, self.T, dim=-1), dim=0)

    def unfold_unet_outputs(self, x, input_size):
        batch_size, n_frames = input_size[0], input_size[-1]
        if x.size(0) == batch_size:
            return x[..., :n_frames]
        x = torch.cat(torch.split(x, batch_size, dim=0), dim=-1)
        return x[..., :n_frames]

    def trim_freq_dim(self, x):
        return x[..., :self.F, :]

    def pad_freq_dim(self, x):
        padding = (self.n_fft // 2 + 1) - x.size(-2)
        return F.pad(x, (0, 0, 0, padding))

    def pad_stft_input(self, x):
        pad_len = (-(x.size(-1) - self.win_length) % self.hop_length) % self.win_length
        return F.pad(x, (0, pad_len))

    def _stft(self, x):
        return torch.stft(input=x, n_fft=self.n_fft, window=self.hann_window,
                          win_length=self.win_length, hop_length=self.hop_length,
                          center=self.center, return_complex=True)

    def _istft(self, x, trim_length=None):
        return torch.istft(input=x, n_fft=self.n_fft, window=self.hann_window,
                           win_length=self.win_length, hop_length=self.hop_length,
                           center=self.center, length=trim_length)

    def batch_stft(self, x, pad=True, return_complex=False):
        x_shape = x.size()
        x = x.reshape(-1, x_shape[-1])
        if pad:
            x = self.pad_stft_input(x)
        S = self._stft(x)
        S = S.reshape(x_shape[:-1] + S.shape[-2:])
        if return_complex:
            return S
        return S.abs(), S.angle()

    def batch_istft(self, magnitude, phase, trim_length=None):
        S = torch.polar(magnitude, phase)
        S_shape = S.size()
        S = S.reshape(-1, S_shape[-2], S_shape[-1])
        x = self._istft(S, trim_length)
        return x.reshape(S_shape[:-2] + x.shape[-1:])


# ─────────────────────────────────────────────
# UNet Blocks
# ─────────────────────────────────────────────

class UNetEncoderBlock(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=(5,5),
                 stride=(2,2), padding=(2,2), relu_slope=0.2):
        super().__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size,
                              stride=stride, padding=padding)
        self.bn = nn.BatchNorm2d(out_channels)
        self.activ = nn.LeakyReLU(relu_slope)
        nn.init.kaiming_uniform_(self.conv.weight, nonlinearity='leaky_relu', a=relu_slope)
        nn.init.zeros_(self.conv.bias)

    def forward(self, x):
        c = self.conv(x)
        return self.activ(self.bn(c)), c


class UNetDecoderBlock(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=(5,5),
                 stride=(2,2), padding=(2,2), output_padding=(1,1), dropout=0.0):
        super().__init__()
        self.conv_trans = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=kernel_size,
                                             stride=stride, padding=padding, output_padding=output_padding)
        self.bn = nn.BatchNorm2d(out_channels)
        self.dropout = nn.Dropout(dropout)
        self.activ = nn.ReLU()

    def forward(self, x):
        return self.dropout(self.bn(self.activ(self.conv_trans(x))))


# ─────────────────────────────────────────────
# UNet Models
# ─────────────────────────────────────────────

class UNet(nn.Module):
    def __init__(self, input_size: Tuple[int, ...] = (2, 2048, 512),
                 power: float = 1.0, device: Optional[str] = None):
        super().__init__()
        self.input_size = input_size
        audio_channels, f_size, t_size = input_size
        self.utils = UNetUtils(F=f_size, T=t_size, device=device)
        self.input_norm = nn.BatchNorm2d(f_size)
        self.enc1 = UNetEncoderBlock(audio_channels, 16)
        self.enc2 = UNetEncoderBlock(16, 32)
        self.enc3 = UNetEncoderBlock(32, 64)
        self.enc4 = UNetEncoderBlock(64, 128)
        self.enc5 = UNetEncoderBlock(128, 256)
        self.enc6 = UNetEncoderBlock(256, 512)
        self.dec1 = UNetDecoderBlock(512, 256, dropout=0.5)
        self.dec2 = UNetDecoderBlock(512, 128, dropout=0.5)
        self.dec3 = UNetDecoderBlock(256, 64, dropout=0.5)
        self.dec4 = UNetDecoderBlock(128, 32)
        self.dec5 = UNetDecoderBlock(64, 16)
        self.dec6 = UNetDecoderBlock(32, audio_channels)
        self.mask_layer = nn.Sequential(
            nn.Conv2d(audio_channels, audio_channels, kernel_size=(4,4), dilation=(2,2), padding=3),
            nn.Sigmoid()
        )
        nn.init.kaiming_uniform_(self.mask_layer[0].weight)
        nn.init.zeros_(self.mask_layer[0].bias)
        if device is not None:
            self.to(device)

    def produce_mask(self, x: Tensor) -> Tensor:
        x = self.input_norm(x.transpose(1, 2)).transpose(1, 2)
        d, c1 = self.enc1(x)
        d, c2 = self.enc2(d)
        d, c3 = self.enc3(d)
        d, c4 = self.enc4(d)
        d, c5 = self.enc5(d)
        _, c6 = self.enc6(d)
        u = self.dec1(c6)
        u = self.dec2(torch.cat([c5, u], dim=1))
        u = self.dec3(torch.cat([c4, u], dim=1))
        u = self.dec4(torch.cat([c3, u], dim=1))
        u = self.dec5(torch.cat([c2, u], dim=1))
        u = self.dec6(torch.cat([c1, u], dim=1))
        return self.mask_layer(u)

    def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
        input_size = x.size()
        x = self.utils.fold_unet_inputs(x)
        i = self.utils.trim_freq_dim(x)
        mask = self.produce_mask(i)
        mask = self.utils.pad_freq_dim(mask)
        return (self.utils.unfold_unet_outputs(x * mask, input_size),
                self.utils.unfold_unet_outputs(mask, input_size))


class UNetWaveform(UNet):
    def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
        if x.dim() == 1:
            x = x.repeat(2, 1)
        if x.dim() == 2:
            x = x.unsqueeze(0)
        mag, phase = self.utils.batch_stft(x)
        mag_hat, mask = super().forward(mag)
        return self.utils.batch_istft(mag_hat, phase, trim_length=x.size(-1)), mask


# ─────────────────────────────────────────────
# LarsNet
# ─────────────────────────────────────────────

class LarsNet(nn.Module):
    def __init__(self, wiener_filter=False, wiener_exponent=1.0,
                 config: Union[str, Path] = "config.yaml",
                 return_stft=False, device='cpu', **kwargs):
        super().__init__(**kwargs)
        with open(config, "r") as f:
            config = yaml.safe_load(f)

        self.device = device
        self.wiener_filter = wiener_filter
        self.wiener_exponent = wiener_exponent
        self.return_stft = return_stft
        self.stems = config['inference_models'].keys()
        self.utils = UNetUtils(device=self.device)
        self.sr = config['global']['sr']
        self.models = {}

        print('Loading UNet models...')
        for stem in tqdm(self.stems):
            checkpoint_path = Path(config['inference_models'][stem])
            F = config[stem]['F']
            T = config[stem]['T']
            model = (UNet if (wiener_filter or return_stft) else UNetWaveform)(
                input_size=(2, F, T), device=self.device
            )
            checkpoint = torch.load(str(checkpoint_path), map_location=device)
            model.load_state_dict(checkpoint['model_state_dict'])
            model.eval()
            self.models[stem] = model

    @staticmethod
    def _fix_dim(x):
        if x.dim() == 1:
            x = x.repeat(2, 1)
        if x.dim() == 2:
            x = x.unsqueeze(0)
        return x

    def separate(self, x):
        out = {}
        x = x.to(self.device)
        for stem, model in tqdm(self.models.items()):
            y, _ = model(x)
            out[stem] = y.squeeze(0).detach()
        return out

    def separate_wiener(self, x):
        out = {}
        mag_pred = []
        x = self._fix_dim(x).to(self.device)
        mag, phase = self.utils.batch_stft(x)
        for stem, model in tqdm(self.models.items()):
            _, mask = model(mag)
            mag_pred.append((mask * mag) ** self.wiener_exponent)
        pred_sum = sum(mag_pred)
        for stem, pred in zip(self.stems, mag_pred):
            wiener_mask = pred / (pred_sum + 1e-7)
            y = self.utils.batch_istft(mag * wiener_mask, phase, trim_length=x.size(-1))
            out[stem] = y.squeeze(0).detach()
        return out

    def separate_stft(self, x):
        out = {}
        x = self._fix_dim(x).to(self.device)
        mag, phase = self.utils.batch_stft(x)
        for stem, model in tqdm(self.models.items()):
            mag_pred, _ = model(mag)
            out[stem] = torch.polar(mag_pred, phase).squeeze(0).detach()
        return out

    def forward(self, x):
        if isinstance(x, (str, Path)):
            x, sr_ = ta.load(str(x))
            if sr_ != self.sr:
                x = ta.functional.resample(x, sr_, self.sr)
        if self.return_stft:
            return self.separate_stft(x)
        elif self.wiener_filter:
            return self.separate_wiener(x)
        else:
            return self.separate(x)


# ─────────────────────────────────────────────
# App
# ─────────────────────────────────────────────

model_card = ModelCard(
    name="LarsNet Drum Stem Separator",
    description="Separates a drum mix into individual drum stems: Kick, Snare, Toms, Hi-Hat, and Cymbals.",
    author="A. I. Mezza, et al.",
    tags=["drums", "demucs", "source-separation", "pyharp", "stems", "multi-output"],
)

MODEL = LarsNet(wiener_filter=False, device="cpu", config="config.yaml")


@torch.inference_mode()
def process_fn(audio_path: str):
    stems = MODEL(audio_path)
    output_dir = Path("outputs")
    output_dir.mkdir(exist_ok=True)
    output_paths = []
    for stem_name in ["kick", "snare", "toms", "hihat", "cymbals"]:
        out_path = output_dir / f"{stem_name}.wav"
        sf.write(out_path, stems[stem_name].cpu().numpy().T, MODEL.sr)
        output_paths.append(str(out_path))
    return tuple(output_paths)


with gr.Blocks() as demo:
    input_audio = gr.Audio(type="filepath", label="Drum Mix (Input)").harp_required(True)
    output_kick    = gr.Audio(type="filepath", label="Kick")
    output_snare   = gr.Audio(type="filepath", label="Snare")
    output_toms    = gr.Audio(type="filepath", label="Toms")
    output_hihat   = gr.Audio(type="filepath", label="Hi-Hat")
    output_cymbals = gr.Audio(type="filepath", label="Cymbals")

    app = build_endpoint(
        model_card=model_card,
        input_components=[input_audio],
        output_components=[output_kick, output_snare, output_toms, output_hihat, output_cymbals],
        process_fn=process_fn,
    )

demo.queue().launch(show_error=True, share=True)