from collections import deque from functools import partial from inspect import isfunction import torch.nn.functional as F import librosa.sequence import numpy as np from torch.nn import Conv1d from torch.nn import Mish import torch from torch import nn from tqdm import tqdm import math def exists(x): return x is not None def default(val, d): if exists(val): return val return d() if isfunction(d) else d def extract(a, t): return a[t].reshape((1, 1, 1, 1)) def noise_like(shape, device, repeat=False): repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) noise = lambda: torch.randn(shape, device=device) return repeat_noise() if repeat else noise() def linear_beta_schedule(timesteps, max_beta=0.02): """ linear schedule """ betas = np.linspace(1e-4, max_beta, timesteps) return betas def cosine_beta_schedule(timesteps, s=0.008): """ cosine schedule as proposed in https://openreview.net/forum?id=-NEXDKk8gZ """ steps = timesteps + 1 x = np.linspace(0, steps, steps) alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2 alphas_cumprod = alphas_cumprod / alphas_cumprod[0] betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1]) return np.clip(betas, a_min=0, a_max=0.999) beta_schedule = { "cosine": cosine_beta_schedule, "linear": linear_beta_schedule, } def extract_1(a, t): return a[t].reshape((1, 1, 1, 1)) def predict_stage0(noise_pred, noise_pred_prev): return (noise_pred + noise_pred_prev) / 2 def predict_stage1(noise_pred, noise_list): return (noise_pred * 3 - noise_list[-1]) / 2 def predict_stage2(noise_pred, noise_list): return (noise_pred * 23 - noise_list[-1] * 16 + noise_list[-2] * 5) / 12 def predict_stage3(noise_pred, noise_list): return (noise_pred * 55 - noise_list[-1] * 59 + noise_list[-2] * 37 - noise_list[-3] * 9) / 24 class SinusoidalPosEmb(nn.Module): def __init__(self, dim): super().__init__() self.dim = dim self.half_dim = dim // 2 self.emb = 9.21034037 / (self.half_dim - 1) self.emb = torch.exp(torch.arange(self.half_dim) * torch.tensor(-self.emb)).unsqueeze(0) self.emb = self.emb.cpu() def forward(self, x): emb = self.emb * x emb = torch.cat((emb.sin(), emb.cos()), dim=-1) return emb class ResidualBlock(nn.Module): def __init__(self, encoder_hidden, residual_channels, dilation): super().__init__() self.residual_channels = residual_channels self.dilated_conv = Conv1d(residual_channels, 2 * residual_channels, 3, padding=dilation, dilation=dilation) self.diffusion_projection = nn.Linear(residual_channels, residual_channels) self.conditioner_projection = Conv1d(encoder_hidden, 2 * residual_channels, 1) self.output_projection = Conv1d(residual_channels, 2 * residual_channels, 1) def forward(self, x, conditioner, diffusion_step): diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1) conditioner = self.conditioner_projection(conditioner) y = x + diffusion_step y = self.dilated_conv(y) + conditioner gate, filter_1 = torch.split(y, [self.residual_channels, self.residual_channels], dim=1) y = torch.sigmoid(gate) * torch.tanh(filter_1) y = self.output_projection(y) residual, skip = torch.split(y, [self.residual_channels, self.residual_channels], dim=1) return (x + residual) / 1.41421356, skip class DiffNet(nn.Module): def __init__(self, in_dims, n_layers, n_chans, n_hidden): super().__init__() self.encoder_hidden = n_hidden self.residual_layers = n_layers self.residual_channels = n_chans self.input_projection = Conv1d(in_dims, self.residual_channels, 1) self.diffusion_embedding = SinusoidalPosEmb(self.residual_channels) dim = self.residual_channels self.mlp = nn.Sequential( nn.Linear(dim, dim * 4), Mish(), nn.Linear(dim * 4, dim) ) self.residual_layers = nn.ModuleList([ ResidualBlock(self.encoder_hidden, self.residual_channels, 1) for i in range(self.residual_layers) ]) self.skip_projection = Conv1d(self.residual_channels, self.residual_channels, 1) self.output_projection = Conv1d(self.residual_channels, in_dims, 1) nn.init.zeros_(self.output_projection.weight) def forward(self, spec, diffusion_step, cond): x = spec.squeeze(0) x = self.input_projection(x) # x [B, residual_channel, T] x = F.relu(x) # skip = torch.randn_like(x) diffusion_step = diffusion_step.float() diffusion_step = self.diffusion_embedding(diffusion_step) diffusion_step = self.mlp(diffusion_step) x, skip = self.residual_layers[0](x, cond, diffusion_step) # noinspection PyTypeChecker for layer in self.residual_layers[1:]: x, skip_connection = layer.forward(x, cond, diffusion_step) skip = skip + skip_connection x = skip / math.sqrt(len(self.residual_layers)) x = self.skip_projection(x) x = F.relu(x) x = self.output_projection(x) # [B, 80, T] return x.unsqueeze(1) class AfterDiffusion(nn.Module): def __init__(self, spec_max, spec_min, v_type='a'): super().__init__() self.spec_max = spec_max self.spec_min = spec_min self.type = v_type def forward(self, x): x = x.squeeze(1).permute(0, 2, 1) mel_out = (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min if self.type == 'nsf-hifigan-log10': mel_out = mel_out * 0.434294 return mel_out.transpose(2, 1) class Pred(nn.Module): def __init__(self, alphas_cumprod): super().__init__() self.alphas_cumprod = alphas_cumprod def forward(self, x_1, noise_t, t_1, t_prev): a_t = extract(self.alphas_cumprod, t_1).cpu() a_prev = extract(self.alphas_cumprod, t_prev).cpu() a_t_sq, a_prev_sq = a_t.sqrt().cpu(), a_prev.sqrt().cpu() x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x_1 - 1 / ( a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t) x_pred = x_1 + x_delta.cpu() return x_pred class GaussianDiffusion(nn.Module): def __init__(self, out_dims=128, n_layers=20, n_chans=384, n_hidden=256, timesteps=1000, k_step=1000, max_beta=0.02, spec_min=-12, spec_max=2): super().__init__() self.denoise_fn = DiffNet(out_dims, n_layers, n_chans, n_hidden) self.out_dims = out_dims self.mel_bins = out_dims self.n_hidden = n_hidden betas = beta_schedule['linear'](timesteps, max_beta=max_beta) alphas = 1. - betas alphas_cumprod = np.cumprod(alphas, axis=0) alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) timesteps, = betas.shape self.num_timesteps = int(timesteps) self.k_step = k_step self.noise_list = deque(maxlen=4) to_torch = partial(torch.tensor, dtype=torch.float32) self.register_buffer('betas', to_torch(betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) # calculations for diffusion q(x_t | x_{t-1}) and others self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) # calculations for posterior q(x_{t-1} | x_t, x_0) posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod) # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) self.register_buffer('posterior_variance', to_torch(posterior_variance)) # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) self.register_buffer('posterior_mean_coef1', to_torch( betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) self.register_buffer('posterior_mean_coef2', to_torch( (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) self.register_buffer('spec_min', torch.FloatTensor([spec_min])[None, None, :out_dims]) self.register_buffer('spec_max', torch.FloatTensor([spec_max])[None, None, :out_dims]) self.ad = AfterDiffusion(self.spec_max, self.spec_min) self.xp = Pred(self.alphas_cumprod) def q_mean_variance(self, x_start, t): mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start variance = extract(1. - self.alphas_cumprod, t, x_start.shape) log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape) return mean, variance, log_variance def predict_start_from_noise(self, x_t, t, noise): return ( extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise ) def q_posterior(self, x_start, x_t, t): posterior_mean = ( extract(self.posterior_mean_coef1, t, x_t.shape) * x_start + extract(self.posterior_mean_coef2, t, x_t.shape) * x_t ) posterior_variance = extract(self.posterior_variance, t, x_t.shape) posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape) return posterior_mean, posterior_variance, posterior_log_variance_clipped def p_mean_variance(self, x, t, cond): noise_pred = self.denoise_fn(x, t, cond=cond) x_recon = self.predict_start_from_noise(x, t=t, noise=noise_pred) x_recon.clamp_(-1., 1.) model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) return model_mean, posterior_variance, posterior_log_variance @torch.no_grad() def p_sample(self, x, t, cond, clip_denoised=True, repeat_noise=False): b, *_, device = *x.shape, x.device model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, cond=cond) noise = noise_like(x.shape, device, repeat_noise) # no noise when t == 0 nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise @torch.no_grad() def p_sample_plms(self, x, t, interval, cond, clip_denoised=True, repeat_noise=False): """ Use the PLMS method from [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778). """ def get_x_pred(x, noise_t, t): a_t = extract(self.alphas_cumprod, t) a_prev = extract(self.alphas_cumprod, torch.max(t - interval, torch.zeros_like(t))) a_t_sq, a_prev_sq = a_t.sqrt(), a_prev.sqrt() x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x - 1 / ( a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t) x_pred = x + x_delta return x_pred noise_list = self.noise_list noise_pred = self.denoise_fn(x, t, cond=cond) if len(noise_list) == 0: x_pred = get_x_pred(x, noise_pred, t) noise_pred_prev = self.denoise_fn(x_pred, max(t - interval, 0), cond=cond) noise_pred_prime = (noise_pred + noise_pred_prev) / 2 elif len(noise_list) == 1: noise_pred_prime = (3 * noise_pred - noise_list[-1]) / 2 elif len(noise_list) == 2: noise_pred_prime = (23 * noise_pred - 16 * noise_list[-1] + 5 * noise_list[-2]) / 12 else: noise_pred_prime = (55 * noise_pred - 59 * noise_list[-1] + 37 * noise_list[-2] - 9 * noise_list[-3]) / 24 x_prev = get_x_pred(x, noise_pred_prime, t) noise_list.append(noise_pred) return x_prev def q_sample(self, x_start, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) return ( extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise ) def p_losses(self, x_start, t, cond, noise=None, loss_type='l2'): noise = default(noise, lambda: torch.randn_like(x_start)) x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) x_recon = self.denoise_fn(x_noisy, t, cond) if loss_type == 'l1': loss = (noise - x_recon).abs().mean() elif loss_type == 'l2': loss = F.mse_loss(noise, x_recon) else: raise NotImplementedError() return loss def org_forward(self, condition, init_noise=None, gt_spec=None, infer=True, infer_speedup=100, method='pndm', k_step=1000, use_tqdm=True): """ conditioning diffusion, use fastspeech2 encoder output as the condition """ cond = condition b, device = condition.shape[0], condition.device if not infer: spec = self.norm_spec(gt_spec) t = torch.randint(0, self.k_step, (b,), device=device).long() norm_spec = spec.transpose(1, 2)[:, None, :, :] # [B, 1, M, T] return self.p_losses(norm_spec, t, cond=cond) else: shape = (cond.shape[0], 1, self.out_dims, cond.shape[2]) if gt_spec is None: t = self.k_step if init_noise is None: x = torch.randn(shape, device=device) else: x = init_noise else: t = k_step norm_spec = self.norm_spec(gt_spec) norm_spec = norm_spec.transpose(1, 2)[:, None, :, :] x = self.q_sample(x_start=norm_spec, t=torch.tensor([t - 1], device=device).long()) if method is not None and infer_speedup > 1: if method == 'dpm-solver': from .dpm_solver_pytorch import NoiseScheduleVP, model_wrapper, DPM_Solver # 1. Define the noise schedule. noise_schedule = NoiseScheduleVP(schedule='discrete', betas=self.betas[:t]) # 2. Convert your discrete-time `model` to the continuous-time # noise prediction model. Here is an example for a diffusion model # `model` with the noise prediction type ("noise") . def my_wrapper(fn): def wrapped(x, t, **kwargs): ret = fn(x, t, **kwargs) if use_tqdm: self.bar.update(1) return ret return wrapped model_fn = model_wrapper( my_wrapper(self.denoise_fn), noise_schedule, model_type="noise", # or "x_start" or "v" or "score" model_kwargs={"cond": cond} ) # 3. Define dpm-solver and sample by singlestep DPM-Solver. # (We recommend singlestep DPM-Solver for unconditional sampling) # You can adjust the `steps` to balance the computation # costs and the sample quality. dpm_solver = DPM_Solver(model_fn, noise_schedule) steps = t // infer_speedup if use_tqdm: self.bar = tqdm(desc="sample time step", total=steps) x = dpm_solver.sample( x, steps=steps, order=3, skip_type="time_uniform", method="singlestep", ) if use_tqdm: self.bar.close() elif method == 'pndm': self.noise_list = deque(maxlen=4) if use_tqdm: for i in tqdm( reversed(range(0, t, infer_speedup)), desc='sample time step', total=t // infer_speedup, ): x = self.p_sample_plms( x, torch.full((b,), i, device=device, dtype=torch.long), infer_speedup, cond=cond ) else: for i in reversed(range(0, t, infer_speedup)): x = self.p_sample_plms( x, torch.full((b,), i, device=device, dtype=torch.long), infer_speedup, cond=cond ) else: raise NotImplementedError(method) else: if use_tqdm: for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t): x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond) else: for i in reversed(range(0, t)): x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond) x = x.squeeze(1).transpose(1, 2) # [B, T, M] return self.denorm_spec(x).transpose(2, 1) def norm_spec(self, x): return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1 def denorm_spec(self, x): return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min def get_x_pred(self, x_1, noise_t, t_1, t_prev): a_t = extract(self.alphas_cumprod, t_1) a_prev = extract(self.alphas_cumprod, t_prev) a_t_sq, a_prev_sq = a_t.sqrt(), a_prev.sqrt() x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x_1 - 1 / ( a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t) x_pred = x_1 + x_delta return x_pred def OnnxExport(self, project_name=None, init_noise=None, hidden_channels=256, export_denoise=True, export_pred=True, export_after=True): cond = torch.randn([1, self.n_hidden, 10]).cpu() if init_noise is None: x = torch.randn((1, 1, self.mel_bins, cond.shape[2]), dtype=torch.float32).cpu() else: x = init_noise pndms = 100 org_y_x = self.org_forward(cond, init_noise=x) device = cond.device n_frames = cond.shape[2] step_range = torch.arange(0, self.k_step, pndms, dtype=torch.long, device=device).flip(0) plms_noise_stage = torch.tensor(0, dtype=torch.long, device=device) noise_list = torch.zeros((0, 1, 1, self.mel_bins, n_frames), device=device) ot = step_range[0] ot_1 = torch.full((1,), ot, device=device, dtype=torch.long) if export_denoise: torch.onnx.export( self.denoise_fn, (x.cpu(), ot_1.cpu(), cond.cpu()), f"{project_name}_denoise.onnx", input_names=["noise", "time", "condition"], output_names=["noise_pred"], dynamic_axes={ "noise": [3], "condition": [2] }, opset_version=16 ) for t in step_range: t_1 = torch.full((1,), t, device=device, dtype=torch.long) noise_pred = self.denoise_fn(x, t_1, cond) t_prev = t_1 - pndms t_prev = t_prev * (t_prev > 0) if plms_noise_stage == 0: if export_pred: torch.onnx.export( self.xp, (x.cpu(), noise_pred.cpu(), t_1.cpu(), t_prev.cpu()), f"{project_name}_pred.onnx", input_names=["noise", "noise_pred", "time", "time_prev"], output_names=["noise_pred_o"], dynamic_axes={ "noise": [3], "noise_pred": [3] }, opset_version=16 ) x_pred = self.get_x_pred(x, noise_pred, t_1, t_prev) noise_pred_prev = self.denoise_fn(x_pred, t_prev, cond=cond) noise_pred_prime = predict_stage0(noise_pred, noise_pred_prev) elif plms_noise_stage == 1: noise_pred_prime = predict_stage1(noise_pred, noise_list) elif plms_noise_stage == 2: noise_pred_prime = predict_stage2(noise_pred, noise_list) else: noise_pred_prime = predict_stage3(noise_pred, noise_list) noise_pred = noise_pred.unsqueeze(0) if plms_noise_stage < 3: noise_list = torch.cat((noise_list, noise_pred), dim=0) plms_noise_stage = plms_noise_stage + 1 else: noise_list = torch.cat((noise_list[-2:], noise_pred), dim=0) x = self.get_x_pred(x, noise_pred_prime, t_1, t_prev) if export_after: torch.onnx.export( self.ad, x.cpu(), f"{project_name}_after.onnx", input_names=["x"], output_names=["mel_out"], dynamic_axes={ "x": [3] }, opset_version=16 ) x = self.ad(x) print((x == org_y_x).all()) return x def forward(self, condition=None, init_noise=None, pndms=None, k_step=None): cond = condition x = init_noise device = cond.device n_frames = cond.shape[2] step_range = torch.arange(0, k_step.item(), pndms.item(), dtype=torch.long, device=device).flip(0) plms_noise_stage = torch.tensor(0, dtype=torch.long, device=device) noise_list = torch.zeros((0, 1, 1, self.mel_bins, n_frames), device=device) ot = step_range[0] ot_1 = torch.full((1,), ot, device=device, dtype=torch.long) for t in step_range: t_1 = torch.full((1,), t, device=device, dtype=torch.long) noise_pred = self.denoise_fn(x, t_1, cond) t_prev = t_1 - pndms t_prev = t_prev * (t_prev > 0) if plms_noise_stage == 0: x_pred = self.get_x_pred(x, noise_pred, t_1, t_prev) noise_pred_prev = self.denoise_fn(x_pred, t_prev, cond=cond) noise_pred_prime = predict_stage0(noise_pred, noise_pred_prev) elif plms_noise_stage == 1: noise_pred_prime = predict_stage1(noise_pred, noise_list) elif plms_noise_stage == 2: noise_pred_prime = predict_stage2(noise_pred, noise_list) else: noise_pred_prime = predict_stage3(noise_pred, noise_list) noise_pred = noise_pred.unsqueeze(0) if plms_noise_stage < 3: noise_list = torch.cat((noise_list, noise_pred), dim=0) plms_noise_stage = plms_noise_stage + 1 else: noise_list = torch.cat((noise_list[-2:], noise_pred), dim=0) x = self.get_x_pred(x, noise_pred_prime, t_1, t_prev) x = self.ad(x) return x