pico_model.py

import random
import numpy as np
from tqdm import tqdm
from einops import repeat
import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers.utils.torch_utils import randn_tensor
from diffusers import DDPMScheduler, UNet2DConditionModel

from audioldm.audio.stft import TacotronSTFT
from audioldm.variational_autoencoder.autoencoder import AutoencoderKL
from audioldm.utils import default_audioldm_config, get_metadata



def build_pretrained_models(name):
    checkpoint = torch.load(get_metadata()[name]["path"], map_location="cpu")
    scale_factor = checkpoint["state_dict"]["scale_factor"].item()

    vae_state_dict = {k[18:]: v for k, v in checkpoint["state_dict"].items() if "first_stage_model." in k}

    config = default_audioldm_config(name)
    vae_config = config["model"]["params"]["first_stage_config"]["params"]
    vae_config["scale_factor"] = scale_factor

    vae = AutoencoderKL(**vae_config)
    vae.load_state_dict(vae_state_dict)

    fn_STFT = TacotronSTFT(
        config["preprocessing"]["stft"]["filter_length"],
        config["preprocessing"]["stft"]["hop_length"],
        config["preprocessing"]["stft"]["win_length"],
        config["preprocessing"]["mel"]["n_mel_channels"],
        config["preprocessing"]["audio"]["sampling_rate"],
        config["preprocessing"]["mel"]["mel_fmin"],
        config["preprocessing"]["mel"]["mel_fmax"],
    )

    vae.eval()
    fn_STFT.eval()

    return vae, fn_STFT

def _init_layer(layer):
    """Initialize a Linear or Convolutional layer. """
    nn.init.xavier_uniform_(layer.weight)
 
    if hasattr(layer, 'bias'):
        if layer.bias is not None:
            layer.bias.data.fill_(0.)

class ClapText_Onset_2_Audio_Diffusion(nn.Module):
    def __init__(
        self,
        scheduler_name,
        unet_model_config_path=None,
        snr_gamma=None,
        uncondition=False,
    ):
        super().__init__()

        assert unet_model_config_path is not None, "Either UNet pretrain model name or a config file path is required"
        self.scheduler_name = scheduler_name
        self.unet_model_config_path = unet_model_config_path
        self.snr_gamma = snr_gamma
        self.uncondition = uncondition
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        # https://huggingface.co./docs/diffusers/v0.14.0/en/api/schedulers/overview
        self.noise_scheduler = DDPMScheduler.from_pretrained(self.scheduler_name, subfolder="scheduler")
        self.inference_scheduler = DDPMScheduler.from_pretrained(self.scheduler_name, subfolder="scheduler")
        unet_config = UNet2DConditionModel.load_config(unet_model_config_path)
        self.unet = UNet2DConditionModel.from_config(unet_config, subfolder="unet")

    def compute_snr(self, timesteps):
        """
        Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
        """
        alphas_cumprod = self.noise_scheduler.alphas_cumprod
        sqrt_alphas_cumprod = alphas_cumprod**0.5
        sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5

        # Expand the tensors.
        # Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
        sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
        while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
            sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
        alpha = sqrt_alphas_cumprod.expand(timesteps.shape)

        sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
        while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
            sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
        sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)

        # Compute SNR.
        snr = (alpha / sigma) ** 2
        return snr    

    def encode_channel(self, input):
        # input [batch, 32, 256] -> [batch, 2, 256, 16]
        return input.reshape(input.shape[0], 2, 16, 256).transpose(2, 3)

    def encode_text(self, input_dict):      
        device = self.device   
        
        encoder_hidden_states = input_dict["event_info"].repeat_interleave(2, -1).unsqueeze(1)
        boolean_encoder_mask = (torch.ones(len(encoder_hidden_states), 1) == 1).to(device)

        return encoder_hidden_states, boolean_encoder_mask

    def forward(self, input_dict, validation_mode=False):
        device = self.device
        latents = input_dict["latent"]
        num_train_timesteps = self.noise_scheduler.num_train_timesteps
        self.noise_scheduler.set_timesteps(num_train_timesteps, device=device)
        # [batch, 1, 1024], [batch, 1]

        encoder_hidden_states, boolean_encoder_mask = self.encode_text(input_dict)
        if self.uncondition:
            mask_indices = [k for k in range(len(latents)) if random.random() < 0.1]
            if len(mask_indices) > 0:
                encoder_hidden_states[mask_indices] = 0

        bsz = latents.shape[0]
        if validation_mode:
            timesteps = (self.noise_scheduler.num_train_timesteps//2) * torch.ones((bsz,), dtype=torch.int64, device=device)
        else:
            # Sample a random timestep for each instance
            timesteps = torch.randint(0, self.noise_scheduler.num_train_timesteps, (bsz,), device=device)
        timesteps = timesteps.long()

        noise = torch.randn_like(latents)
        noisy_latents = self.noise_scheduler.add_noise(latents, noise, timesteps)
        
        onset_emb = self.encode_channel(input_dict["onset"])
        # [batch, channel:8, 256, 16] + [batch, onset:2, 256, 16]
        onset_noisy_latents = torch.cat((onset_emb, noisy_latents), dim=1)

        # Get the target for loss depending on the prediction type
        if self.noise_scheduler.config.prediction_type == "epsilon":
            target = noise
        elif self.noise_scheduler.config.prediction_type == "v_prediction":
            target = self.noise_scheduler.get_velocity(latents, noise, timesteps)
        else:
            raise ValueError(f"Unknown prediction type {self.noise_scheduler.config.prediction_type}")
        
        model_pred = self.unet(
            onset_noisy_latents, timesteps, encoder_hidden_states, 
            #encoder_attention_mask=boolean_encoder_mask
        ).sample

        if self.snr_gamma is None:
            loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
        else:
            # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
            # Adaptef from huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py
            snr = self.compute_snr(timesteps)
            mse_loss_weights = (
                torch.stack([snr, self.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
            )
            loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
            loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
            loss = loss.mean()

        return loss

    def prepare_latents(self, batch_size, inference_scheduler, num_channels_latents, dtype, device):
        shape = (batch_size, num_channels_latents, 256, 16)
        latents = randn_tensor(shape, generator=None, device=device, dtype=dtype)
        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * inference_scheduler.init_noise_sigma
        return latents

    def encode_text_classifier_free(self, input_dict, num_samples_per_prompt):
        device = self.device
        prompt_embeds, boolean_prompt_mask = self.encode_text(input_dict)
        prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
        attention_mask = boolean_prompt_mask.repeat_interleave(num_samples_per_prompt, 0)
        # get unconditional embeddings for classifier free guidance
        negative_prompt_embeds = torch.zeros(prompt_embeds.shape).to(device)
        uncond_attention_mask = (torch.ones(attention_mask.shape) == 1).to(device)            

        # For classifier free guidance, we need to do two forward passes.
        # We concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes
        prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
        prompt_mask = torch.cat([uncond_attention_mask, attention_mask])
        boolean_prompt_mask = (prompt_mask == 1).to(device)

        return prompt_embeds, boolean_prompt_mask

    @torch.no_grad()
    def inference(self, input_dict, inference_scheduler, num_steps=20, guidance_scale=3, num_samples_per_prompt=1, disable_progress=True):
        prompt = input_dict["onset"]
        device = self.device
        classifier_free_guidance = guidance_scale > 1.0
        batch_size = len(prompt) * num_samples_per_prompt

        if classifier_free_guidance:
            prompt_embeds, boolean_prompt_mask = self.encode_text_classifier_free(input_dict, num_samples_per_prompt)
        else:
            prompt_embeds, boolean_prompt_mask = self.encode_text(input_dict)
            prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
            boolean_prompt_mask = boolean_prompt_mask.repeat_interleave(num_samples_per_prompt, 0)

        inference_scheduler.set_timesteps(num_steps, device=device)
        timesteps = inference_scheduler.timesteps
        
        num_channels_latents = self.unet.config.in_channels - 2
        latents = self.prepare_latents(batch_size, inference_scheduler, num_channels_latents, prompt_embeds.dtype, device)
        onset_emb = self.encode_channel(input_dict["onset"]).repeat_interleave(num_samples_per_prompt, 0)
        onset_latents = torch.cat((onset_emb, latents), dim=1)

        num_warmup_steps = len(timesteps) - num_steps * inference_scheduler.order
        progress_bar = tqdm(range(num_steps), disable=disable_progress)
        
        for i, t in tqdm(enumerate(timesteps)):
            # expand the latents if we are doing classifier free guidance
            latent_model_input = torch.cat([onset_latents] * 2) if classifier_free_guidance else onset_latents
            latent_model_input = inference_scheduler.scale_model_input(latent_model_input, t)
            noise_pred = self.unet(
                latent_model_input, t, encoder_hidden_states=prompt_embeds,
                encoder_attention_mask=boolean_prompt_mask
            ).sample

            # perform guidance
            if classifier_free_guidance:
                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

            # compute the previous noisy sample x_t -> x_t-1
            latents = inference_scheduler.step(noise_pred, t, latents).prev_sample
            onset_latents = torch.cat((onset_emb, latents), dim=1)

            # call the callback, if provided
            if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % inference_scheduler.order == 0):
                progress_bar.update(1)

        return latents
    
##############################
### Demo utils
##############################
from sklearn.metrics.pairwise import cosine_similarity
import laion_clap
from laion_clap.clap_module.factory import load_state_dict as clap_load_state_dict

class PicoDiffusion(ClapText_Onset_2_Audio_Diffusion):
    def __init__(self,      
        scheduler_name,
        unet_model_config_path=None,
        snr_gamma=None,
        uncondition=False,
        freeze_text_encoder_ckpt=None,
        diffusion_pt=None,
    ):
        super().__init__(scheduler_name, unet_model_config_path, snr_gamma, uncondition)
        self.freeze_text_encoder = laion_clap.CLAP_Module(enable_fusion=False)
    
        #load pretrain params
        ckpt = clap_load_state_dict(freeze_text_encoder_ckpt, skip_params=True)
        del_parameter_key = ["text_branch.embeddings.position_ids"]
        ckpt = {f"freeze_text_encoder.model.{k}":v for k, v in ckpt.items() if k not in del_parameter_key}
        diffusion_ckpt = torch.load(diffusion_pt)
        del diffusion_ckpt["class_emb.weight"]
        ckpt.update(diffusion_ckpt)
        self.load_state_dict(ckpt)

        self.event_list = [
            "burping_belching",             # 0
            "car_horn_honking",             #
            "cat_meowing",                  #    
            "cow_mooing",                   #
            "dog_barking",                  #  
            "door_knocking",                #
            "door_slamming",                #
            "explosion",                    #  
            "gunshot",                      # 8
            "sheep_goat_bleating",          #
            "sneeze",                       #
            "spraying",                     # 
            "thump_thud",                   #   
            "train_horn",                   #
            "tapping_clicking_clanking",    #
            "woman_laughing",               #         
            "duck_quacking",                # 16   
            "whistling",                    #    
        ]
        self.events_emb = self.freeze_text_encoder.get_text_embedding(self.event_list, use_tensor=False)

        
    @torch.no_grad()
    def demo_inference(self, timestampCaption, scheduler, num_steps=20, guidance_scale=3, num_samples_per_prompt=1, disable_progress=True):
        #"timestampCaption": "event1__onset1-offset1_onset2-offset2--event2__onset1-offset1"
        #"timestampCaption": "event1 at onset1-offset1_onset2-offset2 and event2 at onset1-offset1."
        device = self.device   

        timestamp_matrix = np.zeros((32, 256))
        events = []
        timestampCaption = timestampCaption.rstrip('.')
        for event_timestamp in timestampCaption.split(' and '):
            # event_timestamp : event1__onset1-offset1_onset2-offset2
            (event, instance) = event_timestamp.split(' at ')
            events.append(event)
            # instance : onset1-offset1_onset2-offset2
            event_emb = self.freeze_text_encoder.get_text_embedding([event, ""], use_tensor=False)[0]
            event_id = np.argmax(cosine_similarity(event_emb.reshape(1, -1), self.events_emb))
            for start_end in instance.split('_'):
                (start, end) = start_end.split('-')         
                start, end = int(float(start)*250/10), int(float(end)*250/10)
                if end > 250: break
                timestamp_matrix[event_id, start: end] = 1
        
        #event_info = self.clap_scorer.get_text_embedding([" and ".join(events), ""], use_tensor=False)[0]
        event_info = self.freeze_text_encoder.get_text_embedding([" and ".join(events), ""], use_tensor=True)[0].unsqueeze(0)
        timestamp_matrix = torch.tensor(timestamp_matrix, dtype=torch.float32).unsqueeze(0).to(device)
        latents = self.inference({"onset":timestamp_matrix, "event_info":event_info.to(device)}, scheduler, num_steps, guidance_scale, num_samples_per_prompt, disable_progress)
                                 
        return latents