handler.py

from typing import Dict, List, Any
import logger
import spaces
import gradio as gr
import json
import torch
import wavio
from tqdm import tqdm
from huggingface_hub import snapshot_download
from models import AudioDiffusion, DDPMScheduler
from audioldm.audio.stft import TacotronSTFT
from audioldm.variational_autoencoder import AutoencoderKL
from pydub import AudioSegment
from gradio import Markdown

import torch
#from diffusers.models.autoencoder_kl import AutoencoderKL
from diffusers.models.unet_2d_condition import UNet2DConditionModel
from diffusers import DiffusionPipeline,AudioPipelineOutput
from transformers import CLIPTextModel, T5EncoderModel, AutoModel, T5Tokenizer, T5TokenizerFast
from typing import Union
from diffusers.utils.torch_utils import randn_tensor
from tqdm import tqdm

class Tango:
    def __init__(self, name="declare-lab/tango2", device=device_selection):
        
        path = snapshot_download(repo_id=name)
        
        vae_config = json.load(open("{}/vae_config.json".format(path)))
        stft_config = json.load(open("{}/stft_config.json".format(path)))
        main_config = json.load(open("{}/main_config.json".format(path)))
        
        self.vae = AutoencoderKL(**vae_config).to(device)
        self.stft = TacotronSTFT(**stft_config).to(device)
        self.model = AudioDiffusion(**main_config).to(device)
        
        vae_weights = torch.load("{}/pytorch_model_vae.bin".format(path), map_location=device)
        stft_weights = torch.load("{}/pytorch_model_stft.bin".format(path), map_location=device)
        main_weights = torch.load("{}/pytorch_model_main.bin".format(path), map_location=device)
        
        self.vae.load_state_dict(vae_weights)
        self.stft.load_state_dict(stft_weights)
        self.model.load_state_dict(main_weights)

        print ("Successfully loaded checkpoint from:", name)
        
        self.vae.eval()
        self.stft.eval()
        self.model.eval()
        
        self.scheduler = DDPMScheduler.from_pretrained(main_config["scheduler_name"], subfolder="scheduler")
        
    def chunks(self, lst, n):
        """ Yield successive n-sized chunks from a list. """
        for i in range(0, len(lst), n):
            yield lst[i:i + n]
        
    def generate(self, prompt, steps=100, guidance=3, samples=1, disable_progress=True):
        """ Genrate audio for a single prompt string. """
        with torch.no_grad():
            latents = self.model.inference([prompt], self.scheduler, steps, guidance, samples, disable_progress=disable_progress)
            mel = self.vae.decode_first_stage(latents)
            wave = self.vae.decode_to_waveform(mel)
        return wave[0]
    
    def generate_for_batch(self, prompts, steps=200, guidance=3, samples=1, batch_size=8, disable_progress=True):
        """ Genrate audio for a list of prompt strings. """
        outputs = []
        for k in tqdm(range(0, len(prompts), batch_size)):
            batch = prompts[k: k+batch_size]
            with torch.no_grad():
                latents = self.model.inference(batch, self.scheduler, steps, guidance, samples, disable_progress=disable_progress)
                mel = self.vae.decode_first_stage(latents)
                wave = self.vae.decode_to_waveform(mel)
                outputs += [item for item in wave]
        if samples == 1:
            return outputs
        else:
            return list(self.chunks(outputs, samples))

# Initialize TANGO




class EndpointHandler():
    def __init__(self, path=""):
        # Preload all the elements you are going to need at inference.
        # pseudo:
        self.model= tango(device='cuda')

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
       data args:
            inputs (:obj: `str` | `PIL.Image` | `np.array`)
            kwargs
      Return:
            A :obj:`list` | `dict`: will be serialized and returned
        """

        # pseudo
        # self.model(input)
        inputs = data.pop("inputs", data)

        logger.info(f"Received incoming request with {data=}")

        if "inputs" in data and isinstance(data["inputs"], str):
            prompt = data.pop("inputs")
        elif "prompt" in data and isinstance(data["prompt"], str):
            prompt = data.pop("prompt")
        else:
            raise ValueError(
                "Provided input body must contain either the key `inputs` or `prompt` with the"
                " prompt to use for the image generation, and it needs to be a non-empty string."
            )

        parameters = data.pop("parameters", {})

        num_inference_steps = parameters.get("num_inference_steps", 30)
        width = parameters.get("width", 1024)
        height = parameters.get("height", 768)
        guidance_scale = parameters.get("guidance_scale", 3.5)

        # seed generator (seed cannot be provided as is but via a generator)
        seed = parameters.get("seed", 0)
        generator = torch.manual_seed(seed)