app/app.py

import fastapi 
import shutil 
import os 
import zipfile  
import io 
import uvicorn 
import threading
import glob
from typing import List 
import torch
import gdown
from soundfile import write
from torchaudio import load
from librosa import resample
import logging
logging.basicConfig(level=logging.DEBUG)

from sgmse import ScoreModel
from sgmse.util.other import pad_spec

class ModelAPI:
    
    def __init__(self, host, port):
        
        self.host = host 
        self.port = port
        
        self.base_path = os.path.join(os.path.expanduser("~"), ".modelapi")
        self.noisy_audio_path = os.path.join(self.base_path, "noisy_audio")
        self.enhanced_audio_path = os.path.join(self.base_path, "enhanced_audio")
        app_dir = os.path.dirname(os.path.abspath(__file__))
        self.ckpt_path = os.path.join(app_dir,"weightss.ckpt")
        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
        self.corrector = "ald"
        self.corrector_steps = 1
        self.snr = 0.5
        self.N = 30

        for audio_path in [self.noisy_audio_path, self.enhanced_audio_path]:
            if not os.path.exists(audio_path):
                os.makedirs(audio_path)
                
            for filename in os.listdir(audio_path):
                file_path = os.path.join(audio_path, filename)
                
                try:
                    if os.path.isfile(file_path) or os.path.islink(file_path):
                        os.unlink(file_path)
                    elif os.path.isdir(file_path):
                        shutil.rmtree(file_path)
                except Exception as e:
                    raise e
                
        self.app = fastapi.FastAPI()
        self._setup_routes()

    def _prepare(self):

        self.model = ScoreModel.load_from_checkpoint(self.ckpt_path, self.device)
        self.model.t_eps = 0.03
        self.model.eval()

    def _enhance(self):
        if self.model.backbone == 'ncsnpp_48k':
            target_sr = 48000
            pad_mode = "reflection"
        elif self.model.backbone == 'ncsnpp_v2':
            target_sr = 16000
            pad_mode = "reflection"
            print("using ncsnpp_v2")
        else:
            target_sr = 16000
            pad_mode = "zero_pad"

        noisy_files = sorted(glob.glob(os.path.join(self.noisy_audio_path, '*.wav')))
        for noisy_file in noisy_files: 
            
            filename = noisy_file.replace(self.noisy_audio_path, "")
            filename = filename[1:] if filename.startswith("/") else filename

            y, sr = load(noisy_file)

            if sr != target_sr:
                y = torch.tensor(resample(y.numpy(), orig_sr=sr, target_sr=target_sr))

            T_orig = y.size(1)   

            # Normalize
            norm_factor = y.abs().max()
            y = y / norm_factor
            
            # Prepare DNN input
            Y = torch.unsqueeze(self.model._forward_transform(self.model._stft(y.to(self.device))), 0)
            Y = pad_spec(Y, mode=pad_mode)
            
            # Reverse sampling
            if self.model.sde.__class__.__name__ == 'OUVESDE':
                if self.model.sde.sampler_type == 'pc':
                    sampler = self.model.get_pc_sampler('reverse_diffusion', self.corrector, Y.to(self.device), N=self.N, 
                        corrector_steps=self.corrector_steps, snr=self.snr)
                elif self.model.sde.sampler_type == 'ode':
                    sampler = self.model.get_ode_sampler(Y.to(self.device), N=self.N)
                else:
                    raise ValueError(f"Sampler type {args.sampler_type} not supported")
            elif self.model.sde.__class__.__name__ == 'SBVESDE':
                sampler_type = 'ode' if self.model.sde.sampler_type == 'pc' else self.model.sde.sampler_type
                sampler = self.model.get_sb_sampler(sde=self.model.sde, y=Y.cuda(), sampler_type=sampler_type)
            else:
                raise ValueError(f"SDE {self.model.sde.__class__.__name__} not supported")
                
            sample, _ = sampler()
            
            x_hat = self.model.to_audio(sample.squeeze(), T_orig)

            x_hat = x_hat * norm_factor
            
            os.makedirs(os.path.dirname(os.path.join(self.enhanced_audio_path, filename)), exist_ok=True)
            write(os.path.join(self.enhanced_audio_path, filename), x_hat.cpu().numpy(), target_sr)
        
    def _setup_routes(self):
        self.app.get("/status/")(self.get_status)
        self.app.post("/prepare/")(self.prepare)
        self.app.post("/upload-audio/")(self.upload_audio)
        self.app.post("/enhance/")(self.enhance_audio)
        self.app.get("/download-enhanced/")(self.download_enhanced)
        
    def get_status(self):
        try:
            return {"container_running": True}
        except Exception as e:
            logging.error(f"Error getting status: {e}")
            raise fastapi.HTTPException(status_code=500, detail="An error occurred while fetching API status.")
        
    def prepare(self):
        try:
            self._prepare()
            return {'preparations': True}
        except Exception as e:
            logging.error(f"Error during preparations: {e}")
            return fastapi.HTTPException(status_code=500, detail="An error occurred while fetching API status.")
        
    def upload_audio(self, files: List[fastapi.UploadFile] = fastapi.File(...)):
        
        uploaded_files = []
        
        for file in files:
            try: 
                file_path = os.path.join(self.noisy_audio_path, file.filename)
                
                with open(file_path, "wb") as f:
                    while contents := file.file.read(1024*1024):
                        f.write(contents)

                uploaded_files.append(file.filename)                
                
            except Exception as e:
                logging.error(f"Error uploading files: {e}") 
                raise fastapi.HTTPException(status_code=500, detail="An error occurred while uploading the noisy files.")
            finally:
                file.file.close()
            
        print(f"uploaded files: {uploaded_files}")
            
        return {"uploaded_files": uploaded_files, "status": True}

    def enhance_audio(self):
        try:
            # Enhance audio
            self._enhance()
            # Obtain list of file paths for enhanced audio
            wav_files = glob.glob(os.path.join(self.enhanced_audio_path, '*.wav'))
            # Extract just the file names
            enhanced_files = [os.path.basename(file) for file in wav_files]
            return {"status": True}
    
        except Exception as e:
            print(f"Exception occured during enhancement: {e}")
            raise fastapi.HTTPException(status_code=500, detail="An error occurred while enhancing the noisy files.")
        
    def download_enhanced(self):
        try:
            zip_buffer = io.BytesIO()

            with zipfile.ZipFile(zip_buffer, "w") as zip_file:
                for wav_file in glob.glob(os.path.join(self.enhanced_audio_path, '*.wav')):
                    zip_file.write(wav_file, arcname=os.path.basename(wav_file))
            zip_buffer.seek(0)


            return fastapi.responses.StreamingResponse(
                iter([zip_buffer.getvalue()]),  # Stream the in-memory content
                media_type="application/zip",
                headers={"Content-Disposition": "attachment; filename=enhanced_audio_files.zip"}
            )

        except Exception as e:
            logging.error(f"Error during enhanced files download: {e}")
            raise fastapi.HTTPException(status_code=500, detail=f"An error occurred while creating the download file: {str(e)}")

    def run(self):
        
        uvicorn.run(self.app, host=self.host, port=self.port)