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__))
        
        ckpt_files = glob.glob(os.path.join(app_dir, "*.ckpt"))

        if not ckpt_files:
            raise FileNotFoundError("No .ckpt file found in app_dir.")
        elif len(ckpt_files) > 1:
            raise RuntimeError("Multiple .ckpt files found in app_dir. Please keep only one.")
        else:
            self.ckpt_path = ckpt_files[0]
                    
        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
        self.corrector = "ald"
        self.corrector_steps = 1
        self.snr = 0.5
        self.N = 30

        # Create directories if they do not exist
        for audio_path in [self.noisy_audio_path, self.enhanced_audio_path]:
            if not os.path.exists(audio_path):
                os.makedirs(audio_path)
                
            # Loop through all the files and subdirectories in the directory
            for filename in os.listdir(audio_path):
                file_path = os.path.join(audio_path, filename)
                
                # Check if it's a file or directory and remove accordingly
                try:
                    if os.path.isfile(file_path) or os.path.islink(file_path):
                        os.unlink(file_path)  # Remove the file or link
                    elif os.path.isdir(file_path):
                        shutil.rmtree(file_path)  # Remove the directory and its contents
                except Exception as e:
                    raise e
                
        self.app = fastapi.FastAPI()
        self._setup_routes()
        
    def _prepare(self):
        """Miners should modify this function to fit their fine-tuned models.
        
        This function will make any preparations necessary to initialize the
        speech enhancement model (i.e. downloading checkpoint files, etc.)
        """
        # Initialize model
        self.model = ScoreModel.load_from_checkpoint(self.ckpt_path, self.device)
        self.model.t_eps = 0.03
        self.model.eval()

    def _enhance(self):
        """
        Miners should modify this function to fit their fine-tuned models.
        
        This function will:
        1. Open each noisy .wav file
        2. Enhance the audio with the model 
        3. Save the enhanced audio in .wav format to ModelAPI.enhanced_audio_path
        """
        
        # Check if the model is trained on 48 kHz data
        if self.model.backbone == 'ncsnpp_48k':
            target_sr = 48000
            pad_mode = "reflection"
        elif self.model.backbone == 'ncsnpp_v2':
            target_sr = 16000
            pad_mode = "reflection"
        else:
            target_sr = 16000
            pad_mode = "zero_pad"
        
        # Define file paths for all noisy files to be enhanced
        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

            # Load wav
            y, sr = load(noisy_file)
            # Resample if necessary
            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 {self.model.sde.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()
            
            # Backward transform in time domain
            x_hat = self.model.to_audio(sample.squeeze(), T_orig)
            
            # Renormalize
            x_hat = x_hat * norm_factor
            
            # Write enhanced wav file
            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):
        """
        Setup API routes:
        
        /status/ : Communicates API status
        /upload-audio/ : Upload audio files, save to noisy audio directory
        /enhance/ : Enhance audio files, save to enhanced audio directory
        /download-enhanced/ : Download enhanced audio files
        """
        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: 
                # Define the path to save the file
                file_path = os.path.join(self.noisy_audio_path, file.filename)
                
                # Save the uploaded file
                with open(file_path, "wb") as f:
                    while contents := file.file.read(1024*1024):
                        f.write(contents)
                
                # Append the file name to the list of uploaded files
                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:
            # Create an in-memory zip file to hold all the enhanced audio files
            zip_buffer = io.BytesIO()

            with zipfile.ZipFile(zip_buffer, "w") as zip_file:
                # Add each .wav file in the enhanced_audio_path directory to the 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))

            # Make sure to seek back to the start of the BytesIO object before sending it
            zip_buffer.seek(0)

            # Send the zip file to the client as a downloadable file
            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)