server/preprocess.py

"""
Util functions to process any incoming audio data to be processable by the model 
"""
import os
import librosa
import torch
import torchaudio
from scipy.io import wavfile 
import wget
import requests

DEFAULT_SAMPLE_RATE=48000
DEFAULT_WAVE_LENGTH=3

def process_from_url(url):
    # download UI audio
    req_url = requests.get(url)

    with open('temp.wav', 'wb') as file:
        file.write(req_url.content)

    
    # filename = 'temp.wav'
    # audio = torchaudio.load(filename)

    # # remove wget file
    # os.remove(filename)

    # spec
    spec = process_from_filename('temp.wav')

    os.remove('temp.wav')
    return spec


def process_from_filename(filename, target_sample_rate=DEFAULT_SAMPLE_RATE, wav_length=DEFAULT_WAVE_LENGTH):
    wav, sample_rate = torchaudio.load(filename)

    wav = process_raw_wav(wav, sample_rate, target_sample_rate, wav_length)

    spec = _wav_to_spec(wav, target_sample_rate)

    return spec

def process_raw_wav(wav, sample_rate, target_sample_rate, wav_length):
    num_samples = wav_length * target_sample_rate

    wav = _resample(wav, sample_rate, target_sample_rate)
    wav = _mix_down(wav)
    wav = _cut(wav, num_samples)
    wav = _pad(wav, num_samples)

    return wav

def _wav_to_spec(wav, target_sample_rate):
    mel_spectrogram = torchaudio.transforms.MelSpectrogram(
        sample_rate=target_sample_rate,
        n_fft=2048,
        hop_length=512,
        n_mels=128,
    )

    return mel_spectrogram(wav)

def _resample(wav, sample_rate, target_sample_rate):
    if sample_rate != target_sample_rate:
        resampler = torchaudio.transforms.Resample(sample_rate, target_sample_rate)
        wav = resampler(wav)
    
    return wav

def _mix_down(wav):
    if wav.shape[0] > 1:
        wav = torch.mean(wav, dim=0, keepdim=True)
    
    return wav

def _cut(wav, num_samples):
    if wav.shape[1] > num_samples:
        wav = wav[:, :num_samples]
    
    return wav

def _pad(wav, num_samples):
    if wav.shape[1] < num_samples:
        missing_samples = num_samples - wav.shape[1]
        pad = (0, missing_samples)
        wav = torch.nn.functional.pad(wav, pad)
    
    return wav