audiosr/utilities/audio/tools.py

import torch
import numpy as np
from scipy.io.wavfile import write
import torchaudio

from audiosr.utilities.audio.audio_processing import griffin_lim


def pad_wav(waveform, segment_length):
    waveform_length = waveform.shape[-1]
    assert waveform_length > 100, "Waveform is too short, %s" % waveform_length
    if segment_length is None or waveform_length == segment_length:
        return waveform
    elif waveform_length > segment_length:
        return waveform[:segment_length]
    elif waveform_length < segment_length:
        temp_wav = np.zeros((1, segment_length))
        temp_wav[:, :waveform_length] = waveform
    return temp_wav


def normalize_wav(waveform):
    waveform = waveform - np.mean(waveform)
    waveform = waveform / (np.max(np.abs(waveform)) + 1e-8)
    return waveform * 0.5


def read_wav_file(filename, segment_length):
    # waveform, sr = librosa.load(filename, sr=None, mono=True) # 4 times slower
    waveform, sr = torchaudio.load(filename)  # Faster!!!
    waveform = torchaudio.functional.resample(waveform, orig_freq=sr, new_freq=16000)
    waveform = waveform.numpy()[0, ...]
    waveform = normalize_wav(waveform)
    waveform = waveform[None, ...]
    waveform = pad_wav(waveform, segment_length)

    waveform = waveform / np.max(np.abs(waveform))
    waveform = 0.5 * waveform

    return waveform


def get_mel_from_wav(audio, _stft):
    audio = torch.clip(torch.FloatTensor(audio).unsqueeze(0), -1, 1)
    audio = torch.autograd.Variable(audio, requires_grad=False)
    melspec, magnitudes, phases, energy = _stft.mel_spectrogram(audio)
    melspec = torch.squeeze(melspec, 0).numpy().astype(np.float32)
    magnitudes = torch.squeeze(magnitudes, 0).numpy().astype(np.float32)
    energy = torch.squeeze(energy, 0).numpy().astype(np.float32)
    return melspec, magnitudes, energy


def inv_mel_spec(mel, out_filename, _stft, griffin_iters=60):
    mel = torch.stack([mel])
    mel_decompress = _stft.spectral_de_normalize(mel)
    mel_decompress = mel_decompress.transpose(1, 2).data.cpu()
    spec_from_mel_scaling = 1000
    spec_from_mel = torch.mm(mel_decompress[0], _stft.mel_basis)
    spec_from_mel = spec_from_mel.transpose(0, 1).unsqueeze(0)
    spec_from_mel = spec_from_mel * spec_from_mel_scaling

    audio = griffin_lim(
        torch.autograd.Variable(spec_from_mel[:, :, :-1]), _stft._stft_fn, griffin_iters
    )

    audio = audio.squeeze()
    audio = audio.cpu().numpy()
    audio_path = out_filename
    write(audio_path, _stft.sampling_rate, audio)