audio.py

import os
import numpy as np
from scipy.io import wavfile
from scipy import signal
import resampy
from hparams import hparams as hp

def load_wav(path, sr):
    orig_sr, audio = wavfile.read(path)

    if len(audio) < 100:  # Arbitrary threshold (can be higher for safety)
        raise ValueError(f"Input audio too short: {len(audio)} samples")

    if audio.dtype.kind == 'i':
        audio = audio.astype(np.float32) / np.iinfo(audio.dtype).max
    else:
        audio = audio.astype(np.float32)

    if orig_sr != sr:
        audio = resampy.resample(audio, orig_sr, sr)

    return audio

def save_wav(wav, path, sr):
    """
    Save a float32 waveform to disk as 16-bit PCM WAV.
    """
    wav_int16 = (wav * 32767).clip(-32767, 32767).astype(np.int16)
    wavfile.write(path, sr, wav_int16)

def preemphasis(wav, k, preemphasize=True):
    if preemphasize:
        return signal.lfilter([1, -k], [1], wav)
    return wav

def inv_preemphasis(wav, k, inv_preemphasize=True):
    if inv_preemphasize:
        return signal.lfilter([1], [1, -k], wav)
    return wav

def get_hop_size():
    hop_size = hp.hop_size
    if hop_size is None:
        assert hp.frame_shift_ms is not None
        hop_size = int(hp.frame_shift_ms / 1000 * hp.sample_rate)
    return hop_size

def linearspectrogram(wav):
    D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize))
    S = _amp_to_db(np.abs(D)) - hp.ref_level_db

    return _normalize(S) if hp.signal_normalization else S

def melspectrogram(wav):
    D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize))
    S = _amp_to_db(_linear_to_mel(np.abs(D))) - hp.ref_level_db

    return _normalize(S) if hp.signal_normalization else S

def _lws_processor():
    import lws
    return lws.lws(hp.n_fft, get_hop_size(), fftsize=hp.win_size, mode="speech")

def _stft(y):
    if hp.use_lws:
        return _lws_processor().stft(y).T
    else:
        import librosa  # Safe to import inside function
        return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=get_hop_size(), win_length=hp.win_size)

def num_frames(length, fsize, fshift):
    pad = (fsize - fshift)
    if length % fshift == 0:
        M = (length + pad * 2 - fsize) // fshift + 1
    else:
        M = (length + pad * 2 - fsize) // fshift + 2
    return M

def pad_lr(x, fsize, fshift):
    M = num_frames(len(x), fsize, fshift)
    pad = (fsize - fshift)
    T = len(x) + 2 * pad
    r = (M - 1) * fshift + fsize - T
    return pad, pad + r

def librosa_pad_lr(x, fsize, fshift):
    return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0]

_mel_basis = None

def _linear_to_mel(spectrogram):
    global _mel_basis
    if _mel_basis is None:
        _mel_basis = _build_mel_basis()
    return np.dot(_mel_basis, spectrogram)

def _build_mel_basis():
    import librosa.filters  # Imported only when needed
    assert hp.fmax <= hp.sample_rate // 2
    return librosa.filters.mel(
        sr=hp.sample_rate,
        n_fft=hp.n_fft,
        n_mels=hp.num_mels,
        fmin=hp.fmin,
        fmax=hp.fmax
    )

def _amp_to_db(x):
    min_level = np.exp(hp.min_level_db / 20 * np.log(10))
    return 20 * np.log10(np.maximum(min_level, x))

def _db_to_amp(x):
    return np.power(10.0, x * 0.05)

def _normalize(S):
    if hp.allow_clipping_in_normalization:
        if hp.symmetric_mels:
            return np.clip((2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value,
                           -hp.max_abs_value, hp.max_abs_value)
        else:
            return np.clip(hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)), 0, hp.max_abs_value)
    
    assert S.max() <= 0 and S.min() - hp.min_level_db >= 0
    if hp.symmetric_mels:
        return (2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value
    else:
        return hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db))

def _denormalize(D):
    if hp.allow_clipping_in_normalization:
        if hp.symmetric_mels:
            return (((np.clip(D, -hp.max_abs_value,
                              hp.max_abs_value) + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value))
                    + hp.min_level_db)
        else:
            return ((np.clip(D, 0, hp.max_abs_value) * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db)
    
    if hp.symmetric_mels:
        return (((D + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + hp.min_level_db)
    else:
        return ((D * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db)