diff --git a/TTS/utils/audio/numpy_transforms.py b/TTS/utils/audio/numpy_transforms.py
new file mode 100644
index 00000000..c0a639a2
--- /dev/null
+++ b/TTS/utils/audio/numpy_transforms.py
@@ -0,0 +1,452 @@
+from typing import Callable, Tuple
+import librosa
+import numpy as np
+
+import soundfile as sf
+import pyworld as pw
+import scipy
+
+# from TTS.tts.utils.helpers import StandardScaler
+
+
+def build_mel_basis(
+    *,
+    sample_rate: int = None,
+    fft_size: int = None,
+    num_mels: int = None,
+    mel_fmax: int = None,
+    mel_fmin: int = None,
+    **kwargs,
+) -> np.ndarray:
+    """Build melspectrogram basis.
+
+    Returns:
+        np.ndarray: melspectrogram basis.
+    """
+    if mel_fmax is not None:
+        assert mel_fmax <= sample_rate // 2
+        assert mel_fmax - mel_fmin > 0
+    return librosa.filters.mel(sr=sample_rate, n_fft=fft_size, n_mels=num_mels, fmin=mel_fmin, fmax=mel_fmax)
+
+
+def millisec_to_length(
+    *, frame_length_ms: int = None, frame_shift_ms: int = None, sample_rate: int = None, **kwargs
+) -> Tuple[int, int]:
+    """Compute hop and window length from milliseconds.
+
+    Returns:
+        Tuple[int, int]: hop length and window length for STFT.
+    """
+    factor = frame_length_ms / frame_shift_ms
+    assert (factor).is_integer(), " [!] frame_shift_ms should divide frame_length_ms"
+    hop_length = int(frame_shift_ms / 1000.0 * sample_rate)
+    win_length = int(hop_length * factor)
+    return hop_length, win_length
+
+
+def _log(x, base):
+    if base == 10:
+        return np.log10(x)
+    return np.log(x)
+
+
+def _exp(x, base):
+    if base == 10:
+        return np.power(10, x)
+    return np.exp(x)
+
+
+def amp_to_db(*, x: np.ndarray = None, gain: float = 1, base: int = 10, **kwargs) -> np.ndarray:
+    """Convert amplitude values to decibels.
+
+    Args:
+        x (np.ndarray): Amplitude spectrogram.
+        gain (float): Gain factor. Defaults to 1.
+        base (int): Logarithm base. Defaults to 10.
+
+    Returns:
+        np.ndarray: Decibels spectrogram.
+    """
+    return gain * _log(np.maximum(1e-5, x), base)
+
+
+# pylint: disable=no-self-use
+def db_to_amp(*, x: np.ndarray = None, gain: float = 1, base: int = 10, **kwargs) -> np.ndarray:
+    """Convert decibels spectrogram to amplitude spectrogram.
+
+    Args:
+        x (np.ndarray): Decibels spectrogram.
+        gain (float): Gain factor. Defaults to 1.
+        base (int): Logarithm base. Defaults to 10.
+
+    Returns:
+        np.ndarray: Amplitude spectrogram.
+    """
+    return _exp(x / gain, base)
+
+
+def preemphasis(*, x: np.ndarray, coef: float = 0.97, **kwargs) -> np.ndarray:
+    """Apply pre-emphasis to the audio signal. Useful to reduce the correlation between neighbouring signal values.
+
+    Args:
+        x (np.ndarray): Audio signal.
+
+    Raises:
+        RuntimeError: Preemphasis coeff is set to 0.
+
+    Returns:
+        np.ndarray: Decorrelated audio signal.
+    """
+    if coef == 0:
+        raise RuntimeError(" [!] Preemphasis is set 0.0.")
+    return scipy.signal.lfilter([1, -coef], [1], x)
+
+
+def deemphasis(*, x: np.ndarray = None, coef: float = 0.97, **kwargs) -> np.ndarray:
+    """Reverse pre-emphasis."""
+    if coef == 0:
+        raise RuntimeError(" [!] Preemphasis is set 0.0.")
+    return scipy.signal.lfilter([1], [1, -coef], x)
+
+
+def spec_to_mel(*, spectrogram: np.ndarray = None, mel_basis: np.ndarray = None, **kwargs) -> np.ndarray:
+    """Project a full scale spectrogram to a melspectrogram.
+
+    Args:
+        spectrogram (np.ndarray): Full scale spectrogram.
+
+    Returns:
+        np.ndarray: Melspectrogram
+    """
+    return np.dot(mel_basis, spectrogram)
+
+
+def mel_to_spec(*, mel: np.ndarray = None, mel_basis: np.ndarray = None, **kwargs) -> np.ndarray:
+    """Convert a melspectrogram to full scale spectrogram."""
+    inv_mel_basis = np.linalg.pinv(mel_basis)
+    return np.maximum(1e-10, np.dot(inv_mel_basis, mel))
+
+
+def wav_to_spec(*, y: np.ndarray = None, **kwargs) -> np.ndarray:
+    """Compute a spectrogram from a waveform.
+
+    Args:
+        y (np.ndarray): Waveform.
+
+    Returns:
+        np.ndarray: Spectrogram.
+    """
+    D = stft(y, **kwargs)
+    S = np.abs(D)
+    return S.astype(np.float32)
+
+
+def wav_to_mel(*, y: np.ndarray = None, **kwargs) -> np.ndarray:
+    """Compute a melspectrogram from a waveform."""
+    D = stft(y=y, **kwargs)
+    S = spec_to_mel(spec=np.abs(D), **kwargs)
+    return S.astype(np.float32)
+
+
+def spec_to_wav(*, spec: np.ndarray, power: float = 1.5, denorm_func: Callable = None, **kwargs) -> np.ndarray:
+    """Convert a spectrogram to a waveform using Griffi-Lim vocoder."""
+    S = spec.copy()
+    if denorm_func is not None:
+        S = denorm_func(spec=S, **kwargs)
+    S = db_to_amp(S)
+    return griffin_lim(spec=S**power, **kwargs)
+
+
+def mel_to_wav(*, mel: np.ndarray = None, power: float = 1.5, denorm_func: Callable = None, **kwargs) -> np.ndarray:
+    """Convert a melspectrogram to a waveform using Griffi-Lim vocoder."""
+    S = mel.copy()
+    if denorm_func is not None:
+        S = denorm_func(spec=S, **kwargs)
+    S = db_to_amp(S)
+    S = mel_to_spec(mel=S, mel_basis=kwargs["mel_basis"])  # Convert back to linear
+    return griffin_lim(spec=S**power, **kwargs)
+
+
+def spec_to_mel(*, spec: np.ndarray, mel_basis: np.ndarray = None, **kwargs) -> np.ndarray:
+    """Convert a full scale linear spectrogram output of a network to a melspectrogram.
+
+    Args:
+        linear_spec (np.ndarray): Normalized full scale linear spectrogram.
+
+    Returns:
+        np.ndarray: Normalized melspectrogram.
+    """
+    return np.dot(mel_basis, spec)
+
+
+### STFT and ISTFT ###
+def stft(
+    *,
+    y: np.ndarray = None,
+    fft_size: int = None,
+    hop_length: int = None,
+    win_length: int = None,
+    pad_mode: str = "reflect",
+    window: str = "hann",
+    center: bool = True,
+    **kwargs,
+) -> np.ndarray:
+    """Librosa STFT wrapper.
+
+    Check http://librosa.org/doc/main/generated/librosa.stft.html argument details.
+
+    Returns:
+        np.ndarray: Complex number array.
+    """
+    return librosa.stft(
+        y=y,
+        n_fft=fft_size,
+        hop_length=hop_length,
+        win_length=win_length,
+        pad_mode=pad_mode,
+        window="hann",
+        center=True,
+    )
+
+
+def istft(
+    *,
+    y: np.ndarray = None,
+    fft_size: int = None,
+    hop_length: int = None,
+    win_length: int = None,
+    window: str = "hann",
+    center: bool = True,
+    **kwargs,
+) -> np.ndarray:
+    """Librosa iSTFT wrapper.
+
+    Check http://librosa.org/doc/main/generated/librosa.istft.html argument details.
+
+    Returns:
+        np.ndarray: Complex number array.
+    """
+    return librosa.istft(y, hop_length=hop_length, win_length=win_length, center=center, window=window)
+
+
+def griffin_lim(*, spec: np.ndarray = None, num_iter=60, **kwargs) -> np.ndarray:
+    angles = np.exp(2j * np.pi * np.random.rand(*spec.shape))
+    S_complex = np.abs(spec).astype(np.complex)
+    y = istft(y=S_complex * angles, **kwargs)
+    if not np.isfinite(y).all():
+        print(" [!] Waveform is not finite everywhere. Skipping the GL.")
+        return np.array([0.0])
+    for _ in range(num_iter):
+        angles = np.exp(1j * np.angle(stft(y=y, **kwargs)))
+        y = istft(y=S_complex * angles, **kwargs)
+    return y
+
+
+def compute_stft_paddings(
+    *, x: np.ndarray = None, hop_length: int = None, pad_two_sides: bool = False, **kwargs
+) -> Tuple[int, int]:
+    """Compute paddings used by Librosa's STFT. Compute right padding (final frame) or both sides padding
+    (first and final frames)"""
+    pad = (x.shape[0] // hop_length + 1) * hop_length - x.shape[0]
+    if not pad_two_sides:
+        return 0, pad
+    return pad // 2, pad // 2 + pad % 2
+
+
+def compute_f0(
+    *, x: np.ndarray = None, pitch_fmax: float = None, hop_length: int = None, sample_rate: int = None, **kwargs
+) -> np.ndarray:
+    """Compute pitch (f0) of a waveform using the same parameters used for computing melspectrogram.
+
+    Args:
+        x (np.ndarray): Waveform.
+
+    Returns:
+        np.ndarray: Pitch.
+
+    Examples:
+        >>> WAV_FILE = filename = librosa.util.example_audio_file()
+        >>> from TTS.config import BaseAudioConfig
+        >>> from TTS.utils.audio.processor import AudioProcessor        >>> conf = BaseAudioConfig(pitch_fmax=8000)
+        >>> ap = AudioProcessor(**conf)
+        >>> wav = ap.load_wav(WAV_FILE, sr=22050)[:5 * 22050]
+        >>> pitch = ap.compute_f0(wav)
+    """
+    assert pitch_fmax is not None, " [!] Set `pitch_fmax` before caling `compute_f0`."
+
+    f0, t = pw.dio(
+        x.astype(np.double),
+        fs=sample_rate,
+        f0_ceil=pitch_fmax,
+        frame_period=1000 * hop_length / sample_rate,
+    )
+    f0 = pw.stonemask(x.astype(np.double), f0, t, sample_rate)
+    return f0
+
+
+### Audio Processing ###
+def find_endpoint(
+    *,
+    wav: np.ndarray = None,
+    trim_db: float = None,
+    sample_rate: int = None,
+    min_silence_sec=0.8,
+    gain: float = None,
+    base: int = 10,
+    **kwargs,
+) -> int:
+    """Find the last point without silence at the end of a audio signal.
+
+    Args:
+        wav (np.ndarray): Audio signal.
+        threshold_db (int, optional): Silence threshold in decibels. Defaults to -40.
+        min_silence_sec (float, optional): Ignore silences that are shorter then this in secs. Defaults to 0.8.
+
+    Returns:
+        int: Last point without silence.
+    """
+    window_length = int(sample_rate * min_silence_sec)
+    hop_length = int(window_length / 4)
+    threshold = db_to_amp(x=-trim_db, gain=gain, base=base)
+    for x in range(hop_length, len(wav) - window_length, hop_length):
+        if np.max(wav[x : x + window_length]) < threshold:
+            return x + hop_length
+    return len(wav)
+
+
+def trim_silence(
+    *,
+    wav: np.ndarray = None,
+    sample_rate: int = None,
+    trim_db: float = None,
+    win_length: int = None,
+    hop_length: int = None,
+    **kwargs,
+) -> np.ndarray:
+    """Trim silent parts with a threshold and 0.01 sec margin"""
+    margin = int(sample_rate * 0.01)
+    wav = wav[margin:-margin]
+    return librosa.effects.trim(wav, top_db=trim_db, frame_length=win_length, hop_length=hop_length)[0]
+
+
+def sound_norm(*, x: np.ndarray = None, coef: float = 0.95, **kwargs) -> np.ndarray:
+    """Normalize the volume of an audio signal.
+
+    Args:
+        x (np.ndarray): Raw waveform.
+        coef (float): Coefficient to rescale the maximum value. Defaults to 0.95.
+
+    Returns:
+        np.ndarray: Volume normalized waveform.
+    """
+    return x / abs(x).max() * coef
+
+
+def rms_norm(*, wav: np.ndarray = None, db_level: float = -27.0, **kwargs) -> np.ndarray:
+    r = 10 ** (db_level / 20)
+    a = np.sqrt((len(wav) * (r**2)) / np.sum(wav**2))
+    return wav * a
+
+
+def rms_volume_norm(*, x: np.ndarray, db_level: float = -27.0, **kwargs) -> np.ndarray:
+    """Normalize the volume based on RMS of the signal.
+
+    Args:
+        x (np.ndarray): Raw waveform.
+        db_level (float): Target dB level in RMS. Defaults to -27.0.
+
+    Returns:
+        np.ndarray: RMS normalized waveform.
+    """
+    if db_level is None:
+        db_level = db_level
+    assert -99 <= db_level <= 0, " [!] db_level should be between -99 and 0"
+    wav = rms_norm(wav=x, db_level=db_level)
+    return wav
+
+
+def load_wav(*, filename: str, sample_rate: int = None, resample: bool = False, **kwargs) -> np.ndarray:
+    """Read a wav file using Librosa and optionally resample, silence trim, volume normalize.
+
+    Resampling slows down loading the file significantly. Therefore it is recommended to resample the file before.
+
+    Args:
+        filename (str): Path to the wav file.
+        sr (int, optional): Sampling rate for resampling. Defaults to None.
+        resample (bool, optional): Resample the audio file when loading. Slows down the I/O time. Defaults to False.
+
+    Returns:
+        np.ndarray: Loaded waveform.
+    """
+    if resample:
+        # loading with resampling. It is significantly slower.
+        x, sr = librosa.load(filename, sr=sample_rate)
+    elif sr is None:
+        # SF is faster than librosa for loading files
+        x, sr = sf.read(filename)
+        assert sample_rate == sr, "%s vs %s" % (sample_rate, sr)
+    else:
+        x, sr = librosa.load(filename, sr=sr)
+    return x
+
+
+def save_wav(*, wav: np.ndarray, path: str, sample_rate: int = None, **kwargs) -> None:
+    """Save float waveform to a file using Scipy.
+
+    Args:
+        wav (np.ndarray): Waveform with float values in range [-1, 1] to save.
+        path (str): Path to a output file.
+        sr (int, optional): Sampling rate used for saving to the file. Defaults to None.
+    """
+    wav_norm = wav * (32767 / max(0.01, np.max(np.abs(wav))))
+    scipy.io.wavfile.write(path, sample_rate, wav_norm.astype(np.int16))
+
+
+def mulaw_encode(*, wav: np.ndarray, mulaw_qc: int, **kwargs) -> np.ndarray:
+    mu = 2**mulaw_qc - 1
+    signal = np.sign(wav) * np.log(1 + mu * np.abs(wav)) / np.log(1.0 + mu)
+    signal = (signal + 1) / 2 * mu + 0.5
+    return np.floor(
+        signal,
+    )
+
+
+def mulaw_decode(*, wav, mulaw_qc: int, **kwargs) -> np.ndarray:
+    """Recovers waveform from quantized values."""
+    mu = 2**mulaw_qc - 1
+    x = np.sign(wav) / mu * ((1 + mu) ** np.abs(wav) - 1)
+    return x
+
+
+def encode_16bits(*, x: np.ndarray, **kwargs) -> np.ndarray:
+    return np.clip(x * 2**15, -(2**15), 2**15 - 1).astype(np.int16)
+
+
+def quantize(*, x: np.ndarray, quantize_bits: int, **kwargs) -> np.ndarray:
+    """Quantize a waveform to a given number of bits.
+
+    Args:
+        x (np.ndarray): Waveform to quantize. Must be normalized into the range `[-1, 1]`.
+        quantize_bits (int): Number of quantization bits.
+
+    Returns:
+        np.ndarray: Quantized waveform.
+    """
+    return (x + 1.0) * (2**quantize_bits - 1) / 2
+
+
+def dequantize(*, x, quantize_bits, **kwargs) -> np.ndarray:
+    """Dequantize a waveform from the given number of bits."""
+    return 2 * x / (2**quantize_bits - 1) - 1
+
+
+def _log(x, base):
+    if base == 10:
+        return np.log10(x)
+    return np.log(x)
+
+
+def _exp(x, base):
+    if base == 10:
+        return np.power(10, x)
+    return np.exp(x)
diff --git a/TTS/utils/audio/torch_transforms.py b/TTS/utils/audio/torch_transforms.py
new file mode 100644
index 00000000..2e415df1
--- /dev/null
+++ b/TTS/utils/audio/torch_transforms.py
@@ -0,0 +1,165 @@
+import torch
+from torch import nn
+import librosa
+
+
+class TorchSTFT(nn.Module):  # pylint: disable=abstract-method
+    """Some of the audio processing funtions using Torch for faster batch processing.
+
+    TODO: Merge this with audio.py
+
+    Args:
+
+        n_fft (int):
+            FFT window size for STFT.
+
+        hop_length (int):
+            number of frames between STFT columns.
+
+        win_length (int, optional):
+            STFT window length.
+
+        pad_wav (bool, optional):
+            If True pad the audio with (n_fft - hop_length) / 2). Defaults to False.
+
+        window (str, optional):
+            The name of a function to create a window tensor that is applied/multiplied to each frame/window. Defaults to "hann_window"
+
+        sample_rate (int, optional):
+            target audio sampling rate. Defaults to None.
+
+        mel_fmin (int, optional):
+            minimum filter frequency for computing melspectrograms. Defaults to None.
+
+        mel_fmax (int, optional):
+            maximum filter frequency for computing melspectrograms. Defaults to None.
+
+        n_mels (int, optional):
+            number of melspectrogram dimensions. Defaults to None.
+
+        use_mel (bool, optional):
+            If True compute the melspectrograms otherwise. Defaults to False.
+
+        do_amp_to_db_linear (bool, optional):
+            enable/disable amplitude to dB conversion of linear spectrograms. Defaults to False.
+
+        spec_gain (float, optional):
+            gain applied when converting amplitude to DB. Defaults to 1.0.
+
+        power (float, optional):
+            Exponent for the magnitude spectrogram, e.g., 1 for energy, 2 for power, etc.  Defaults to None.
+
+        use_htk (bool, optional):
+            Use HTK formula in mel filter instead of Slaney.
+
+        mel_norm (None, 'slaney', or number, optional):
+            If 'slaney', divide the triangular mel weights by the width of the mel band
+            (area normalization).
+
+            If numeric, use `librosa.util.normalize` to normalize each filter by to unit l_p norm.
+            See `librosa.util.normalize` for a full description of supported norm values
+            (including `+-np.inf`).
+
+            Otherwise, leave all the triangles aiming for a peak value of 1.0. Defaults to "slaney".
+    """
+
+    def __init__(
+        self,
+        n_fft,
+        hop_length,
+        win_length,
+        pad_wav=False,
+        window="hann_window",
+        sample_rate=None,
+        mel_fmin=0,
+        mel_fmax=None,
+        n_mels=80,
+        use_mel=False,
+        do_amp_to_db=False,
+        spec_gain=1.0,
+        power=None,
+        use_htk=False,
+        mel_norm="slaney",
+    ):
+        super().__init__()
+        self.n_fft = n_fft
+        self.hop_length = hop_length
+        self.win_length = win_length
+        self.pad_wav = pad_wav
+        self.sample_rate = sample_rate
+        self.mel_fmin = mel_fmin
+        self.mel_fmax = mel_fmax
+        self.n_mels = n_mels
+        self.use_mel = use_mel
+        self.do_amp_to_db = do_amp_to_db
+        self.spec_gain = spec_gain
+        self.power = power
+        self.use_htk = use_htk
+        self.mel_norm = mel_norm
+        self.window = nn.Parameter(getattr(torch, window)(win_length), requires_grad=False)
+        self.mel_basis = None
+        if use_mel:
+            self._build_mel_basis()
+
+    def __call__(self, x):
+        """Compute spectrogram frames by torch based stft.
+
+        Args:
+            x (Tensor): input waveform
+
+        Returns:
+            Tensor: spectrogram frames.
+
+        Shapes:
+            x: [B x T] or [:math:`[B, 1, T]`]
+        """
+        if x.ndim == 2:
+            x = x.unsqueeze(1)
+        if self.pad_wav:
+            padding = int((self.n_fft - self.hop_length) / 2)
+            x = torch.nn.functional.pad(x, (padding, padding), mode="reflect")
+        # B x D x T x 2
+        o = torch.stft(
+            x.squeeze(1),
+            self.n_fft,
+            self.hop_length,
+            self.win_length,
+            self.window,
+            center=True,
+            pad_mode="reflect",  # compatible with audio.py
+            normalized=False,
+            onesided=True,
+            return_complex=False,
+        )
+        M = o[:, :, :, 0]
+        P = o[:, :, :, 1]
+        S = torch.sqrt(torch.clamp(M**2 + P**2, min=1e-8))
+
+        if self.power is not None:
+            S = S**self.power
+
+        if self.use_mel:
+            S = torch.matmul(self.mel_basis.to(x), S)
+        if self.do_amp_to_db:
+            S = self._amp_to_db(S, spec_gain=self.spec_gain)
+        return S
+
+    def _build_mel_basis(self):
+        mel_basis = librosa.filters.mel(
+            self.sample_rate,
+            self.n_fft,
+            n_mels=self.n_mels,
+            fmin=self.mel_fmin,
+            fmax=self.mel_fmax,
+            htk=self.use_htk,
+            norm=self.mel_norm,
+        )
+        self.mel_basis = torch.from_numpy(mel_basis).float()
+
+    @staticmethod
+    def _amp_to_db(x, spec_gain=1.0):
+        return torch.log(torch.clamp(x, min=1e-5) * spec_gain)
+
+    @staticmethod
+    def _db_to_amp(x, spec_gain=1.0):
+        return torch.exp(x) / spec_gain