Shortcuts

Source code for torchaudio.functional.functional

# -*- coding: utf-8 -*-

from collections.abc import Sequence
import io
import math
import warnings
from typing import Optional, Tuple

import torch
from torch import Tensor
from torchaudio._internal import module_utils as _mod_utils
import torchaudio

__all__ = [
    "spectrogram",
    "inverse_spectrogram",
    "griffinlim",
    "amplitude_to_DB",
    "DB_to_amplitude",
    "compute_deltas",
    "compute_kaldi_pitch",
    "melscale_fbanks",
    "linear_fbanks",
    "create_dct",
    "compute_deltas",
    "detect_pitch_frequency",
    "DB_to_amplitude",
    "mu_law_encoding",
    "mu_law_decoding",
    "phase_vocoder",
    'mask_along_axis',
    'mask_along_axis_iid',
    'sliding_window_cmn',
    "spectral_centroid",
    "apply_codec",
    "resample",
    "edit_distance",
    "pitch_shift",
    "rnnt_loss",
]


[docs]def spectrogram( waveform: Tensor, pad: int, window: Tensor, n_fft: int, hop_length: int, win_length: int, power: Optional[float], normalized: bool, center: bool = True, pad_mode: str = "reflect", onesided: bool = True, return_complex: Optional[bool] = None, ) -> Tensor: r"""Create a spectrogram or a batch of spectrograms from a raw audio signal. The spectrogram can be either magnitude-only or complex. Args: waveform (Tensor): Tensor of audio of dimension `(..., time)` pad (int): Two sided padding of signal window (Tensor): Window tensor that is applied/multiplied to each frame/window n_fft (int): Size of FFT hop_length (int): Length of hop between STFT windows win_length (int): Window size power (float or None): Exponent for the magnitude spectrogram, (must be > 0) e.g., 1 for energy, 2 for power, etc. If None, then the complex spectrum is returned instead. normalized (bool): Whether to normalize by magnitude after stft center (bool, optional): whether to pad :attr:`waveform` on both sides so that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`. Default: ``True`` pad_mode (string, optional): controls the padding method used when :attr:`center` is ``True``. Default: ``"reflect"`` onesided (bool, optional): controls whether to return half of results to avoid redundancy. Default: ``True`` return_complex (bool, optional): Deprecated and not used. Returns: Tensor: Dimension `(..., freq, time)`, freq is ``n_fft // 2 + 1`` and ``n_fft`` is the number of Fourier bins, and time is the number of window hops (n_frame). """ if return_complex is not None: warnings.warn( "`return_complex` argument is now deprecated and is not effective." "`torchaudio.functional.spectrogram(power=None)` always returns a tensor with " "complex dtype. Please remove the argument in the function call." ) if pad > 0: # TODO add "with torch.no_grad():" back when JIT supports it waveform = torch.nn.functional.pad(waveform, (pad, pad), "constant") # pack batch shape = waveform.size() waveform = waveform.reshape(-1, shape[-1]) # default values are consistent with librosa.core.spectrum._spectrogram spec_f = torch.stft( input=waveform, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, center=center, pad_mode=pad_mode, normalized=False, onesided=onesided, return_complex=True, ) # unpack batch spec_f = spec_f.reshape(shape[:-1] + spec_f.shape[-2:]) if normalized: spec_f /= window.pow(2.).sum().sqrt() if power is not None: if power == 1.0: return spec_f.abs() return spec_f.abs().pow(power) return spec_f
[docs]def inverse_spectrogram( spectrogram: Tensor, length: Optional[int], pad: int, window: Tensor, n_fft: int, hop_length: int, win_length: int, normalized: bool, center: bool = True, pad_mode: str = "reflect", onesided: bool = True, ) -> Tensor: r"""Create an inverse spectrogram or a batch of inverse spectrograms from the provided complex-valued spectrogram. Args: spectrogram (Tensor): Complex tensor of audio of dimension (..., freq, time). length (int or None): The output length of the waveform. pad (int): Two sided padding of signal. It is only effective when ``length`` is provided. window (Tensor): Window tensor that is applied/multiplied to each frame/window n_fft (int): Size of FFT hop_length (int): Length of hop between STFT windows win_length (int): Window size normalized (bool): Whether the stft output was normalized by magnitude center (bool, optional): whether the waveform was padded on both sides so that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`. Default: ``True`` pad_mode (string, optional): controls the padding method used when :attr:`center` is ``True``. This parameter is provided for compatibility with the spectrogram function and is not used. Default: ``"reflect"`` onesided (bool, optional): controls whether spectrogram was done in onesided mode. Default: ``True`` Returns: Tensor: Dimension `(..., time)`. Least squares estimation of the original signal. """ if not spectrogram.is_complex(): raise ValueError("Expected `spectrogram` to be complex dtype.") if normalized: spectrogram = spectrogram * window.pow(2.).sum().sqrt() # pack batch shape = spectrogram.size() spectrogram = spectrogram.reshape(-1, shape[-2], shape[-1]) # default values are consistent with librosa.core.spectrum._spectrogram waveform = torch.istft( input=spectrogram, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, center=center, normalized=False, onesided=onesided, length=length + 2 * pad if length is not None else None, return_complex=False, ) if length is not None and pad > 0: # remove padding from front and back waveform = waveform[:, pad:-pad] # unpack batch waveform = waveform.reshape(shape[:-2] + waveform.shape[-1:]) return waveform
def _get_complex_dtype(real_dtype: torch.dtype): if real_dtype == torch.double: return torch.cdouble if real_dtype == torch.float: return torch.cfloat if real_dtype == torch.half: return torch.complex32 raise ValueError(f'Unexpected dtype {real_dtype}')
[docs]def griffinlim( specgram: Tensor, window: Tensor, n_fft: int, hop_length: int, win_length: int, power: float, n_iter: int, momentum: float, length: Optional[int], rand_init: bool ) -> Tensor: r"""Compute waveform from a linear scale magnitude spectrogram using the Griffin-Lim transformation. Implementation ported from *librosa* [:footcite:`brian_mcfee-proc-scipy-2015`], *A fast Griffin-Lim algorithm* [:footcite:`6701851`] and *Signal estimation from modified short-time Fourier transform* [:footcite:`1172092`]. Args: specgram (Tensor): A magnitude-only STFT spectrogram of dimension `(..., freq, frames)` where freq is ``n_fft // 2 + 1``. window (Tensor): Window tensor that is applied/multiplied to each frame/window n_fft (int): Size of FFT, creates ``n_fft // 2 + 1`` bins hop_length (int): Length of hop between STFT windows. ( Default: ``win_length // 2``) win_length (int): Window size. (Default: ``n_fft``) power (float): Exponent for the magnitude spectrogram, (must be > 0) e.g., 1 for energy, 2 for power, etc. n_iter (int): Number of iteration for phase recovery process. momentum (float): The momentum parameter for fast Griffin-Lim. Setting this to 0 recovers the original Griffin-Lim method. Values near 1 can lead to faster convergence, but above 1 may not converge. length (int or None): Array length of the expected output. rand_init (bool): Initializes phase randomly if True, to zero otherwise. Returns: Tensor: waveform of `(..., time)`, where time equals the ``length`` parameter if given. """ assert momentum < 1, 'momentum={} > 1 can be unstable'.format(momentum) assert momentum >= 0, 'momentum={} < 0'.format(momentum) # pack batch shape = specgram.size() specgram = specgram.reshape([-1] + list(shape[-2:])) specgram = specgram.pow(1 / power) # initialize the phase if rand_init: angles = torch.rand( specgram.size(), dtype=_get_complex_dtype(specgram.dtype), device=specgram.device) else: angles = torch.full( specgram.size(), 1, dtype=_get_complex_dtype(specgram.dtype), device=specgram.device) # And initialize the previous iterate to 0 tprev = torch.tensor(0., dtype=specgram.dtype, device=specgram.device) for _ in range(n_iter): # Invert with our current estimate of the phases inverse = torch.istft(specgram * angles, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, length=length) # Rebuild the spectrogram rebuilt = torch.stft( input=inverse, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, center=True, pad_mode='reflect', normalized=False, onesided=True, return_complex=True, ) # Update our phase estimates angles = rebuilt if momentum: angles = angles - tprev.mul_(momentum / (1 + momentum)) angles = angles.div(angles.abs().add(1e-16)) # Store the previous iterate tprev = rebuilt # Return the final phase estimates waveform = torch.istft(specgram * angles, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, length=length) # unpack batch waveform = waveform.reshape(shape[:-2] + waveform.shape[-1:]) return waveform
[docs]def amplitude_to_DB( x: Tensor, multiplier: float, amin: float, db_multiplier: float, top_db: Optional[float] = None ) -> Tensor: r"""Turn a spectrogram from the power/amplitude scale to the decibel scale. The output of each tensor in a batch depends on the maximum value of that tensor, and so may return different values for an audio clip split into snippets vs. a full clip. Args: x (Tensor): Input spectrogram(s) before being converted to decibel scale. Input should take the form `(..., freq, time)`. Batched inputs should include a channel dimension and have the form `(batch, channel, freq, time)`. multiplier (float): Use 10. for power and 20. for amplitude amin (float): Number to clamp ``x`` db_multiplier (float): Log10(max(reference value and amin)) top_db (float or None, optional): Minimum negative cut-off in decibels. A reasonable number is 80. (Default: ``None``) Returns: Tensor: Output tensor in decibel scale """ x_db = multiplier * torch.log10(torch.clamp(x, min=amin)) x_db -= multiplier * db_multiplier if top_db is not None: # Expand batch shape = x_db.size() packed_channels = shape[-3] if x_db.dim() > 2 else 1 x_db = x_db.reshape(-1, packed_channels, shape[-2], shape[-1]) x_db = torch.max(x_db, (x_db.amax(dim=(-3, -2, -1)) - top_db).view(-1, 1, 1, 1)) # Repack batch x_db = x_db.reshape(shape) return x_db
[docs]def DB_to_amplitude( x: Tensor, ref: float, power: float ) -> Tensor: r"""Turn a tensor from the decibel scale to the power/amplitude scale. Args: x (Tensor): Input tensor before being converted to power/amplitude scale. ref (float): Reference which the output will be scaled by. power (float): If power equals 1, will compute DB to power. If 0.5, will compute DB to amplitude. Returns: Tensor: Output tensor in power/amplitude scale. """ return ref * torch.pow(torch.pow(10.0, 0.1 * x), power)
def _hz_to_mel(freq: float, mel_scale: str = "htk") -> float: r"""Convert Hz to Mels. Args: freqs (float): Frequencies in Hz mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``) Returns: mels (float): Frequency in Mels """ if mel_scale not in ['slaney', 'htk']: raise ValueError('mel_scale should be one of "htk" or "slaney".') if mel_scale == "htk": return 2595.0 * math.log10(1.0 + (freq / 700.0)) # Fill in the linear part f_min = 0.0 f_sp = 200.0 / 3 mels = (freq - f_min) / f_sp # Fill in the log-scale part min_log_hz = 1000.0 min_log_mel = (min_log_hz - f_min) / f_sp logstep = math.log(6.4) / 27.0 if freq >= min_log_hz: mels = min_log_mel + math.log(freq / min_log_hz) / logstep return mels def _mel_to_hz(mels: Tensor, mel_scale: str = "htk") -> Tensor: """Convert mel bin numbers to frequencies. Args: mels (Tensor): Mel frequencies mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``) Returns: freqs (Tensor): Mels converted in Hz """ if mel_scale not in ['slaney', 'htk']: raise ValueError('mel_scale should be one of "htk" or "slaney".') if mel_scale == "htk": return 700.0 * (10.0**(mels / 2595.0) - 1.0) # Fill in the linear scale f_min = 0.0 f_sp = 200.0 / 3 freqs = f_min + f_sp * mels # And now the nonlinear scale min_log_hz = 1000.0 min_log_mel = (min_log_hz - f_min) / f_sp logstep = math.log(6.4) / 27.0 log_t = (mels >= min_log_mel) freqs[log_t] = min_log_hz * torch.exp(logstep * (mels[log_t] - min_log_mel)) return freqs def _create_triangular_filterbank( all_freqs: Tensor, f_pts: Tensor, ) -> Tensor: """Create a triangular filter bank. Args: all_freqs (Tensor): STFT freq points of size (`n_freqs`). f_pts (Tensor): Filter mid points of size (`n_filter`). Returns: fb (Tensor): The filter bank of size (`n_freqs`, `n_filter`). """ # Adopted from Librosa # calculate the difference between each filter mid point and each stft freq point in hertz f_diff = f_pts[1:] - f_pts[:-1] # (n_filter + 1) slopes = f_pts.unsqueeze(0) - all_freqs.unsqueeze(1) # (n_freqs, n_filter + 2) # create overlapping triangles zero = torch.zeros(1) down_slopes = (-1.0 * slopes[:, :-2]) / f_diff[:-1] # (n_freqs, n_filter) up_slopes = slopes[:, 2:] / f_diff[1:] # (n_freqs, n_filter) fb = torch.max(zero, torch.min(down_slopes, up_slopes)) return fb
[docs]def melscale_fbanks( n_freqs: int, f_min: float, f_max: float, n_mels: int, sample_rate: int, norm: Optional[str] = None, mel_scale: str = "htk", ) -> Tensor: r"""Create a frequency bin conversion matrix. Note: For the sake of the numerical compatibility with librosa, not all the coefficients in the resulting filter bank has magnitude of 1. .. image:: https://download.pytorch.org/torchaudio/doc-assets/mel_fbanks.png :alt: Visualization of generated filter bank Args: n_freqs (int): Number of frequencies to highlight/apply f_min (float): Minimum frequency (Hz) f_max (float): Maximum frequency (Hz) n_mels (int): Number of mel filterbanks sample_rate (int): Sample rate of the audio waveform norm (str or None, optional): If 'slaney', divide the triangular mel weights by the width of the mel band (area normalization). (Default: ``None``) mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``) Returns: Tensor: Triangular filter banks (fb matrix) of size (``n_freqs``, ``n_mels``) meaning number of frequencies to highlight/apply to x the number of filterbanks. Each column is a filterbank so that assuming there is a matrix A of size (..., ``n_freqs``), the applied result would be ``A * melscale_fbanks(A.size(-1), ...)``. """ if norm is not None and norm != "slaney": raise ValueError("norm must be one of None or 'slaney'") # freq bins all_freqs = torch.linspace(0, sample_rate // 2, n_freqs) # calculate mel freq bins m_min = _hz_to_mel(f_min, mel_scale=mel_scale) m_max = _hz_to_mel(f_max, mel_scale=mel_scale) m_pts = torch.linspace(m_min, m_max, n_mels + 2) f_pts = _mel_to_hz(m_pts, mel_scale=mel_scale) # create filterbank fb = _create_triangular_filterbank(all_freqs, f_pts) if norm is not None and norm == "slaney": # Slaney-style mel is scaled to be approx constant energy per channel enorm = 2.0 / (f_pts[2:n_mels + 2] - f_pts[:n_mels]) fb *= enorm.unsqueeze(0) if (fb.max(dim=0).values == 0.).any(): warnings.warn( "At least one mel filterbank has all zero values. " f"The value for `n_mels` ({n_mels}) may be set too high. " f"Or, the value for `n_freqs` ({n_freqs}) may be set too low." ) return fb
[docs]def linear_fbanks( n_freqs: int, f_min: float, f_max: float, n_filter: int, sample_rate: int, ) -> Tensor: r"""Creates a linear triangular filterbank. Note: For the sake of the numerical compatibility with librosa, not all the coefficients in the resulting filter bank has magnitude of 1. .. image:: https://download.pytorch.org/torchaudio/doc-assets/lin_fbanks.png :alt: Visualization of generated filter bank Args: n_freqs (int): Number of frequencies to highlight/apply f_min (float): Minimum frequency (Hz) f_max (float): Maximum frequency (Hz) n_filter (int): Number of (linear) triangular filter sample_rate (int): Sample rate of the audio waveform Returns: Tensor: Triangular filter banks (fb matrix) of size (``n_freqs``, ``n_filter``) meaning number of frequencies to highlight/apply to x the number of filterbanks. Each column is a filterbank so that assuming there is a matrix A of size (..., ``n_freqs``), the applied result would be ``A * linear_fbanks(A.size(-1), ...)``. """ # freq bins all_freqs = torch.linspace(0, sample_rate // 2, n_freqs) # filter mid-points f_pts = torch.linspace(f_min, f_max, n_filter + 2) # create filterbank fb = _create_triangular_filterbank(all_freqs, f_pts) return fb
[docs]def create_dct( n_mfcc: int, n_mels: int, norm: Optional[str] ) -> Tensor: r"""Create a DCT transformation matrix with shape (``n_mels``, ``n_mfcc``), normalized depending on norm. Args: n_mfcc (int): Number of mfc coefficients to retain n_mels (int): Number of mel filterbanks norm (str or None): Norm to use (either 'ortho' or None) Returns: Tensor: The transformation matrix, to be right-multiplied to row-wise data of size (``n_mels``, ``n_mfcc``). """ # http://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-II n = torch.arange(float(n_mels)) k = torch.arange(float(n_mfcc)).unsqueeze(1) dct = torch.cos(math.pi / float(n_mels) * (n + 0.5) * k) # size (n_mfcc, n_mels) if norm is None: dct *= 2.0 else: assert norm == "ortho" dct[0] *= 1.0 / math.sqrt(2.0) dct *= math.sqrt(2.0 / float(n_mels)) return dct.t()
[docs]def mu_law_encoding( x: Tensor, quantization_channels: int ) -> Tensor: r"""Encode signal based on mu-law companding. For more info see the `Wikipedia Entry <https://en.wikipedia.org/wiki/%CE%9C-law_algorithm>`_ This algorithm expects the signal has been scaled to between -1 and 1 and returns a signal encoded with values from 0 to quantization_channels - 1. Args: x (Tensor): Input tensor quantization_channels (int): Number of channels Returns: Tensor: Input after mu-law encoding """ mu = quantization_channels - 1.0 if not x.is_floating_point(): warnings.warn("The input Tensor must be of floating type. \ This will be an error in the v0.12 release.") x = x.to(torch.float) mu = torch.tensor(mu, dtype=x.dtype) x_mu = torch.sign(x) * torch.log1p(mu * torch.abs(x)) / torch.log1p(mu) x_mu = ((x_mu + 1) / 2 * mu + 0.5).to(torch.int64) return x_mu
[docs]def mu_law_decoding( x_mu: Tensor, quantization_channels: int ) -> Tensor: r"""Decode mu-law encoded signal. For more info see the `Wikipedia Entry <https://en.wikipedia.org/wiki/%CE%9C-law_algorithm>`_ This expects an input with values between 0 and quantization_channels - 1 and returns a signal scaled between -1 and 1. Args: x_mu (Tensor): Input tensor quantization_channels (int): Number of channels Returns: Tensor: Input after mu-law decoding """ mu = quantization_channels - 1.0 if not x_mu.is_floating_point(): x_mu = x_mu.to(torch.float) mu = torch.tensor(mu, dtype=x_mu.dtype) x = ((x_mu) / mu) * 2 - 1.0 x = torch.sign(x) * (torch.exp(torch.abs(x) * torch.log1p(mu)) - 1.0) / mu return x
[docs]def phase_vocoder( complex_specgrams: Tensor, rate: float, phase_advance: Tensor ) -> Tensor: r"""Given a STFT tensor, speed up in time without modifying pitch by a factor of ``rate``. Args: complex_specgrams (Tensor): A tensor of dimension `(..., freq, num_frame)` with complex dtype. rate (float): Speed-up factor phase_advance (Tensor): Expected phase advance in each bin. Dimension of `(freq, 1)` Returns: Tensor: Stretched spectrogram. The resulting tensor is of the same dtype as the input spectrogram, but the number of frames is changed to ``ceil(num_frame / rate)``. Example >>> freq, hop_length = 1025, 512 >>> # (channel, freq, time) >>> complex_specgrams = torch.randn(2, freq, 300, dtype=torch.cfloat) >>> rate = 1.3 # Speed up by 30% >>> phase_advance = torch.linspace( >>> 0, math.pi * hop_length, freq)[..., None] >>> x = phase_vocoder(complex_specgrams, rate, phase_advance) >>> x.shape # with 231 == ceil(300 / 1.3) torch.Size([2, 1025, 231]) """ if rate == 1.0: return complex_specgrams # pack batch shape = complex_specgrams.size() complex_specgrams = complex_specgrams.reshape([-1] + list(shape[-2:])) # Figures out the corresponding real dtype, i.e. complex128 -> float64, complex64 -> float32 # Note torch.real is a view so it does not incur any memory copy. real_dtype = torch.real(complex_specgrams).dtype time_steps = torch.arange( 0, complex_specgrams.size(-1), rate, device=complex_specgrams.device, dtype=real_dtype) alphas = time_steps % 1.0 phase_0 = complex_specgrams[..., :1].angle() # Time Padding complex_specgrams = torch.nn.functional.pad(complex_specgrams, [0, 2]) # (new_bins, freq, 2) complex_specgrams_0 = complex_specgrams.index_select(-1, time_steps.long()) complex_specgrams_1 = complex_specgrams.index_select(-1, (time_steps + 1).long()) angle_0 = complex_specgrams_0.angle() angle_1 = complex_specgrams_1.angle() norm_0 = complex_specgrams_0.abs() norm_1 = complex_specgrams_1.abs() phase = angle_1 - angle_0 - phase_advance phase = phase - 2 * math.pi * torch.round(phase / (2 * math.pi)) # Compute Phase Accum phase = phase + phase_advance phase = torch.cat([phase_0, phase[..., :-1]], dim=-1) phase_acc = torch.cumsum(phase, -1) mag = alphas * norm_1 + (1 - alphas) * norm_0 complex_specgrams_stretch = torch.polar(mag, phase_acc) # unpack batch complex_specgrams_stretch = complex_specgrams_stretch.reshape(shape[:-2] + complex_specgrams_stretch.shape[1:]) return complex_specgrams_stretch
[docs]def mask_along_axis_iid( specgrams: Tensor, mask_param: int, mask_value: float, axis: int ) -> Tensor: r""" Apply a mask along ``axis``. Mask will be applied from indices ``[v_0, v_0 + v)``, where ``v`` is sampled from ``uniform(0, mask_param)``, and ``v_0`` from ``uniform(0, max_v - v)``. Args: specgrams (Tensor): Real spectrograms `(batch, channel, freq, time)` mask_param (int): Number of columns to be masked will be uniformly sampled from [0, mask_param] mask_value (float): Value to assign to the masked columns axis (int): Axis to apply masking on (2 -> frequency, 3 -> time) Returns: Tensor: Masked spectrograms of dimensions `(batch, channel, freq, time)` """ if axis not in [2, 3]: raise ValueError('Only Frequency and Time masking are supported') device = specgrams.device dtype = specgrams.dtype value = torch.rand(specgrams.shape[:2], device=device, dtype=dtype) * mask_param min_value = torch.rand(specgrams.shape[:2], device=device, dtype=dtype) * (specgrams.size(axis) - value) # Create broadcastable mask mask_start = min_value[..., None, None] mask_end = (min_value + value)[..., None, None] mask = torch.arange(0, specgrams.size(axis), device=device, dtype=dtype) # Per batch example masking specgrams = specgrams.transpose(axis, -1) specgrams = specgrams.masked_fill((mask >= mask_start) & (mask < mask_end), mask_value) specgrams = specgrams.transpose(axis, -1) return specgrams
[docs]def mask_along_axis( specgram: Tensor, mask_param: int, mask_value: float, axis: int ) -> Tensor: r""" Apply a mask along ``axis``. Mask will be applied from indices ``[v_0, v_0 + v)``, where ``v`` is sampled from ``uniform(0, mask_param)``, and ``v_0`` from ``uniform(0, max_v - v)``. All examples will have the same mask interval. Args: specgram (Tensor): Real spectrogram `(channel, freq, time)` mask_param (int): Number of columns to be masked will be uniformly sampled from [0, mask_param] mask_value (float): Value to assign to the masked columns axis (int): Axis to apply masking on (1 -> frequency, 2 -> time) Returns: Tensor: Masked spectrogram of dimensions `(channel, freq, time)` """ if axis not in [1, 2]: raise ValueError('Only Frequency and Time masking are supported') # pack batch shape = specgram.size() specgram = specgram.reshape([-1] + list(shape[-2:])) value = torch.rand(1) * mask_param min_value = torch.rand(1) * (specgram.size(axis) - value) mask_start = (min_value.long()).squeeze() mask_end = (min_value.long() + value.long()).squeeze() mask = torch.arange(0, specgram.shape[axis], device=specgram.device, dtype=specgram.dtype) mask = (mask >= mask_start) & (mask < mask_end) if axis == 1: mask = mask.unsqueeze(-1) assert mask_end - mask_start < mask_param specgram = specgram.masked_fill(mask, mask_value) # unpack batch specgram = specgram.reshape(shape[:-2] + specgram.shape[-2:]) return specgram
[docs]def compute_deltas( specgram: Tensor, win_length: int = 5, mode: str = "replicate" ) -> Tensor: r"""Compute delta coefficients of a tensor, usually a spectrogram: .. math:: d_t = \frac{\sum_{n=1}^{\text{N}} n (c_{t+n} - c_{t-n})}{2 \sum_{n=1}^{\text{N}} n^2} where :math:`d_t` is the deltas at time :math:`t`, :math:`c_t` is the spectrogram coeffcients at time :math:`t`, :math:`N` is ``(win_length-1)//2``. Args: specgram (Tensor): Tensor of audio of dimension `(..., freq, time)` win_length (int, optional): The window length used for computing delta (Default: ``5``) mode (str, optional): Mode parameter passed to padding (Default: ``"replicate"``) Returns: Tensor: Tensor of deltas of dimension `(..., freq, time)` Example >>> specgram = torch.randn(1, 40, 1000) >>> delta = compute_deltas(specgram) >>> delta2 = compute_deltas(delta) """ device = specgram.device dtype = specgram.dtype # pack batch shape = specgram.size() specgram = specgram.reshape(1, -1, shape[-1]) assert win_length >= 3 n = (win_length - 1) // 2 # twice sum of integer squared denom = n * (n + 1) * (2 * n + 1) / 3 specgram = torch.nn.functional.pad(specgram, (n, n), mode=mode) kernel = torch.arange(-n, n + 1, 1, device=device, dtype=dtype).repeat(specgram.shape[1], 1, 1) output = torch.nn.functional.conv1d(specgram, kernel, groups=specgram.shape[1]) / denom # unpack batch output = output.reshape(shape) return output
def _compute_nccf( waveform: Tensor, sample_rate: int, frame_time: float, freq_low: int ) -> Tensor: r""" Compute Normalized Cross-Correlation Function (NCCF). .. math:: \phi_i(m) = \frac{\sum_{n=b_i}^{b_i + N-1} w(n) w(m+n)}{\sqrt{E(b_i) E(m+b_i)}}, where :math:`\phi_i(m)` is the NCCF at frame :math:`i` with lag :math:`m`, :math:`w` is the waveform, :math:`N` is the length of a frame, :math:`b_i` is the beginning of frame :math:`i`, :math:`E(j)` is the energy :math:`\sum_{n=j}^{j+N-1} w^2(n)`. """ EPSILON = 10 ** (-9) # Number of lags to check lags = int(math.ceil(sample_rate / freq_low)) frame_size = int(math.ceil(sample_rate * frame_time)) waveform_length = waveform.size()[-1] num_of_frames = int(math.ceil(waveform_length / frame_size)) p = lags + num_of_frames * frame_size - waveform_length waveform = torch.nn.functional.pad(waveform, (0, p)) # Compute lags output_lag = [] for lag in range(1, lags + 1): s1 = waveform[..., :-lag].unfold(-1, frame_size, frame_size)[..., :num_of_frames, :] s2 = waveform[..., lag:].unfold(-1, frame_size, frame_size)[..., :num_of_frames, :] output_frames = ( (s1 * s2).sum(-1) / (EPSILON + torch.norm(s1, p=2, dim=-1)).pow(2) / (EPSILON + torch.norm(s2, p=2, dim=-1)).pow(2) ) output_lag.append(output_frames.unsqueeze(-1)) nccf = torch.cat(output_lag, -1) return nccf def _combine_max( a: Tuple[Tensor, Tensor], b: Tuple[Tensor, Tensor], thresh: float = 0.99 ) -> Tuple[Tensor, Tensor]: """ Take value from first if bigger than a multiplicative factor of the second, elementwise. """ mask = (a[0] > thresh * b[0]) values = mask * a[0] + ~mask * b[0] indices = mask * a[1] + ~mask * b[1] return values, indices def _find_max_per_frame( nccf: Tensor, sample_rate: int, freq_high: int ) -> Tensor: r""" For each frame, take the highest value of NCCF, apply centered median smoothing, and convert to frequency. Note: If the max among all the lags is very close to the first half of lags, then the latter is taken. """ lag_min = int(math.ceil(sample_rate / freq_high)) # Find near enough max that is smallest best = torch.max(nccf[..., lag_min:], -1) half_size = nccf.shape[-1] // 2 half = torch.max(nccf[..., lag_min:half_size], -1) best = _combine_max(half, best) indices = best[1] # Add back minimal lag indices += lag_min # Add 1 empirical calibration offset indices += 1 return indices def _median_smoothing( indices: Tensor, win_length: int ) -> Tensor: r""" Apply median smoothing to the 1D tensor over the given window. """ # Centered windowed pad_length = (win_length - 1) // 2 # "replicate" padding in any dimension indices = torch.nn.functional.pad( indices, (pad_length, 0), mode="constant", value=0. ) indices[..., :pad_length] = torch.cat(pad_length * [indices[..., pad_length].unsqueeze(-1)], dim=-1) roll = indices.unfold(-1, win_length, 1) values, _ = torch.median(roll, -1) return values
[docs]def detect_pitch_frequency( waveform: Tensor, sample_rate: int, frame_time: float = 10 ** (-2), win_length: int = 30, freq_low: int = 85, freq_high: int = 3400, ) -> Tensor: r"""Detect pitch frequency. It is implemented using normalized cross-correlation function and median smoothing. Args: waveform (Tensor): Tensor of audio of dimension `(..., freq, time)` sample_rate (int): The sample rate of the waveform (Hz) frame_time (float, optional): Duration of a frame (Default: ``10 ** (-2)``). win_length (int, optional): The window length for median smoothing (in number of frames) (Default: ``30``). freq_low (int, optional): Lowest frequency that can be detected (Hz) (Default: ``85``). freq_high (int, optional): Highest frequency that can be detected (Hz) (Default: ``3400``). Returns: Tensor: Tensor of freq of dimension `(..., frame)` """ # pack batch shape = list(waveform.size()) waveform = waveform.reshape([-1] + shape[-1:]) nccf = _compute_nccf(waveform, sample_rate, frame_time, freq_low) indices = _find_max_per_frame(nccf, sample_rate, freq_high) indices = _median_smoothing(indices, win_length) # Convert indices to frequency EPSILON = 10 ** (-9) freq = sample_rate / (EPSILON + indices.to(torch.float)) # unpack batch freq = freq.reshape(shape[:-1] + list(freq.shape[-1:])) return freq
[docs]def sliding_window_cmn( specgram: Tensor, cmn_window: int = 600, min_cmn_window: int = 100, center: bool = False, norm_vars: bool = False, ) -> Tensor: r""" Apply sliding-window cepstral mean (and optionally variance) normalization per utterance. Args: specgram (Tensor): Tensor of spectrogram of dimension `(..., time, freq)` cmn_window (int, optional): Window in frames for running average CMN computation (int, default = 600) min_cmn_window (int, optional): Minimum CMN window used at start of decoding (adds latency only at start). Only applicable if center == false, ignored if center==true (int, default = 100) center (bool, optional): If true, use a window centered on the current frame (to the extent possible, modulo end effects). If false, window is to the left. (bool, default = false) norm_vars (bool, optional): If true, normalize variance to one. (bool, default = false) Returns: Tensor: Tensor matching input shape `(..., freq, time)` """ input_shape = specgram.shape num_frames, num_feats = input_shape[-2:] specgram = specgram.view(-1, num_frames, num_feats) num_channels = specgram.shape[0] dtype = specgram.dtype device = specgram.device last_window_start = last_window_end = -1 cur_sum = torch.zeros(num_channels, num_feats, dtype=dtype, device=device) cur_sumsq = torch.zeros(num_channels, num_feats, dtype=dtype, device=device) cmn_specgram = torch.zeros( num_channels, num_frames, num_feats, dtype=dtype, device=device) for t in range(num_frames): window_start = 0 window_end = 0 if center: window_start = t - cmn_window // 2 window_end = window_start + cmn_window else: window_start = t - cmn_window window_end = t + 1 if window_start < 0: window_end -= window_start window_start = 0 if not center: if window_end > t: window_end = max(t + 1, min_cmn_window) if window_end > num_frames: window_start -= (window_end - num_frames) window_end = num_frames if window_start < 0: window_start = 0 if last_window_start == -1: input_part = specgram[:, window_start: window_end - window_start, :] cur_sum += torch.sum(input_part, 1) if norm_vars: cur_sumsq += torch.cumsum(input_part ** 2, 1)[:, -1, :] else: if window_start > last_window_start: frame_to_remove = specgram[:, last_window_start, :] cur_sum -= frame_to_remove if norm_vars: cur_sumsq -= (frame_to_remove ** 2) if window_end > last_window_end: frame_to_add = specgram[:, last_window_end, :] cur_sum += frame_to_add if norm_vars: cur_sumsq += (frame_to_add ** 2) window_frames = window_end - window_start last_window_start = window_start last_window_end = window_end cmn_specgram[:, t, :] = specgram[:, t, :] - cur_sum / window_frames if norm_vars: if window_frames == 1: cmn_specgram[:, t, :] = torch.zeros( num_channels, num_feats, dtype=dtype, device=device) else: variance = cur_sumsq variance = variance / window_frames variance -= ((cur_sum ** 2) / (window_frames ** 2)) variance = torch.pow(variance, -0.5) cmn_specgram[:, t, :] *= variance cmn_specgram = cmn_specgram.view(input_shape[:-2] + (num_frames, num_feats)) if len(input_shape) == 2: cmn_specgram = cmn_specgram.squeeze(0) return cmn_specgram
[docs]def spectral_centroid( waveform: Tensor, sample_rate: int, pad: int, window: Tensor, n_fft: int, hop_length: int, win_length: int, ) -> Tensor: r""" Compute the spectral centroid for each channel along the time axis. The spectral centroid is defined as the weighted average of the frequency values, weighted by their magnitude. Args: waveform (Tensor): Tensor of audio of dimension `(..., time)` sample_rate (int): Sample rate of the audio waveform pad (int): Two sided padding of signal window (Tensor): Window tensor that is applied/multiplied to each frame/window n_fft (int): Size of FFT hop_length (int): Length of hop between STFT windows win_length (int): Window size Returns: Tensor: Dimension `(..., time)` """ specgram = spectrogram(waveform, pad=pad, window=window, n_fft=n_fft, hop_length=hop_length, win_length=win_length, power=1., normalized=False) freqs = torch.linspace(0, sample_rate // 2, steps=1 + n_fft // 2, device=specgram.device).reshape((-1, 1)) freq_dim = -2 return (freqs * specgram).sum(dim=freq_dim) / specgram.sum(dim=freq_dim)
[docs]@_mod_utils.requires_sox() def apply_codec( waveform: Tensor, sample_rate: int, format: str, channels_first: bool = True, compression: Optional[float] = None, encoding: Optional[str] = None, bits_per_sample: Optional[int] = None, ) -> Tensor: r""" Apply codecs as a form of augmentation. Args: waveform (Tensor): Audio data. Must be 2 dimensional. See also ```channels_first```. sample_rate (int): Sample rate of the audio waveform. format (str): File format. channels_first (bool, optional): When True, both the input and output Tensor have dimension `(channel, time)`. Otherwise, they have dimension `(time, channel)`. compression (float or None, optional): Used for formats other than WAV. For more details see :py:func:`torchaudio.backend.sox_io_backend.save`. encoding (str or None, optional): Changes the encoding for the supported formats. For more details see :py:func:`torchaudio.backend.sox_io_backend.save`. bits_per_sample (int or None, optional): Changes the bit depth for the supported formats. For more details see :py:func:`torchaudio.backend.sox_io_backend.save`. Returns: Tensor: Resulting Tensor. If ``channels_first=True``, it has `(channel, time)` else `(time, channel)`. """ bytes = io.BytesIO() torchaudio.backend.sox_io_backend.save(bytes, waveform, sample_rate, channels_first, compression, format, encoding, bits_per_sample ) bytes.seek(0) augmented, _ = torchaudio.sox_effects.sox_effects.apply_effects_file( bytes, effects=[["rate", f"{sample_rate}"]], channels_first=channels_first, format=format) return augmented
[docs]@_mod_utils.requires_kaldi() def compute_kaldi_pitch( waveform: torch.Tensor, sample_rate: float, frame_length: float = 25.0, frame_shift: float = 10.0, min_f0: float = 50, max_f0: float = 400, soft_min_f0: float = 10.0, penalty_factor: float = 0.1, lowpass_cutoff: float = 1000, resample_frequency: float = 4000, delta_pitch: float = 0.005, nccf_ballast: float = 7000, lowpass_filter_width: int = 1, upsample_filter_width: int = 5, max_frames_latency: int = 0, frames_per_chunk: int = 0, simulate_first_pass_online: bool = False, recompute_frame: int = 500, snip_edges: bool = True, ) -> torch.Tensor: """Extract pitch based on method described in *A pitch extraction algorithm tuned for automatic speech recognition* [:footcite:`6854049`]. This function computes the equivalent of `compute-kaldi-pitch-feats` from Kaldi. Args: waveform (Tensor): The input waveform of shape `(..., time)`. sample_rate (float): Sample rate of `waveform`. frame_length (float, optional): Frame length in milliseconds. (default: 25.0) frame_shift (float, optional): Frame shift in milliseconds. (default: 10.0) min_f0 (float, optional): Minimum F0 to search for (Hz) (default: 50.0) max_f0 (float, optional): Maximum F0 to search for (Hz) (default: 400.0) soft_min_f0 (float, optional): Minimum f0, applied in soft way, must not exceed min-f0 (default: 10.0) penalty_factor (float, optional): Cost factor for FO change. (default: 0.1) lowpass_cutoff (float, optional): Cutoff frequency for LowPass filter (Hz) (default: 1000) resample_frequency (float, optional): Frequency that we down-sample the signal to. Must be more than twice lowpass-cutoff. (default: 4000) delta_pitch( float, optional): Smallest relative change in pitch that our algorithm measures. (default: 0.005) nccf_ballast (float, optional): Increasing this factor reduces NCCF for quiet frames (default: 7000) lowpass_filter_width (int, optional): Integer that determines filter width of lowpass filter, more gives sharper filter. (default: 1) upsample_filter_width (int, optional): Integer that determines filter width when upsampling NCCF. (default: 5) max_frames_latency (int, optional): Maximum number of frames of latency that we allow pitch tracking to introduce into the feature processing (affects output only if ``frames_per_chunk > 0`` and ``simulate_first_pass_online=True``) (default: 0) frames_per_chunk (int, optional): The number of frames used for energy normalization. (default: 0) simulate_first_pass_online (bool, optional): If true, the function will output features that correspond to what an online decoder would see in the first pass of decoding -- not the final version of the features, which is the default. (default: False) Relevant if ``frames_per_chunk > 0``. recompute_frame (int, optional): Only relevant for compatibility with online pitch extraction. A non-critical parameter; the frame at which we recompute some of the forward pointers, after revising our estimate of the signal energy. Relevant if ``frames_per_chunk > 0``. (default: 500) snip_edges (bool, optional): If this is set to false, the incomplete frames near the ending edge won't be snipped, so that the number of frames is the file size divided by the frame-shift. This makes different types of features give the same number of frames. (default: True) Returns: Tensor: Pitch feature. Shape: `(batch, frames 2)` where the last dimension corresponds to pitch and NCCF. """ shape = waveform.shape waveform = waveform.reshape(-1, shape[-1]) result = torch.ops.torchaudio.kaldi_ComputeKaldiPitch( waveform, sample_rate, frame_length, frame_shift, min_f0, max_f0, soft_min_f0, penalty_factor, lowpass_cutoff, resample_frequency, delta_pitch, nccf_ballast, lowpass_filter_width, upsample_filter_width, max_frames_latency, frames_per_chunk, simulate_first_pass_online, recompute_frame, snip_edges, ) result = result.reshape(shape[:-1] + result.shape[-2:]) return result
def _get_sinc_resample_kernel( orig_freq: int, new_freq: int, gcd: int, lowpass_filter_width: int, rolloff: float, resampling_method: str, beta: Optional[float], device: torch.device = torch.device("cpu"), dtype: Optional[torch.dtype] = None): if not (int(orig_freq) == orig_freq and int(new_freq) == new_freq): raise Exception( "Frequencies must be of integer type to ensure quality resampling computation. " "To work around this, manually convert both frequencies to integer values " "that maintain their resampling rate ratio before passing them into the function. " "Example: To downsample a 44100 hz waveform by a factor of 8, use " "`orig_freq=8` and `new_freq=1` instead of `orig_freq=44100` and `new_freq=5512.5`. " "For more information, please refer to https://github.com/pytorch/audio/issues/1487." ) if resampling_method not in ['sinc_interpolation', 'kaiser_window']: raise ValueError('Invalid resampling method: {}'.format(resampling_method)) orig_freq = int(orig_freq) // gcd new_freq = int(new_freq) // gcd assert lowpass_filter_width > 0 kernels = [] base_freq = min(orig_freq, new_freq) # This will perform antialiasing filtering by removing the highest frequencies. # At first I thought I only needed this when downsampling, but when upsampling # you will get edge artifacts without this, as the edge is equivalent to zero padding, # which will add high freq artifacts. base_freq *= rolloff # The key idea of the algorithm is that x(t) can be exactly reconstructed from x[i] (tensor) # using the sinc interpolation formula: # x(t) = sum_i x[i] sinc(pi * orig_freq * (i / orig_freq - t)) # We can then sample the function x(t) with a different sample rate: # y[j] = x(j / new_freq) # or, # y[j] = sum_i x[i] sinc(pi * orig_freq * (i / orig_freq - j / new_freq)) # We see here that y[j] is the convolution of x[i] with a specific filter, for which # we take an FIR approximation, stopping when we see at least `lowpass_filter_width` zeros crossing. # But y[j+1] is going to have a different set of weights and so on, until y[j + new_freq]. # Indeed: # y[j + new_freq] = sum_i x[i] sinc(pi * orig_freq * ((i / orig_freq - (j + new_freq) / new_freq)) # = sum_i x[i] sinc(pi * orig_freq * ((i - orig_freq) / orig_freq - j / new_freq)) # = sum_i x[i + orig_freq] sinc(pi * orig_freq * (i / orig_freq - j / new_freq)) # so y[j+new_freq] uses the same filter as y[j], but on a shifted version of x by `orig_freq`. # This will explain the F.conv1d after, with a stride of orig_freq. width = math.ceil(lowpass_filter_width * orig_freq / base_freq) # If orig_freq is still big after GCD reduction, most filters will be very unbalanced, i.e., # they will have a lot of almost zero values to the left or to the right... # There is probably a way to evaluate those filters more efficiently, but this is kept for # future work. idx_dtype = dtype if dtype is not None else torch.float64 idx = torch.arange(-width, width + orig_freq, device=device, dtype=idx_dtype) for i in range(new_freq): t = (-i / new_freq + idx / orig_freq) * base_freq t = t.clamp_(-lowpass_filter_width, lowpass_filter_width) # we do not use built in torch windows here as we need to evaluate the window # at specific positions, not over a regular grid. if resampling_method == "sinc_interpolation": window = torch.cos(t * math.pi / lowpass_filter_width / 2)**2 else: # kaiser_window if beta is None: beta = 14.769656459379492 beta_tensor = torch.tensor(float(beta)) window = torch.i0(beta_tensor * torch.sqrt(1 - (t / lowpass_filter_width) ** 2)) / torch.i0(beta_tensor) t *= math.pi kernel = torch.where(t == 0, torch.tensor(1.).to(t), torch.sin(t) / t) kernel.mul_(window) kernels.append(kernel) scale = base_freq / orig_freq kernels = torch.stack(kernels).view(new_freq, 1, -1).mul_(scale) if dtype is None: kernels = kernels.to(dtype=torch.float32) return kernels, width def _apply_sinc_resample_kernel( waveform: Tensor, orig_freq: int, new_freq: int, gcd: int, kernel: Tensor, width: int, ): orig_freq = int(orig_freq) // gcd new_freq = int(new_freq) // gcd # pack batch shape = waveform.size() waveform = waveform.view(-1, shape[-1]) num_wavs, length = waveform.shape waveform = torch.nn.functional.pad(waveform, (width, width + orig_freq)) resampled = torch.nn.functional.conv1d(waveform[:, None], kernel, stride=orig_freq) resampled = resampled.transpose(1, 2).reshape(num_wavs, -1) target_length = int(math.ceil(new_freq * length / orig_freq)) resampled = resampled[..., :target_length] # unpack batch resampled = resampled.view(shape[:-1] + resampled.shape[-1:]) return resampled
[docs]def resample( waveform: Tensor, orig_freq: int, new_freq: int, lowpass_filter_width: int = 6, rolloff: float = 0.99, resampling_method: str = "sinc_interpolation", beta: Optional[float] = None, ) -> Tensor: r"""Resamples the waveform at the new frequency using bandlimited interpolation. https://ccrma.stanford.edu/~jos/resample/Theory_Ideal_Bandlimited_Interpolation.html Note: ``transforms.Resample`` precomputes and reuses the resampling kernel, so using it will result in more efficient computation if resampling multiple waveforms with the same resampling parameters. Args: waveform (Tensor): The input signal of dimension `(..., time)` orig_freq (int): The original frequency of the signal new_freq (int): The desired frequency lowpass_filter_width (int, optional): Controls the sharpness of the filter, more == sharper but less efficient. (Default: ``6``) rolloff (float, optional): The roll-off frequency of the filter, as a fraction of the Nyquist. Lower values reduce anti-aliasing, but also reduce some of the highest frequencies. (Default: ``0.99``) resampling_method (str, optional): The resampling method to use. Options: [``sinc_interpolation``, ``kaiser_window``] (Default: ``'sinc_interpolation'``) beta (float or None, optional): The shape parameter used for kaiser window. Returns: Tensor: The waveform at the new frequency of dimension `(..., time).` """ assert orig_freq > 0.0 and new_freq > 0.0 if orig_freq == new_freq: return waveform gcd = math.gcd(int(orig_freq), int(new_freq)) kernel, width = _get_sinc_resample_kernel(orig_freq, new_freq, gcd, lowpass_filter_width, rolloff, resampling_method, beta, waveform.device, waveform.dtype) resampled = _apply_sinc_resample_kernel(waveform, orig_freq, new_freq, gcd, kernel, width) return resampled
[docs]@torch.jit.unused def edit_distance(seq1: Sequence, seq2: Sequence) -> int: """ Calculate the word level edit (Levenshtein) distance between two sequences. The function computes an edit distance allowing deletion, insertion and substitution. The result is an integer. For most applications, the two input sequences should be the same type. If two strings are given, the output is the edit distance between the two strings (character edit distance). If two lists of strings are given, the output is the edit distance between sentences (word edit distance). Users may want to normalize the output by the length of the reference sequence. torchscipt is not supported for this function. Args: seq1 (Sequence): the first sequence to compare. seq2 (Sequence): the second sequence to compare. Returns: int: The distance between the first and second sequences. """ len_sent2 = len(seq2) dold = list(range(len_sent2 + 1)) dnew = [0 for _ in range(len_sent2 + 1)] for i in range(1, len(seq1) + 1): dnew[0] = i for j in range(1, len_sent2 + 1): if seq1[i - 1] == seq2[j - 1]: dnew[j] = dold[j - 1] else: substitution = dold[j - 1] + 1 insertion = dnew[j - 1] + 1 deletion = dold[j] + 1 dnew[j] = min(substitution, insertion, deletion) dnew, dold = dold, dnew return int(dold[-1])
[docs]def pitch_shift( waveform: Tensor, sample_rate: int, n_steps: int, bins_per_octave: int = 12, n_fft: int = 512, win_length: Optional[int] = None, hop_length: Optional[int] = None, window: Optional[Tensor] = None, ) -> Tensor: """ Shift the pitch of a waveform by ``n_steps`` steps. Args: waveform (Tensor): The input waveform of shape `(..., time)`. sample_rate (int): Sample rate of `waveform`. n_steps (int): The (fractional) steps to shift `waveform`. bins_per_octave (int, optional): The number of steps per octave (Default: ``12``). n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins (Default: ``512``). win_length (int or None, optional): Window size. If None, then ``n_fft`` is used. (Default: ``None``). hop_length (int or None, optional): Length of hop between STFT windows. If None, then ``win_length // 4`` is used (Default: ``None``). window (Tensor or None, optional): Window tensor that is applied/multiplied to each frame/window. If None, then ``torch.hann_window(win_length)`` is used (Default: ``None``). Returns: Tensor: The pitch-shifted audio waveform of shape `(..., time)`. """ if hop_length is None: hop_length = n_fft // 4 if win_length is None: win_length = n_fft if window is None: window = torch.hann_window(window_length=win_length, device=waveform.device) # pack batch shape = waveform.size() waveform = waveform.reshape(-1, shape[-1]) ori_len = shape[-1] rate = 2.0 ** (-float(n_steps) / bins_per_octave) spec_f = torch.stft(input=waveform, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, center=True, pad_mode='reflect', normalized=False, onesided=True, return_complex=True) phase_advance = torch.linspace(0, math.pi * hop_length, spec_f.shape[-2], device=spec_f.device)[..., None] spec_stretch = phase_vocoder(spec_f, rate, phase_advance) len_stretch = int(round(ori_len / rate)) waveform_stretch = torch.istft(spec_stretch, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, length=len_stretch) waveform_shift = resample(waveform_stretch, int(sample_rate / rate), sample_rate) shift_len = waveform_shift.size()[-1] if shift_len > ori_len: waveform_shift = waveform_shift[..., :ori_len] else: waveform_shift = torch.nn.functional.pad(waveform_shift, [0, ori_len - shift_len]) # unpack batch waveform_shift = waveform_shift.view(shape[:-1] + waveform_shift.shape[-1:]) return waveform_shift
[docs]def rnnt_loss( logits: Tensor, targets: Tensor, logit_lengths: Tensor, target_lengths: Tensor, blank: int = -1, clamp: float = -1, reduction: str = "mean", ): """Compute the RNN Transducer loss from *Sequence Transduction with Recurrent Neural Networks* [:footcite:`graves2012sequence`]. The RNN Transducer loss extends the CTC loss by defining a distribution over output sequences of all lengths, and by jointly modelling both input-output and output-output dependencies. Args: logits (Tensor): Tensor of dimension `(batch, max seq length, max target length + 1, class)` containing output from joiner targets (Tensor): Tensor of dimension `(batch, max target length)` containing targets with zero padded logit_lengths (Tensor): Tensor of dimension `(batch)` containing lengths of each sequence from encoder target_lengths (Tensor): Tensor of dimension `(batch)` containing lengths of targets for each sequence blank (int, optional): blank label (Default: ``-1``) clamp (float, optional): clamp for gradients (Default: ``-1``) reduction (string, optional): Specifies the reduction to apply to the output: ``'none'`` | ``'mean'`` | ``'sum'``. (Default: ``'mean'``) Returns: Tensor: Loss with the reduction option applied. If ``reduction`` is ``'none'``, then size `(batch)`, otherwise scalar. """ if reduction not in ['none', 'mean', 'sum']: raise ValueError("reduction should be one of 'none', 'mean', or 'sum'") if blank < 0: # reinterpret blank index if blank < 0. blank = logits.shape[-1] + blank costs, _ = torch.ops.torchaudio.rnnt_loss( logits=logits, targets=targets, logit_lengths=logit_lengths, target_lengths=target_lengths, blank=blank, clamp=clamp, ) if reduction == 'mean': return costs.mean() elif reduction == 'sum': return costs.sum() return costs

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources