Initial checkin of nvidia tacotron model & dataset

These two are tested, full support for training to come.

codes/data/__init__.py

@@ -2,11 +2,12 @@
 import logging
 import torch
 import torch.utils.data
+from munch import munchify
 
 from utils.util import opt_get
 
 
-def create_dataloader(dataset, dataset_opt, opt=None, sampler=None):
+def create_dataloader(dataset, dataset_opt, opt=None, sampler=None, collate_fn=None):
     phase = dataset_opt['phase']
     if phase == 'train':
         if opt_get(opt, ['dist'], False):
@@ -21,15 +22,17 @@ def create_dataloader(dataset, dataset_opt, opt=None, sampler=None):
             shuffle = True
         return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle,
                                            num_workers=num_workers, sampler=sampler, drop_last=True,
-                                           pin_memory=True)
+                                           pin_memory=True, collate_fn=collate_fn)
     else:
         batch_size = dataset_opt['batch_size'] or 1
         return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=0,
-                                           pin_memory=True)
+                                           pin_memory=True, collate_fn=collate_fn)
 
 
-def create_dataset(dataset_opt):
+def create_dataset(dataset_opt, return_collate=False):
     mode = dataset_opt['mode']
+    collate = None
+
     # datasets for image restoration
     if mode == 'fullimage':
         from data.full_image_dataset import FullImageDataset as D
@@ -59,8 +62,19 @@ def create_dataset(dataset_opt):
         from data.random_dataset import RandomDataset as D
     elif mode == 'zipfile':
         from data.zip_file_dataset import ZipFileDataset as D
+    elif mode == 'nv_tacotron':
+        from data.audio.nv_tacotron_dataset import TextMelLoader as D
+        from data.audio.nv_tacotron_dataset import TextMelCollate as C
+        from models.tacotron2.hparams import create_hparams
+        default_params = create_hparams()
+        dataset_opt.update(default_params)
+        dataset_opt = munchify(dataset_opt)
+        collate = C(dataset_opt.n_frames_per_step)
     else:
         raise NotImplementedError('Dataset [{:s}] is not recognized.'.format(mode))
     dataset = D(dataset_opt)
 
+    if return_collate:
+        return dataset, collate
+    else:
         return dataset

codes/data/audio/nv_tacotron_dataset.py

@@ -0,0 +1,127 @@
+import os
+import random
+import numpy as np
+import torch
+import torch.utils.data
+
+import models.tacotron2.layers as layers
+from models.tacotron2.taco_utils import load_wav_to_torch, load_filepaths_and_text
+
+from models.tacotron2.text import text_to_sequence
+
+
+class TextMelLoader(torch.utils.data.Dataset):
+    """
+        1) loads audio,text pairs
+        2) normalizes text and converts them to sequences of one-hot vectors
+        3) computes mel-spectrograms from audio files.
+    """
+    def __init__(self, hparams):
+        self.path = os.path.dirname(hparams['path'])
+        self.audiopaths_and_text = load_filepaths_and_text(hparams['path'])
+        self.text_cleaners = hparams.text_cleaners
+        self.max_wav_value = hparams.max_wav_value
+        self.sampling_rate = hparams.sampling_rate
+        self.load_mel_from_disk = hparams.load_mel_from_disk
+        self.stft = layers.TacotronSTFT(
+            hparams.filter_length, hparams.hop_length, hparams.win_length,
+            hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin,
+            hparams.mel_fmax)
+        random.seed(hparams.seed)
+        random.shuffle(self.audiopaths_and_text)
+
+    def get_mel_text_pair(self, audiopath_and_text):
+        # separate filename and text
+        audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
+        audiopath = os.path.join(self.path, audiopath)
+        text = self.get_text(text)
+        mel = self.get_mel(audiopath)
+        return (text, mel)
+
+    def get_mel(self, filename):
+        if not self.load_mel_from_disk:
+            audio, sampling_rate = load_wav_to_torch(filename)
+            if sampling_rate != self.stft.sampling_rate:
+                raise ValueError("{} {} SR doesn't match target {} SR".format(
+                    sampling_rate, self.stft.sampling_rate))
+            audio_norm = audio / self.max_wav_value
+            audio_norm = audio_norm.unsqueeze(0)
+            audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
+            melspec = self.stft.mel_spectrogram(audio_norm)
+            melspec = torch.squeeze(melspec, 0)
+        else:
+            melspec = torch.from_numpy(np.load(filename))
+            assert melspec.size(0) == self.stft.n_mel_channels, (
+                'Mel dimension mismatch: given {}, expected {}'.format(
+                    melspec.size(0), self.stft.n_mel_channels))
+
+        return melspec
+
+    def get_text(self, text):
+        text_norm = torch.IntTensor(text_to_sequence(text, self.text_cleaners))
+        return text_norm
+
+    def __getitem__(self, index):
+        return self.get_mel_text_pair(self.audiopaths_and_text[index])
+
+    def __len__(self):
+        return len(self.audiopaths_and_text)
+
+
+class TextMelCollate():
+    """ Zero-pads model inputs and targets based on number of frames per setep
+    """
+    def __init__(self, n_frames_per_step):
+        self.n_frames_per_step = n_frames_per_step
+
+    def __call__(self, batch):
+        """Collate's training batch from normalized text and mel-spectrogram
+        PARAMS
+        ------
+        batch: [text_normalized, mel_normalized]
+        """
+        # Right zero-pad all one-hot text sequences to max input length
+        input_lengths, ids_sorted_decreasing = torch.sort(
+            torch.LongTensor([len(x[0]) for x in batch]),
+            dim=0, descending=True)
+        max_input_len = input_lengths[0]
+
+        text_padded = torch.LongTensor(len(batch), max_input_len)
+        text_padded.zero_()
+        for i in range(len(ids_sorted_decreasing)):
+            text = batch[ids_sorted_decreasing[i]][0]
+            text_padded[i, :text.size(0)] = text
+
+        # Right zero-pad mel-spec
+        num_mels = batch[0][1].size(0)
+        max_target_len = max([x[1].size(1) for x in batch])
+        if max_target_len % self.n_frames_per_step != 0:
+            max_target_len += self.n_frames_per_step - max_target_len % self.n_frames_per_step
+            assert max_target_len % self.n_frames_per_step == 0
+
+        # include mel padded and gate padded
+        mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len)
+        mel_padded.zero_()
+        gate_padded = torch.FloatTensor(len(batch), max_target_len)
+        gate_padded.zero_()
+        output_lengths = torch.LongTensor(len(batch))
+        for i in range(len(ids_sorted_decreasing)):
+            mel = batch[ids_sorted_decreasing[i]][1]
+            mel_padded[i, :, :mel.size(1)] = mel
+            gate_padded[i, mel.size(1)-1:] = 1
+            output_lengths[i] = mel.size(1)
+
+        return text_padded, input_lengths, mel_padded, gate_padded, \
+            output_lengths
+
+
+if __name__ == '__main__':
+    params = {
+        'mode': 'nv_tacotron',
+        'path': 'E:\\4k6k\\datasets\\audio\\LJSpeech-1.1\\ljs_audio_text_train_filelist.txt',
+
+    }
+    from data import create_dataset
+    ds = create_dataset(params)
+    j = ds[0]
+    print(j)

codes/models/tacotron2/LICENSE

@@ -0,0 +1,32 @@
+This directory contains works with the below licenses, which should be considered in addition
+to the base repository license.
+
+BSD 3-Clause License
+
+Copyright (c) 2018, NVIDIA Corporation
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

codes/models/tacotron2/audio_processing.py

@@ -0,0 +1,93 @@
+import torch
+import numpy as np
+from scipy.signal import get_window
+import librosa.util as librosa_util
+
+
+def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
+                     n_fft=800, dtype=np.float32, norm=None):
+    """
+    # from librosa 0.6
+    Compute the sum-square envelope of a window function at a given hop length.
+
+    This is used to estimate modulation effects induced by windowing
+    observations in short-time fourier transforms.
+
+    Parameters
+    ----------
+    window : string, tuple, number, callable, or list-like
+        Window specification, as in `get_window`
+
+    n_frames : int > 0
+        The number of analysis frames
+
+    hop_length : int > 0
+        The number of samples to advance between frames
+
+    win_length : [optional]
+        The length of the window function.  By default, this matches `n_fft`.
+
+    n_fft : int > 0
+        The length of each analysis frame.
+
+    dtype : np.dtype
+        The data type of the output
+
+    Returns
+    -------
+    wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
+        The sum-squared envelope of the window function
+    """
+    if win_length is None:
+        win_length = n_fft
+
+    n = n_fft + hop_length * (n_frames - 1)
+    x = np.zeros(n, dtype=dtype)
+
+    # Compute the squared window at the desired length
+    win_sq = get_window(window, win_length, fftbins=True)
+    win_sq = librosa_util.normalize(win_sq, norm=norm)**2
+    win_sq = librosa_util.pad_center(win_sq, n_fft)
+
+    # Fill the envelope
+    for i in range(n_frames):
+        sample = i * hop_length
+        x[sample:min(n, sample + n_fft)] += win_sq[:max(0, min(n_fft, n - sample))]
+    return x
+
+
+def griffin_lim(magnitudes, stft_fn, n_iters=30):
+    """
+    PARAMS
+    ------
+    magnitudes: spectrogram magnitudes
+    stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods
+    """
+
+    angles = np.angle(np.exp(2j * np.pi * np.random.rand(*magnitudes.size())))
+    angles = angles.astype(np.float32)
+    angles = torch.autograd.Variable(torch.from_numpy(angles))
+    signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
+
+    for i in range(n_iters):
+        _, angles = stft_fn.transform(signal)
+        signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
+    return signal
+
+
+def dynamic_range_compression(x, C=1, clip_val=1e-5):
+    """
+    PARAMS
+    ------
+    C: compression factor
+    """
+    return torch.log(torch.clamp(x, min=clip_val) * C)
+
+
+def dynamic_range_decompression(x, C=1):
+    """
+    PARAMS
+    ------
+    C: compression factor used to compress
+    """
+    return torch.exp(x) / C

codes/models/tacotron2/hparams.py

@@ -0,0 +1,95 @@
+import tensorflow as tf
+from models.tacotron2.text import symbols
+
+
+def create_hparams(hparams_string=None, verbose=False):
+    """Create model hyperparameters. Parse nondefault from given string."""
+
+    hparams = dict(
+        ################################
+        # Experiment Parameters        #
+        ################################
+        epochs=500,
+        iters_per_checkpoint=1000,
+        seed=1234,
+        dynamic_loss_scaling=True,
+        fp16_run=False,
+        distributed_run=False,
+        dist_backend="nccl",
+        dist_url="tcp://localhost:54321",
+        cudnn_enabled=True,
+        cudnn_benchmark=False,
+        ignore_layers=['embedding.weight'],
+
+        ################################
+        # Data Parameters             #
+        ################################
+        load_mel_from_disk=False,
+        training_files='filelists/ljs_audio_text_train_filelist.txt',
+        validation_files='filelists/ljs_audio_text_val_filelist.txt',
+        text_cleaners=['english_cleaners'],
+
+        ################################
+        # Audio Parameters             #
+        ################################
+        max_wav_value=32768.0,
+        sampling_rate=22050,
+        filter_length=1024,
+        hop_length=256,
+        win_length=1024,
+        n_mel_channels=80,
+        mel_fmin=0.0,
+        mel_fmax=8000.0,
+
+        ################################
+        # Model Parameters             #
+        ################################
+        n_symbols=len(symbols),
+        symbols_embedding_dim=512,
+
+        # Encoder parameters
+        encoder_kernel_size=5,
+        encoder_n_convolutions=3,
+        encoder_embedding_dim=512,
+
+        # Decoder parameters
+        n_frames_per_step=1,  # currently only 1 is supported
+        decoder_rnn_dim=1024,
+        prenet_dim=256,
+        max_decoder_steps=1000,
+        gate_threshold=0.5,
+        p_attention_dropout=0.1,
+        p_decoder_dropout=0.1,
+
+        # Attention parameters
+        attention_rnn_dim=1024,
+        attention_dim=128,
+
+        # Location Layer parameters
+        attention_location_n_filters=32,
+        attention_location_kernel_size=31,
+
+        # Mel-post processing network parameters
+        postnet_embedding_dim=512,
+        postnet_kernel_size=5,
+        postnet_n_convolutions=5,
+
+        ################################
+        # Optimization Hyperparameters #
+        ################################
+        use_saved_learning_rate=False,
+        learning_rate=1e-3,
+        weight_decay=1e-6,
+        grad_clip_thresh=1.0,
+        batch_size=64,
+        mask_padding=True  # set model's padded outputs to padded values
+    )
+
+    if hparams_string:
+        tf.logging.info('Parsing command line hparams: %s', hparams_string)
+        hparams.parse(hparams_string)
+
+    if verbose:
+        tf.logging.info('Final parsed hparams: %s', hparams.values())
+
+    return hparams

codes/models/tacotron2/layers.py

@@ -0,0 +1,80 @@
+import torch
+from librosa.filters import mel as librosa_mel_fn
+from models.tacotron2.audio_processing import dynamic_range_compression
+from models.tacotron2.audio_processing import dynamic_range_decompression
+from models.tacotron2.stft import STFT
+
+
+class LinearNorm(torch.nn.Module):
+    def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
+        super(LinearNorm, self).__init__()
+        self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
+
+        torch.nn.init.xavier_uniform_(
+            self.linear_layer.weight,
+            gain=torch.nn.init.calculate_gain(w_init_gain))
+
+    def forward(self, x):
+        return self.linear_layer(x)
+
+
+class ConvNorm(torch.nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
+                 padding=None, dilation=1, bias=True, w_init_gain='linear'):
+        super(ConvNorm, self).__init__()
+        if padding is None:
+            assert(kernel_size % 2 == 1)
+            padding = int(dilation * (kernel_size - 1) / 2)
+
+        self.conv = torch.nn.Conv1d(in_channels, out_channels,
+                                    kernel_size=kernel_size, stride=stride,
+                                    padding=padding, dilation=dilation,
+                                    bias=bias)
+
+        torch.nn.init.xavier_uniform_(
+            self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
+
+    def forward(self, signal):
+        conv_signal = self.conv(signal)
+        return conv_signal
+
+
+class TacotronSTFT(torch.nn.Module):
+    def __init__(self, filter_length=1024, hop_length=256, win_length=1024,
+                 n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0,
+                 mel_fmax=8000.0):
+        super(TacotronSTFT, self).__init__()
+        self.n_mel_channels = n_mel_channels
+        self.sampling_rate = sampling_rate
+        self.stft_fn = STFT(filter_length, hop_length, win_length)
+        mel_basis = librosa_mel_fn(
+            sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax)
+        mel_basis = torch.from_numpy(mel_basis).float()
+        self.register_buffer('mel_basis', mel_basis)
+
+    def spectral_normalize(self, magnitudes):
+        output = dynamic_range_compression(magnitudes)
+        return output
+
+    def spectral_de_normalize(self, magnitudes):
+        output = dynamic_range_decompression(magnitudes)
+        return output
+
+    def mel_spectrogram(self, y):
+        """Computes mel-spectrograms from a batch of waves
+        PARAMS
+        ------
+        y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
+
+        RETURNS
+        -------
+        mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
+        """
+        assert(torch.min(y.data) >= -1)
+        assert(torch.max(y.data) <= 1)
+
+        magnitudes, phases = self.stft_fn.transform(y)
+        magnitudes = magnitudes.data
+        mel_output = torch.matmul(self.mel_basis, magnitudes)
+        mel_output = self.spectral_normalize(mel_output)
+        return mel_output

codes/models/tacotron2/loss.py

@@ -0,0 +1,19 @@
+from torch import nn
+
+
+class Tacotron2Loss(nn.Module):
+    def __init__(self):
+        super(Tacotron2Loss, self).__init__()
+
+    def forward(self, model_output, targets):
+        mel_target, gate_target = targets[0], targets[1]
+        mel_target.requires_grad = False
+        gate_target.requires_grad = False
+        gate_target = gate_target.view(-1, 1)
+
+        mel_out, mel_out_postnet, gate_out, _ = model_output
+        gate_out = gate_out.view(-1, 1)
+        mel_loss = nn.MSELoss()(mel_out, mel_target) + \
+            nn.MSELoss()(mel_out_postnet, mel_target)
+        gate_loss = nn.BCEWithLogitsLoss()(gate_out, gate_target)
+        return mel_loss + gate_loss

codes/models/tacotron2/stft.py

@@ -0,0 +1,141 @@
+"""
+BSD 3-Clause License
+
+Copyright (c) 2017, Prem Seetharaman
+All rights reserved.
+
+* Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice,
+  this list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice, this
+  list of conditions and the following disclaimer in the
+  documentation and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from this
+  software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""
+
+import torch
+import numpy as np
+import torch.nn.functional as F
+from torch.autograd import Variable
+from scipy.signal import get_window
+from librosa.util import pad_center, tiny
+from models.tacotron2.audio_processing import window_sumsquare
+
+
+class STFT(torch.nn.Module):
+    """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft"""
+    def __init__(self, filter_length=800, hop_length=200, win_length=800,
+                 window='hann'):
+        super(STFT, self).__init__()
+        self.filter_length = filter_length
+        self.hop_length = hop_length
+        self.win_length = win_length
+        self.window = window
+        self.forward_transform = None
+        scale = self.filter_length / self.hop_length
+        fourier_basis = np.fft.fft(np.eye(self.filter_length))
+
+        cutoff = int((self.filter_length / 2 + 1))
+        fourier_basis = np.vstack([np.real(fourier_basis[:cutoff, :]),
+                                   np.imag(fourier_basis[:cutoff, :])])
+
+        forward_basis = torch.FloatTensor(fourier_basis[:, None, :])
+        inverse_basis = torch.FloatTensor(
+            np.linalg.pinv(scale * fourier_basis).T[:, None, :])
+
+        if window is not None:
+            assert(filter_length >= win_length)
+            # get window and zero center pad it to filter_length
+            fft_window = get_window(window, win_length, fftbins=True)
+            fft_window = pad_center(fft_window, filter_length)
+            fft_window = torch.from_numpy(fft_window).float()
+
+            # window the bases
+            forward_basis *= fft_window
+            inverse_basis *= fft_window
+
+        self.register_buffer('forward_basis', forward_basis.float())
+        self.register_buffer('inverse_basis', inverse_basis.float())
+
+    def transform(self, input_data):
+        num_batches = input_data.size(0)
+        num_samples = input_data.size(1)
+
+        self.num_samples = num_samples
+
+        # similar to librosa, reflect-pad the input
+        input_data = input_data.view(num_batches, 1, num_samples)
+        input_data = F.pad(
+            input_data.unsqueeze(1),
+            (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0),
+            mode='reflect')
+        input_data = input_data.squeeze(1)
+
+        forward_transform = F.conv1d(
+            input_data,
+            Variable(self.forward_basis, requires_grad=False),
+            stride=self.hop_length,
+            padding=0)
+
+        cutoff = int((self.filter_length / 2) + 1)
+        real_part = forward_transform[:, :cutoff, :]
+        imag_part = forward_transform[:, cutoff:, :]
+
+        magnitude = torch.sqrt(real_part**2 + imag_part**2)
+        phase = torch.autograd.Variable(
+            torch.atan2(imag_part.data, real_part.data))
+
+        return magnitude, phase
+
+    def inverse(self, magnitude, phase):
+        recombine_magnitude_phase = torch.cat(
+            [magnitude*torch.cos(phase), magnitude*torch.sin(phase)], dim=1)
+
+        inverse_transform = F.conv_transpose1d(
+            recombine_magnitude_phase,
+            Variable(self.inverse_basis, requires_grad=False),
+            stride=self.hop_length,
+            padding=0)
+
+        if self.window is not None:
+            window_sum = window_sumsquare(
+                self.window, magnitude.size(-1), hop_length=self.hop_length,
+                win_length=self.win_length, n_fft=self.filter_length,
+                dtype=np.float32)
+            # remove modulation effects
+            approx_nonzero_indices = torch.from_numpy(
+                np.where(window_sum > tiny(window_sum))[0])
+            window_sum = torch.autograd.Variable(
+                torch.from_numpy(window_sum), requires_grad=False)
+            window_sum = window_sum.cuda() if magnitude.is_cuda else window_sum
+            inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices]
+
+            # scale by hop ratio
+            inverse_transform *= float(self.filter_length) / self.hop_length
+
+        inverse_transform = inverse_transform[:, :, int(self.filter_length/2):]
+        inverse_transform = inverse_transform[:, :, :-int(self.filter_length/2):]
+
+        return inverse_transform
+
+    def forward(self, input_data):
+        self.magnitude, self.phase = self.transform(input_data)
+        reconstruction = self.inverse(self.magnitude, self.phase)
+        return reconstruction

codes/models/tacotron2/taco_utils.py

@@ -0,0 +1,29 @@
+import numpy as np
+from scipy.io.wavfile import read
+import torch
+
+
+def get_mask_from_lengths(lengths):
+    max_len = torch.max(lengths).item()
+    ids = torch.arange(0, max_len, out=torch.LongTensor(max_len, device=lengths.device))
+    mask = (ids < lengths.unsqueeze(1)).bool()
+    return mask
+
+
+def load_wav_to_torch(full_path):
+    sampling_rate, data = read(full_path)
+    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
+
+
+def load_filepaths_and_text(filename, split="|"):
+    with open(filename, encoding='utf-8') as f:
+        filepaths_and_text = [line.strip().split(split) for line in f]
+    return filepaths_and_text
+
+
+def to_gpu(x):
+    x = x.contiguous()
+
+    if torch.cuda.is_available():
+        x = x.cuda(non_blocking=True)
+    return torch.autograd.Variable(x)

codes/models/tacotron2/tacotron2.py

@@ -0,0 +1,549 @@
+from math import sqrt
+import torch
+from munch import munchify
+from torch.autograd import Variable
+from torch import nn
+from torch.nn import functional as F
+from layers import ConvNorm, LinearNorm
+from models.tacotron2.hparams import create_hparams
+from trainer.networks import register_model
+from taco_utils import to_gpu, get_mask_from_lengths
+from utils.util import opt_get
+
+
+class LocationLayer(nn.Module):
+    def __init__(self, attention_n_filters, attention_kernel_size,
+                 attention_dim):
+        super(LocationLayer, self).__init__()
+        padding = int((attention_kernel_size - 1) / 2)
+        self.location_conv = ConvNorm(2, attention_n_filters,
+                                      kernel_size=attention_kernel_size,
+                                      padding=padding, bias=False, stride=1,
+                                      dilation=1)
+        self.location_dense = LinearNorm(attention_n_filters, attention_dim,
+                                         bias=False, w_init_gain='tanh')
+
+    def forward(self, attention_weights_cat):
+        processed_attention = self.location_conv(attention_weights_cat)
+        processed_attention = processed_attention.transpose(1, 2)
+        processed_attention = self.location_dense(processed_attention)
+        return processed_attention
+
+
+class Attention(nn.Module):
+    def __init__(self, attention_rnn_dim, embedding_dim, attention_dim,
+                 attention_location_n_filters, attention_location_kernel_size):
+        super(Attention, self).__init__()
+        self.query_layer = LinearNorm(attention_rnn_dim, attention_dim,
+                                      bias=False, w_init_gain='tanh')
+        self.memory_layer = LinearNorm(embedding_dim, attention_dim, bias=False,
+                                       w_init_gain='tanh')
+        self.v = LinearNorm(attention_dim, 1, bias=False)
+        self.location_layer = LocationLayer(attention_location_n_filters,
+                                            attention_location_kernel_size,
+                                            attention_dim)
+        self.score_mask_value = -float("inf")
+
+    def get_alignment_energies(self, query, processed_memory,
+                               attention_weights_cat):
+        """
+        PARAMS
+        ------
+        query: decoder output (batch, n_mel_channels * n_frames_per_step)
+        processed_memory: processed encoder outputs (B, T_in, attention_dim)
+        attention_weights_cat: cumulative and prev. att weights (B, 2, max_time)
+
+        RETURNS
+        -------
+        alignment (batch, max_time)
+        """
+
+        processed_query = self.query_layer(query.unsqueeze(1))
+        processed_attention_weights = self.location_layer(attention_weights_cat)
+        energies = self.v(torch.tanh(
+            processed_query + processed_attention_weights + processed_memory))
+
+        energies = energies.squeeze(-1)
+        return energies
+
+    def forward(self, attention_hidden_state, memory, processed_memory,
+                attention_weights_cat, mask):
+        """
+        PARAMS
+        ------
+        attention_hidden_state: attention rnn last output
+        memory: encoder outputs
+        processed_memory: processed encoder outputs
+        attention_weights_cat: previous and cummulative attention weights
+        mask: binary mask for padded data
+        """
+        alignment = self.get_alignment_energies(
+            attention_hidden_state, processed_memory, attention_weights_cat)
+
+        if mask is not None:
+            alignment.data.masked_fill_(mask, self.score_mask_value)
+
+        attention_weights = F.softmax(alignment, dim=1)
+        attention_context = torch.bmm(attention_weights.unsqueeze(1), memory)
+        attention_context = attention_context.squeeze(1)
+
+        return attention_context, attention_weights
+
+
+class Prenet(nn.Module):
+    def __init__(self, in_dim, sizes):
+        super(Prenet, self).__init__()
+        in_sizes = [in_dim] + sizes[:-1]
+        self.layers = nn.ModuleList(
+            [LinearNorm(in_size, out_size, bias=False)
+             for (in_size, out_size) in zip(in_sizes, sizes)])
+
+    def forward(self, x):
+        for linear in self.layers:
+            x = F.dropout(F.relu(linear(x)), p=0.5, training=True)
+        return x
+
+
+class Postnet(nn.Module):
+    """Postnet
+        - Five 1-d convolution with 512 channels and kernel size 5
+    """
+
+    def __init__(self, hparams):
+        super(Postnet, self).__init__()
+        self.convolutions = nn.ModuleList()
+
+        self.convolutions.append(
+            nn.Sequential(
+                ConvNorm(hparams.n_mel_channels, hparams.postnet_embedding_dim,
+                         kernel_size=hparams.postnet_kernel_size, stride=1,
+                         padding=int((hparams.postnet_kernel_size - 1) / 2),
+                         dilation=1, w_init_gain='tanh'),
+                nn.BatchNorm1d(hparams.postnet_embedding_dim))
+        )
+
+        for i in range(1, hparams.postnet_n_convolutions - 1):
+            self.convolutions.append(
+                nn.Sequential(
+                    ConvNorm(hparams.postnet_embedding_dim,
+                             hparams.postnet_embedding_dim,
+                             kernel_size=hparams.postnet_kernel_size, stride=1,
+                             padding=int((hparams.postnet_kernel_size - 1) / 2),
+                             dilation=1, w_init_gain='tanh'),
+                    nn.BatchNorm1d(hparams.postnet_embedding_dim))
+            )
+
+        self.convolutions.append(
+            nn.Sequential(
+                ConvNorm(hparams.postnet_embedding_dim, hparams.n_mel_channels,
+                         kernel_size=hparams.postnet_kernel_size, stride=1,
+                         padding=int((hparams.postnet_kernel_size - 1) / 2),
+                         dilation=1, w_init_gain='linear'),
+                nn.BatchNorm1d(hparams.n_mel_channels))
+            )
+
+    def forward(self, x):
+        for i in range(len(self.convolutions) - 1):
+            x = F.dropout(torch.tanh(self.convolutions[i](x)), 0.5, self.training)
+        x = F.dropout(self.convolutions[-1](x), 0.5, self.training)
+
+        return x
+
+
+class Encoder(nn.Module):
+    """Encoder module:
+        - Three 1-d convolution banks
+        - Bidirectional LSTM
+    """
+    def __init__(self, hparams):
+        super(Encoder, self).__init__()
+
+        convolutions = []
+        for _ in range(hparams.encoder_n_convolutions):
+            conv_layer = nn.Sequential(
+                ConvNorm(hparams.encoder_embedding_dim,
+                         hparams.encoder_embedding_dim,
+                         kernel_size=hparams.encoder_kernel_size, stride=1,
+                         padding=int((hparams.encoder_kernel_size - 1) / 2),
+                         dilation=1, w_init_gain='relu'),
+                nn.BatchNorm1d(hparams.encoder_embedding_dim))
+            convolutions.append(conv_layer)
+        self.convolutions = nn.ModuleList(convolutions)
+
+        self.lstm = nn.LSTM(hparams.encoder_embedding_dim,
+                            int(hparams.encoder_embedding_dim / 2), 1,
+                            batch_first=True, bidirectional=True)
+
+    def forward(self, x, input_lengths):
+        for conv in self.convolutions:
+            x = F.dropout(F.relu(conv(x)), 0.5, self.training)
+
+        x = x.transpose(1, 2)
+
+        # pytorch tensor are not reversible, hence the conversion
+        input_lengths = input_lengths.cpu().numpy()
+        x = nn.utils.rnn.pack_padded_sequence(
+            x, input_lengths, batch_first=True)
+
+        self.lstm.flatten_parameters()
+        outputs, _ = self.lstm(x)
+
+        outputs, _ = nn.utils.rnn.pad_packed_sequence(
+            outputs, batch_first=True)
+
+        return outputs
+
+    def inference(self, x):
+        for conv in self.convolutions:
+            x = F.dropout(F.relu(conv(x)), 0.5, self.training)
+
+        x = x.transpose(1, 2)
+
+        self.lstm.flatten_parameters()
+        outputs, _ = self.lstm(x)
+
+        return outputs
+
+
+class Decoder(nn.Module):
+    def __init__(self, hparams):
+        super(Decoder, self).__init__()
+        self.n_mel_channels = hparams.n_mel_channels
+        self.n_frames_per_step = hparams.n_frames_per_step
+        self.encoder_embedding_dim = hparams.encoder_embedding_dim
+        self.attention_rnn_dim = hparams.attention_rnn_dim
+        self.decoder_rnn_dim = hparams.decoder_rnn_dim
+        self.prenet_dim = hparams.prenet_dim
+        self.max_decoder_steps = hparams.max_decoder_steps
+        self.gate_threshold = hparams.gate_threshold
+        self.p_attention_dropout = hparams.p_attention_dropout
+        self.p_decoder_dropout = hparams.p_decoder_dropout
+
+        self.prenet = Prenet(
+            hparams.n_mel_channels * hparams.n_frames_per_step,
+            [hparams.prenet_dim, hparams.prenet_dim])
+
+        self.attention_rnn = nn.LSTMCell(
+            hparams.prenet_dim + hparams.encoder_embedding_dim,
+            hparams.attention_rnn_dim)
+
+        self.attention_layer = Attention(
+            hparams.attention_rnn_dim, hparams.encoder_embedding_dim,
+            hparams.attention_dim, hparams.attention_location_n_filters,
+            hparams.attention_location_kernel_size)
+
+        self.decoder_rnn = nn.LSTMCell(
+            hparams.attention_rnn_dim + hparams.encoder_embedding_dim,
+            hparams.decoder_rnn_dim, 1)
+
+        self.linear_projection = LinearNorm(
+            hparams.decoder_rnn_dim + hparams.encoder_embedding_dim,
+            hparams.n_mel_channels * hparams.n_frames_per_step)
+
+        self.gate_layer = LinearNorm(
+            hparams.decoder_rnn_dim + hparams.encoder_embedding_dim, 1,
+            bias=True, w_init_gain='sigmoid')
+
+    def get_go_frame(self, memory):
+        """ Gets all zeros frames to use as first decoder input
+        PARAMS
+        ------
+        memory: decoder outputs
+
+        RETURNS
+        -------
+        decoder_input: all zeros frames
+        """
+        B = memory.size(0)
+        decoder_input = Variable(memory.data.new(
+            B, self.n_mel_channels * self.n_frames_per_step).zero_())
+        return decoder_input
+
+    def initialize_decoder_states(self, memory, mask):
+        """ Initializes attention rnn states, decoder rnn states, attention
+        weights, attention cumulative weights, attention context, stores memory
+        and stores processed memory
+        PARAMS
+        ------
+        memory: Encoder outputs
+        mask: Mask for padded data if training, expects None for inference
+        """
+        B = memory.size(0)
+        MAX_TIME = memory.size(1)
+
+        self.attention_hidden = Variable(memory.data.new(
+            B, self.attention_rnn_dim).zero_())
+        self.attention_cell = Variable(memory.data.new(
+            B, self.attention_rnn_dim).zero_())
+
+        self.decoder_hidden = Variable(memory.data.new(
+            B, self.decoder_rnn_dim).zero_())
+        self.decoder_cell = Variable(memory.data.new(
+            B, self.decoder_rnn_dim).zero_())
+
+        self.attention_weights = Variable(memory.data.new(
+            B, MAX_TIME).zero_())
+        self.attention_weights_cum = Variable(memory.data.new(
+            B, MAX_TIME).zero_())
+        self.attention_context = Variable(memory.data.new(
+            B, self.encoder_embedding_dim).zero_())
+
+        self.memory = memory
+        self.processed_memory = self.attention_layer.memory_layer(memory)
+        self.mask = mask
+
+    def parse_decoder_inputs(self, decoder_inputs):
+        """ Prepares decoder inputs, i.e. mel outputs
+        PARAMS
+        ------
+        decoder_inputs: inputs used for teacher-forced training, i.e. mel-specs
+
+        RETURNS
+        -------
+        inputs: processed decoder inputs
+
+        """
+        # (B, n_mel_channels, T_out) -> (B, T_out, n_mel_channels)
+        decoder_inputs = decoder_inputs.transpose(1, 2)
+        decoder_inputs = decoder_inputs.view(
+            decoder_inputs.size(0),
+            int(decoder_inputs.size(1)/self.n_frames_per_step), -1)
+        # (B, T_out, n_mel_channels) -> (T_out, B, n_mel_channels)
+        decoder_inputs = decoder_inputs.transpose(0, 1)
+        return decoder_inputs
+
+    def parse_decoder_outputs(self, mel_outputs, gate_outputs, alignments):
+        """ Prepares decoder outputs for output
+        PARAMS
+        ------
+        mel_outputs:
+        gate_outputs: gate output energies
+        alignments:
+
+        RETURNS
+        -------
+        mel_outputs:
+        gate_outpust: gate output energies
+        alignments:
+        """
+        # (T_out, B) -> (B, T_out)
+        alignments = torch.stack(alignments).transpose(0, 1)
+        # (T_out, B) -> (B, T_out)
+        gate_outputs = torch.stack(gate_outputs).transpose(0, 1)
+        gate_outputs = gate_outputs.contiguous()
+        # (T_out, B, n_mel_channels) -> (B, T_out, n_mel_channels)
+        mel_outputs = torch.stack(mel_outputs).transpose(0, 1).contiguous()
+        # decouple frames per step
+        mel_outputs = mel_outputs.view(
+            mel_outputs.size(0), -1, self.n_mel_channels)
+        # (B, T_out, n_mel_channels) -> (B, n_mel_channels, T_out)
+        mel_outputs = mel_outputs.transpose(1, 2)
+
+        return mel_outputs, gate_outputs, alignments
+
+    def decode(self, decoder_input):
+        """ Decoder step using stored states, attention and memory
+        PARAMS
+        ------
+        decoder_input: previous mel output
+
+        RETURNS
+        -------
+        mel_output:
+        gate_output: gate output energies
+        attention_weights:
+        """
+        cell_input = torch.cat((decoder_input, self.attention_context), -1)
+        self.attention_hidden, self.attention_cell = self.attention_rnn(
+            cell_input, (self.attention_hidden, self.attention_cell))
+        self.attention_hidden = F.dropout(
+            self.attention_hidden, self.p_attention_dropout, self.training)
+
+        attention_weights_cat = torch.cat(
+            (self.attention_weights.unsqueeze(1),
+             self.attention_weights_cum.unsqueeze(1)), dim=1)
+        self.attention_context, self.attention_weights = self.attention_layer(
+            self.attention_hidden, self.memory, self.processed_memory,
+            attention_weights_cat, self.mask)
+
+        self.attention_weights_cum += self.attention_weights
+        decoder_input = torch.cat(
+            (self.attention_hidden, self.attention_context), -1)
+        self.decoder_hidden, self.decoder_cell = self.decoder_rnn(
+            decoder_input, (self.decoder_hidden, self.decoder_cell))
+        self.decoder_hidden = F.dropout(
+            self.decoder_hidden, self.p_decoder_dropout, self.training)
+
+        decoder_hidden_attention_context = torch.cat(
+            (self.decoder_hidden, self.attention_context), dim=1)
+        decoder_output = self.linear_projection(
+            decoder_hidden_attention_context)
+
+        gate_prediction = self.gate_layer(decoder_hidden_attention_context)
+        return decoder_output, gate_prediction, self.attention_weights
+
+    def forward(self, memory, decoder_inputs, memory_lengths):
+        """ Decoder forward pass for training
+        PARAMS
+        ------
+        memory: Encoder outputs
+        decoder_inputs: Decoder inputs for teacher forcing. i.e. mel-specs
+        memory_lengths: Encoder output lengths for attention masking.
+
+        RETURNS
+        -------
+        mel_outputs: mel outputs from the decoder
+        gate_outputs: gate outputs from the decoder
+        alignments: sequence of attention weights from the decoder
+        """
+
+        decoder_input = self.get_go_frame(memory).unsqueeze(0)
+        decoder_inputs = self.parse_decoder_inputs(decoder_inputs)
+        decoder_inputs = torch.cat((decoder_input, decoder_inputs), dim=0)
+        decoder_inputs = self.prenet(decoder_inputs)
+
+        self.initialize_decoder_states(
+            memory, mask=~get_mask_from_lengths(memory_lengths))
+
+        mel_outputs, gate_outputs, alignments = [], [], []
+        while len(mel_outputs) < decoder_inputs.size(0) - 1:
+            decoder_input = decoder_inputs[len(mel_outputs)]
+            mel_output, gate_output, attention_weights = self.decode(
+                decoder_input)
+            mel_outputs += [mel_output.squeeze(1)]
+            gate_outputs += [gate_output.squeeze(1)]
+            alignments += [attention_weights]
+
+        mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs(
+            mel_outputs, gate_outputs, alignments)
+
+        return mel_outputs, gate_outputs, alignments
+
+    def inference(self, memory):
+        """ Decoder inference
+        PARAMS
+        ------
+        memory: Encoder outputs
+
+        RETURNS
+        -------
+        mel_outputs: mel outputs from the decoder
+        gate_outputs: gate outputs from the decoder
+        alignments: sequence of attention weights from the decoder
+        """
+        decoder_input = self.get_go_frame(memory)
+
+        self.initialize_decoder_states(memory, mask=None)
+
+        mel_outputs, gate_outputs, alignments = [], [], []
+        while True:
+            decoder_input = self.prenet(decoder_input)
+            mel_output, gate_output, alignment = self.decode(decoder_input)
+
+            mel_outputs += [mel_output.squeeze(1)]
+            gate_outputs += [gate_output]
+            alignments += [alignment]
+
+            if torch.sigmoid(gate_output.data) > self.gate_threshold:
+                break
+            elif len(mel_outputs) == self.max_decoder_steps:
+                print("Warning! Reached max decoder steps")
+                break
+
+            decoder_input = mel_output
+
+        mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs(
+            mel_outputs, gate_outputs, alignments)
+
+        return mel_outputs, gate_outputs, alignments
+
+
+class Tacotron2(nn.Module):
+    def __init__(self, hparams):
+        super(Tacotron2, self).__init__()
+        self.mask_padding = hparams.mask_padding
+        self.fp16_run = hparams.fp16_run
+        self.n_mel_channels = hparams.n_mel_channels
+        self.n_frames_per_step = hparams.n_frames_per_step
+        self.embedding = nn.Embedding(
+            hparams.n_symbols, hparams.symbols_embedding_dim)
+        std = sqrt(2.0 / (hparams.n_symbols + hparams.symbols_embedding_dim))
+        val = sqrt(3.0) * std  # uniform bounds for std
+        self.embedding.weight.data.uniform_(-val, val)
+        self.encoder = Encoder(hparams)
+        self.decoder = Decoder(hparams)
+        self.postnet = Postnet(hparams)
+
+    def parse_batch(self, batch):
+        text_padded, input_lengths, mel_padded, gate_padded, \
+            output_lengths = batch
+        text_padded = to_gpu(text_padded).long()
+        input_lengths = to_gpu(input_lengths).long()
+        max_len = torch.max(input_lengths.data).item()
+        mel_padded = to_gpu(mel_padded).float()
+        gate_padded = to_gpu(gate_padded).float()
+        output_lengths = to_gpu(output_lengths).long()
+
+        return (
+            (text_padded, input_lengths, mel_padded, max_len, output_lengths),
+            (mel_padded, gate_padded))
+
+    def parse_output(self, outputs, output_lengths=None):
+        if self.mask_padding and output_lengths is not None:
+            mask = ~get_mask_from_lengths(output_lengths)
+            mask = mask.expand(self.n_mel_channels, mask.size(0), mask.size(1))
+            mask = mask.permute(1, 0, 2)
+
+            outputs[0].data.masked_fill_(mask, 0.0)
+            outputs[1].data.masked_fill_(mask, 0.0)
+            outputs[2].data.masked_fill_(mask[:, 0, :], 1e3)  # gate energies
+
+        return outputs
+
+    def forward(self, inputs):
+        text_inputs, text_lengths, mels, max_len, output_lengths = inputs
+        text_lengths, output_lengths = text_lengths.data, output_lengths.data
+
+        embedded_inputs = self.embedding(text_inputs).transpose(1, 2)
+
+        encoder_outputs = self.encoder(embedded_inputs, text_lengths)
+
+        mel_outputs, gate_outputs, alignments = self.decoder(
+            encoder_outputs, mels, memory_lengths=text_lengths)
+
+        mel_outputs_postnet = self.postnet(mel_outputs)
+        mel_outputs_postnet = mel_outputs + mel_outputs_postnet
+
+        return self.parse_output(
+            [mel_outputs, mel_outputs_postnet, gate_outputs, alignments],
+            output_lengths)
+
+    def inference(self, inputs):
+        embedded_inputs = self.embedding(inputs).transpose(1, 2)
+        encoder_outputs = self.encoder.inference(embedded_inputs)
+        mel_outputs, gate_outputs, alignments = self.decoder.inference(
+            encoder_outputs)
+
+        mel_outputs_postnet = self.postnet(mel_outputs)
+        mel_outputs_postnet = mel_outputs + mel_outputs_postnet
+
+        outputs = self.parse_output(
+            [mel_outputs, mel_outputs_postnet, gate_outputs, alignments])
+
+        return outputs
+
+
+@register_model
+def register_nv_tacotron2(opt_net, opt):
+    kw = opt_get(opt_net, ['kwargs'], {})
+    hparams = create_hparams()
+    hparams.update(kw)
+    hparams = munchify(hparams)
+    return Tacotron2(hparams)
+
+
+if __name__ == '__main__':
+    tron = register_nv_tacotron2({}, {})
+    inputs = torch.randint(high=24, size=(1,12)), torch.tensor([12]), torch.randn((1,80,749)), 800, torch.tensor([749])
+    out = tron(inputs)
+    print(out)

codes/models/tacotron2/text/LICENSE

@@ -0,0 +1,19 @@
+Copyright (c) 2017 Keith Ito
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.

codes/models/tacotron2/text/__init__.py

@@ -0,0 +1,74 @@
+""" from https://github.com/keithito/tacotron """
+import re
+from models.tacotron2.text import cleaners
+from models.tacotron2.text.symbols import symbols
+
+
+# Mappings from symbol to numeric ID and vice versa:
+_symbol_to_id = {s: i for i, s in enumerate(symbols)}
+_id_to_symbol = {i: s for i, s in enumerate(symbols)}
+
+# Regular expression matching text enclosed in curly braces:
+_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)')
+
+
+def text_to_sequence(text, cleaner_names):
+  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
+
+    The text can optionally have ARPAbet sequences enclosed in curly braces embedded
+    in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
+
+    Args:
+      text: string to convert to a sequence
+      cleaner_names: names of the cleaner functions to run the text through
+
+    Returns:
+      List of integers corresponding to the symbols in the text
+  '''
+  sequence = []
+
+  # Check for curly braces and treat their contents as ARPAbet:
+  while len(text):
+    m = _curly_re.match(text)
+    if not m:
+      sequence += _symbols_to_sequence(_clean_text(text, cleaner_names))
+      break
+    sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names))
+    sequence += _arpabet_to_sequence(m.group(2))
+    text = m.group(3)
+
+  return sequence
+
+
+def sequence_to_text(sequence):
+  '''Converts a sequence of IDs back to a string'''
+  result = ''
+  for symbol_id in sequence:
+    if symbol_id in _id_to_symbol:
+      s = _id_to_symbol[symbol_id]
+      # Enclose ARPAbet back in curly braces:
+      if len(s) > 1 and s[0] == '@':
+        s = '{%s}' % s[1:]
+      result += s
+  return result.replace('}{', ' ')
+
+
+def _clean_text(text, cleaner_names):
+  for name in cleaner_names:
+    cleaner = getattr(cleaners, name)
+    if not cleaner:
+      raise Exception('Unknown cleaner: %s' % name)
+    text = cleaner(text)
+  return text
+
+
+def _symbols_to_sequence(symbols):
+  return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)]
+
+
+def _arpabet_to_sequence(text):
+  return _symbols_to_sequence(['@' + s for s in text.split()])
+
+
+def _should_keep_symbol(s):
+  return s in _symbol_to_id and s is not '_' and s is not '~'

codes/models/tacotron2/text/cleaners.py

@@ -0,0 +1,90 @@
+""" from https://github.com/keithito/tacotron """
+
+'''
+Cleaners are transformations that run over the input text at both training and eval time.
+
+Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
+hyperparameter. Some cleaners are English-specific. You'll typically want to use:
+  1. "english_cleaners" for English text
+  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
+     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
+  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
+     the symbols in symbols.py to match your data).
+'''
+
+import re
+from unidecode import unidecode
+from .numbers import normalize_numbers
+
+
+# Regular expression matching whitespace:
+_whitespace_re = re.compile(r'\s+')
+
+# List of (regular expression, replacement) pairs for abbreviations:
+_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
+  ('mrs', 'misess'),
+  ('mr', 'mister'),
+  ('dr', 'doctor'),
+  ('st', 'saint'),
+  ('co', 'company'),
+  ('jr', 'junior'),
+  ('maj', 'major'),
+  ('gen', 'general'),
+  ('drs', 'doctors'),
+  ('rev', 'reverend'),
+  ('lt', 'lieutenant'),
+  ('hon', 'honorable'),
+  ('sgt', 'sergeant'),
+  ('capt', 'captain'),
+  ('esq', 'esquire'),
+  ('ltd', 'limited'),
+  ('col', 'colonel'),
+  ('ft', 'fort'),
+]]
+
+
+def expand_abbreviations(text):
+  for regex, replacement in _abbreviations:
+    text = re.sub(regex, replacement, text)
+  return text
+
+
+def expand_numbers(text):
+  return normalize_numbers(text)
+
+
+def lowercase(text):
+  return text.lower()
+
+
+def collapse_whitespace(text):
+  return re.sub(_whitespace_re, ' ', text)
+
+
+def convert_to_ascii(text):
+  return unidecode(text)
+
+
+def basic_cleaners(text):
+  '''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
+  text = lowercase(text)
+  text = collapse_whitespace(text)
+  return text
+
+
+def transliteration_cleaners(text):
+  '''Pipeline for non-English text that transliterates to ASCII.'''
+  text = convert_to_ascii(text)
+  text = lowercase(text)
+  text = collapse_whitespace(text)
+  return text
+
+
+def english_cleaners(text):
+  '''Pipeline for English text, including number and abbreviation expansion.'''
+  text = convert_to_ascii(text)
+  text = lowercase(text)
+  text = expand_numbers(text)
+  text = expand_abbreviations(text)
+  text = collapse_whitespace(text)
+  return text

codes/models/tacotron2/text/cmudict.py

@@ -0,0 +1,65 @@
+""" from https://github.com/keithito/tacotron """
+
+import re
+
+
+valid_symbols = [
+  'AA', 'AA0', 'AA1', 'AA2', 'AE', 'AE0', 'AE1', 'AE2', 'AH', 'AH0', 'AH1', 'AH2',
+  'AO', 'AO0', 'AO1', 'AO2', 'AW', 'AW0', 'AW1', 'AW2', 'AY', 'AY0', 'AY1', 'AY2',
+  'B', 'CH', 'D', 'DH', 'EH', 'EH0', 'EH1', 'EH2', 'ER', 'ER0', 'ER1', 'ER2', 'EY',
+  'EY0', 'EY1', 'EY2', 'F', 'G', 'HH', 'IH', 'IH0', 'IH1', 'IH2', 'IY', 'IY0', 'IY1',
+  'IY2', 'JH', 'K', 'L', 'M', 'N', 'NG', 'OW', 'OW0', 'OW1', 'OW2', 'OY', 'OY0',
+  'OY1', 'OY2', 'P', 'R', 'S', 'SH', 'T', 'TH', 'UH', 'UH0', 'UH1', 'UH2', 'UW',
+  'UW0', 'UW1', 'UW2', 'V', 'W', 'Y', 'Z', 'ZH'
+]
+
+_valid_symbol_set = set(valid_symbols)
+
+
+class CMUDict:
+  '''Thin wrapper around CMUDict data. http://www.speech.cs.cmu.edu/cgi-bin/cmudict'''
+  def __init__(self, file_or_path, keep_ambiguous=True):
+    if isinstance(file_or_path, str):
+      with open(file_or_path, encoding='latin-1') as f:
+        entries = _parse_cmudict(f)
+    else:
+      entries = _parse_cmudict(file_or_path)
+    if not keep_ambiguous:
+      entries = {word: pron for word, pron in entries.items() if len(pron) == 1}
+    self._entries = entries
+
+
+  def __len__(self):
+    return len(self._entries)
+
+
+  def lookup(self, word):
+    '''Returns list of ARPAbet pronunciations of the given word.'''
+    return self._entries.get(word.upper())
+
+
+
+_alt_re = re.compile(r'\([0-9]+\)')
+
+
+def _parse_cmudict(file):
+  cmudict = {}
+  for line in file:
+    if len(line) and (line[0] >= 'A' and line[0] <= 'Z' or line[0] == "'"):
+      parts = line.split('  ')
+      word = re.sub(_alt_re, '', parts[0])
+      pronunciation = _get_pronunciation(parts[1])
+      if pronunciation:
+        if word in cmudict:
+          cmudict[word].append(pronunciation)
+        else:
+          cmudict[word] = [pronunciation]
+  return cmudict
+
+
+def _get_pronunciation(s):
+  parts = s.strip().split(' ')
+  for part in parts:
+    if part not in _valid_symbol_set:
+      return None
+  return ' '.join(parts)

codes/models/tacotron2/text/numbers.py

@@ -0,0 +1,71 @@
+""" from https://github.com/keithito/tacotron """
+
+import inflect
+import re
+
+
+_inflect = inflect.engine()
+_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
+_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
+_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
+_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
+_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
+_number_re = re.compile(r'[0-9]+')
+
+
+def _remove_commas(m):
+  return m.group(1).replace(',', '')
+
+
+def _expand_decimal_point(m):
+  return m.group(1).replace('.', ' point ')
+
+
+def _expand_dollars(m):
+  match = m.group(1)
+  parts = match.split('.')
+  if len(parts) > 2:
+    return match + ' dollars'  # Unexpected format
+  dollars = int(parts[0]) if parts[0] else 0
+  cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
+  if dollars and cents:
+    dollar_unit = 'dollar' if dollars == 1 else 'dollars'
+    cent_unit = 'cent' if cents == 1 else 'cents'
+    return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
+  elif dollars:
+    dollar_unit = 'dollar' if dollars == 1 else 'dollars'
+    return '%s %s' % (dollars, dollar_unit)
+  elif cents:
+    cent_unit = 'cent' if cents == 1 else 'cents'
+    return '%s %s' % (cents, cent_unit)
+  else:
+    return 'zero dollars'
+
+
+def _expand_ordinal(m):
+  return _inflect.number_to_words(m.group(0))
+
+
+def _expand_number(m):
+  num = int(m.group(0))
+  if num > 1000 and num < 3000:
+    if num == 2000:
+      return 'two thousand'
+    elif num > 2000 and num < 2010:
+      return 'two thousand ' + _inflect.number_to_words(num % 100)
+    elif num % 100 == 0:
+      return _inflect.number_to_words(num // 100) + ' hundred'
+    else:
+      return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
+  else:
+    return _inflect.number_to_words(num, andword='')
+
+
+def normalize_numbers(text):
+  text = re.sub(_comma_number_re, _remove_commas, text)
+  text = re.sub(_pounds_re, r'\1 pounds', text)
+  text = re.sub(_dollars_re, _expand_dollars, text)
+  text = re.sub(_decimal_number_re, _expand_decimal_point, text)
+  text = re.sub(_ordinal_re, _expand_ordinal, text)
+  text = re.sub(_number_re, _expand_number, text)
+  return text

codes/models/tacotron2/text/symbols.py

@@ -0,0 +1,18 @@
+""" from https://github.com/keithito/tacotron """
+
+'''
+Defines the set of symbols used in text input to the model.
+
+The default is a set of ASCII characters that works well for English or text that has been run through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details. '''
+from models.tacotron2.text import cmudict
+
+_pad        = '_'
+_punctuation = '!\'(),.:;? '
+_special = '-'
+_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
+
+# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters):
+_arpabet = ['@' + s for s in cmudict.valid_symbols]
+
+# Export all symbols:
+symbols = [_pad] + list(_special) + list(_punctuation) + list(_letters) + _arpabet

codes/requirements.txt

@@ -1,5 +1,5 @@
+# Fundamentals
 numpy
-opencv-python
 pyyaml
 tb-nightly
 future
@@ -11,12 +11,20 @@ munch
 tqdm
 scp
 tensorboard
-pytorch_fid==0.1.1
-kornia
 linear_attention_transformer
-vector_quantize_pytorch
 orjson
 einops
-gsa-pytorch
 lambda-networks
+
+# For image generation stuff
+opencv-python
+kornia
 pytorch_ssim
+gsa-pytorch
+vector_quantize_pytorch
+pytorch_fid==0.1.1
+
+# For audio generation stuff
+inflect==0.2.5
+librosa==0.6.0
+Unidecode==1.0.22

codes/train.py

@@ -107,7 +107,7 @@ class Trainer:
         dataset_ratio = 1  # enlarge the size of each epoch
         for phase, dataset_opt in opt['datasets'].items():
             if phase == 'train':
-                self.train_set = create_dataset(dataset_opt)
+                self.train_set, collate_fn = create_dataset(dataset_opt, return_collate=True)
                 train_size = int(math.ceil(len(self.train_set) / dataset_opt['batch_size']))
                 total_iters = int(opt['train']['niter'])
                 self.total_epochs = int(math.ceil(total_iters / train_size))
@@ -116,15 +116,15 @@ class Trainer:
                     self.total_epochs = int(math.ceil(total_iters / (train_size * dataset_ratio)))
                 else:
                     self.train_sampler = None
-                self.train_loader = create_dataloader(self.train_set, dataset_opt, opt, self.train_sampler)
+                self.train_loader = create_dataloader(self.train_set, dataset_opt, opt, self.train_sampler, collate_fn=collate_fn)
                 if self.rank <= 0:
                     self.logger.info('Number of train images: {:,d}, iters: {:,d}'.format(
                         len(self.train_set), train_size))
                     self.logger.info('Total epochs needed: {:d} for iters {:,d}'.format(
                         self.total_epochs, total_iters))
             elif phase == 'val':
-                self.val_set = create_dataset(dataset_opt)
-                self.val_loader = create_dataloader(self.val_set, dataset_opt, opt, None)
+                self.val_set, collate_fn = create_dataset(dataset_opt, return_collate=True)
+                self.val_loader = create_dataloader(self.val_set, dataset_opt, opt, None, collate_fn=collate_fn)
                 if self.rank <= 0:
                     self.logger.info('Number of val images in [{:s}]: {:d}'.format(
                         dataset_opt['name'], len(self.val_set)))

codes/trainer/losses.py

@@ -56,6 +56,9 @@ def create_loss(opt_loss, env):
     elif type == 'switch_transformer_balance':
         from models.switched_conv.mixture_of_experts import SwitchTransformersLoadBalancingLoss
         return SwitchTransformersLoadBalancingLoss(opt_loss, env)
+    elif type == 'nv_tacotron2_loss':
+        from models.tacotron2.loss import Tacotron2Loss
+        return Tacotron2Loss()
     else:
         raise NotImplementedError