增加了pytorch框架下的音频处理模型FastSpeech和ECAPA-TDNN的测试代码

PyTorch/Speech/FastSpeech2/audio/audio_processing.py

+import torch
+import numpy as np
+import librosa.util as librosa_util
+from scipy.signal import get_window
+
+
+def window_sumsquare(
+    window,
+    n_frames,
+    hop_length,
+    win_length,
+    n_fft,
+    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

PyTorch/Speech/FastSpeech2/audio/stft.py

+import torch
+import torch.nn.functional as F
+import numpy as np
+from scipy.signal import get_window
+from librosa.util import pad_center, tiny
+from librosa.filters import mel as librosa_mel_fn
+
+from audio.audio_processing import (
+    dynamic_range_compression,
+    dynamic_range_decompression,
+    window_sumsquare,
+)
+
+
+class STFT(torch.nn.Module):
+    """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft"""
+
+    def __init__(self, filter_length, hop_length, win_length, 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.cuda(),
+            torch.autograd.Variable(self.forward_basis, requires_grad=False).cuda(),
+            stride=self.hop_length,
+            padding=0,
+        ).cpu()
+
+        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,
+            torch.autograd.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
+
+
+class TacotronSTFT(torch.nn.Module):
+    def __init__(
+        self,
+        filter_length,
+        hop_length,
+        win_length,
+        n_mel_channels,
+        sampling_rate,
+        mel_fmin,
+        mel_fmax,
+    ):
+        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)
+        energy = torch.norm(magnitudes, dim=1)
+
+        return mel_output, energy

PyTorch/Speech/FastSpeech2/audio/tools.py

+import torch
+import numpy as np
+from scipy.io.wavfile import write
+
+from audio.audio_processing import griffin_lim
+
+
+def get_mel_from_wav(audio, _stft):
+    audio = torch.clip(torch.FloatTensor(audio).unsqueeze(0), -1, 1)
+    audio = torch.autograd.Variable(audio, requires_grad=False)
+    melspec, energy = _stft.mel_spectrogram(audio)
+    melspec = torch.squeeze(melspec, 0).numpy().astype(np.float32)
+    energy = torch.squeeze(energy, 0).numpy().astype(np.float32)
+
+    return melspec, energy
+
+
+def inv_mel_spec(mel, out_filename, _stft, griffin_iters=60):
+    mel = torch.stack([mel])
+    mel_decompress = _stft.spectral_de_normalize(mel)
+    mel_decompress = mel_decompress.transpose(1, 2).data.cpu()
+    spec_from_mel_scaling = 1000
+    spec_from_mel = torch.mm(mel_decompress[0], _stft.mel_basis)
+    spec_from_mel = spec_from_mel.transpose(0, 1).unsqueeze(0)
+    spec_from_mel = spec_from_mel * spec_from_mel_scaling
+
+    audio = griffin_lim(
+        torch.autograd.Variable(spec_from_mel[:, :, :-1]), _stft._stft_fn, griffin_iters
+    )
+
+    audio = audio.squeeze()
+    audio = audio.cpu().numpy()
+    audio_path = out_filename
+    write(audio_path, _stft.sampling_rate, audio)

PyTorch/Speech/FastSpeech2/config/AISHELL3/model.yaml

+transformer:
+  encoder_layer: 4
+  encoder_head: 2
+  encoder_hidden: 256
+  decoder_layer: 6
+  decoder_head: 2
+  decoder_hidden: 256
+  conv_filter_size: 1024
+  conv_kernel_size: [9, 1]
+  encoder_dropout: 0.2
+  decoder_dropout: 0.2
+
+variance_predictor:
+  filter_size: 256
+  kernel_size: 3
+  dropout: 0.5
+
+variance_embedding:
+  pitch_quantization: "linear" # support 'linear' or 'log', 'log' is allowed only if the pitch values are not normalized during preprocessing
+  energy_quantization: "linear" # support 'linear' or 'log', 'log' is allowed only if the energy values are not normalized during preprocessing
+  n_bins: 256
+
+# gst:
+#   use_gst: False
+#   conv_filters: [32, 32, 64, 64, 128, 128]
+#   gru_hidden: 128
+#   token_size: 128
+#   n_style_token: 10
+#   attn_head: 4
+
+multi_speaker: True
+
+max_seq_len: 1000
+
+vocoder:
+  model: "HiFi-GAN" # support 'HiFi-GAN', 'MelGAN'
+  speaker: "universal" # support  'LJSpeech', 'universal'

PyTorch/Speech/FastSpeech2/config/AISHELL3/preprocess.yaml

+dataset: "AISHELL3"
+
+path:
+  corpus_path: "/home/ming/Data/AISHELL-3"
+  lexicon_path: "lexicon/pinyin-lexicon-r.txt"
+  raw_path: "./raw_data/AISHELL3"
+  preprocessed_path: "./preprocessed_data/AISHELL3"
+
+preprocessing:
+  val_size: 512
+  text:
+    text_cleaners: []
+    language: "zh"
+  audio:
+    sampling_rate: 22050
+    max_wav_value: 32768.0
+  stft:
+    filter_length: 1024
+    hop_length: 256
+    win_length: 1024
+  mel:
+    n_mel_channels: 80
+    mel_fmin: 0
+    mel_fmax: 8000 # please set to 8000 for HiFi-GAN vocoder, set to null for MelGAN vocoder
+  pitch:
+    feature: "phoneme_level" # support 'phoneme_level' or 'frame_level'
+    normalization: True
+  energy:
+    feature: "phoneme_level" # support 'phoneme_level' or 'frame_level'
+    normalization: True

PyTorch/Speech/FastSpeech2/config/AISHELL3/train.yaml

+path:
+  ckpt_path: "./output/ckpt/AISHELL3"
+  log_path: "./output/log/AISHELL3"
+  result_path: "./output/result/AISHELL3"
+optimizer:
+  batch_size: 16
+  betas: [0.9, 0.98]
+  eps: 0.000000001
+  weight_decay: 0.0
+  grad_clip_thresh: 1.0
+  grad_acc_step: 1
+  warm_up_step: 4000
+  anneal_steps: [300000, 400000, 500000]
+  anneal_rate: 0.3
+step:
+  total_step: 900000
+  log_step: 100
+  synth_step: 1000
+  val_step: 1000
+  save_step: 100000

PyTorch/Speech/FastSpeech2/config/LJSpeech/model.yaml

+transformer:
+  encoder_layer: 4
+  encoder_head: 2
+  encoder_hidden: 256
+  decoder_layer: 6
+  decoder_head: 2
+  decoder_hidden: 256
+  conv_filter_size: 1024
+  conv_kernel_size: [9, 1]
+  encoder_dropout: 0.2
+  decoder_dropout: 0.2
+
+variance_predictor:
+  filter_size: 256
+  kernel_size: 3
+  dropout: 0.5
+
+variance_embedding:
+  pitch_quantization: "linear" # support 'linear' or 'log', 'log' is allowed only if the pitch values are not normalized during preprocessing
+  energy_quantization: "linear" # support 'linear' or 'log', 'log' is allowed only if the energy values are not normalized during preprocessing
+  n_bins: 256
+
+# gst:
+#   use_gst: False
+#   conv_filters: [32, 32, 64, 64, 128, 128]
+#   gru_hidden: 128
+#   token_size: 128
+#   n_style_token: 10
+#   attn_head: 4
+
+multi_speaker: False
+
+max_seq_len: 1000
+
+vocoder:
+  model: "HiFi-GAN" # support 'HiFi-GAN', 'MelGAN'
+  speaker: "LJSpeech" # support  'LJSpeech', 'universal'

PyTorch/Speech/FastSpeech2/config/LJSpeech/preprocess.yaml

+dataset: "LJSpeech"
+
+path:
+  corpus_path: "/home/ming/Data/LJSpeech-1.1"
+  lexicon_path: "lexicon/librispeech-lexicon.txt"
+  raw_path: "./raw_data/LJSpeech"
+  preprocessed_path: "./preprocessed_data/LJSpeech"
+
+preprocessing:
+  val_size: 512
+  text:
+    text_cleaners: ["english_cleaners"]
+    language: "en"
+  audio:
+    sampling_rate: 22050
+    max_wav_value: 32768.0
+  stft:
+    filter_length: 1024
+    hop_length: 256
+    win_length: 1024
+  mel:
+    n_mel_channels: 80
+    mel_fmin: 0
+    mel_fmax: 8000 # please set to 8000 for HiFi-GAN vocoder, set to null for MelGAN vocoder
+  pitch:
+    feature: "phoneme_level" # support 'phoneme_level' or 'frame_level'
+    normalization: True
+  energy:
+    feature: "phoneme_level" # support 'phoneme_level' or 'frame_level'
+    normalization: True

PyTorch/Speech/FastSpeech2/config/LJSpeech/train.yaml

+path:
+  ckpt_path: "./output/ckpt/LJSpeech"
+  log_path: "./output/log/LJSpeech"
+  result_path: "./output/result/LJSpeech"
+optimizer:
+  batch_size: 16
+  betas: [0.9, 0.98]
+  eps: 0.000000001
+  weight_decay: 0.0
+  grad_clip_thresh: 1.0
+  grad_acc_step: 1
+  warm_up_step: 4000
+  anneal_steps: [300000, 400000, 500000]
+  anneal_rate: 0.3
+step:
+  total_step: 900000
+  log_step: 100
+  synth_step: 1000
+  val_step: 1000
+  save_step: 100000

PyTorch/Speech/FastSpeech2/config/LJSpeech_paper/model.yaml

+transformer:
+  encoder_layer: 4
+  encoder_head: 2
+  encoder_hidden: 256
+  decoder_layer: 4
+  decoder_head: 2
+  decoder_hidden: 256
+  conv_filter_size: 1024
+  conv_kernel_size: [9, 1]
+  encoder_dropout: 0.2
+  decoder_dropout: 0.2
+
+variance_predictor:
+  filter_size: 256
+  kernel_size: 3
+  dropout: 0.5
+
+variance_embedding:
+  pitch_quantization: "log" # support 'linear' or 'log', 'log' is allowed only if the pitch values are not normalized during preprocessing
+  energy_quantization: "linear" # support 'linear' or 'log', 'log' is allowed only if the energy values are not normalized during preprocessing
+  n_bins: 256
+
+# gst:
+#   use_gst: False
+#   conv_filters: [32, 32, 64, 64, 128, 128]
+#   gru_hidden: 128
+#   token_size: 128
+#   n_style_token: 10
+#   attn_head: 4
+
+multi_speaker: False
+
+max_seq_len: 1000
+
+vocoder:
+  model: "HiFi-GAN" # support 'HiFi-GAN', 'MelGAN'
+  speaker: "LJSpeech" # support  'LJSpeech', 'universal'

PyTorch/Speech/FastSpeech2/config/LJSpeech_paper/preprocess.yaml

+dataset: "LJSpeech_paper"
+
+path:
+  corpus_path: "/home/ming/Data/LJSpeech-1.1"
+  lexicon_path: "lexicon/librispeech-lexicon.txt"
+  raw_path: "./raw_data/LJSpeech"
+  preprocessed_path: "./preprocessed_data/LJSpeech_paper"
+
+preprocessing:
+  val_size: 512
+  text:
+    text_cleaners: ["english_cleaners"]
+    language: "en"
+  audio:
+    sampling_rate: 22050
+    max_wav_value: 32768.0
+  stft:
+    filter_length: 1024
+    hop_length: 256
+    win_length: 1024
+  mel:
+    n_mel_channels: 80
+    mel_fmin: 0
+    mel_fmax: 8000 # please set to 8000 for HiFi-GAN vocoder, set to null for MelGAN vocoder
+  pitch:
+    feature: "frame_level" # support 'phoneme_level' or 'frame_level'
+    normalization: False
+  energy:
+    feature: "frame_level" # support 'phoneme_level' or 'frame_level'
+    normalization: False

PyTorch/Speech/FastSpeech2/config/LJSpeech_paper/train.yaml

+path:
+  ckpt_path: "./output/ckpt/LJSpeech"
+  log_path: "./output/log/LJSpeech"
+  result_path: "./output/result/LJSpeech"
+optimizer:
+  batch_size: 48
+  betas: [0.9, 0.98]
+  eps: 0.000000001
+  weight_decay: 0.0
+  grad_clip_thresh: 1.0
+  grad_acc_step: 1
+  warm_up_step: 4000
+  anneal_steps: []
+  anneal_rate: 1.0
+step:
+  total_step: 160000
+  log_step: 100
+  synth_step: 1000
+  val_step: 1000
+  save_step: 10000

PyTorch/Speech/FastSpeech2/config/LibriTTS/model.yaml

+transformer:
+  encoder_layer: 4
+  encoder_head: 2
+  encoder_hidden: 256
+  decoder_layer: 6
+  decoder_head: 2
+  decoder_hidden: 256
+  conv_filter_size: 1024
+  conv_kernel_size: [9, 1]
+  encoder_dropout: 0.2
+  decoder_dropout: 0.2
+
+variance_predictor:
+  filter_size: 256
+  kernel_size: 3
+  dropout: 0.5
+
+variance_embedding:
+  pitch_quantization: "linear" # support 'linear' or 'log', 'log' is allowed only if the pitch values are not normalized during preprocessing
+  energy_quantization: "linear" # support 'linear' or 'log', 'log' is allowed only if the energy values are not normalized during preprocessing
+  n_bins: 256
+
+# gst:
+#   use_gst: False
+#   conv_filters: [32, 32, 64, 64, 128, 128]
+#   gru_hidden: 128
+#   token_size: 128
+#   n_style_token: 10
+#   attn_head: 4
+
+multi_speaker: True
+
+max_seq_len: 1000
+
+vocoder:
+  model: "HiFi-GAN" # support 'HiFi-GAN', 'MelGAN'
+  speaker: "universal" # support  'LJSpeech', 'universal'

PyTorch/Speech/FastSpeech2/config/LibriTTS/preprocess.yaml

+dataset: "LibriTTS"
+
+path:
+  corpus_path: "/home/ming/Data/LibriTTS/train-clean-360"
+  lexicon_path: "lexicon/librispeech-lexicon.txt"
+  raw_path: "./raw_data/LibriTTS"
+  preprocessed_path: "./preprocessed_data/LibriTTS"
+
+preprocessing:
+  val_size: 512
+  text:
+    text_cleaners: ["english_cleaners"]
+    language: "en"
+  audio:
+    sampling_rate: 22050
+    max_wav_value: 32768.0
+  stft:
+    filter_length: 1024
+    hop_length: 256
+    win_length: 1024
+  mel:
+    n_mel_channels: 80
+    mel_fmin: 0
+    mel_fmax: 8000 # please set to 8000 for HiFi-GAN vocoder, set to null for MelGAN vocoder
+  pitch:
+    feature: "phoneme_level" # support 'phoneme_level' or 'frame_level'
+    normalization: True
+  energy:
+    feature: "phoneme_level" # support 'phoneme_level' or 'frame_level'
+    normalization: True

PyTorch/Speech/FastSpeech2/config/LibriTTS/train.yaml

+path:
+  ckpt_path: "./output/ckpt/LibriTTS"
+  log_path: "./output/log/LibriTTS"
+  result_path: "./output/result/LibriTTS"
+optimizer:
+  batch_size: 16
+  betas: [0.9, 0.98]
+  eps: 0.000000001
+  weight_decay: 0.0
+  grad_clip_thresh: 1.0
+  grad_acc_step: 1
+  warm_up_step: 4000
+  anneal_steps: [300000, 400000, 500000]
+  anneal_rate: 0.3
+step:
+  total_step: 900000
+  log_step: 100
+  synth_step: 1000
+  val_step: 1000
+  save_step: 100000

PyTorch/Speech/FastSpeech2/config/README.md

+# Config
+Here are the config files used to train the single/multi-speaker TTS models.
+4 different configurations are given:
+- LJSpeech: suggested configuration for LJSpeech dataset.
+- LibriTTS: suggested configuration for LibriTTS dataset.
+- AISHELL3: suggested configuration for AISHELL-3 dataset.
+- LJSpeech_paper: closed to the setting proposed in the original FastSpeech 2 paper.
+
+Some important hyper-parameters are explained here.
+
+## preprocess.yaml
+- **path.lexicon_path**: the lexicon (which maps words to phonemes) used by Montreal Forced Aligner. 
+  We provide an English lexicon and a Mandarin lexicon. 
+  Erhua (ㄦ化音) is handled in the Mandarin lexicon.
+- **mel.stft.mel_fmax**: set it to 8000 if HiFi-GAN vocoder is used, and set it to null if MelGAN is used.
+- **pitch.feature & energy.feature**: the original paper proposed to predict and apply frame-level pitch and energy features to the inputs of the TTS decoder to control the pitch and energy of the synthesized utterances. 
+  However, in our experiments, we find that using phoneme-level features makes the prosody of the synthesized utterances more natural.
+- **pitch.normalization & energy.normalization**: to normalize the pitch and energy values or not. 
+  The original paper did not normalize these values.
+
+## train.yaml
+- **optimizer.grad_acc_step**: the number of batches of gradient accumulation before updating the model parameters and call optimizer.zero_grad(), which is useful if you wish to train the model with a large batch size but you do not have sufficient GPU memory.
+- **optimizer.anneal_steps & optimizer.anneal_rate**: the learning rate is reduced at the **anneal_steps** by the ratio specified with **anneal_rate**.
+
+## model.yaml
+- **transformer.decoder_layer**: the original paper used a 4-layer decoder, but we find it better to use a 6-layer decoder, especially for multi-speaker TTS.
+- **variance_embedding.pitch_quantization**: when the pitch values are normalized as specified in ``preprocess.yaml``, it is not valid to use log-scale quantization bins as proposed in the original paper, so we use linear-scaled bins instead. 
+- **multi_speaker**: to apply a speaker embedding table to enable multi-speaker TTS or not.
+- **vocoder.speaker**: should be set to 'universal' if any dataset other than LJSpeech is used.
\ No newline at end of file

PyTorch/Speech/FastSpeech2/dataset.py

+import json
+import math
+import os
+
+import numpy as np
+from torch.utils.data import Dataset
+
+from text import text_to_sequence
+from utils.tools import pad_1D, pad_2D
+
+
+class Dataset(Dataset):
+    def __init__(
+        self, filename, preprocess_config, train_config, sort=False, drop_last=False
+    ):
+        self.dataset_name = preprocess_config["dataset"]
+        self.preprocessed_path = preprocess_config["path"]["preprocessed_path"]
+        self.cleaners = preprocess_config["preprocessing"]["text"]["text_cleaners"]
+        self.batch_size = train_config["optimizer"]["batch_size"]
+
+        self.basename, self.speaker, self.text, self.raw_text = self.process_meta(
+            filename
+        )
+        with open(os.path.join(self.preprocessed_path, "speakers.json")) as f:
+            self.speaker_map = json.load(f)
+        self.sort = sort
+        self.drop_last = drop_last
+
+    def __len__(self):
+        return len(self.text)
+
+    def __getitem__(self, idx):
+        basename = self.basename[idx]
+        speaker = self.speaker[idx]
+        speaker_id = self.speaker_map[speaker]
+        raw_text = self.raw_text[idx]
+        phone = np.array(text_to_sequence(self.text[idx], self.cleaners))
+        mel_path = os.path.join(
+            self.preprocessed_path,
+            "mel",
+            "{}-mel-{}.npy".format(speaker, basename),
+        )
+        mel = np.load(mel_path)
+        pitch_path = os.path.join(
+            self.preprocessed_path,
+            "pitch",
+            "{}-pitch-{}.npy".format(speaker, basename),
+        )
+        pitch = np.load(pitch_path)
+        energy_path = os.path.join(
+            self.preprocessed_path,
+            "energy",
+            "{}-energy-{}.npy".format(speaker, basename),
+        )
+        energy = np.load(energy_path)
+        duration_path = os.path.join(
+            self.preprocessed_path,
+            "duration",
+            "{}-duration-{}.npy".format(speaker, basename),
+        )
+        duration = np.load(duration_path)
+
+        sample = {
+            "id": basename,
+            "speaker": speaker_id,
+            "text": phone,
+            "raw_text": raw_text,
+            "mel": mel,
+            "pitch": pitch,
+            "energy": energy,
+            "duration": duration,
+        }
+
+        return sample
+
+    def process_meta(self, filename):
+        with open(
+            os.path.join(self.preprocessed_path, filename), "r", encoding="utf-8"
+        ) as f:
+            name = []
+            speaker = []
+            text = []
+            raw_text = []
+            for line in f.readlines():
+                n, s, t, r = line.strip("\n").split("|")
+                name.append(n)
+                speaker.append(s)
+                text.append(t)
+                raw_text.append(r)
+            return name, speaker, text, raw_text
+
+    def reprocess(self, data, idxs):
+        ids = [data[idx]["id"] for idx in idxs]
+        speakers = [data[idx]["speaker"] for idx in idxs]
+        texts = [data[idx]["text"] for idx in idxs]
+        raw_texts = [data[idx]["raw_text"] for idx in idxs]
+        mels = [data[idx]["mel"] for idx in idxs]
+        pitches = [data[idx]["pitch"] for idx in idxs]
+        energies = [data[idx]["energy"] for idx in idxs]
+        durations = [data[idx]["duration"] for idx in idxs]
+
+        text_lens = np.array([text.shape[0] for text in texts])
+        mel_lens = np.array([mel.shape[0] for mel in mels])
+
+        speakers = np.array(speakers)
+        texts = pad_1D(texts)
+        mels = pad_2D(mels)
+        pitches = pad_1D(pitches)
+        energies = pad_1D(energies)
+        durations = pad_1D(durations)
+
+        return (
+            ids,
+            raw_texts,
+            speakers,
+            texts,
+            text_lens,
+            max(text_lens),
+            mels,
+            mel_lens,
+            max(mel_lens),
+            pitches,
+            energies,
+            durations,
+        )
+
+    def collate_fn(self, data):
+        data_size = len(data)
+
+        if self.sort:
+            len_arr = np.array([d["text"].shape[0] for d in data])
+            idx_arr = np.argsort(-len_arr)
+        else:
+            idx_arr = np.arange(data_size)
+
+        tail = idx_arr[len(idx_arr) - (len(idx_arr) % self.batch_size) :]
+        idx_arr = idx_arr[: len(idx_arr) - (len(idx_arr) % self.batch_size)]
+        idx_arr = idx_arr.reshape((-1, self.batch_size)).tolist()
+        if not self.drop_last and len(tail) > 0:
+            idx_arr += [tail.tolist()]
+
+        output = list()
+        for idx in idx_arr:
+            output.append(self.reprocess(data, idx))
+
+        return output
+
+
+class TextDataset(Dataset):
+    def __init__(self, filepath, preprocess_config):
+        self.cleaners = preprocess_config["preprocessing"]["text"]["text_cleaners"]
+
+        self.basename, self.speaker, self.text, self.raw_text = self.process_meta(
+            filepath
+        )
+        with open(
+            os.path.join(
+                preprocess_config["path"]["preprocessed_path"], "speakers.json"
+            )
+        ) as f:
+            self.speaker_map = json.load(f)
+
+    def __len__(self):
+        return len(self.text)
+
+    def __getitem__(self, idx):
+        basename = self.basename[idx]
+        speaker = self.speaker[idx]
+        speaker_id = self.speaker_map[speaker]
+        raw_text = self.raw_text[idx]
+        phone = np.array(text_to_sequence(self.text[idx], self.cleaners))
+
+        return (basename, speaker_id, phone, raw_text)
+
+    def process_meta(self, filename):
+        with open(filename, "r", encoding="utf-8") as f:
+            name = []
+            speaker = []
+            text = []
+            raw_text = []
+            for line in f.readlines():
+                n, s, t, r = line.strip("\n").split("|")
+                name.append(n)
+                speaker.append(s)
+                text.append(t)
+                raw_text.append(r)
+            return name, speaker, text, raw_text
+
+    def collate_fn(self, data):
+        ids = [d[0] for d in data]
+        speakers = np.array([d[1] for d in data])
+        texts = [d[2] for d in data]
+        raw_texts = [d[3] for d in data]
+        text_lens = np.array([text.shape[0] for text in texts])
+
+        texts = pad_1D(texts)
+
+        return ids, raw_texts, speakers, texts, text_lens, max(text_lens)
+
+
+if __name__ == "__main__":
+    # Test
+    import torch
+    import yaml
+    from torch.utils.data import DataLoader
+    from utils.utils import to_device
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    preprocess_config = yaml.load(
+        open("./config/LJSpeech/preprocess.yaml", "r"), Loader=yaml.FullLoader
+    )
+    train_config = yaml.load(
+        open("./config/LJSpeech/train.yaml", "r"), Loader=yaml.FullLoader
+    )
+
+    train_dataset = Dataset(
+        "train.txt", preprocess_config, train_config, sort=True, drop_last=True
+    )
+    val_dataset = Dataset(
+        "val.txt", preprocess_config, train_config, sort=False, drop_last=False
+    )
+    train_loader = DataLoader(
+        train_dataset,
+        batch_size=train_config["optimizer"]["batch_size"] * 4,
+        shuffle=True,
+        collate_fn=train_dataset.collate_fn,
+    )
+    val_loader = DataLoader(
+        val_dataset,
+        batch_size=train_config["optimizer"]["batch_size"],
+        shuffle=False,
+        collate_fn=val_dataset.collate_fn,
+    )
+
+    n_batch = 0
+    for batchs in train_loader:
+        for batch in batchs:
+            to_device(batch, device)
+            n_batch += 1
+    print(
+        "Training set  with size {} is composed of {} batches.".format(
+            len(train_dataset), n_batch
+        )
+    )
+
+    n_batch = 0
+    for batchs in val_loader:
+        for batch in batchs:
+            to_device(batch, device)
+            n_batch += 1
+    print(
+        "Validation set  with size {} is composed of {} batches.".format(
+            len(val_dataset), n_batch
+        )
+    )
\ No newline at end of file