1102025

.python-version

@@ -0,0 +1 @@
+FS2_deploy

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 Chung-Ming Chien
+
+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.

README.md

@@ -1,12 +1,13 @@
 ---
 title: Frysk TTS
-emoji: 👀
-colorFrom: blue
-colorTo: purple
+emoji: 🔥
+colorFrom: pink
+colorTo: pink
+python_version: 3.8.6
 sdk: gradio
-sdk_version: 5.15.0
+sdk_version: 4.44.1
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import numpy as np
+
+import os, subprocess, time, torch, yaml, re
+import synthesize
+
+from pandas import describe_option
+from scipy.io.wavfile import read
+from utils.model import get_model_infer, get_vocoder
+
+gr.close_all()
+
+title = "Open-source and open-access Frisian TTS<br>(from Phat Do's PhD research)"
+description = """
+<center>
+<img src="https://unishare.nl/index.php/apps/files_sharing/publicpreview/ND3yDPiL9do69TC?x=1908&y=590&a=true&file=friesland_transparent.png" width=200px><br>
+These are the prototype Frisian synthetic speech models intended to showcase the findings of my PhD research, which are described in Section 7.6 of the dissertation.<br>
+The single-speaker model was trained on only <strong>20 minutes</strong> of data (recordings) using transfer learning from Dutch data. The multi-speaker model was trained from scratch wholly on <strong>32 hours</strong> of Frisian data.<br><br>
+Please select the model's tab above, enter any Frisian text (or choose from the examples for convenience), select a speaker ID below (for the multi-speaker model), and click on Submit to synthesize the speech.<br>
+</center>
+"""
+article = "<center>This is a basic demo version intended to accompany my PhD dissertation, showcasing the effects of several findings included in the research (please see the dissertation for details).<br> Due to hosting constraints, the model may be rather slow in synthesizing. Thank you for your patience!<br> Please reach out to <a href='https://www.rug.nl/staff/t.p.do/' target='_blank'>Phat Do</a> or email me at <a href='mailto:[email protected]' target='_blank'>[email protected]</a> if you are interested in knowing more!</center><br><br>To phonetically transcribe the input Frisian text, this work gratefully uses the G2P Frysk model and script kindly provided by the Fryske Akademy:<br> Heeringa, Wilbert & Drenth, Eduard & Van de Velde, Hans (2024). G2P Frysk [computer program]. Retrieved 4 July 2024 from <a href='https://www.fa.knaw.nl/fa-apps/graph2phon/' target='_blank'>https://www.fa.knaw.nl/fa-apps/graph2phon/</a>."
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+def load_models(config_path, model_name):
+    # getting configs
+    preprocess_config = yaml.load(open(os.path.join(os.getcwd(), config_path + "preprocess.yaml"), "r"), Loader=yaml.FullLoader)
+    model_config = yaml.load(open(os.path.join(os.getcwd(), config_path + "model.yaml"), "r"), Loader=yaml.FullLoader)
+    train_config = yaml.load(open(os.path.join(os.getcwd(), config_path + "train.yaml"), "r"), Loader=yaml.FullLoader)
+    configs = (preprocess_config, model_config, train_config)
+    # loading models
+    model = get_model_infer(os.path.join(os.getcwd(), "assets/model/" + model_name), configs, device)
+    vocoder = get_vocoder(model_config, device)    
+    return configs, model, vocoder
+
+configs_single, model_single, vocoder_single = load_models(config_path = "config/Frysk_CV_speaker_29/",
+                                                           model_name = "Frysk_CV_speaker_29_350000.pth.tar")
+configs_multi, model_multi, vocoder_multi = load_models(config_path = "config/Frysk_CV/",
+                                                        model_name = "Frysk_CV_300000.pth.tar")
+
+def infer(text, speaker_ID, configs, model, vocoder):
+    speakers = np.array([int(speaker_ID)])
+    texts = np.array([synthesize.preprocess_frysk(text, configs[0])])
+    text_lens = np.array([len(texts[0])])
+    
+    ids = raw_texts = [text[:100]]
+    batchs = [(ids, raw_texts, speakers, texts, text_lens, max(text_lens))]
+
+    synthesize.synthesize(model, "step", configs, vocoder, batchs, (1.0, 1.0, 1.0))
+
+    while True:
+        if not os.path.exists(os.path.join(os.getcwd(), 'assets/audio/result.wav')):
+            time.sleep(0.01)
+        else:
+            sr, audio = read(os.path.join(os.getcwd(), 'assets/audio/result.wav'))
+            break    
+    return sr, audio
+
+# main inference function
+def run_single(text):
+    speaker_ID = 0
+    output = infer(text, 0, configs_single, model_single, vocoder_single)
+    return output
+
+def run_multi(text, speaker_ID):
+    dic = {1: 0, 2: 11, 3: 22, 4: 24, 5: 25, 6: 26, 7: 27, 8: 28, 9: 29, 10: 1, 11: 2, 12: 3, 13: 4, 14: 5, 15: 6, 16: 7, 17: 8, 18: 9, 19: 10, 20: 12, 21: 13, 22: 14, 23: 15, 24: 16, 25: 17, 26: 18, 27: 19, 28: 20, 29: 21, 30: 23}
+    output = infer(text, dic[speaker_ID], configs_multi, model_multi, vocoder_multi)
+    return output
+
+iface_single = gr.Interface(fn=run_single,
+                     inputs=[
+                            gr.components.Textbox(lines=3, placeholder="Please input Frisian text to synthesize", label='Text to synthesize'),
+                            ],
+                     outputs=gr.components.Audio(type="numpy", label='Synthesized speech'),
+                     title=title,
+                     description=description,
+                     article=article,
+                     theme='huggingface',
+                     examples=[
+                            ["Praat mar Frysk!", 0],
+                            ["Bûter, brea en griene tsiis, wat’dat net sizze kin is gjin oprjochte Fries!", 0],
+                            ["In lyk man is in ryk man.", 0],
+                            ["As pake it net meitsje kin, dan slagget it gjinien!", 0],
+                            ["As it net kin sa moat, moat it mar sa’t kin.", 0],
+                            ["Elk sprekt fan myn sûpen, mar nimmen wit fan myn toarst!", 0],
+                            ["Pikerje net it komt dochs oars.", 0]
+                              ],
+                     )
+
+iface_multi = gr.Interface(fn=run_multi,
+                     inputs=[
+                            gr.components.Textbox(lines=3, placeholder="Please input Frisian text to synthesize", label='Text to synthesize'),
+                            gr.components.Slider(minimum=1, maximum=30, step=1, label="Speaker ID (1 to 30)") 
+                            ],
+                     outputs=gr.components.Audio(type="numpy", label='Synthesized speech'),
+                     title=title,
+                     description=description,
+                     article=article,
+                     theme='huggingface',
+                     examples=[
+                            ["Praat mar Frysk!", 1],
+                            ["Bûter, brea en griene tsiis, wat’dat net sizze kin is gjin oprjochte Fries!", 5],
+                            ["In lyk man is in ryk man.", 11],
+                            ["As pake it net meitsje kin, dan slagget it gjinien!", 17],
+                            ["As it net kin sa moat, moat it mar sa’t kin.", 20],
+                            ["Elk sprekt fan myn sûpen, mar nimmen wit fan myn toarst!", 25],
+                            ["Pikerje net it komt dochs oars.", 29]
+                              ],
+                     )
+
+demo = gr.TabbedInterface([iface_single, iface_multi], ["Single-speaker (transfer learning)", "Multi-speaker (trained from scratch)"])
+
+if __name__ == "__main__":
+    demo.launch(share=False, inline=False, server_name="0.0.0.0", server_port=7860)

audio/__init__.py

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+import audio.tools
+import audio.stft
+import audio.audio_processing

audio/audio_processing.py

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+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

audio/stft.py

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+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

audio/tools.py

@@ -0,0 +1,34 @@
+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)

config/Frysk_CV/model.yaml

@@ -0,0 +1,37 @@
+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'

config/Frysk_CV/preprocess.yaml

@@ -0,0 +1,30 @@
+dataset: "Frysk_CV"
+
+path:
+  corpus_path: "../corpus_frisian/30-speakers"
+  lexicon_path: "./config/Frysk_CV/Frysk_CV.dict"
+  raw_path: "../corpus_frisian/30-speakers"
+  preprocessed_path: "./preprocessed_data/Frysk_CV"
+
+preprocessing:
+  val_size: 512
+  text:
+    text_cleaners: ["english_cleaners"]
+    language: "fy"
+  audio:
+    sampling_rate: 22050
+    max_wav_value: 32767
+  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

config/Frysk_CV/train.yaml

@@ -0,0 +1,20 @@
+path:
+  ckpt_path: "./output/ckpt/Frysk_CV"
+  log_path: "./output/log/Frysk_CV"
+  result_path: "./output/result/Frysk_CV"
+optimizer:
+  batch_size: 32
+  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: 200000
+  log_step: 100
+  synth_step: 1000
+  val_step: 1000
+  save_step: 5000

config/Frysk_CV_speaker_29/model.yaml

@@ -0,0 +1,37 @@
+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: "universal" # support  'LJSpeech', 'universal'

config/Frysk_CV_speaker_29/preprocess.yaml

@@ -0,0 +1,30 @@
+dataset: "Frysk_CV_speaker_29"
+
+path:
+  corpus_path: "../corpus_frisian/1-speaker"
+  lexicon_path: "./config/Frysk_CV/Frysk_CV.dict"
+  raw_path: "../corpus_frisian/1-speaker"
+  preprocessed_path: "./preprocessed_data/Frysk_CV_speaker_29"
+
+preprocessing:
+  val_size: 16
+  text:
+    text_cleaners: ["english_cleaners"]
+    language: "fy"
+  audio:
+    sampling_rate: 22050
+    max_wav_value: 32767
+  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

config/Frysk_CV_speaker_29/train.yaml

@@ -0,0 +1,20 @@
+path:
+  ckpt_path: "./output/ckpt/Frysk_CV_speaker_29"
+  log_path: "./output/log/Frysk_CV_speaker_29"
+  result_path: "./output/result/Frysk_CV_speaker_29"
+optimizer:
+  batch_size: 4
+  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: 350000
+  log_step: 100
+  synth_step: 1000
+  val_step: 1000
+  save_step: 2500

config/README.md

@@ -0,0 +1,29 @@
+# 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.

dataset.py

@@ -0,0 +1,256 @@
+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) # LABEL-INPUT VERSION
+        texts = pad_2D(texts) # FEATURE-INPUT VERSION
+        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
+        )
+    )

evaluate.py

@@ -0,0 +1,120 @@
+import argparse
+import os
+
+import torch
+import yaml
+import torch.nn as nn
+from torch.utils.data import DataLoader
+
+from utils.model import get_model, get_vocoder
+from utils.tools import to_device, log, synth_one_sample
+from model import FastSpeech2Loss
+from dataset import Dataset
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def evaluate(model, step, configs, logger=None, vocoder=None):
+    preprocess_config, model_config, train_config = configs
+
+    # Get dataset
+    dataset = Dataset(
+        "val.txt", preprocess_config, train_config, sort=False, drop_last=False
+    )
+    batch_size = train_config["optimizer"]["batch_size"]
+    loader = DataLoader(
+        dataset,
+        batch_size=batch_size,
+        shuffle=False,
+        collate_fn=dataset.collate_fn,
+    )
+
+    # Get loss function
+    Loss = FastSpeech2Loss(preprocess_config, model_config).to(device)
+
+    # Evaluation
+    loss_sums = [0 for _ in range(6)]
+    for batchs in loader:
+        for batch in batchs:
+            batch = to_device(batch, device)
+            with torch.no_grad():
+                # Forward
+                output = model(*(batch[2:]))
+
+                # Cal Loss
+                losses = Loss(batch, output)
+
+                for i in range(len(losses)):
+                    loss_sums[i] += losses[i].item() * len(batch[0])
+
+    loss_means = [loss_sum / len(dataset) for loss_sum in loss_sums]
+
+    message = "Validation Step {}, Total Loss: {:.4f}, Mel Loss: {:.4f}, Mel PostNet Loss: {:.4f}, Pitch Loss: {:.4f}, Energy Loss: {:.4f}, Duration Loss: {:.4f}".format(
+        *([step] + [l for l in loss_means])
+    )
+
+    if logger is not None:
+        fig, wav_reconstruction, wav_prediction, tag = synth_one_sample(
+            batch,
+            output,
+            vocoder,
+            model_config,
+            preprocess_config,
+        )
+
+        log(logger, step, losses=loss_means)
+        log(
+            logger,
+            fig=fig,
+            tag="Validation/step_{}_{}".format(step, tag),
+        )
+        sampling_rate = preprocess_config["preprocessing"]["audio"]["sampling_rate"]
+        log(
+            logger,
+            audio=wav_reconstruction,
+            sampling_rate=sampling_rate,
+            tag="Validation/step_{}_{}_reconstructed".format(step, tag),
+        )
+        log(
+            logger,
+            audio=wav_prediction,
+            sampling_rate=sampling_rate,
+            tag="Validation/step_{}_{}_synthesized".format(step, tag),
+        )
+
+    return message
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--restore_step", type=int, default=30000)
+    parser.add_argument(
+        "-p",
+        "--preprocess_config",
+        type=str,
+        required=True,
+        help="path to preprocess.yaml",
+    )
+    parser.add_argument(
+        "-m", "--model_config", type=str, required=True, help="path to model.yaml"
+    )
+    parser.add_argument(
+        "-t", "--train_config", type=str, required=True, help="path to train.yaml"
+    )
+    args = parser.parse_args()
+
+    # Read Config
+    preprocess_config = yaml.load(
+        open(args.preprocess_config, "r"), Loader=yaml.FullLoader
+    )
+    model_config = yaml.load(open(args.model_config, "r"), Loader=yaml.FullLoader)
+    train_config = yaml.load(open(args.train_config, "r"), Loader=yaml.FullLoader)
+    configs = (preprocess_config, model_config, train_config)
+
+    # Get model
+    model = get_model(args, configs, device, train=False).to(device)
+
+    message = evaluate(model, args.restore_step, configs)
+    print(message)

hifigan/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 Jungil Kong
+
+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.

hifigan/__init__.py

@@ -0,0 +1,7 @@
+from .models import Generator
+
+
+class AttrDict(dict):
+    def __init__(self, *args, **kwargs):
+        super(AttrDict, self).__init__(*args, **kwargs)
+        self.__dict__ = self

hifigan/config.json

@@ -0,0 +1,37 @@
+{
+    "resblock": "1",
+    "num_gpus": 0,
+    "batch_size": 16,
+    "learning_rate": 0.0002,
+    "adam_b1": 0.8,
+    "adam_b2": 0.99,
+    "lr_decay": 0.999,
+    "seed": 1234,
+
+    "upsample_rates": [8,8,2,2],
+    "upsample_kernel_sizes": [16,16,4,4],
+    "upsample_initial_channel": 512,
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+
+    "segment_size": 8192,
+    "num_mels": 80,
+    "num_freq": 1025,
+    "n_fft": 1024,
+    "hop_size": 256,
+    "win_size": 1024,
+
+    "sampling_rate": 22050,
+
+    "fmin": 0,
+    "fmax": 8000,
+    "fmax_for_loss": null,
+
+    "num_workers": 4,
+
+    "dist_config": {
+        "dist_backend": "nccl",
+        "dist_url": "tcp://localhost:54321",
+        "world_size": 1
+    }
+}

hifigan/models.py

@@ -0,0 +1,174 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import Conv1d, ConvTranspose1d
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+LRELU_SLOPE = 0.1
+
+
+def init_weights(m, mean=0.0, std=0.01):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size * dilation - dilation) / 2)
+
+
+class ResBlock(torch.nn.Module):
+    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock, self).__init__()
+        self.h = h
+        self.convs1 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[0],
+                        padding=get_padding(kernel_size, dilation[0]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[1],
+                        padding=get_padding(kernel_size, dilation[1]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[2],
+                        padding=get_padding(kernel_size, dilation[2]),
+                    )
+                ),
+            ]
+        )
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+            ]
+        )
+        self.convs2.apply(init_weights)
+
+    def forward(self, x):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            xt = c2(xt)
+            x = xt + x
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+
+class Generator(torch.nn.Module):
+    def __init__(self, h):
+        super(Generator, self).__init__()
+        self.h = h
+        self.num_kernels = len(h.resblock_kernel_sizes)
+        self.num_upsamples = len(h.upsample_rates)
+        self.conv_pre = weight_norm(
+            Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3)
+        )
+        resblock = ResBlock
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        h.upsample_initial_channel // (2 ** i),
+                        h.upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = h.upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(
+                zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)
+            ):
+                self.resblocks.append(resblock(h, ch, k, d))
+
+        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
+        self.ups.apply(init_weights)
+        self.conv_post.apply(init_weights)
+
+    def forward(self, x):
+        x = self.conv_pre(x)
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print("Removing weight norm...")
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+        remove_weight_norm(self.conv_pre)
+        remove_weight_norm(self.conv_post)

lexicon/pinyin-lexicon-r.txt

@@ -0,0 +1,4120 @@
+a1 a1
+a2 a2
+a3 a3
+a4 a4
+a5 a5
+ai1 ai1
+ai2 ai2
+ai3 ai3
+ai4 ai4
+ai5 ai5
+an1 an1
+an2 an2
+an3 an3
+an4 an4
+an5 an5
+ang1 ang1
+ang2 ang2
+ang3 ang3
+ang4 ang4
+ang5 ang5
+ao1 ao1
+ao2 ao2
+ao3 ao3
+ao4 ao4
+ao5 ao5
+ba1 b a1
+ba2 b a2
+ba3 b a3
+ba4 b a4
+ba5 b a5
+bai1 b ai1
+bai2 b ai2
+bai3 b ai3
+bai4 b ai4
+bai5 b ai5
+ban1 b an1
+ban2 b an2
+ban3 b an3
+ban4 b an4
+ban5 b an5
+bang1 b ang1
+bang2 b ang2
+bang3 b ang3
+bang4 b ang4
+bang5 b ang5
+bao1 b ao1
+bao2 b ao2
+bao3 b ao3
+bao4 b ao4
+bao5 b ao5
+bei1 b ei1
+bei2 b ei2
+bei3 b ei3
+bei4 b ei4
+bei5 b ei5
+ben1 b en1
+ben2 b en2
+ben3 b en3
+ben4 b en4
+ben5 b en5
+beng1 b eng1
+beng2 b eng2
+beng3 b eng3
+beng4 b eng4
+beng5 b eng5
+bi1 b i1
+bi2 b i2
+bi3 b i3
+bi4 b i4
+bi5 b i5
+bian1 b ian1
+bian2 b ian2
+bian3 b ian3
+bian4 b ian4
+bian5 b ian5
+biao1 b iao1
+biao2 b iao2
+biao3 b iao3
+biao4 b iao4
+biao5 b iao5
+bie1 b ie1
+bie2 b ie2
+bie3 b ie3
+bie4 b ie4
+bie5 b ie5
+bin1 b in1
+bin2 b in2
+bin3 b in3
+bin4 b in4
+bin5 b in5
+bing1 b ing1
+bing2 b ing2
+bing3 b ing3
+bing4 b ing4
+bing5 b ing5
+bo1 b o1
+bo2 b o2
+bo3 b o3
+bo4 b o4
+bo5 b o5
+bu1 b u1
+bu2 b u2
+bu3 b u3
+bu4 b u4
+bu5 b u5
+ca1 c a1
+ca2 c a2
+ca3 c a3
+ca4 c a4
+ca5 c a5
+cai1 c ai1
+cai2 c ai2
+cai3 c ai3
+cai4 c ai4
+cai5 c ai5
+can1 c an1
+can2 c an2
+can3 c an3
+can4 c an4
+can5 c an5
+cang1 c ang1
+cang2 c ang2
+cang3 c ang3
+cang4 c ang4
+cang5 c ang5
+cao1 c ao1
+cao2 c ao2
+cao3 c ao3
+cao4 c ao4
+cao5 c ao5
+ce1 c e1
+ce2 c e2
+ce3 c e3
+ce4 c e4
+ce5 c e5
+cen1 c en1
+cen2 c en2
+cen3 c en3
+cen4 c en4
+cen5 c en5
+ceng1 c eng1
+ceng2 c eng2
+ceng3 c eng3
+ceng4 c eng4
+ceng5 c eng5
+cha1 ch a1
+cha2 ch a2
+cha3 ch a3
+cha4 ch a4
+cha5 ch a5
+chai1 ch ai1
+chai2 ch ai2
+chai3 ch ai3
+chai4 ch ai4
+chai5 ch ai5
+chan1 ch an1
+chan2 ch an2
+chan3 ch an3
+chan4 ch an4
+chan5 ch an5
+chang1 ch ang1
+chang2 ch ang2
+chang3 ch ang3
+chang4 ch ang4
+chang5 ch ang5
+chao1 ch ao1
+chao2 ch ao2
+chao3 ch ao3
+chao4 ch ao4
+chao5 ch ao5
+che1 ch e1
+che2 ch e2
+che3 ch e3
+che4 ch e4
+che5 ch e5
+chen1 ch en1
+chen2 ch en2
+chen3 ch en3
+chen4 ch en4
+chen5 ch en5
+cheng1 ch eng1
+cheng2 ch eng2
+cheng3 ch eng3
+cheng4 ch eng4
+cheng5 ch eng5
+chi1 ch iii1
+chi2 ch iii2
+chi3 ch iii3
+chi4 ch iii4
+chi5 ch iii5
+chong1 ch ong1
+chong2 ch ong2
+chong3 ch ong3
+chong4 ch ong4
+chong5 ch ong5
+chou1 ch ou1
+chou2 ch ou2
+chou3 ch ou3
+chou4 ch ou4
+chou5 ch ou5
+chu1 ch u1
+chu2 ch u2
+chu3 ch u3
+chu4 ch u4
+chu5 ch u5
+chuai1 ch uai1
+chuai2 ch uai2
+chuai3 ch uai3
+chuai4 ch uai4
+chuai5 ch uai5
+chuan1 ch uan1
+chuan2 ch uan2
+chuan3 ch uan3
+chuan4 ch uan4
+chuan5 ch uan5
+chuang1 ch uang1
+chuang2 ch uang2
+chuang3 ch uang3
+chuang4 ch uang4
+chuang5 ch uang5
+chui1 ch uei1
+chui2 ch uei2
+chui3 ch uei3
+chui4 ch uei4
+chui5 ch uei5
+chun1 ch uen1
+chun2 ch uen2
+chun3 ch uen3
+chun4 ch uen4
+chun5 ch uen5
+chuo1 ch uo1
+chuo2 ch uo2
+chuo3 ch uo3
+chuo4 ch uo4
+chuo5 ch uo5
+ci1 c ii1
+ci2 c ii2
+ci3 c ii3
+ci4 c ii4
+ci5 c ii5
+cong1 c ong1
+cong2 c ong2
+cong3 c ong3
+cong4 c ong4
+cong5 c ong5
+cou1 c ou1
+cou2 c ou2
+cou3 c ou3
+cou4 c ou4
+cou5 c ou5
+cu1 c u1
+cu2 c u2
+cu3 c u3
+cu4 c u4
+cu5 c u5
+cuan1 c uan1
+cuan2 c uan2
+cuan3 c uan3
+cuan4 c uan4
+cuan5 c uan5
+cui1 c uei1
+cui2 c uei2
+cui3 c uei3
+cui4 c uei4
+cui5 c uei5
+cun1 c uen1
+cun2 c uen2
+cun3 c uen3
+cun4 c uen4
+cun5 c uen5
+cuo1 c uo1
+cuo2 c uo2
+cuo3 c uo3
+cuo4 c uo4
+cuo5 c uo5
+da1 d a1
+da2 d a2
+da3 d a3
+da4 d a4
+da5 d a5
+dai1 d ai1
+dai2 d ai2
+dai3 d ai3
+dai4 d ai4
+dai5 d ai5
+dan1 d an1
+dan2 d an2
+dan3 d an3
+dan4 d an4
+dan5 d an5
+dang1 d ang1
+dang2 d ang2
+dang3 d ang3
+dang4 d ang4
+dang5 d ang5
+dao1 d ao1
+dao2 d ao2
+dao3 d ao3
+dao4 d ao4
+dao5 d ao5
+de1 d e1
+de2 d e2
+de3 d e3
+de4 d e4
+de5 d e5
+dei1 d ei1
+dei2 d ei2
+dei3 d ei3
+dei4 d ei4
+dei5 d ei5
+den1 d en1
+den2 d en2
+den3 d en3
+den4 d en4
+den5 d en5
+deng1 d eng1
+deng2 d eng2
+deng3 d eng3
+deng4 d eng4
+deng5 d eng5
+di1 d i1
+di2 d i2
+di3 d i3
+di4 d i4
+di5 d i5
+dia1 d ia1
+dia2 d ia2
+dia3 d ia3
+dia4 d ia4
+dia5 d ia5
+dian1 d ian1
+dian2 d ian2
+dian3 d ian3
+dian4 d ian4
+dian5 d ian5
+diao1 d iao1
+diao2 d iao2
+diao3 d iao3
+diao4 d iao4
+diao5 d iao5
+die1 d ie1
+die2 d ie2
+die3 d ie3
+die4 d ie4
+die5 d ie5
+ding1 d ing1
+ding2 d ing2
+ding3 d ing3
+ding4 d ing4
+ding5 d ing5
+diu1 d iou1
+diu2 d iou2
+diu3 d iou3
+diu4 d iou4
+diu5 d iou5
+dong1 d ong1
+dong2 d ong2
+dong3 d ong3
+dong4 d ong4
+dong5 d ong5
+dou1 d ou1
+dou2 d ou2
+dou3 d ou3
+dou4 d ou4
+dou5 d ou5
+du1 d u1
+du2 d u2
+du3 d u3
+du4 d u4
+du5 d u5
+duan1 d uan1
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+duan3 d uan3
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+duan5 d uan5
+dui1 d uei1
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+dui5 d uei5
+dun1 d uen1
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+duo1 d uo1
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+duo3 d uo3
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+e1 e1
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+e3 e3
+e4 e4
+e5 e5
+ei1 ei1
+ei2 ei2
+ei3 ei3
+ei4 ei4
+ei5 ei5
+en1 en1
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+en3 en3
+en4 en4
+en5 en5
+eng1 eng1
+eng2 eng2
+eng3 eng3
+eng4 eng4
+eng5 eng5
+r1 er1
+r2 er2
+r3 er3
+r4 er4
+r5 er5
+er1 er1
+er2 er2
+er3 er3
+er4 er4
+er5 er5
+fa1 f a1
+fa2 f a2
+fa3 f a3
+fa4 f a4
+fa5 f a5
+fan1 f an1
+fan2 f an2
+fan3 f an3
+fan4 f an4
+fan5 f an5
+fang1 f ang1
+fang2 f ang2
+fang3 f ang3
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+fang5 f ang5
+fei1 f ei1
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+fen1 f en1
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+feng1 f eng1
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+fo1 f o1
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+fu1 f u1
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+ga1 g a1
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+gai1 g ai1
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+gai3 g ai3
+gai4 g ai4
+gai5 g ai5
+gan1 g an1
+gan2 g an2
+gan3 g an3
+gan4 g an4
+gan5 g an5
+gang1 g ang1
+gang2 g ang2
+gang3 g ang3
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+gang5 g ang5
+gao1 g ao1
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+ge1 g e1
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+geng3 g eng3
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+gong1 g ong1
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+gou1 g ou1
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+gui1 g uei1
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+ha1 h a1
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+hai1 h ai1
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+jiong1 j iong1
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+jiong3 j iong3
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+kao1 k ao1
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+keng1 k eng1
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+keng4 k eng4
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+kong1 k ong1
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+kong3 k ong3
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+kou1 k ou1
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+kou5 k ou5
+ku1 k u1
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+kuai4 k uai4
+kuai5 k uai5
+kuan1 k uan1
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+kuan4 k uan4
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+kuang1 k uang1
+kuang2 k uang2
+kuang3 k uang3
+kuang4 k uang4
+kuang5 k uang5
+kui1 k uei1
+kui2 k uei2
+kui3 k uei3
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+kui5 k uei5
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+kun5 k uen5
+kuo1 k uo1
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+zhuangr1 zh uang1 rr
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+zhuangr3 zh uang3 rr
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+zhuir1 zh uei1 rr
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+zhuir3 zh uei3 rr
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+zhuir5 zh uei5 rr
+zhunr1 zh uen1 rr
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+zir1 z ii1 rr
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+zir3 z ii3 rr
+zir4 z ii4 rr
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+zongr1 z ong1 rr
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+zur4 z u4 rr
+zur5 z u5 rr
+zuanr1 z uan1 rr
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+zuanr3 z uan3 rr
+zuanr4 z uan4 rr
+zuanr5 z uan5 rr
+zuir1 z uei1 rr
+zuir2 z uei2 rr
+zuir3 z uei3 rr
+zuir4 z uei4 rr
+zuir5 z uei5 rr
+zunr1 z uen1 rr
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+zunr3 z uen3 rr
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+zuor1 z uo1 rr
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+zuor3 z uo3 rr
+zuor4 z uo4 rr
+zuor5 z uo5 rr

model/__init__.py

@@ -0,0 +1,3 @@
+from .fastspeech2 import FastSpeech2
+from .loss import FastSpeech2Loss
+from .optimizer import ScheduledOptim

model/fastspeech2.py

@@ -0,0 +1,110 @@
+import os
+import json
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from transformer import Encoder, Decoder, PostNet
+from .modules import VarianceAdaptor
+from utils.tools import get_mask_from_lengths
+
+
+class FastSpeech2(nn.Module):
+    """ FastSpeech2 """
+
+    def __init__(self, preprocess_config, model_config):
+        super(FastSpeech2, self).__init__()
+        self.model_config = model_config
+
+        self.encoder = Encoder(model_config)
+        self.variance_adaptor = VarianceAdaptor(preprocess_config, model_config)
+        self.decoder = Decoder(model_config)
+        self.mel_linear = nn.Linear(
+            model_config["transformer"]["decoder_hidden"],
+            preprocess_config["preprocessing"]["mel"]["n_mel_channels"],
+        )
+        self.postnet = PostNet()
+
+        self.speaker_emb = None
+        if model_config["multi_speaker"]:
+            with open(
+                os.path.join(
+                    preprocess_config["path"]["preprocessed_path"], "speakers.json"
+                ),
+                "r",
+            ) as f:
+                n_speaker = len(json.load(f))
+            self.speaker_emb = nn.Embedding(
+                n_speaker,
+                model_config["transformer"]["encoder_hidden"],
+            )
+
+    def forward(
+        self,
+        speakers,
+        texts,
+        src_lens,
+        max_src_len,
+        mels=None,
+        mel_lens=None,
+        max_mel_len=None,
+        p_targets=None,
+        e_targets=None,
+        d_targets=None,
+        p_control=1.0,
+        e_control=1.0,
+        d_control=1.0,
+    ):
+        src_masks = get_mask_from_lengths(src_lens, max_src_len)
+        mel_masks = (
+            get_mask_from_lengths(mel_lens, max_mel_len)
+            if mel_lens is not None
+            else None
+        )
+
+        output = self.encoder(texts, src_masks)
+
+        if self.speaker_emb is not None:
+            output = output + self.speaker_emb(speakers).unsqueeze(1).expand(
+                -1, max_src_len, -1
+            )
+
+        (
+            output,
+            p_predictions,
+            e_predictions,
+            log_d_predictions,
+            d_rounded,
+            mel_lens,
+            mel_masks,
+        ) = self.variance_adaptor(
+            output,
+            src_masks,
+            mel_masks,
+            max_mel_len,
+            p_targets,
+            e_targets,
+            d_targets,
+            p_control,
+            e_control,
+            d_control,
+        )
+
+        output, mel_masks = self.decoder(output, mel_masks)
+        output = self.mel_linear(output)
+
+        postnet_output = self.postnet(output) + output
+
+        return (
+            output,
+            postnet_output,
+            p_predictions,
+            e_predictions,
+            log_d_predictions,
+            d_rounded,
+            src_masks,
+            mel_masks,
+            src_lens,
+            mel_lens,
+        )

model/loss.py

@@ -0,0 +1,92 @@
+import torch
+import torch.nn as nn
+
+
+class FastSpeech2Loss(nn.Module):
+    """ FastSpeech2 Loss """
+
+    def __init__(self, preprocess_config, model_config):
+        super(FastSpeech2Loss, self).__init__()
+        self.pitch_feature_level = preprocess_config["preprocessing"]["pitch"][
+            "feature"
+        ]
+        self.energy_feature_level = preprocess_config["preprocessing"]["energy"][
+            "feature"
+        ]
+        self.mse_loss = nn.MSELoss()
+        self.mae_loss = nn.L1Loss()
+
+    def forward(self, inputs, predictions):
+        (
+            mel_targets,
+            _,
+            _,
+            pitch_targets,
+            energy_targets,
+            duration_targets,
+        ) = inputs[6:]
+        (
+            mel_predictions,
+            postnet_mel_predictions,
+            pitch_predictions,
+            energy_predictions,
+            log_duration_predictions,
+            _,
+            src_masks,
+            mel_masks,
+            _,
+            _,
+        ) = predictions
+        src_masks = ~src_masks
+        mel_masks = ~mel_masks
+        log_duration_targets = torch.log(duration_targets.float() + 1)
+        mel_targets = mel_targets[:, : mel_masks.shape[1], :]
+        mel_masks = mel_masks[:, :mel_masks.shape[1]]
+
+        log_duration_targets.requires_grad = False
+        pitch_targets.requires_grad = False
+        energy_targets.requires_grad = False
+        mel_targets.requires_grad = False
+
+        if self.pitch_feature_level == "phoneme_level":
+            pitch_predictions = pitch_predictions.masked_select(src_masks)
+            pitch_targets = pitch_targets.masked_select(src_masks)
+        elif self.pitch_feature_level == "frame_level":
+            pitch_predictions = pitch_predictions.masked_select(mel_masks)
+            pitch_targets = pitch_targets.masked_select(mel_masks)
+
+        if self.energy_feature_level == "phoneme_level":
+            energy_predictions = energy_predictions.masked_select(src_masks)
+            energy_targets = energy_targets.masked_select(src_masks)
+        if self.energy_feature_level == "frame_level":
+            energy_predictions = energy_predictions.masked_select(mel_masks)
+            energy_targets = energy_targets.masked_select(mel_masks)
+
+        log_duration_predictions = log_duration_predictions.masked_select(src_masks)
+        log_duration_targets = log_duration_targets.masked_select(src_masks)
+
+        mel_predictions = mel_predictions.masked_select(mel_masks.unsqueeze(-1))
+        postnet_mel_predictions = postnet_mel_predictions.masked_select(
+            mel_masks.unsqueeze(-1)
+        )
+        mel_targets = mel_targets.masked_select(mel_masks.unsqueeze(-1))
+
+        mel_loss = self.mae_loss(mel_predictions, mel_targets)
+        postnet_mel_loss = self.mae_loss(postnet_mel_predictions, mel_targets)
+
+        pitch_loss = self.mse_loss(pitch_predictions, pitch_targets)
+        energy_loss = self.mse_loss(energy_predictions, energy_targets)
+        duration_loss = self.mse_loss(log_duration_predictions, log_duration_targets)
+
+        total_loss = (
+            mel_loss + postnet_mel_loss + duration_loss + pitch_loss + energy_loss
+        )
+
+        return (
+            total_loss,
+            mel_loss,
+            postnet_mel_loss,
+            pitch_loss,
+            energy_loss,
+            duration_loss,
+        )

model/modules.py

@@ -0,0 +1,296 @@
+import os
+import json
+import copy
+import math
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+import numpy as np
+import torch.nn.functional as F
+
+from utils.tools import get_mask_from_lengths, pad
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+class VarianceAdaptor(nn.Module):
+    """Variance Adaptor"""
+
+    def __init__(self, preprocess_config, model_config):
+        super(VarianceAdaptor, self).__init__()
+        self.duration_predictor = VariancePredictor(model_config)
+        self.length_regulator = LengthRegulator()
+        self.pitch_predictor = VariancePredictor(model_config)
+        self.energy_predictor = VariancePredictor(model_config)
+
+        self.pitch_feature_level = preprocess_config["preprocessing"]["pitch"][
+            "feature"
+        ]
+        self.energy_feature_level = preprocess_config["preprocessing"]["energy"][
+            "feature"
+        ]
+        assert self.pitch_feature_level in ["phoneme_level", "frame_level"]
+        assert self.energy_feature_level in ["phoneme_level", "frame_level"]
+
+        pitch_quantization = model_config["variance_embedding"]["pitch_quantization"]
+        energy_quantization = model_config["variance_embedding"]["energy_quantization"]
+        n_bins = model_config["variance_embedding"]["n_bins"]
+        assert pitch_quantization in ["linear", "log"]
+        assert energy_quantization in ["linear", "log"]
+        with open(
+            os.path.join(preprocess_config["path"]["preprocessed_path"], "stats.json")
+        ) as f:
+            stats = json.load(f)
+            pitch_min, pitch_max = stats["pitch"][:2]
+            energy_min, energy_max = stats["energy"][:2]
+
+        if pitch_quantization == "log":
+            self.pitch_bins = nn.Parameter(
+                torch.exp(
+                    torch.linspace(np.log(pitch_min), np.log(pitch_max), n_bins - 1)
+                ),
+                requires_grad=False,
+            )
+        else:
+            self.pitch_bins = nn.Parameter(
+                torch.linspace(pitch_min, pitch_max, n_bins - 1),
+                requires_grad=False,
+            )
+        if energy_quantization == "log":
+            self.energy_bins = nn.Parameter(
+                torch.exp(
+                    torch.linspace(np.log(energy_min), np.log(energy_max), n_bins - 1)
+                ),
+                requires_grad=False,
+            )
+        else:
+            self.energy_bins = nn.Parameter(
+                torch.linspace(energy_min, energy_max, n_bins - 1),
+                requires_grad=False,
+            )
+
+        self.pitch_embedding = nn.Embedding(
+            n_bins, model_config["transformer"]["encoder_hidden"]
+        )
+        self.energy_embedding = nn.Embedding(
+            n_bins, model_config["transformer"]["encoder_hidden"]
+        )
+
+    def get_pitch_embedding(self, x, target, mask, control):
+        prediction = self.pitch_predictor(x, mask)
+        if target is not None:
+            embedding = self.pitch_embedding(torch.bucketize(target, self.pitch_bins))
+        else:
+            prediction = prediction * control
+            embedding = self.pitch_embedding(
+                torch.bucketize(prediction, self.pitch_bins)
+            )
+        return prediction, embedding
+
+    def get_energy_embedding(self, x, target, mask, control):
+        prediction = self.energy_predictor(x, mask)
+        if target is not None:
+            embedding = self.energy_embedding(torch.bucketize(target, self.energy_bins))
+        else:
+            prediction = prediction * control
+            embedding = self.energy_embedding(
+                torch.bucketize(prediction, self.energy_bins)
+            )
+        return prediction, embedding
+
+    def forward(
+        self,
+        x,
+        src_mask,
+        mel_mask=None,
+        max_len=None,
+        pitch_target=None,
+        energy_target=None,
+        duration_target=None,
+        p_control=1.0,
+        e_control=1.0,
+        d_control=1.0,
+    ):
+
+        log_duration_prediction = self.duration_predictor(x, src_mask)
+        if self.pitch_feature_level == "phoneme_level":
+            pitch_prediction, pitch_embedding = self.get_pitch_embedding(
+                x, pitch_target, src_mask, p_control
+            )
+            x = x + pitch_embedding
+        if self.energy_feature_level == "phoneme_level":
+            energy_prediction, energy_embedding = self.get_energy_embedding(
+                x, energy_target, src_mask, e_control
+            )
+            x = x + energy_embedding
+
+        if duration_target is not None:
+            x, mel_len = self.length_regulator(x, duration_target, max_len)
+            duration_rounded = duration_target
+        else:
+            duration_rounded = torch.clamp(
+                (torch.round(torch.exp(log_duration_prediction) - 1) * d_control),
+                min=0,
+            )
+            x, mel_len = self.length_regulator(x, duration_rounded, max_len)
+            mel_mask = get_mask_from_lengths(mel_len)
+
+        if self.pitch_feature_level == "frame_level":
+            pitch_prediction, pitch_embedding = self.get_pitch_embedding(
+                x, pitch_target, mel_mask, p_control
+            )
+            x = x + pitch_embedding
+        if self.energy_feature_level == "frame_level":
+            energy_prediction, energy_embedding = self.get_energy_embedding(
+                x, energy_target, mel_mask, p_control
+            )
+            x = x + energy_embedding
+
+        return (
+            x,
+            pitch_prediction,
+            energy_prediction,
+            log_duration_prediction,
+            duration_rounded,
+            mel_len,
+            mel_mask,
+        )
+
+
+class LengthRegulator(nn.Module):
+    """Length Regulator"""
+
+    def __init__(self):
+        super(LengthRegulator, self).__init__()
+
+    def LR(self, x, duration, max_len):
+        output = list()
+        mel_len = list()
+        for batch, expand_target in zip(x, duration):
+            expanded = self.expand(batch, expand_target)
+            output.append(expanded)
+            mel_len.append(expanded.shape[0])
+
+        if max_len is not None:
+            output = pad(output, max_len)
+        else:
+            output = pad(output)
+
+        return output, torch.LongTensor(mel_len).to(device)
+
+    def expand(self, batch, predicted):
+        out = list()
+
+        for i, vec in enumerate(batch):
+            expand_size = predicted[i].item()
+            out.append(vec.expand(max(int(expand_size), 0), -1))
+        out = torch.cat(out, 0)
+
+        return out
+
+    def forward(self, x, duration, max_len):
+        output, mel_len = self.LR(x, duration, max_len)
+        return output, mel_len
+
+
+class VariancePredictor(nn.Module):
+    """Duration, Pitch and Energy Predictor"""
+
+    def __init__(self, model_config):
+        super(VariancePredictor, self).__init__()
+
+        self.input_size = model_config["transformer"]["encoder_hidden"]
+        self.filter_size = model_config["variance_predictor"]["filter_size"]
+        self.kernel = model_config["variance_predictor"]["kernel_size"]
+        self.conv_output_size = model_config["variance_predictor"]["filter_size"]
+        self.dropout = model_config["variance_predictor"]["dropout"]
+
+        self.conv_layer = nn.Sequential(
+            OrderedDict(
+                [
+                    (
+                        "conv1d_1",
+                        Conv(
+                            self.input_size,
+                            self.filter_size,
+                            kernel_size=self.kernel,
+                            padding=(self.kernel - 1) // 2,
+                        ),
+                    ),
+                    ("relu_1", nn.ReLU()),
+                    ("layer_norm_1", nn.LayerNorm(self.filter_size)),
+                    ("dropout_1", nn.Dropout(self.dropout)),
+                    (
+                        "conv1d_2",
+                        Conv(
+                            self.filter_size,
+                            self.filter_size,
+                            kernel_size=self.kernel,
+                            padding=1,
+                        ),
+                    ),
+                    ("relu_2", nn.ReLU()),
+                    ("layer_norm_2", nn.LayerNorm(self.filter_size)),
+                    ("dropout_2", nn.Dropout(self.dropout)),
+                ]
+            )
+        )
+
+        self.linear_layer = nn.Linear(self.conv_output_size, 1)
+
+    def forward(self, encoder_output, mask):
+        out = self.conv_layer(encoder_output)
+        out = self.linear_layer(out)
+        out = out.squeeze(-1)
+
+        if mask is not None:
+            out = out.masked_fill(mask, 0.0)
+
+        return out
+
+
+class Conv(nn.Module):
+    """
+    Convolution Module
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size=1,
+        stride=1,
+        padding=0,
+        dilation=1,
+        bias=True,
+        w_init="linear",
+    ):
+        """
+        :param in_channels: dimension of input
+        :param out_channels: dimension of output
+        :param kernel_size: size of kernel
+        :param stride: size of stride
+        :param padding: size of padding
+        :param dilation: dilation rate
+        :param bias: boolean. if True, bias is included.
+        :param w_init: str. weight inits with xavier initialization.
+        """
+        super(Conv, self).__init__()
+
+        self.conv = nn.Conv1d(
+            in_channels,
+            out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            bias=bias,
+        )
+
+    def forward(self, x):
+        x = x.contiguous().transpose(1, 2)
+        x = self.conv(x)
+        x = x.contiguous().transpose(1, 2)
+
+        return x