Initial commit

IMSToucan/.gitignore

@@ -0,0 +1,20 @@
+.idea
+*.pyc
+*.png
+*.pdf
+tensorboard_logs
+Corpora
+Models
+*_graph
+*.out
+*.wav
+audios/notes.txt
+audios/
+*playground*
+apex/
+pretrained_models/
+*.json
+.tmp/
+.vscode/
+split/
+singing/

IMSToucan/InferenceInterfaces/AnonFastSpeech2.py

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+import librosa.display as lbd
+import matplotlib.pyplot as plt
+import soundfile
+import torch
+
+from .InferenceArchitectures.InferenceFastSpeech2 import FastSpeech2
+from .InferenceArchitectures.InferenceHiFiGAN import HiFiGANGenerator
+from ..Preprocessing.ArticulatoryCombinedTextFrontend import ArticulatoryCombinedTextFrontend
+from ..Preprocessing.ArticulatoryCombinedTextFrontend import get_language_id
+
+
+class AnonFastSpeech2(torch.nn.Module):
+
+    def __init__(self, device: str, path_to_hifigan_model: str, path_to_fastspeech_model: str):
+        """
+        Args:
+            device: Device to run on. CPU is feasible, still faster than real-time, but a GPU is significantly faster.
+            path_to_hifigan_model: Path to the vocoder model, including filename and suffix.
+            path_to_fastspeech_model: Path to the synthesis model, including filename and suffix.
+
+        """
+        super().__init__()
+        language = "en"
+        self.device = device
+        self.text2phone = ArticulatoryCombinedTextFrontend(language=language, add_silence_to_end=True)
+        checkpoint = torch.load(path_to_fastspeech_model, map_location='cpu')
+        self.phone2mel = FastSpeech2(weights=checkpoint["model"], lang_embs=None).to(torch.device(device))
+        self.mel2wav = HiFiGANGenerator(path_to_weights=path_to_hifigan_model).to(torch.device(device))
+        self.default_utterance_embedding = checkpoint["default_emb"].to(self.device)
+        self.phone2mel.eval()
+        self.mel2wav.eval()
+        self.lang_id = get_language_id(language)
+        self.to(torch.device(device))
+
+    def forward(self, text, view=False, text_is_phonemes=False):
+        """
+        Args:
+            text: The text that the TTS should convert to speech
+            view: Boolean flag whether to produce and display a graphic showing the generated audio
+            text_is_phonemes: Boolean flag whether the text parameter contains phonemes (True) or graphemes (False)
+
+        Returns:
+            48kHz waveform as 1d tensor
+
+        """
+        with torch.no_grad():
+            phones = self.text2phone.string_to_tensor(text, input_phonemes=text_is_phonemes).to(torch.device(self.device))
+            mel, durations, pitch, energy = self.phone2mel(phones,
+                                                           return_duration_pitch_energy=True,
+                                                           utterance_embedding=self.default_utterance_embedding)
+            mel = mel.transpose(0, 1)
+            wave = self.mel2wav(mel)
+        if view:
+            from Utility.utils import cumsum_durations
+            fig, ax = plt.subplots(nrows=2, ncols=1)
+            ax[0].plot(wave.cpu().numpy())
+            lbd.specshow(mel.cpu().numpy(),
+                         ax=ax[1],
+                         sr=16000,
+                         cmap='GnBu',
+                         y_axis='mel',
+                         x_axis=None,
+                         hop_length=256)
+            ax[0].yaxis.set_visible(False)
+            ax[1].yaxis.set_visible(False)
+            duration_splits, label_positions = cumsum_durations(durations.cpu().numpy())
+            ax[1].set_xticks(duration_splits, minor=True)
+            ax[1].xaxis.grid(True, which='minor')
+            ax[1].set_xticks(label_positions, minor=False)
+            ax[1].set_xticklabels(self.text2phone.get_phone_string(text))
+            ax[0].set_title(text)
+            plt.subplots_adjust(left=0.05, bottom=0.1, right=0.95, top=.9, wspace=0.0, hspace=0.0)
+            plt.show()
+        return wave
+
+    def anonymize_to_file(self, text: str, text_is_phonemes: bool, target_speaker_embedding: torch.tensor, path_to_result_file: str):
+        """
+        Args:
+            text: The text that the TTS should convert to speech
+            text_is_phonemes: Boolean flag whether the text parameter contains phonemes (True) or graphemes (False)
+            target_speaker_embedding: The speaker embedding that should be used for the produced speech
+            path_to_result_file: The path to the location where the resulting speech should be saved (including the filename and .wav suffix)
+
+        """
+
+        assert text.strip() != ""
+        assert path_to_result_file.endswith(".wav")
+
+        self.default_utterance_embedding = target_speaker_embedding.to(self.device)
+        wav = self(text=text, text_is_phonemes=text_is_phonemes)
+        soundfile.write(file=path_to_result_file, data=wav.cpu().numpy(), samplerate=48000)

IMSToucan/InferenceInterfaces/InferenceArchitectures/InferenceFastSpeech2.py

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+from abc import ABC
+
+import torch
+
+from ...Layers.Conformer import Conformer
+from ...Layers.DurationPredictor import DurationPredictor
+from ...Layers.LengthRegulator import LengthRegulator
+from ...Layers.PostNet import PostNet
+from ...Layers.VariancePredictor import VariancePredictor
+from ...Utility.utils import make_non_pad_mask
+from ...Utility.utils import make_pad_mask
+
+
+class FastSpeech2(torch.nn.Module, ABC):
+
+    def __init__(self,  # network structure related
+                 weights,
+                 idim=66,
+                 odim=80,
+                 adim=384,
+                 aheads=4,
+                 elayers=6,
+                 eunits=1536,
+                 dlayers=6,
+                 dunits=1536,
+                 postnet_layers=5,
+                 postnet_chans=256,
+                 postnet_filts=5,
+                 positionwise_conv_kernel_size=1,
+                 use_scaled_pos_enc=True,
+                 use_batch_norm=True,
+                 encoder_normalize_before=True,
+                 decoder_normalize_before=True,
+                 encoder_concat_after=False,
+                 decoder_concat_after=False,
+                 reduction_factor=1,
+                 # encoder / decoder
+                 use_macaron_style_in_conformer=True,
+                 use_cnn_in_conformer=True,
+                 conformer_enc_kernel_size=7,
+                 conformer_dec_kernel_size=31,
+                 # duration predictor
+                 duration_predictor_layers=2,
+                 duration_predictor_chans=256,
+                 duration_predictor_kernel_size=3,
+                 # energy predictor
+                 energy_predictor_layers=2,
+                 energy_predictor_chans=256,
+                 energy_predictor_kernel_size=3,
+                 energy_predictor_dropout=0.5,
+                 energy_embed_kernel_size=1,
+                 energy_embed_dropout=0.0,
+                 stop_gradient_from_energy_predictor=True,
+                 # pitch predictor
+                 pitch_predictor_layers=5,
+                 pitch_predictor_chans=256,
+                 pitch_predictor_kernel_size=5,
+                 pitch_predictor_dropout=0.5,
+                 pitch_embed_kernel_size=1,
+                 pitch_embed_dropout=0.0,
+                 stop_gradient_from_pitch_predictor=True,
+                 # training related
+                 transformer_enc_dropout_rate=0.2,
+                 transformer_enc_positional_dropout_rate=0.2,
+                 transformer_enc_attn_dropout_rate=0.2,
+                 transformer_dec_dropout_rate=0.2,
+                 transformer_dec_positional_dropout_rate=0.2,
+                 transformer_dec_attn_dropout_rate=0.2,
+                 duration_predictor_dropout_rate=0.2,
+                 postnet_dropout_rate=0.5,
+                 # additional features
+                 utt_embed_dim=704,
+                 connect_utt_emb_at_encoder_out=True,
+                 lang_embs=100):
+        super().__init__()
+        self.idim = idim
+        self.odim = odim
+        self.reduction_factor = reduction_factor
+        self.stop_gradient_from_pitch_predictor = stop_gradient_from_pitch_predictor
+        self.stop_gradient_from_energy_predictor = stop_gradient_from_energy_predictor
+        self.use_scaled_pos_enc = use_scaled_pos_enc
+        embed = torch.nn.Sequential(torch.nn.Linear(idim, 100),
+                                    torch.nn.Tanh(),
+                                    torch.nn.Linear(100, adim))
+        self.encoder = Conformer(idim=idim, attention_dim=adim, attention_heads=aheads, linear_units=eunits, num_blocks=elayers,
+                                 input_layer=embed, dropout_rate=transformer_enc_dropout_rate,
+                                 positional_dropout_rate=transformer_enc_positional_dropout_rate, attention_dropout_rate=transformer_enc_attn_dropout_rate,
+                                 normalize_before=encoder_normalize_before, concat_after=encoder_concat_after,
+                                 positionwise_conv_kernel_size=positionwise_conv_kernel_size, macaron_style=use_macaron_style_in_conformer,
+                                 use_cnn_module=use_cnn_in_conformer, cnn_module_kernel=conformer_enc_kernel_size, zero_triu=False,
+                                 utt_embed=utt_embed_dim, connect_utt_emb_at_encoder_out=connect_utt_emb_at_encoder_out, lang_embs=lang_embs)
+        self.duration_predictor = DurationPredictor(idim=adim, n_layers=duration_predictor_layers,
+                                                    n_chans=duration_predictor_chans,
+                                                    kernel_size=duration_predictor_kernel_size,
+                                                    dropout_rate=duration_predictor_dropout_rate, )
+        self.pitch_predictor = VariancePredictor(idim=adim, n_layers=pitch_predictor_layers,
+                                                 n_chans=pitch_predictor_chans,
+                                                 kernel_size=pitch_predictor_kernel_size,
+                                                 dropout_rate=pitch_predictor_dropout)
+        self.pitch_embed = torch.nn.Sequential(torch.nn.Conv1d(in_channels=1, out_channels=adim,
+                                                               kernel_size=pitch_embed_kernel_size,
+                                                               padding=(pitch_embed_kernel_size - 1) // 2),
+                                               torch.nn.Dropout(pitch_embed_dropout))
+        self.energy_predictor = VariancePredictor(idim=adim, n_layers=energy_predictor_layers,
+                                                  n_chans=energy_predictor_chans,
+                                                  kernel_size=energy_predictor_kernel_size,
+                                                  dropout_rate=energy_predictor_dropout)
+        self.energy_embed = torch.nn.Sequential(torch.nn.Conv1d(in_channels=1, out_channels=adim,
+                                                                kernel_size=energy_embed_kernel_size,
+                                                                padding=(energy_embed_kernel_size - 1) // 2),
+                                                torch.nn.Dropout(energy_embed_dropout))
+        self.length_regulator = LengthRegulator()
+        self.decoder = Conformer(idim=0,
+                                 attention_dim=adim,
+                                 attention_heads=aheads,
+                                 linear_units=dunits,
+                                 num_blocks=dlayers,
+                                 input_layer=None,
+                                 dropout_rate=transformer_dec_dropout_rate,
+                                 positional_dropout_rate=transformer_dec_positional_dropout_rate,
+                                 attention_dropout_rate=transformer_dec_attn_dropout_rate,
+                                 normalize_before=decoder_normalize_before,
+                                 concat_after=decoder_concat_after,
+                                 positionwise_conv_kernel_size=positionwise_conv_kernel_size,
+                                 macaron_style=use_macaron_style_in_conformer,
+                                 use_cnn_module=use_cnn_in_conformer,
+                                 cnn_module_kernel=conformer_dec_kernel_size)
+        self.feat_out = torch.nn.Linear(adim, odim * reduction_factor)
+        self.postnet = PostNet(idim=idim,
+                               odim=odim,
+                               n_layers=postnet_layers,
+                               n_chans=postnet_chans,
+                               n_filts=postnet_filts,
+                               use_batch_norm=use_batch_norm,
+                               dropout_rate=postnet_dropout_rate)
+        self.load_state_dict(weights)
+
+    def _forward(self, text_tensors, text_lens, gold_speech=None, speech_lens=None,
+                 gold_durations=None, gold_pitch=None, gold_energy=None,
+                 is_inference=False, alpha=1.0, utterance_embedding=None, lang_ids=None):
+        # forward encoder
+        text_masks = self._source_mask(text_lens)
+
+        encoded_texts, _ = self.encoder(text_tensors, text_masks, utterance_embedding=utterance_embedding, lang_ids=lang_ids)  # (B, Tmax, adim)
+
+        # forward duration predictor and variance predictors
+        duration_masks = make_pad_mask(text_lens, device=text_lens.device)
+
+        if self.stop_gradient_from_pitch_predictor:
+            pitch_predictions = self.pitch_predictor(encoded_texts.detach(), duration_masks.unsqueeze(-1))
+        else:
+            pitch_predictions = self.pitch_predictor(encoded_texts, duration_masks.unsqueeze(-1))
+
+        if self.stop_gradient_from_energy_predictor:
+            energy_predictions = self.energy_predictor(encoded_texts.detach(), duration_masks.unsqueeze(-1))
+        else:
+            energy_predictions = self.energy_predictor(encoded_texts, duration_masks.unsqueeze(-1))
+
+        if is_inference:
+            if gold_durations is not None:
+                duration_predictions = gold_durations
+            else:
+                duration_predictions = self.duration_predictor.inference(encoded_texts, duration_masks)
+            if gold_pitch is not None:
+                pitch_predictions = gold_pitch
+            if gold_energy is not None:
+                energy_predictions = gold_energy
+            pitch_embeddings = self.pitch_embed(pitch_predictions.transpose(1, 2)).transpose(1, 2)
+            energy_embeddings = self.energy_embed(energy_predictions.transpose(1, 2)).transpose(1, 2)
+            encoded_texts = encoded_texts + energy_embeddings + pitch_embeddings
+            encoded_texts = self.length_regulator(encoded_texts, duration_predictions, alpha)
+        else:
+            duration_predictions = self.duration_predictor(encoded_texts, duration_masks)
+
+            # use groundtruth in training
+            pitch_embeddings = self.pitch_embed(gold_pitch.transpose(1, 2)).transpose(1, 2)
+            energy_embeddings = self.energy_embed(gold_energy.transpose(1, 2)).transpose(1, 2)
+            encoded_texts = encoded_texts + energy_embeddings + pitch_embeddings
+            encoded_texts = self.length_regulator(encoded_texts, gold_durations)  # (B, Lmax, adim)
+
+        # forward decoder
+        if speech_lens is not None and not is_inference:
+            if self.reduction_factor > 1:
+                olens_in = speech_lens.new([olen // self.reduction_factor for olen in speech_lens])
+            else:
+                olens_in = speech_lens
+            h_masks = self._source_mask(olens_in)
+        else:
+            h_masks = None
+        zs, _ = self.decoder(encoded_texts, h_masks)  # (B, Lmax, adim)
+        before_outs = self.feat_out(zs).view(zs.size(0), -1, self.odim)  # (B, Lmax, odim)
+
+        # postnet -> (B, Lmax//r * r, odim)
+        after_outs = before_outs + self.postnet(before_outs.transpose(1, 2)).transpose(1, 2)
+
+        return before_outs, after_outs, duration_predictions, pitch_predictions, energy_predictions
+
+    @torch.no_grad()
+    def forward(self,
+                text,
+                speech=None,
+                durations=None,
+                pitch=None,
+                energy=None,
+                utterance_embedding=None,
+                return_duration_pitch_energy=False,
+                lang_id=None):
+        """
+        Generate the sequence of features given the sequences of characters.
+
+        Args:
+            text: Input sequence of characters
+            speech: Feature sequence to extract style
+            durations: Groundtruth of duration
+            pitch: Groundtruth of token-averaged pitch
+            energy: Groundtruth of token-averaged energy
+            return_duration_pitch_energy: whether to return the list of predicted durations for nicer plotting
+            utterance_embedding: embedding of utterance wide parameters
+
+        Returns:
+            Mel Spectrogram
+
+        """
+        self.eval()
+        # setup batch axis
+        ilens = torch.tensor([text.shape[0]], dtype=torch.long, device=text.device)
+        if speech is not None:
+            gold_speech = speech.unsqueeze(0)
+        else:
+            gold_speech = None
+        if durations is not None:
+            durations = durations.unsqueeze(0)
+        if pitch is not None:
+            pitch = pitch.unsqueeze(0)
+        if energy is not None:
+            energy = energy.unsqueeze(0)
+        if lang_id is not None:
+            lang_id = lang_id.unsqueeze(0)
+
+        before_outs, after_outs, d_outs, pitch_predictions, energy_predictions = self._forward(text.unsqueeze(0),
+                                                                                               ilens,
+                                                                                               gold_speech=gold_speech,
+                                                                                               gold_durations=durations,
+                                                                                               is_inference=True,
+                                                                                               gold_pitch=pitch,
+                                                                                               gold_energy=energy,
+                                                                                               utterance_embedding=utterance_embedding.unsqueeze(0),
+                                                                                               lang_ids=lang_id)
+        self.train()
+        if return_duration_pitch_energy:
+            return after_outs[0], d_outs[0], pitch_predictions[0], energy_predictions[0]
+        return after_outs[0]
+
+    def _source_mask(self, ilens):
+        x_masks = make_non_pad_mask(ilens).to(next(self.parameters()).device)
+        return x_masks.unsqueeze(-2)

IMSToucan/InferenceInterfaces/InferenceArchitectures/InferenceHiFiGAN.py

@@ -0,0 +1,91 @@
+import torch
+
+from ...Layers.ResidualBlock import HiFiGANResidualBlock as ResidualBlock
+
+
+class HiFiGANGenerator(torch.nn.Module):
+
+    def __init__(self,
+                 path_to_weights,
+                 in_channels=80,
+                 out_channels=1,
+                 channels=512,
+                 kernel_size=7,
+                 upsample_scales=(8, 6, 4, 4),
+                 upsample_kernel_sizes=(16, 12, 8, 8),
+                 resblock_kernel_sizes=(3, 7, 11),
+                 resblock_dilations=[(1, 3, 5), (1, 3, 5), (1, 3, 5)],
+                 use_additional_convs=True,
+                 bias=True,
+                 nonlinear_activation="LeakyReLU",
+                 nonlinear_activation_params={"negative_slope": 0.1},
+                 use_weight_norm=True, ):
+        super().__init__()
+        assert kernel_size % 2 == 1, "Kernal size must be odd number."
+        assert len(upsample_scales) == len(upsample_kernel_sizes)
+        assert len(resblock_dilations) == len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_kernel_sizes)
+        self.num_blocks = len(resblock_kernel_sizes)
+        self.input_conv = torch.nn.Conv1d(in_channels,
+                                          channels,
+                                          kernel_size,
+                                          1,
+                                          padding=(kernel_size - 1) // 2, )
+        self.upsamples = torch.nn.ModuleList()
+        self.blocks = torch.nn.ModuleList()
+        for i in range(len(upsample_kernel_sizes)):
+            self.upsamples += [
+                torch.nn.Sequential(getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
+                                    torch.nn.ConvTranspose1d(channels // (2 ** i),
+                                                             channels // (2 ** (i + 1)),
+                                                             upsample_kernel_sizes[i],
+                                                             upsample_scales[i],
+                                                             padding=(upsample_kernel_sizes[i] - upsample_scales[i]) // 2, ), )]
+            for j in range(len(resblock_kernel_sizes)):
+                self.blocks += [ResidualBlock(kernel_size=resblock_kernel_sizes[j],
+                                              channels=channels // (2 ** (i + 1)),
+                                              dilations=resblock_dilations[j],
+                                              bias=bias,
+                                              use_additional_convs=use_additional_convs,
+                                              nonlinear_activation=nonlinear_activation,
+                                              nonlinear_activation_params=nonlinear_activation_params, )]
+        self.output_conv = torch.nn.Sequential(
+            torch.nn.LeakyReLU(),
+            torch.nn.Conv1d(channels // (2 ** (i + 1)),
+                            out_channels,
+                            kernel_size,
+                            1,
+                            padding=(kernel_size - 1) // 2, ),
+            torch.nn.Tanh(), )
+        if use_weight_norm:
+            self.apply_weight_norm()
+        self.load_state_dict(torch.load(path_to_weights, map_location='cpu')["generator"])
+
+    def forward(self, c, normalize_before=False):
+        if normalize_before:
+            c = (c - self.mean) / self.scale
+        c = self.input_conv(c.unsqueeze(0))
+        for i in range(self.num_upsamples):
+            c = self.upsamples[i](c)
+            cs = 0.0  # initialize
+            for j in range(self.num_blocks):
+                cs = cs + self.blocks[i * self.num_blocks + j](c)
+            c = cs / self.num_blocks
+        c = self.output_conv(c)
+        return c.squeeze(0).squeeze(0)
+
+    def remove_weight_norm(self):
+        def _remove_weight_norm(m):
+            try:
+                torch.nn.utils.remove_weight_norm(m)
+            except ValueError:
+                return
+
+        self.apply(_remove_weight_norm)
+
+    def apply_weight_norm(self):
+        def _apply_weight_norm(m):
+            if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.ConvTranspose1d):
+                torch.nn.utils.weight_norm(m)
+
+        self.apply(_apply_weight_norm)

IMSToucan/LICENSE

@@ -0,0 +1,202 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
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+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
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+
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+   Copyright for most of the PyTorch modules: 2017 Johns Hopkins University (Shinji Watanabe)
+   Copyright for the rest: 2021 University of Stuttgart (Florian Lux)
+
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+   you may not use this file except in compliance with the License.
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+
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+
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IMSToucan/Layers/Attention.py

@@ -0,0 +1,324 @@
+# Written by Shigeki Karita, 2019
+# Published under Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+# Adapted by Florian Lux, 2021
+
+"""Multi-Head Attention layer definition."""
+
+import math
+
+import numpy
+import torch
+from torch import nn
+
+from ..Utility.utils import make_non_pad_mask
+
+
+class MultiHeadedAttention(nn.Module):
+    """
+    Multi-Head Attention layer.
+
+    Args:
+        n_head (int): The number of heads.
+        n_feat (int): The number of features.
+        dropout_rate (float): Dropout rate.
+    """
+
+    def __init__(self, n_head, n_feat, dropout_rate):
+        """
+        Construct an MultiHeadedAttention object.
+        """
+        super(MultiHeadedAttention, self).__init__()
+        assert n_feat % n_head == 0
+        # We assume d_v always equals d_k
+        self.d_k = n_feat // n_head
+        self.h = n_head
+        self.linear_q = nn.Linear(n_feat, n_feat)
+        self.linear_k = nn.Linear(n_feat, n_feat)
+        self.linear_v = nn.Linear(n_feat, n_feat)
+        self.linear_out = nn.Linear(n_feat, n_feat)
+        self.attn = None
+        self.dropout = nn.Dropout(p=dropout_rate)
+
+    def forward_qkv(self, query, key, value):
+        """
+        Transform query, key and value.
+
+        Args:
+            query (torch.Tensor): Query tensor (#batch, time1, size).
+            key (torch.Tensor): Key tensor (#batch, time2, size).
+            value (torch.Tensor): Value tensor (#batch, time2, size).
+
+        Returns:
+            torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k).
+            torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k).
+            torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k).
+        """
+        n_batch = query.size(0)
+        q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
+        k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
+        v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
+        q = q.transpose(1, 2)  # (batch, head, time1, d_k)
+        k = k.transpose(1, 2)  # (batch, head, time2, d_k)
+        v = v.transpose(1, 2)  # (batch, head, time2, d_k)
+
+        return q, k, v
+
+    def forward_attention(self, value, scores, mask):
+        """
+        Compute attention context vector.
+
+        Args:
+            value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k).
+            scores (torch.Tensor): Attention score (#batch, n_head, time1, time2).
+            mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2).
+
+        Returns:
+            torch.Tensor: Transformed value (#batch, time1, d_model)
+                weighted by the attention score (#batch, time1, time2).
+        """
+        n_batch = value.size(0)
+        if mask is not None:
+            mask = mask.unsqueeze(1).eq(0)  # (batch, 1, *, time2)
+            min_value = float(numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min)
+            scores = scores.masked_fill(mask, min_value)
+            self.attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0)  # (batch, head, time1, time2)
+        else:
+            self.attn = torch.softmax(scores, dim=-1)  # (batch, head, time1, time2)
+
+        p_attn = self.dropout(self.attn)
+        x = torch.matmul(p_attn, value)  # (batch, head, time1, d_k)
+        x = (x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k))  # (batch, time1, d_model)
+
+        return self.linear_out(x)  # (batch, time1, d_model)
+
+    def forward(self, query, key, value, mask):
+        """
+        Compute scaled dot product attention.
+
+        Args:
+            query (torch.Tensor): Query tensor (#batch, time1, size).
+            key (torch.Tensor): Key tensor (#batch, time2, size).
+            value (torch.Tensor): Value tensor (#batch, time2, size).
+            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
+                (#batch, time1, time2).
+
+        Returns:
+            torch.Tensor: Output tensor (#batch, time1, d_model).
+        """
+        q, k, v = self.forward_qkv(query, key, value)
+        scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
+        return self.forward_attention(v, scores, mask)
+
+
+class RelPositionMultiHeadedAttention(MultiHeadedAttention):
+    """
+    Multi-Head Attention layer with relative position encoding.
+    Details can be found in https://github.com/espnet/espnet/pull/2816.
+    Paper: https://arxiv.org/abs/1901.02860
+    Args:
+        n_head (int): The number of heads.
+        n_feat (int): The number of features.
+        dropout_rate (float): Dropout rate.
+        zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
+    """
+
+    def __init__(self, n_head, n_feat, dropout_rate, zero_triu=False):
+        """Construct an RelPositionMultiHeadedAttention object."""
+        super().__init__(n_head, n_feat, dropout_rate)
+        self.zero_triu = zero_triu
+        # linear transformation for positional encoding
+        self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
+        # these two learnable bias are used in matrix c and matrix d
+        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
+        self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
+        self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
+        torch.nn.init.xavier_uniform_(self.pos_bias_u)
+        torch.nn.init.xavier_uniform_(self.pos_bias_v)
+
+    def rel_shift(self, x):
+        """
+        Compute relative positional encoding.
+        Args:
+            x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1).
+            time1 means the length of query vector.
+        Returns:
+            torch.Tensor: Output tensor.
+        """
+        zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype)
+        x_padded = torch.cat([zero_pad, x], dim=-1)
+
+        x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2))
+        x = x_padded[:, :, 1:].view_as(x)[:, :, :, : x.size(-1) // 2 + 1]  # only keep the positions from 0 to time2
+
+        if self.zero_triu:
+            ones = torch.ones((x.size(2), x.size(3)), device=x.device)
+            x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :]
+
+        return x
+
+    def forward(self, query, key, value, pos_emb, mask):
+        """
+        Compute 'Scaled Dot Product Attention' with rel. positional encoding.
+        Args:
+            query (torch.Tensor): Query tensor (#batch, time1, size).
+            key (torch.Tensor): Key tensor (#batch, time2, size).
+            value (torch.Tensor): Value tensor (#batch, time2, size).
+            pos_emb (torch.Tensor): Positional embedding tensor
+                (#batch, 2*time1-1, size).
+            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
+                (#batch, time1, time2).
+        Returns:
+            torch.Tensor: Output tensor (#batch, time1, d_model).
+        """
+        q, k, v = self.forward_qkv(query, key, value)
+        q = q.transpose(1, 2)  # (batch, time1, head, d_k)
+
+        n_batch_pos = pos_emb.size(0)
+        p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
+        p = p.transpose(1, 2)  # (batch, head, 2*time1-1, d_k)
+
+        # (batch, head, time1, d_k)
+        q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
+        # (batch, head, time1, d_k)
+        q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
+
+        # compute attention score
+        # first compute matrix a and matrix c
+        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
+        # (batch, head, time1, time2)
+        matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
+
+        # compute matrix b and matrix d
+        # (batch, head, time1, 2*time1-1)
+        matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
+        matrix_bd = self.rel_shift(matrix_bd)
+
+        scores = (matrix_ac + matrix_bd) / math.sqrt(self.d_k)  # (batch, head, time1, time2)
+
+        return self.forward_attention(v, scores, mask)
+
+
+class GuidedAttentionLoss(torch.nn.Module):
+    """
+    Guided attention loss function module.
+
+    This module calculates the guided attention loss described
+    in `Efficiently Trainable Text-to-Speech System Based
+    on Deep Convolutional Networks with Guided Attention`_,
+    which forces the attention to be diagonal.
+
+    .. _`Efficiently Trainable Text-to-Speech System
+        Based on Deep Convolutional Networks with Guided Attention`:
+        https://arxiv.org/abs/1710.08969
+    """
+
+    def __init__(self, sigma=0.4, alpha=1.0):
+        """
+        Initialize guided attention loss module.
+
+        Args:
+            sigma (float, optional): Standard deviation to control
+                how close attention to a diagonal.
+            alpha (float, optional): Scaling coefficient (lambda).
+            reset_always (bool, optional): Whether to always reset masks.
+        """
+        super(GuidedAttentionLoss, self).__init__()
+        self.sigma = sigma
+        self.alpha = alpha
+        self.guided_attn_masks = None
+        self.masks = None
+
+    def _reset_masks(self):
+        self.guided_attn_masks = None
+        self.masks = None
+
+    def forward(self, att_ws, ilens, olens):
+        """
+        Calculate forward propagation.
+
+        Args:
+            att_ws (Tensor): Batch of attention weights (B, T_max_out, T_max_in).
+            ilens (LongTensor): Batch of input lenghts (B,).
+            olens (LongTensor): Batch of output lenghts (B,).
+
+        Returns:
+            Tensor: Guided attention loss value.
+        """
+        self._reset_masks()
+        self.guided_attn_masks = self._make_guided_attention_masks(ilens, olens).to(att_ws.device)
+        self.masks = self._make_masks(ilens, olens).to(att_ws.device)
+        losses = self.guided_attn_masks * att_ws
+        loss = torch.mean(losses.masked_select(self.masks))
+        self._reset_masks()
+        return self.alpha * loss
+
+    def _make_guided_attention_masks(self, ilens, olens):
+        n_batches = len(ilens)
+        max_ilen = max(ilens)
+        max_olen = max(olens)
+        guided_attn_masks = torch.zeros((n_batches, max_olen, max_ilen), device=ilens.device)
+        for idx, (ilen, olen) in enumerate(zip(ilens, olens)):
+            guided_attn_masks[idx, :olen, :ilen] = self._make_guided_attention_mask(ilen, olen, self.sigma)
+        return guided_attn_masks
+
+    @staticmethod
+    def _make_guided_attention_mask(ilen, olen, sigma):
+        """
+        Make guided attention mask.
+        """
+        grid_x, grid_y = torch.meshgrid(torch.arange(olen, device=olen.device).float(), torch.arange(ilen, device=ilen.device).float())
+        return 1.0 - torch.exp(-((grid_y / ilen - grid_x / olen) ** 2) / (2 * (sigma ** 2)))
+
+    @staticmethod
+    def _make_masks(ilens, olens):
+        """
+        Make masks indicating non-padded part.
+
+        Args:
+            ilens (LongTensor or List): Batch of lengths (B,).
+            olens (LongTensor or List): Batch of lengths (B,).
+
+        Returns:
+            Tensor: Mask tensor indicating non-padded part.
+                    dtype=torch.uint8 in PyTorch 1.2-
+                    dtype=torch.bool in PyTorch 1.2+ (including 1.2)
+        """
+        in_masks = make_non_pad_mask(ilens, device=ilens.device)  # (B, T_in)
+        out_masks = make_non_pad_mask(olens, device=olens.device)  # (B, T_out)
+        return out_masks.unsqueeze(-1) & in_masks.unsqueeze(-2)  # (B, T_out, T_in)
+
+
+class GuidedMultiHeadAttentionLoss(GuidedAttentionLoss):
+    """
+    Guided attention loss function module for multi head attention.
+
+    Args:
+        sigma (float, optional): Standard deviation to control
+        how close attention to a diagonal.
+        alpha (float, optional): Scaling coefficient (lambda).
+        reset_always (bool, optional): Whether to always reset masks.
+    """
+
+    def forward(self, att_ws, ilens, olens):
+        """
+        Calculate forward propagation.
+
+        Args:
+            att_ws (Tensor):
+                Batch of multi head attention weights (B, H, T_max_out, T_max_in).
+            ilens (LongTensor): Batch of input lenghts (B,).
+            olens (LongTensor): Batch of output lenghts (B,).
+
+        Returns:
+            Tensor: Guided attention loss value.
+        """
+        if self.guided_attn_masks is None:
+            self.guided_attn_masks = (self._make_guided_attention_masks(ilens, olens).to(att_ws.device).unsqueeze(1))
+        if self.masks is None:
+            self.masks = self._make_masks(ilens, olens).to(att_ws.device).unsqueeze(1)
+        losses = self.guided_attn_masks * att_ws
+        loss = torch.mean(losses.masked_select(self.masks))
+        if self.reset_always:
+            self._reset_masks()
+
+        return self.alpha * loss

IMSToucan/Layers/Conformer.py

@@ -0,0 +1,144 @@
+"""
+Taken from ESPNet
+"""
+
+import torch
+import torch.nn.functional as F
+
+from .Attention import RelPositionMultiHeadedAttention
+from .Convolution import ConvolutionModule
+from .EncoderLayer import EncoderLayer
+from .LayerNorm import LayerNorm
+from .MultiLayeredConv1d import MultiLayeredConv1d
+from .MultiSequential import repeat
+from .PositionalEncoding import RelPositionalEncoding
+from .Swish import Swish
+
+
+class Conformer(torch.nn.Module):
+    """
+    Conformer encoder module.
+
+    Args:
+        idim (int): Input dimension.
+        attention_dim (int): Dimension of attention.
+        attention_heads (int): The number of heads of multi head attention.
+        linear_units (int): The number of units of position-wise feed forward.
+        num_blocks (int): The number of decoder blocks.
+        dropout_rate (float): Dropout rate.
+        positional_dropout_rate (float): Dropout rate after adding positional encoding.
+        attention_dropout_rate (float): Dropout rate in attention.
+        input_layer (Union[str, torch.nn.Module]): Input layer type.
+        normalize_before (bool): Whether to use layer_norm before the first block.
+        concat_after (bool): Whether to concat attention layer's input and output.
+            if True, additional linear will be applied.
+            i.e. x -> x + linear(concat(x, att(x)))
+            if False, no additional linear will be applied. i.e. x -> x + att(x)
+        positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear".
+        positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
+        macaron_style (bool): Whether to use macaron style for positionwise layer.
+        pos_enc_layer_type (str): Conformer positional encoding layer type.
+        selfattention_layer_type (str): Conformer attention layer type.
+        activation_type (str): Conformer activation function type.
+        use_cnn_module (bool): Whether to use convolution module.
+        cnn_module_kernel (int): Kernerl size of convolution module.
+        padding_idx (int): Padding idx for input_layer=embed.
+
+    """
+
+    def __init__(self, idim, attention_dim=256, attention_heads=4, linear_units=2048, num_blocks=6, dropout_rate=0.1, positional_dropout_rate=0.1,
+                 attention_dropout_rate=0.0, input_layer="conv2d", normalize_before=True, concat_after=False, positionwise_conv_kernel_size=1,
+                 macaron_style=False, use_cnn_module=False, cnn_module_kernel=31, zero_triu=False, utt_embed=None, connect_utt_emb_at_encoder_out=True,
+                 spk_emb_bottleneck_size=128, lang_embs=None):
+        super(Conformer, self).__init__()
+
+        activation = Swish()
+        self.conv_subsampling_factor = 1
+
+        if isinstance(input_layer, torch.nn.Module):
+            self.embed = input_layer
+            self.pos_enc = RelPositionalEncoding(attention_dim, positional_dropout_rate)
+        elif input_layer is None:
+            self.embed = None
+            self.pos_enc = torch.nn.Sequential(RelPositionalEncoding(attention_dim, positional_dropout_rate))
+        else:
+            raise ValueError("unknown input_layer: " + input_layer)
+
+        self.normalize_before = normalize_before
+
+        self.connect_utt_emb_at_encoder_out = connect_utt_emb_at_encoder_out
+        if utt_embed is not None:
+            self.hs_emb_projection = torch.nn.Linear(attention_dim + spk_emb_bottleneck_size, attention_dim)
+            # embedding projection derived from https://arxiv.org/pdf/1705.08947.pdf
+            self.embedding_projection = torch.nn.Sequential(torch.nn.Linear(utt_embed, spk_emb_bottleneck_size),
+                                                            torch.nn.Softsign())
+        if lang_embs is not None:
+            self.language_embedding = torch.nn.Embedding(num_embeddings=lang_embs, embedding_dim=attention_dim)
+
+        # self-attention module definition
+        encoder_selfattn_layer = RelPositionMultiHeadedAttention
+        encoder_selfattn_layer_args = (attention_heads, attention_dim, attention_dropout_rate, zero_triu)
+
+        # feed-forward module definition
+        positionwise_layer = MultiLayeredConv1d
+        positionwise_layer_args = (attention_dim, linear_units, positionwise_conv_kernel_size, dropout_rate,)
+
+        # convolution module definition
+        convolution_layer = ConvolutionModule
+        convolution_layer_args = (attention_dim, cnn_module_kernel, activation)
+
+        self.encoders = repeat(num_blocks, lambda lnum: EncoderLayer(attention_dim, encoder_selfattn_layer(*encoder_selfattn_layer_args),
+                                                                     positionwise_layer(*positionwise_layer_args),
+                                                                     positionwise_layer(*positionwise_layer_args) if macaron_style else None,
+                                                                     convolution_layer(*convolution_layer_args) if use_cnn_module else None, dropout_rate,
+                                                                     normalize_before, concat_after))
+        if self.normalize_before:
+            self.after_norm = LayerNorm(attention_dim)
+
+    def forward(self, xs, masks, utterance_embedding=None, lang_ids=None):
+        """
+        Encode input sequence.
+
+        Args:
+            utterance_embedding: embedding containing lots of conditioning signals
+            step: indicator for when to start updating the embedding function
+            xs (torch.Tensor): Input tensor (#batch, time, idim).
+            masks (torch.Tensor): Mask tensor (#batch, time).
+
+        Returns:
+            torch.Tensor: Output tensor (#batch, time, attention_dim).
+            torch.Tensor: Mask tensor (#batch, time).
+
+        """
+
+        if self.embed is not None:
+            xs = self.embed(xs)
+
+        if lang_ids is not None:
+            lang_embs = self.language_embedding(lang_ids)
+            xs = xs + lang_embs  # offset the phoneme distribution of a language
+
+        if utterance_embedding is not None and not self.connect_utt_emb_at_encoder_out:
+            xs = self._integrate_with_utt_embed(xs, utterance_embedding)
+
+        xs = self.pos_enc(xs)
+
+        xs, masks = self.encoders(xs, masks)
+        if isinstance(xs, tuple):
+            xs = xs[0]
+
+        if self.normalize_before:
+            xs = self.after_norm(xs)
+
+        if utterance_embedding is not None and self.connect_utt_emb_at_encoder_out:
+            xs = self._integrate_with_utt_embed(xs, utterance_embedding)
+
+        return xs, masks
+
+    def _integrate_with_utt_embed(self, hs, utt_embeddings):
+        # project embedding into smaller space
+        speaker_embeddings_projected = self.embedding_projection(utt_embeddings)
+        # concat hidden states with spk embeds and then apply projection
+        speaker_embeddings_expanded = F.normalize(speaker_embeddings_projected).unsqueeze(1).expand(-1, hs.size(1), -1)
+        hs = self.hs_emb_projection(torch.cat([hs, speaker_embeddings_expanded], dim=-1))
+        return hs

IMSToucan/Layers/Convolution.py

@@ -0,0 +1,55 @@
+# Copyright 2020 Johns Hopkins University (Shinji Watanabe)
+#                Northwestern Polytechnical University (Pengcheng Guo)
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+# Adapted by Florian Lux 2021
+
+
+from torch import nn
+
+
+class ConvolutionModule(nn.Module):
+    """
+    ConvolutionModule in Conformer model.
+
+    Args:
+        channels (int): The number of channels of conv layers.
+        kernel_size (int): Kernel size of conv layers.
+
+    """
+
+    def __init__(self, channels, kernel_size, activation=nn.ReLU(), bias=True):
+        super(ConvolutionModule, self).__init__()
+        # kernel_size should be an odd number for 'SAME' padding
+        assert (kernel_size - 1) % 2 == 0
+
+        self.pointwise_conv1 = nn.Conv1d(channels, 2 * channels, kernel_size=1, stride=1, padding=0, bias=bias, )
+        self.depthwise_conv = nn.Conv1d(channels, channels, kernel_size, stride=1, padding=(kernel_size - 1) // 2, groups=channels, bias=bias, )
+        self.norm = nn.GroupNorm(num_groups=32, num_channels=channels)
+        self.pointwise_conv2 = nn.Conv1d(channels, channels, kernel_size=1, stride=1, padding=0, bias=bias, )
+        self.activation = activation
+
+    def forward(self, x):
+        """
+        Compute convolution module.
+
+        Args:
+            x (torch.Tensor): Input tensor (#batch, time, channels).
+
+        Returns:
+            torch.Tensor: Output tensor (#batch, time, channels).
+
+        """
+        # exchange the temporal dimension and the feature dimension
+        x = x.transpose(1, 2)
+
+        # GLU mechanism
+        x = self.pointwise_conv1(x)  # (batch, 2*channel, dim)
+        x = nn.functional.glu(x, dim=1)  # (batch, channel, dim)
+
+        # 1D Depthwise Conv
+        x = self.depthwise_conv(x)
+        x = self.activation(self.norm(x))
+
+        x = self.pointwise_conv2(x)
+
+        return x.transpose(1, 2)

IMSToucan/Layers/DurationPredictor.py

@@ -0,0 +1,139 @@
+# Copyright 2019 Tomoki Hayashi
+# MIT License (https://opensource.org/licenses/MIT)
+# Adapted by Florian Lux 2021
+
+
+import torch
+
+from .LayerNorm import LayerNorm
+
+
+class DurationPredictor(torch.nn.Module):
+    """
+    Duration predictor module.
+
+    This is a module of duration predictor described
+    in `FastSpeech: Fast, Robust and Controllable Text to Speech`_.
+    The duration predictor predicts a duration of each frame in log domain
+    from the hidden embeddings of encoder.
+
+    .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
+        https://arxiv.org/pdf/1905.09263.pdf
+
+    Note:
+        The calculation domain of outputs is different
+        between in `forward` and in `inference`. In `forward`,
+        the outputs are calculated in log domain but in `inference`,
+        those are calculated in linear domain.
+
+    """
+
+    def __init__(self, idim, n_layers=2, n_chans=384, kernel_size=3, dropout_rate=0.1, offset=1.0):
+        """
+        Initialize duration predictor module.
+
+        Args:
+            idim (int): Input dimension.
+            n_layers (int, optional): Number of convolutional layers.
+            n_chans (int, optional): Number of channels of convolutional layers.
+            kernel_size (int, optional): Kernel size of convolutional layers.
+            dropout_rate (float, optional): Dropout rate.
+            offset (float, optional): Offset value to avoid nan in log domain.
+
+        """
+        super(DurationPredictor, self).__init__()
+        self.offset = offset
+        self.conv = torch.nn.ModuleList()
+        for idx in range(n_layers):
+            in_chans = idim if idx == 0 else n_chans
+            self.conv += [torch.nn.Sequential(torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=(kernel_size - 1) // 2, ), torch.nn.ReLU(),
+                                              LayerNorm(n_chans, dim=1), torch.nn.Dropout(dropout_rate), )]
+        self.linear = torch.nn.Linear(n_chans, 1)
+
+    def _forward(self, xs, x_masks=None, is_inference=False):
+        xs = xs.transpose(1, -1)  # (B, idim, Tmax)
+        for f in self.conv:
+            xs = f(xs)  # (B, C, Tmax)
+
+        # NOTE: calculate in log domain
+        xs = self.linear(xs.transpose(1, -1)).squeeze(-1)  # (B, Tmax)
+
+        if is_inference:
+            # NOTE: calculate in linear domain
+            xs = torch.clamp(torch.round(xs.exp() - self.offset), min=0).long()  # avoid negative value
+
+        if x_masks is not None:
+            xs = xs.masked_fill(x_masks, 0.0)
+
+        return xs
+
+    def forward(self, xs, x_masks=None):
+        """
+        Calculate forward propagation.
+
+        Args:
+            xs (Tensor): Batch of input sequences (B, Tmax, idim).
+            x_masks (ByteTensor, optional):
+                Batch of masks indicating padded part (B, Tmax).
+
+        Returns:
+            Tensor: Batch of predicted durations in log domain (B, Tmax).
+
+        """
+        return self._forward(xs, x_masks, False)
+
+    def inference(self, xs, x_masks=None):
+        """
+        Inference duration.
+
+        Args:
+            xs (Tensor): Batch of input sequences (B, Tmax, idim).
+            x_masks (ByteTensor, optional):
+                Batch of masks indicating padded part (B, Tmax).
+
+        Returns:
+            LongTensor: Batch of predicted durations in linear domain (B, Tmax).
+
+        """
+        return self._forward(xs, x_masks, True)
+
+
+class DurationPredictorLoss(torch.nn.Module):
+    """
+    Loss function module for duration predictor.
+
+    The loss value is Calculated in log domain to make it Gaussian.
+
+    """
+
+    def __init__(self, offset=1.0, reduction="mean"):
+        """
+        Args:
+            offset (float, optional): Offset value to avoid nan in log domain.
+            reduction (str): Reduction type in loss calculation.
+
+        """
+        super(DurationPredictorLoss, self).__init__()
+        self.criterion = torch.nn.MSELoss(reduction=reduction)
+        self.offset = offset
+
+    def forward(self, outputs, targets):
+        """
+        Calculate forward propagation.
+
+        Args:
+            outputs (Tensor): Batch of prediction durations in log domain (B, T)
+            targets (LongTensor): Batch of groundtruth durations in linear domain (B, T)
+
+        Returns:
+            Tensor: Mean squared error loss value.
+
+        Note:
+            `outputs` is in log domain but `targets` is in linear domain.
+
+        """
+        # NOTE: outputs is in log domain while targets in linear
+        targets = torch.log(targets.float() + self.offset)
+        loss = self.criterion(outputs, targets)
+
+        return loss

IMSToucan/Layers/EncoderLayer.py

@@ -0,0 +1,144 @@
+# Copyright 2020 Johns Hopkins University (Shinji Watanabe)
+#                Northwestern Polytechnical University (Pengcheng Guo)
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+# Adapted by Florian Lux 2021
+
+
+import torch
+from torch import nn
+
+from .LayerNorm import LayerNorm
+
+
+class EncoderLayer(nn.Module):
+    """
+    Encoder layer module.
+
+    Args:
+        size (int): Input dimension.
+        self_attn (torch.nn.Module): Self-attention module instance.
+            `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` instance
+            can be used as the argument.
+        feed_forward (torch.nn.Module): Feed-forward module instance.
+            `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
+            can be used as the argument.
+        feed_forward_macaron (torch.nn.Module): Additional feed-forward module instance.
+            `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
+            can be used as the argument.
+        conv_module (torch.nn.Module): Convolution module instance.
+            `ConvlutionModule` instance can be used as the argument.
+        dropout_rate (float): Dropout rate.
+        normalize_before (bool): Whether to use layer_norm before the first block.
+        concat_after (bool): Whether to concat attention layer's input and output.
+            if True, additional linear will be applied.
+            i.e. x -> x + linear(concat(x, att(x)))
+            if False, no additional linear will be applied. i.e. x -> x + att(x)
+
+    """
+
+    def __init__(self, size, self_attn, feed_forward, feed_forward_macaron, conv_module, dropout_rate, normalize_before=True, concat_after=False, ):
+        super(EncoderLayer, self).__init__()
+        self.self_attn = self_attn
+        self.feed_forward = feed_forward
+        self.feed_forward_macaron = feed_forward_macaron
+        self.conv_module = conv_module
+        self.norm_ff = LayerNorm(size)  # for the FNN module
+        self.norm_mha = LayerNorm(size)  # for the MHA module
+        if feed_forward_macaron is not None:
+            self.norm_ff_macaron = LayerNorm(size)
+            self.ff_scale = 0.5
+        else:
+            self.ff_scale = 1.0
+        if self.conv_module is not None:
+            self.norm_conv = LayerNorm(size)  # for the CNN module
+            self.norm_final = LayerNorm(size)  # for the final output of the block
+        self.dropout = nn.Dropout(dropout_rate)
+        self.size = size
+        self.normalize_before = normalize_before
+        self.concat_after = concat_after
+        if self.concat_after:
+            self.concat_linear = nn.Linear(size + size, size)
+
+    def forward(self, x_input, mask, cache=None):
+        """
+        Compute encoded features.
+
+        Args:
+            x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb.
+                - w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)].
+                - w/o pos emb: Tensor (#batch, time, size).
+            mask (torch.Tensor): Mask tensor for the input (#batch, time).
+            cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
+
+        Returns:
+            torch.Tensor: Output tensor (#batch, time, size).
+            torch.Tensor: Mask tensor (#batch, time).
+
+        """
+        if isinstance(x_input, tuple):
+            x, pos_emb = x_input[0], x_input[1]
+        else:
+            x, pos_emb = x_input, None
+
+        # whether to use macaron style
+        if self.feed_forward_macaron is not None:
+            residual = x
+            if self.normalize_before:
+                x = self.norm_ff_macaron(x)
+            x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
+            if not self.normalize_before:
+                x = self.norm_ff_macaron(x)
+
+        # multi-headed self-attention module
+        residual = x
+        if self.normalize_before:
+            x = self.norm_mha(x)
+
+        if cache is None:
+            x_q = x
+        else:
+            assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size)
+            x_q = x[:, -1:, :]
+            residual = residual[:, -1:, :]
+            mask = None if mask is None else mask[:, -1:, :]
+
+        if pos_emb is not None:
+            x_att = self.self_attn(x_q, x, x, pos_emb, mask)
+        else:
+            x_att = self.self_attn(x_q, x, x, mask)
+
+        if self.concat_after:
+            x_concat = torch.cat((x, x_att), dim=-1)
+            x = residual + self.concat_linear(x_concat)
+        else:
+            x = residual + self.dropout(x_att)
+        if not self.normalize_before:
+            x = self.norm_mha(x)
+
+        # convolution module
+        if self.conv_module is not None:
+            residual = x
+            if self.normalize_before:
+                x = self.norm_conv(x)
+            x = residual + self.dropout(self.conv_module(x))
+            if not self.normalize_before:
+                x = self.norm_conv(x)
+
+        # feed forward module
+        residual = x
+        if self.normalize_before:
+            x = self.norm_ff(x)
+        x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
+        if not self.normalize_before:
+            x = self.norm_ff(x)
+
+        if self.conv_module is not None:
+            x = self.norm_final(x)
+
+        if cache is not None:
+            x = torch.cat([cache, x], dim=1)
+
+        if pos_emb is not None:
+            return (x, pos_emb), mask
+
+        return x, mask

IMSToucan/Layers/LayerNorm.py

@@ -0,0 +1,36 @@
+# Written by Shigeki Karita, 2019
+# Published under Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+# Adapted by Florian Lux, 2021
+
+import torch
+
+
+class LayerNorm(torch.nn.LayerNorm):
+    """
+    Layer normalization module.
+
+    Args:
+        nout (int): Output dim size.
+        dim (int): Dimension to be normalized.
+    """
+
+    def __init__(self, nout, dim=-1):
+        """
+        Construct an LayerNorm object.
+        """
+        super(LayerNorm, self).__init__(nout, eps=1e-12)
+        self.dim = dim
+
+    def forward(self, x):
+        """
+        Apply layer normalization.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+
+        Returns:
+            torch.Tensor: Normalized tensor.
+        """
+        if self.dim == -1:
+            return super(LayerNorm, self).forward(x)
+        return super(LayerNorm, self).forward(x.transpose(1, -1)).transpose(1, -1)

IMSToucan/Layers/LengthRegulator.py

@@ -0,0 +1,62 @@
+# Copyright 2019 Tomoki Hayashi
+# MIT License (https://opensource.org/licenses/MIT)
+# Adapted by Florian Lux 2021
+
+from abc import ABC
+
+import torch
+
+from ..Utility.utils import pad_list
+
+
+class LengthRegulator(torch.nn.Module, ABC):
+    """
+    Length regulator module for feed-forward Transformer.
+
+    This is a module of length regulator described in
+    `FastSpeech: Fast, Robust and Controllable Text to Speech`_.
+    The length regulator expands char or
+    phoneme-level embedding features to frame-level by repeating each
+    feature based on the corresponding predicted durations.
+
+    .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
+        https://arxiv.org/pdf/1905.09263.pdf
+
+    """
+
+    def __init__(self, pad_value=0.0):
+        """
+        Initialize length regulator module.
+
+        Args:
+            pad_value (float, optional): Value used for padding.
+        """
+        super(LengthRegulator, self).__init__()
+        self.pad_value = pad_value
+
+    def forward(self, xs, ds, alpha=1.0):
+        """
+        Calculate forward propagation.
+
+        Args:
+            xs (Tensor): Batch of sequences of char or phoneme embeddings (B, Tmax, D).
+            ds (LongTensor): Batch of durations of each frame (B, T).
+            alpha (float, optional): Alpha value to control speed of speech.
+
+        Returns:
+            Tensor: replicated input tensor based on durations (B, T*, D).
+        """
+        if alpha != 1.0:
+            assert alpha > 0
+            ds = torch.round(ds.float() * alpha).long()
+
+        if ds.sum() == 0:
+            ds[ds.sum(dim=1).eq(0)] = 1
+
+        return pad_list([self._repeat_one_sequence(x, d) for x, d in zip(xs, ds)], self.pad_value)
+
+    def _repeat_one_sequence(self, x, d):
+        """
+        Repeat each frame according to duration
+        """
+        return torch.repeat_interleave(x, d, dim=0)

IMSToucan/Layers/MultiLayeredConv1d.py

@@ -0,0 +1,87 @@
+# Copyright 2019 Tomoki Hayashi
+# MIT License (https://opensource.org/licenses/MIT)
+# Adapted by Florian Lux 2021
+
+"""
+Layer modules for FFT block in FastSpeech (Feed-forward Transformer).
+"""
+
+import torch
+
+
+class MultiLayeredConv1d(torch.nn.Module):
+    """
+    Multi-layered conv1d for Transformer block.
+
+    This is a module of multi-layered conv1d designed
+    to replace positionwise feed-forward network
+    in Transformer block, which is introduced in
+    `FastSpeech: Fast, Robust and Controllable Text to Speech`_.
+
+    .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
+        https://arxiv.org/pdf/1905.09263.pdf
+    """
+
+    def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
+        """
+        Initialize MultiLayeredConv1d module.
+
+        Args:
+            in_chans (int): Number of input channels.
+            hidden_chans (int): Number of hidden channels.
+            kernel_size (int): Kernel size of conv1d.
+            dropout_rate (float): Dropout rate.
+        """
+        super(MultiLayeredConv1d, self).__init__()
+        self.w_1 = torch.nn.Conv1d(in_chans, hidden_chans, kernel_size, stride=1, padding=(kernel_size - 1) // 2, )
+        self.w_2 = torch.nn.Conv1d(hidden_chans, in_chans, kernel_size, stride=1, padding=(kernel_size - 1) // 2, )
+        self.dropout = torch.nn.Dropout(dropout_rate)
+
+    def forward(self, x):
+        """
+        Calculate forward propagation.
+
+        Args:
+            x (torch.Tensor): Batch of input tensors (B, T, in_chans).
+
+        Returns:
+            torch.Tensor: Batch of output tensors (B, T, hidden_chans).
+        """
+        x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
+        return self.w_2(self.dropout(x).transpose(-1, 1)).transpose(-1, 1)
+
+
+class Conv1dLinear(torch.nn.Module):
+    """
+    Conv1D + Linear for Transformer block.
+
+    A variant of MultiLayeredConv1d, which replaces second conv-layer to linear.
+    """
+
+    def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
+        """
+        Initialize Conv1dLinear module.
+
+        Args:
+            in_chans (int): Number of input channels.
+            hidden_chans (int): Number of hidden channels.
+            kernel_size (int): Kernel size of conv1d.
+            dropout_rate (float): Dropout rate.
+        """
+        super(Conv1dLinear, self).__init__()
+        self.w_1 = torch.nn.Conv1d(in_chans, hidden_chans, kernel_size, stride=1, padding=(kernel_size - 1) // 2, )
+        self.w_2 = torch.nn.Linear(hidden_chans, in_chans)
+        self.dropout = torch.nn.Dropout(dropout_rate)
+
+    def forward(self, x):
+        """
+        Calculate forward propagation.
+
+        Args:
+            x (torch.Tensor): Batch of input tensors (B, T, in_chans).
+
+        Returns:
+            torch.Tensor: Batch of output tensors (B, T, hidden_chans).
+        """
+        x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
+        return self.w_2(self.dropout(x))

IMSToucan/Layers/MultiSequential.py

@@ -0,0 +1,33 @@
+# Written by Shigeki Karita, 2019
+# Published under Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+# Adapted by Florian Lux, 2021
+
+import torch
+
+
+class MultiSequential(torch.nn.Sequential):
+    """
+    Multi-input multi-output torch.nn.Sequential.
+    """
+
+    def forward(self, *args):
+        """
+        Repeat.
+        """
+        for m in self:
+            args = m(*args)
+        return args
+
+
+def repeat(N, fn):
+    """
+    Repeat module N times.
+
+    Args:
+        N (int): Number of repeat time.
+        fn (Callable): Function to generate module.
+
+    Returns:
+        MultiSequential: Repeated model instance.
+    """
+    return MultiSequential(*[fn(n) for n in range(N)])

IMSToucan/Layers/PositionalEncoding.py

@@ -0,0 +1,166 @@
+"""
+Taken from ESPNet
+"""
+
+import math
+
+import torch
+
+
+class PositionalEncoding(torch.nn.Module):
+    """
+    Positional encoding.
+
+    Args:
+        d_model (int): Embedding dimension.
+        dropout_rate (float): Dropout rate.
+        max_len (int): Maximum input length.
+        reverse (bool): Whether to reverse the input position.
+    """
+
+    def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False):
+        """
+        Construct an PositionalEncoding object.
+        """
+        super(PositionalEncoding, self).__init__()
+        self.d_model = d_model
+        self.reverse = reverse
+        self.xscale = math.sqrt(self.d_model)
+        self.dropout = torch.nn.Dropout(p=dropout_rate)
+        self.pe = None
+        self.extend_pe(torch.tensor(0.0, device=d_model.device).expand(1, max_len))
+
+    def extend_pe(self, x):
+        """
+        Reset the positional encodings.
+        """
+        if self.pe is not None:
+            if self.pe.size(1) >= x.size(1):
+                if self.pe.dtype != x.dtype or self.pe.device != x.device:
+                    self.pe = self.pe.to(dtype=x.dtype, device=x.device)
+                return
+        pe = torch.zeros(x.size(1), self.d_model)
+        if self.reverse:
+            position = torch.arange(x.size(1) - 1, -1, -1.0, dtype=torch.float32).unsqueeze(1)
+        else:
+            position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, self.d_model, 2, dtype=torch.float32) * -(math.log(10000.0) / self.d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+        self.pe = pe.to(device=x.device, dtype=x.dtype)
+
+    def forward(self, x):
+        """
+        Add positional encoding.
+
+        Args:
+            x (torch.Tensor): Input tensor (batch, time, `*`).
+
+        Returns:
+            torch.Tensor: Encoded tensor (batch, time, `*`).
+        """
+        self.extend_pe(x)
+        x = x * self.xscale + self.pe[:, : x.size(1)]
+        return self.dropout(x)
+
+
+class RelPositionalEncoding(torch.nn.Module):
+    """
+    Relative positional encoding module (new implementation).
+    Details can be found in https://github.com/espnet/espnet/pull/2816.
+    See : Appendix B in https://arxiv.org/abs/1901.02860
+    Args:
+        d_model (int): Embedding dimension.
+        dropout_rate (float): Dropout rate.
+        max_len (int): Maximum input length.
+    """
+
+    def __init__(self, d_model, dropout_rate, max_len=5000):
+        """
+        Construct an PositionalEncoding object.
+        """
+        super(RelPositionalEncoding, self).__init__()
+        self.d_model = d_model
+        self.xscale = math.sqrt(self.d_model)
+        self.dropout = torch.nn.Dropout(p=dropout_rate)
+        self.pe = None
+        self.extend_pe(torch.tensor(0.0).expand(1, max_len))
+
+    def extend_pe(self, x):
+        """Reset the positional encodings."""
+        if self.pe is not None:
+            # self.pe contains both positive and negative parts
+            # the length of self.pe is 2 * input_len - 1
+            if self.pe.size(1) >= x.size(1) * 2 - 1:
+                if self.pe.dtype != x.dtype or self.pe.device != x.device:
+                    self.pe = self.pe.to(dtype=x.dtype, device=x.device)
+                return
+        # Suppose `i` means to the position of query vecotr and `j` means the
+        # position of key vector. We use position relative positions when keys
+        # are to the left (i>j) and negative relative positions otherwise (i<j).
+        pe_positive = torch.zeros(x.size(1), self.d_model, device=x.device)
+        pe_negative = torch.zeros(x.size(1), self.d_model, device=x.device)
+        position = torch.arange(0, x.size(1), dtype=torch.float32, device=x.device).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, self.d_model, 2, dtype=torch.float32, device=x.device) * -(math.log(10000.0) / self.d_model))
+        pe_positive[:, 0::2] = torch.sin(position * div_term)
+        pe_positive[:, 1::2] = torch.cos(position * div_term)
+        pe_negative[:, 0::2] = torch.sin(-1 * position * div_term)
+        pe_negative[:, 1::2] = torch.cos(-1 * position * div_term)
+
+        # Reserve the order of positive indices and concat both positive and
+        # negative indices. This is used to support the shifting trick
+        # as in https://arxiv.org/abs/1901.02860
+        pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
+        pe_negative = pe_negative[1:].unsqueeze(0)
+        pe = torch.cat([pe_positive, pe_negative], dim=1)
+        self.pe = pe.to(dtype=x.dtype)
+
+    def forward(self, x):
+        """
+        Add positional encoding.
+        Args:
+            x (torch.Tensor): Input tensor (batch, time, `*`).
+        Returns:
+            torch.Tensor: Encoded tensor (batch, time, `*`).
+        """
+        self.extend_pe(x)
+        x = x * self.xscale
+        pos_emb = self.pe[:, self.pe.size(1) // 2 - x.size(1) + 1: self.pe.size(1) // 2 + x.size(1), ]
+        return self.dropout(x), self.dropout(pos_emb)
+
+
+class ScaledPositionalEncoding(PositionalEncoding):
+    """
+    Scaled positional encoding module.
+
+    See Sec. 3.2  https://arxiv.org/abs/1809.08895
+
+    Args:
+        d_model (int): Embedding dimension.
+        dropout_rate (float): Dropout rate.
+        max_len (int): Maximum input length.
+
+    """
+
+    def __init__(self, d_model, dropout_rate, max_len=5000):
+        super().__init__(d_model=d_model, dropout_rate=dropout_rate, max_len=max_len)
+        self.alpha = torch.nn.Parameter(torch.tensor(1.0))
+
+    def reset_parameters(self):
+        self.alpha.data = torch.tensor(1.0)
+
+    def forward(self, x):
+        """
+        Add positional encoding.
+
+        Args:
+            x (torch.Tensor): Input tensor (batch, time, `*`).
+
+        Returns:
+            torch.Tensor: Encoded tensor (batch, time, `*`).
+
+        """
+        self.extend_pe(x)
+        x = x + self.alpha * self.pe[:, : x.size(1)]
+        return self.dropout(x)

IMSToucan/Layers/PositionwiseFeedForward.py

@@ -0,0 +1,26 @@
+# Written by Shigeki Karita, 2019
+# Published under Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+# Adapted by Florian Lux, 2021
+
+
+import torch
+
+
+class PositionwiseFeedForward(torch.nn.Module):
+    """
+    Args:
+        idim (int): Input dimenstion.
+        hidden_units (int): The number of hidden units.
+        dropout_rate (float): Dropout rate.
+
+    """
+
+    def __init__(self, idim, hidden_units, dropout_rate, activation=torch.nn.ReLU()):
+        super(PositionwiseFeedForward, self).__init__()
+        self.w_1 = torch.nn.Linear(idim, hidden_units)
+        self.w_2 = torch.nn.Linear(hidden_units, idim)
+        self.dropout = torch.nn.Dropout(dropout_rate)
+        self.activation = activation
+
+    def forward(self, x):
+        return self.w_2(self.dropout(self.activation(self.w_1(x))))

IMSToucan/Layers/PostNet.py

@@ -0,0 +1,74 @@
+"""
+Taken from ESPNet
+"""
+
+import torch
+
+
+class PostNet(torch.nn.Module):
+    """
+    From Tacotron2
+
+    Postnet module for Spectrogram prediction network.
+
+    This is a module of Postnet in Spectrogram prediction network,
+    which described in `Natural TTS Synthesis by
+    Conditioning WaveNet on Mel Spectrogram Predictions`_.
+    The Postnet refines the predicted
+    Mel-filterbank of the decoder,
+    which helps to compensate the detail sturcture of spectrogram.
+
+    .. _`Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions`:
+       https://arxiv.org/abs/1712.05884
+    """
+
+    def __init__(self, idim, odim, n_layers=5, n_chans=512, n_filts=5, dropout_rate=0.5, use_batch_norm=True):
+        """
+        Initialize postnet module.
+
+        Args:
+            idim (int): Dimension of the inputs.
+            odim (int): Dimension of the outputs.
+            n_layers (int, optional): The number of layers.
+            n_filts (int, optional): The number of filter size.
+            n_units (int, optional): The number of filter channels.
+            use_batch_norm (bool, optional): Whether to use batch normalization..
+            dropout_rate (float, optional): Dropout rate..
+        """
+        super(PostNet, self).__init__()
+        self.postnet = torch.nn.ModuleList()
+        for layer in range(n_layers - 1):
+            ichans = odim if layer == 0 else n_chans
+            ochans = odim if layer == n_layers - 1 else n_chans
+            if use_batch_norm:
+                self.postnet += [torch.nn.Sequential(torch.nn.Conv1d(ichans, ochans, n_filts, stride=1, padding=(n_filts - 1) // 2, bias=False, ),
+                                                     torch.nn.GroupNorm(num_groups=32, num_channels=ochans), torch.nn.Tanh(),
+                                                     torch.nn.Dropout(dropout_rate), )]
+
+            else:
+                self.postnet += [
+                    torch.nn.Sequential(torch.nn.Conv1d(ichans, ochans, n_filts, stride=1, padding=(n_filts - 1) // 2, bias=False, ), torch.nn.Tanh(),
+                                        torch.nn.Dropout(dropout_rate), )]
+        ichans = n_chans if n_layers != 1 else odim
+        if use_batch_norm:
+            self.postnet += [torch.nn.Sequential(torch.nn.Conv1d(ichans, odim, n_filts, stride=1, padding=(n_filts - 1) // 2, bias=False, ),
+                                                 torch.nn.GroupNorm(num_groups=20, num_channels=odim),
+                                                 torch.nn.Dropout(dropout_rate), )]
+
+        else:
+            self.postnet += [torch.nn.Sequential(torch.nn.Conv1d(ichans, odim, n_filts, stride=1, padding=(n_filts - 1) // 2, bias=False, ),
+                                                 torch.nn.Dropout(dropout_rate), )]
+
+    def forward(self, xs):
+        """
+        Calculate forward propagation.
+
+        Args:
+            xs (Tensor): Batch of the sequences of padded input tensors (B, idim, Tmax).
+
+        Returns:
+            Tensor: Batch of padded output tensor. (B, odim, Tmax).
+        """
+        for i in range(len(self.postnet)):
+            xs = self.postnet[i](xs)
+        return xs

IMSToucan/Layers/RNNAttention.py

@@ -0,0 +1,282 @@
+# Published under Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+# Adapted by Florian Lux, 2021
+
+import torch
+import torch.nn.functional as F
+
+from ..Utility.utils import make_pad_mask
+from ..Utility.utils import to_device
+
+
+def _apply_attention_constraint(e, last_attended_idx, backward_window=1, forward_window=3):
+    """
+    Apply monotonic attention constraint.
+
+    This function apply the monotonic attention constraint
+    introduced in `Deep Voice 3: Scaling
+    Text-to-Speech with Convolutional Sequence Learning`_.
+
+    Args:
+        e (Tensor): Attention energy before applying softmax (1, T).
+        last_attended_idx (int): The index of the inputs of the last attended [0, T].
+        backward_window (int, optional): Backward window size in attention constraint.
+        forward_window (int, optional): Forward window size in attetion constraint.
+
+    Returns:
+        Tensor: Monotonic constrained attention energy (1, T).
+
+    .. _`Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning`:
+        https://arxiv.org/abs/1710.07654
+    """
+    if e.size(0) != 1:
+        raise NotImplementedError("Batch attention constraining is not yet supported.")
+    backward_idx = last_attended_idx - backward_window
+    forward_idx = last_attended_idx + forward_window
+    if backward_idx > 0:
+        e[:, :backward_idx] = -float("inf")
+    if forward_idx < e.size(1):
+        e[:, forward_idx:] = -float("inf")
+    return e
+
+
+class AttLoc(torch.nn.Module):
+    """
+    location-aware attention module.
+
+    Reference: Attention-Based Models for Speech Recognition
+        (https://arxiv.org/pdf/1506.07503.pdf)
+
+    :param int eprojs: # projection-units of encoder
+    :param int dunits: # units of decoder
+    :param int att_dim: attention dimension
+    :param int aconv_chans: # channels of attention convolution
+    :param int aconv_filts: filter size of attention convolution
+    :param bool han_mode: flag to switch on mode of hierarchical attention
+        and not store pre_compute_enc_h
+    """
+
+    def __init__(self, eprojs, dunits, att_dim, aconv_chans, aconv_filts, han_mode=False):
+        super(AttLoc, self).__init__()
+        self.mlp_enc = torch.nn.Linear(eprojs, att_dim)
+        self.mlp_dec = torch.nn.Linear(dunits, att_dim, bias=False)
+        self.mlp_att = torch.nn.Linear(aconv_chans, att_dim, bias=False)
+        self.loc_conv = torch.nn.Conv2d(1, aconv_chans, (1, 2 * aconv_filts + 1), padding=(0, aconv_filts), bias=False, )
+        self.gvec = torch.nn.Linear(att_dim, 1)
+
+        self.dunits = dunits
+        self.eprojs = eprojs
+        self.att_dim = att_dim
+        self.h_length = None
+        self.enc_h = None
+        self.pre_compute_enc_h = None
+        self.mask = None
+        self.han_mode = han_mode
+
+    def reset(self):
+        """reset states"""
+        self.h_length = None
+        self.enc_h = None
+        self.pre_compute_enc_h = None
+        self.mask = None
+
+    def forward(self,
+                enc_hs_pad,
+                enc_hs_len,
+                dec_z,
+                att_prev,
+                scaling=2.0,
+                last_attended_idx=None,
+                backward_window=1,
+                forward_window=3):
+        """
+        Calculate AttLoc forward propagation.
+
+        :param torch.Tensor enc_hs_pad: padded encoder hidden state (B x T_max x D_enc)
+        :param list enc_hs_len: padded encoder hidden state length (B)
+        :param torch.Tensor dec_z: decoder hidden state (B x D_dec)
+        :param torch.Tensor att_prev: previous attention weight (B x T_max)
+        :param float scaling: scaling parameter before applying softmax
+        :param torch.Tensor forward_window:
+            forward window size when constraining attention
+        :param int last_attended_idx: index of the inputs of the last attended
+        :param int backward_window: backward window size in attention constraint
+        :param int forward_window: forward window size in attention constraint
+        :return: attention weighted encoder state (B, D_enc)
+        :rtype: torch.Tensor
+        :return: previous attention weights (B x T_max)
+        :rtype: torch.Tensor
+        """
+        batch = len(enc_hs_pad)
+        # pre-compute all h outside the decoder loop
+        if self.pre_compute_enc_h is None or self.han_mode:
+            self.enc_h = enc_hs_pad  # utt x frame x hdim
+            self.h_length = self.enc_h.size(1)
+            # utt x frame x att_dim
+            self.pre_compute_enc_h = self.mlp_enc(self.enc_h)
+        if dec_z is None:
+            dec_z = enc_hs_pad.new_zeros(batch, self.dunits)
+        else:
+            dec_z = dec_z.view(batch, self.dunits)
+
+        # initialize attention weight with uniform dist.
+        if att_prev is None:
+            # if no bias, 0 0-pad goes 0
+            att_prev = 1.0 - make_pad_mask(enc_hs_len, device=dec_z.device).to(dtype=dec_z.dtype)
+            att_prev = att_prev / att_prev.new(enc_hs_len).unsqueeze(-1)
+
+        # att_prev: utt x frame -> utt x 1 x 1 x frame
+        # -> utt x att_conv_chans x 1 x frame
+        att_conv = self.loc_conv(att_prev.view(batch, 1, 1, self.h_length))
+        # att_conv: utt x att_conv_chans x 1 x frame -> utt x frame x att_conv_chans
+        att_conv = att_conv.squeeze(2).transpose(1, 2)
+        # att_conv: utt x frame x att_conv_chans -> utt x frame x att_dim
+        att_conv = self.mlp_att(att_conv)
+
+        # dec_z_tiled: utt x frame x att_dim
+        dec_z_tiled = self.mlp_dec(dec_z).view(batch, 1, self.att_dim)
+
+        # dot with gvec
+        # utt x frame x att_dim -> utt x frame
+        e = self.gvec(torch.tanh(att_conv + self.pre_compute_enc_h + dec_z_tiled)).squeeze(2)
+
+        # NOTE: consider zero padding when compute w.
+        if self.mask is None:
+            self.mask = to_device(enc_hs_pad, make_pad_mask(enc_hs_len))
+        e.masked_fill_(self.mask, -float("inf"))
+
+        # apply monotonic attention constraint (mainly for TTS)
+        if last_attended_idx is not None:
+            e = _apply_attention_constraint(e, last_attended_idx, backward_window, forward_window)
+
+        w = F.softmax(scaling * e, dim=1)
+
+        # weighted sum over flames
+        # utt x hdim
+        c = torch.sum(self.enc_h * w.view(batch, self.h_length, 1), dim=1)
+
+        return c, w
+
+
+class AttForwardTA(torch.nn.Module):
+    """Forward attention with transition agent module.
+    Reference:
+    Forward attention in sequence-to-sequence acoustic modeling for speech synthesis
+        (https://arxiv.org/pdf/1807.06736.pdf)
+    :param int eunits: # units of encoder
+    :param int dunits: # units of decoder
+    :param int att_dim: attention dimension
+    :param int aconv_chans: # channels of attention convolution
+    :param int aconv_filts: filter size of attention convolution
+    :param int odim: output dimension
+    """
+
+    def __init__(self, eunits, dunits, att_dim, aconv_chans, aconv_filts, odim):
+        super(AttForwardTA, self).__init__()
+        self.mlp_enc = torch.nn.Linear(eunits, att_dim)
+        self.mlp_dec = torch.nn.Linear(dunits, att_dim, bias=False)
+        self.mlp_ta = torch.nn.Linear(eunits + dunits + odim, 1)
+        self.mlp_att = torch.nn.Linear(aconv_chans, att_dim, bias=False)
+        self.loc_conv = torch.nn.Conv2d(1, aconv_chans, (1, 2 * aconv_filts + 1), padding=(0, aconv_filts), bias=False, )
+        self.gvec = torch.nn.Linear(att_dim, 1)
+        self.dunits = dunits
+        self.eunits = eunits
+        self.att_dim = att_dim
+        self.h_length = None
+        self.enc_h = None
+        self.pre_compute_enc_h = None
+        self.mask = None
+        self.trans_agent_prob = 0.5
+
+    def reset(self):
+        self.h_length = None
+        self.enc_h = None
+        self.pre_compute_enc_h = None
+        self.mask = None
+        self.trans_agent_prob = 0.5
+
+    def forward(self,
+                enc_hs_pad,
+                enc_hs_len,
+                dec_z,
+                att_prev,
+                out_prev,
+                scaling=1.0,
+                last_attended_idx=None,
+                backward_window=1,
+                forward_window=3):
+        """
+        Calculate AttForwardTA forward propagation.
+
+        :param torch.Tensor enc_hs_pad: padded encoder hidden state (B, Tmax, eunits)
+        :param list enc_hs_len: padded encoder hidden state length (B)
+        :param torch.Tensor dec_z: decoder hidden state (B, dunits)
+        :param torch.Tensor att_prev: attention weights of previous step
+        :param torch.Tensor out_prev: decoder outputs of previous step (B, odim)
+        :param float scaling: scaling parameter before applying softmax
+        :param int last_attended_idx: index of the inputs of the last attended
+        :param int backward_window: backward window size in attention constraint
+        :param int forward_window: forward window size in attetion constraint
+        :return: attention weighted encoder state (B, dunits)
+        :rtype: torch.Tensor
+        :return: previous attention weights (B, Tmax)
+        :rtype: torch.Tensor
+        """
+        batch = len(enc_hs_pad)
+        # pre-compute all h outside the decoder loop
+        if self.pre_compute_enc_h is None:
+            self.enc_h = enc_hs_pad  # utt x frame x hdim
+            self.h_length = self.enc_h.size(1)
+            # utt x frame x att_dim
+            self.pre_compute_enc_h = self.mlp_enc(self.enc_h)
+
+        if dec_z is None:
+            dec_z = enc_hs_pad.new_zeros(batch, self.dunits)
+        else:
+            dec_z = dec_z.view(batch, self.dunits)
+
+        if att_prev is None:
+            # initial attention will be [1, 0, 0, ...]
+            att_prev = enc_hs_pad.new_zeros(*enc_hs_pad.size()[:2])
+            att_prev[:, 0] = 1.0
+
+        # att_prev: utt x frame -> utt x 1 x 1 x frame
+        # -> utt x att_conv_chans x 1 x frame
+        att_conv = self.loc_conv(att_prev.view(batch, 1, 1, self.h_length))
+        # att_conv: utt x att_conv_chans x 1 x frame -> utt x frame x att_conv_chans
+        att_conv = att_conv.squeeze(2).transpose(1, 2)
+        # att_conv: utt x frame x att_conv_chans -> utt x frame x att_dim
+        att_conv = self.mlp_att(att_conv)
+
+        # dec_z_tiled: utt x frame x att_dim
+        dec_z_tiled = self.mlp_dec(dec_z).view(batch, 1, self.att_dim)
+
+        # dot with gvec
+        # utt x frame x att_dim -> utt x frame
+        e = self.gvec(torch.tanh(att_conv + self.pre_compute_enc_h + dec_z_tiled)).squeeze(2)
+
+        # NOTE consider zero padding when compute w.
+        if self.mask is None:
+            self.mask = to_device(enc_hs_pad, make_pad_mask(enc_hs_len))
+        e.masked_fill_(self.mask, -float("inf"))
+
+        # apply monotonic attention constraint (mainly for TTS)
+        if last_attended_idx is not None:
+            e = _apply_attention_constraint(e, last_attended_idx, backward_window, forward_window)
+
+        w = F.softmax(scaling * e, dim=1)
+
+        # forward attention
+        att_prev_shift = F.pad(att_prev, (1, 0))[:, :-1]
+        w = (self.trans_agent_prob * att_prev + (1 - self.trans_agent_prob) * att_prev_shift) * w
+        # NOTE: clamp is needed to avoid nan gradient
+        w = F.normalize(torch.clamp(w, 1e-6), p=1, dim=1)
+
+        # weighted sum over flames
+        # utt x hdim
+        # NOTE use bmm instead of sum(*)
+        c = torch.sum(self.enc_h * w.view(batch, self.h_length, 1), dim=1)
+
+        # update transition agent prob
+        self.trans_agent_prob = torch.sigmoid(self.mlp_ta(torch.cat([c, out_prev, dec_z], dim=1)))
+
+        return c, w

IMSToucan/Layers/ResidualBlock.py

@@ -0,0 +1,98 @@
+# -*- coding: utf-8 -*-
+
+"""
+References:
+    - https://github.com/jik876/hifi-gan
+    - https://github.com/kan-bayashi/ParallelWaveGAN
+"""
+
+import torch
+
+
+class Conv1d(torch.nn.Conv1d):
+    """
+    Conv1d module with customized initialization.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super(Conv1d, self).__init__(*args, **kwargs)
+
+    def reset_parameters(self):
+        torch.nn.init.kaiming_normal_(self.weight, nonlinearity="relu")
+        if self.bias is not None:
+            torch.nn.init.constant_(self.bias, 0.0)
+
+
+class Conv1d1x1(Conv1d):
+    """
+    1x1 Conv1d with customized initialization.
+    """
+
+    def __init__(self, in_channels, out_channels, bias):
+        super(Conv1d1x1, self).__init__(in_channels, out_channels, kernel_size=1, padding=0, dilation=1, bias=bias)
+
+
+class HiFiGANResidualBlock(torch.nn.Module):
+    """Residual block module in HiFiGAN."""
+
+    def __init__(self,
+                 kernel_size=3,
+                 channels=512,
+                 dilations=(1, 3, 5),
+                 bias=True,
+                 use_additional_convs=True,
+                 nonlinear_activation="LeakyReLU",
+                 nonlinear_activation_params={"negative_slope": 0.1}, ):
+        """
+        Initialize HiFiGANResidualBlock module.
+
+        Args:
+            kernel_size (int): Kernel size of dilation convolution layer.
+            channels (int): Number of channels for convolution layer.
+            dilations (List[int]): List of dilation factors.
+            use_additional_convs (bool): Whether to use additional convolution layers.
+            bias (bool): Whether to add bias parameter in convolution layers.
+            nonlinear_activation (str): Activation function module name.
+            nonlinear_activation_params (dict): Hyperparameters for activation function.
+        """
+        super().__init__()
+        self.use_additional_convs = use_additional_convs
+        self.convs1 = torch.nn.ModuleList()
+        if use_additional_convs:
+            self.convs2 = torch.nn.ModuleList()
+        assert kernel_size % 2 == 1, "Kernel size must be odd number."
+        for dilation in dilations:
+            self.convs1 += [torch.nn.Sequential(getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
+                                                torch.nn.Conv1d(channels,
+                                                                channels,
+                                                                kernel_size,
+                                                                1,
+                                                                dilation=dilation,
+                                                                bias=bias,
+                                                                padding=(kernel_size - 1) // 2 * dilation, ), )]
+            if use_additional_convs:
+                self.convs2 += [torch.nn.Sequential(getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
+                                                    torch.nn.Conv1d(channels,
+                                                                    channels,
+                                                                    kernel_size,
+                                                                    1,
+                                                                    dilation=1,
+                                                                    bias=bias,
+                                                                    padding=(kernel_size - 1) // 2, ), )]
+
+    def forward(self, x):
+        """
+        Calculate forward propagation.
+
+        Args:
+            x (Tensor): Input tensor (B, channels, T).
+
+        Returns:
+            Tensor: Output tensor (B, channels, T).
+        """
+        for idx in range(len(self.convs1)):
+            xt = self.convs1[idx](x)
+            if self.use_additional_convs:
+                xt = self.convs2[idx](xt)
+            x = xt + x
+        return x

IMSToucan/Layers/ResidualStack.py

@@ -0,0 +1,51 @@
+# Copyright 2019 Tomoki Hayashi
+# MIT License (https://opensource.org/licenses/MIT)
+# Adapted by Florian Lux 2021
+
+
+import torch
+
+
+class ResidualStack(torch.nn.Module):
+
+    def __init__(self, kernel_size=3, channels=32, dilation=1, bias=True, nonlinear_activation="LeakyReLU", nonlinear_activation_params={"negative_slope": 0.2},
+                 pad="ReflectionPad1d", pad_params={}, ):
+        """
+        Initialize ResidualStack module.
+
+        Args:
+            kernel_size (int): Kernel size of dilation convolution layer.
+            channels (int): Number of channels of convolution layers.
+            dilation (int): Dilation factor.
+            bias (bool): Whether to add bias parameter in convolution layers.
+            nonlinear_activation (str): Activation function module name.
+            nonlinear_activation_params (dict): Hyperparameters for activation function.
+            pad (str): Padding function module name before dilated convolution layer.
+            pad_params (dict): Hyperparameters for padding function.
+
+        """
+        super(ResidualStack, self).__init__()
+
+        # defile residual stack part
+        assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
+        self.stack = torch.nn.Sequential(getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
+                                         getattr(torch.nn, pad)((kernel_size - 1) // 2 * dilation, **pad_params),
+                                         torch.nn.Conv1d(channels, channels, kernel_size, dilation=dilation, bias=bias),
+                                         getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
+                                         torch.nn.Conv1d(channels, channels, 1, bias=bias), )
+
+        # defile extra layer for skip connection
+        self.skip_layer = torch.nn.Conv1d(channels, channels, 1, bias=bias)
+
+    def forward(self, c):
+        """
+        Calculate forward propagation.
+
+        Args:
+            c (Tensor): Input tensor (B, channels, T).
+
+        Returns:
+            Tensor: Output tensor (B, chennels, T).
+
+        """
+        return self.stack(c) + self.skip_layer(c)

IMSToucan/Layers/STFT.py

@@ -0,0 +1,118 @@
+"""
+Taken from ESPNet
+"""
+
+import torch
+from torch.functional import stft as torch_stft
+from torch_complex.tensor import ComplexTensor
+
+from ..Utility.utils import make_pad_mask
+
+
+class STFT(torch.nn.Module):
+
+    def __init__(self, n_fft=512, win_length=None, hop_length=128, window="hann", center=True, normalized=False,
+                 onesided=True):
+        super().__init__()
+        self.n_fft = n_fft
+        if win_length is None:
+            self.win_length = n_fft
+        else:
+            self.win_length = win_length
+        self.hop_length = hop_length
+        self.center = center
+        self.normalized = normalized
+        self.onesided = onesided
+        self.window = window
+
+    def extra_repr(self):
+        return (f"n_fft={self.n_fft}, "
+                f"win_length={self.win_length}, "
+                f"hop_length={self.hop_length}, "
+                f"center={self.center}, "
+                f"normalized={self.normalized}, "
+                f"onesided={self.onesided}")
+
+    def forward(self, input_wave, ilens=None):
+        """
+        STFT forward function.
+        Args:
+            input_wave: (Batch, Nsamples) or (Batch, Nsample, Channels)
+            ilens: (Batch)
+        Returns:
+            output: (Batch, Frames, Freq, 2) or (Batch, Frames, Channels, Freq, 2)
+        """
+        bs = input_wave.size(0)
+
+        if input_wave.dim() == 3:
+            multi_channel = True
+            # input: (Batch, Nsample, Channels) -> (Batch * Channels, Nsample)
+            input_wave = input_wave.transpose(1, 2).reshape(-1, input_wave.size(1))
+        else:
+            multi_channel = False
+
+        # output: (Batch, Freq, Frames, 2=real_imag)
+        # or (Batch, Channel, Freq, Frames, 2=real_imag)
+        if self.window is not None:
+            window_func = getattr(torch, f"{self.window}_window")
+            window = window_func(self.win_length, dtype=input_wave.dtype, device=input_wave.device)
+        else:
+            window = None
+
+        complex_output = torch_stft(input=input_wave,
+                                    n_fft=self.n_fft,
+                                    win_length=self.win_length,
+                                    hop_length=self.hop_length,
+                                    center=self.center,
+                                    window=window,
+                                    normalized=self.normalized,
+                                    onesided=self.onesided,
+                                    return_complex=True)
+        output = torch.view_as_real(complex_output)
+        # output: (Batch, Freq, Frames, 2=real_imag)
+        # -> (Batch, Frames, Freq, 2=real_imag)
+        output = output.transpose(1, 2)
+        if multi_channel:
+            # output: (Batch * Channel, Frames, Freq, 2=real_imag)
+            # -> (Batch, Frame, Channel, Freq, 2=real_imag)
+            output = output.view(bs, -1, output.size(1), output.size(2), 2).transpose(1, 2)
+
+        if ilens is not None:
+            if self.center:
+                pad = self.win_length // 2
+                ilens = ilens + 2 * pad
+
+            olens = (ilens - self.win_length) // self.hop_length + 1
+            output.masked_fill_(make_pad_mask(olens, output, 1), 0.0)
+        else:
+            olens = None
+
+        return output, olens
+
+    def inverse(self, input, ilens=None):
+        """
+        Inverse STFT.
+        Args:
+            input: Tensor(batch, T, F, 2) or ComplexTensor(batch, T, F)
+            ilens: (batch,)
+        Returns:
+            wavs: (batch, samples)
+            ilens: (batch,)
+        """
+        istft = torch.functional.istft
+
+        if self.window is not None:
+            window_func = getattr(torch, f"{self.window}_window")
+            window = window_func(self.win_length, dtype=input.dtype, device=input.device)
+        else:
+            window = None
+
+        if isinstance(input, ComplexTensor):
+            input = torch.stack([input.real, input.imag], dim=-1)
+        assert input.shape[-1] == 2
+        input = input.transpose(1, 2)
+
+        wavs = istft(input, n_fft=self.n_fft, hop_length=self.hop_length, win_length=self.win_length, window=window, center=self.center,
+                     normalized=self.normalized, onesided=self.onesided, length=ilens.max() if ilens is not None else ilens)
+
+        return wavs, ilens

IMSToucan/Layers/Swish.py

@@ -0,0 +1,18 @@
+# Copyright 2020 Johns Hopkins University (Shinji Watanabe)
+#                Northwestern Polytechnical University (Pengcheng Guo)
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+# Adapted by Florian Lux 2021
+
+import torch
+
+
+class Swish(torch.nn.Module):
+    """
+    Construct an Swish activation function for Conformer.
+    """
+
+    def forward(self, x):
+        """
+        Return Swish activation function.
+        """
+        return x * torch.sigmoid(x)

IMSToucan/Layers/VariancePredictor.py

@@ -0,0 +1,65 @@
+# Copyright 2019 Tomoki Hayashi
+# MIT License (https://opensource.org/licenses/MIT)
+# Adapted by Florian Lux 2021
+
+from abc import ABC
+
+import torch
+
+from .LayerNorm import LayerNorm
+
+
+class VariancePredictor(torch.nn.Module, ABC):
+    """
+    Variance predictor module.
+
+    This is a module of variance predictor described in `FastSpeech 2:
+    Fast and High-Quality End-to-End Text to Speech`_.
+
+    .. _`FastSpeech 2: Fast and High-Quality End-to-End Text to Speech`:
+        https://arxiv.org/abs/2006.04558
+
+    """
+
+    def __init__(self, idim, n_layers=2, n_chans=384, kernel_size=3, bias=True, dropout_rate=0.5, ):
+        """
+        Initilize duration predictor module.
+
+        Args:
+            idim (int): Input dimension.
+            n_layers (int, optional): Number of convolutional layers.
+            n_chans (int, optional): Number of channels of convolutional layers.
+            kernel_size (int, optional): Kernel size of convolutional layers.
+            dropout_rate (float, optional): Dropout rate.
+        """
+        super().__init__()
+        self.conv = torch.nn.ModuleList()
+        for idx in range(n_layers):
+            in_chans = idim if idx == 0 else n_chans
+            self.conv += [
+                torch.nn.Sequential(torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=(kernel_size - 1) // 2, bias=bias, ), torch.nn.ReLU(),
+                                    LayerNorm(n_chans, dim=1), torch.nn.Dropout(dropout_rate), )]
+        self.linear = torch.nn.Linear(n_chans, 1)
+
+    def forward(self, xs, x_masks=None):
+        """
+        Calculate forward propagation.
+
+        Args:
+            xs (Tensor): Batch of input sequences (B, Tmax, idim).
+            x_masks (ByteTensor, optional):
+                Batch of masks indicating padded part (B, Tmax).
+
+        Returns:
+            Tensor: Batch of predicted sequences (B, Tmax, 1).
+        """
+        xs = xs.transpose(1, -1)  # (B, idim, Tmax)
+        for f in self.conv:
+            xs = f(xs)  # (B, C, Tmax)
+
+        xs = self.linear(xs.transpose(1, 2))  # (B, Tmax, 1)
+
+        if x_masks is not None:
+            xs = xs.masked_fill(x_masks, 0.0)
+
+        return xs

IMSToucan/Preprocessing/ArticulatoryCombinedTextFrontend.py

@@ -0,0 +1,323 @@
+import re
+import sys
+
+import panphon
+import phonemizer
+import torch
+
+from .papercup_features import generate_feature_table
+
+
+class ArticulatoryCombinedTextFrontend:
+
+    def __init__(self,
+                 language,
+                 use_word_boundaries=False,  # goes together well with 
+                 # parallel models and a aligner. Doesn't go together 
+                 # well with autoregressive models.
+                 use_explicit_eos=True,
+                 use_prosody=False,  # unfortunately the non-segmental
+                 # nature of prosodic markers mixed with the sequential
+                 # phonemes hurts the performance of end-to-end models a
+                 # lot, even though one might think enriching the input
+                 # with such information would help.
+                 use_lexical_stress=False,
+                 silent=True,
+                 allow_unknown=False,
+                 add_silence_to_end=True,
+                 strip_silence=True):
+        """
+        Mostly preparing ID lookups
+        """
+        self.strip_silence = strip_silence
+        self.use_word_boundaries = use_word_boundaries
+        self.allow_unknown = allow_unknown
+        self.use_explicit_eos = use_explicit_eos
+        self.use_prosody = use_prosody
+        self.use_stress = use_lexical_stress
+        self.add_silence_to_end = add_silence_to_end
+        self.feature_table = panphon.FeatureTable()
+
+        if language == "en":
+            self.g2p_lang = "en-us"
+            self.expand_abbreviations = english_text_expansion
+            if not silent:
+                print("Created an English Text-Frontend")
+
+        elif language == "de":
+            self.g2p_lang = "de"
+            self.expand_abbreviations = lambda x: x
+            if not silent:
+                print("Created a German Text-Frontend")
+
+        elif language == "el":
+            self.g2p_lang = "el"
+            self.expand_abbreviations = lambda x: x
+            if not silent:
+                print("Created a Greek Text-Frontend")
+
+        elif language == "es":
+            self.g2p_lang = "es"
+            self.expand_abbreviations = lambda x: x
+            if not silent:
+                print("Created a Spanish Text-Frontend")
+
+        elif language == "fi":
+            self.g2p_lang = "fi"
+            self.expand_abbreviations = lambda x: x
+            if not silent:
+                print("Created a Finnish Text-Frontend")
+
+        elif language == "ru":
+            self.g2p_lang = "ru"
+            self.expand_abbreviations = lambda x: x
+            if not silent:
+                print("Created a Russian Text-Frontend")
+
+        elif language == "hu":
+            self.g2p_lang = "hu"
+            self.expand_abbreviations = lambda x: x
+            if not silent:
+                print("Created a Hungarian Text-Frontend")
+
+        elif language == "nl":
+            self.g2p_lang = "nl"
+            self.expand_abbreviations = lambda x: x
+            if not silent:
+                print("Created a Dutch Text-Frontend")
+
+        elif language == "fr":
+            self.g2p_lang = "fr-fr"
+            self.expand_abbreviations = lambda x: x
+            if not silent:
+                print("Created a French Text-Frontend")
+
+        elif language == "it":
+            self.g2p_lang = "it"
+            self.expand_abbreviations = lambda x: x
+            if not silent:
+                print("Created a Italian Text-Frontend")
+
+        elif language == "pt":
+            self.g2p_lang = "pt"
+            self.expand_abbreviations = lambda x: x
+            if not silent:
+                print("Created a Portuguese Text-Frontend")
+
+        elif language == "pl":
+            self.g2p_lang = "pl"
+            self.expand_abbreviations = lambda x: x
+            if not silent:
+                print("Created a Polish Text-Frontend")
+
+        # remember to also update get_language_id() when adding something here
+
+        else:
+            print("Language not supported yet")
+            sys.exit()
+
+        self.phone_to_vector_papercup = generate_feature_table()
+
+        self.phone_to_vector = dict()
+        for phone in self.phone_to_vector_papercup:
+            panphon_features = self.feature_table.word_to_vector_list(phone, numeric=True)
+            if panphon_features == []:
+                panphon_features = [[0] * 24]
+            papercup_features = self.phone_to_vector_papercup[phone]
+            self.phone_to_vector[phone] = papercup_features + panphon_features[0]
+
+        self.phone_to_id = {  # this lookup must be updated manually, because the only
+            # other way would be extracting them from a set, which can be non-deterministic
+            '~': 0,
+            '#': 1,
+            '?': 2,
+            '!': 3,
+            '.': 4,
+            'ɜ': 5,
+            'ɫ': 6,
+            'ə': 7,
+            'ɚ': 8,
+            'a': 9,
+            'ð': 10,
+            'ɛ': 11,
+            'ɪ': 12,
+            'ᵻ': 13,
+            'ŋ': 14,
+            'ɔ': 15,
+            'ɒ': 16,
+            'ɾ': 17,
+            'ʃ': 18,
+            'θ': 19,
+            'ʊ': 20,
+            'ʌ': 21,
+            'ʒ': 22,
+            'æ': 23,
+            'b': 24,
+            'ʔ': 25,
+            'd': 26,
+            'e': 27,
+            'f': 28,
+            'g': 29,
+            'h': 30,
+            'i': 31,
+            'j': 32,
+            'k': 33,
+            'l': 34,
+            'm': 35,
+            'n': 36,
+            'ɳ': 37,
+            'o': 38,
+            'p': 39,
+            'ɡ': 40,
+            'ɹ': 41,
+            'r': 42,
+            's': 43,
+            't': 44,
+            'u': 45,
+            'v': 46,
+            'w': 47,
+            'x': 48,
+            'z': 49,
+            'ʀ': 50,
+            'ø': 51,
+            'ç': 52,
+            'ɐ': 53,
+            'œ': 54,
+            'y': 55,
+            'ʏ': 56,
+            'ɑ': 57,
+            'c': 58,
+            'ɲ': 59,
+            'ɣ': 60,
+            'ʎ': 61,
+            'β': 62,
+            'ʝ': 63,
+            'ɟ': 64,
+            'q': 65,
+            'ɕ': 66,
+            'ʲ': 67,
+            'ɭ': 68,
+            'ɵ': 69,
+            'ʑ': 70,
+            'ʋ': 71,
+            'ʁ': 72,
+            'ɨ': 73,
+            'ʂ': 74,
+            'ɬ': 75,
+            }  # for the states of the ctc loss and dijkstra/mas in the aligner
+
+        self.id_to_phone = {v: k for k, v in self.phone_to_id.items()}
+
+    def string_to_tensor(self, text, view=False, device="cpu", handle_missing=True, input_phonemes=False):
+        """
+        Fixes unicode errors, expands some abbreviations,
+        turns graphemes into phonemes and then vectorizes
+        the sequence as articulatory features
+        """
+        if input_phonemes:
+            phones = text
+        else:
+            phones = self.get_phone_string(text=text, include_eos_symbol=True)
+        if view:
+            print("Phonemes: \n{}\n".format(phones))
+        phones_vector = list()
+        # turn into numeric vectors
+        for char in phones:
+            if handle_missing:
+                try:
+                    phones_vector.append(self.phone_to_vector[char])
+                except KeyError:
+                    print("unknown phoneme: {}".format(char))
+            else:
+                phones_vector.append(self.phone_to_vector[char])  # leave error handling to elsewhere
+
+        return torch.Tensor(phones_vector, device=device)
+
+    def get_phone_string(self, text, include_eos_symbol=True):
+        # expand abbreviations
+        utt = self.expand_abbreviations(text)
+        # phonemize
+        phones = phonemizer.phonemize(utt,
+                                      language_switch='remove-flags',
+                                      backend="espeak",
+                                      language=self.g2p_lang,
+                                      preserve_punctuation=True,
+                                      strip=True,
+                                      punctuation_marks=';:,.!?¡¿—…"«»“”~/',
+                                      with_stress=self.use_stress).replace(";", ",").replace("/", " ").replace("—", "") \
+            .replace(":", ",").replace('"', ",").replace("-", ",").replace("...", ",").replace("-", ",").replace("\n", " ") \
+            .replace("\t", " ").replace("¡", "").replace("¿", "").replace(",", "~").replace(" ̃", "").replace('̩', "").replace("̃", "").replace("̪", "")
+        # less than 1 wide characters hidden here
+        phones = re.sub("~+", "~", phones)
+        if not self.use_prosody:
+            # retain ~ as heuristic pause marker, even though all other symbols are removed with this option.
+            # also retain . ? and ! since they can be indicators for the stop token
+            phones = phones.replace("ˌ", "").replace("ː", "").replace("ˑ", "") \
+                .replace("˘", "").replace("|", "").replace("‖", "")
+        if not self.use_word_boundaries:
+            phones = phones.replace(" ", "")
+        else:
+            phones = re.sub(r"\s+", " ", phones)
+            phones = re.sub(" ", "~", phones)
+        if self.strip_silence:
+            phones = phones.lstrip("~").rstrip("~")
+        if self.add_silence_to_end:
+            phones += "~"  # adding a silence in the end during add_silence_to_end produces more natural sounding prosody
+        if include_eos_symbol:
+            phones += "#"
+
+        phones = "~" + phones
+        phones = re.sub("~+", "~", phones)
+
+        return phones
+
+
+def english_text_expansion(text):
+    """
+    Apply as small part of the tacotron style text cleaning pipeline, suitable for e.g. LJSpeech.
+    See https://github.com/keithito/tacotron/
+    Careful: Only apply to english datasets. Different languages need different cleaners.
+    """
+    _abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in
+                      [('Mrs.', 'misess'), ('Mr.', 'mister'), ('Dr.', 'doctor'), ('St.', 'saint'), ('Co.', 'company'), ('Jr.', 'junior'), ('Maj.', 'major'),
+                       ('Gen.', 'general'), ('Drs.', 'doctors'), ('Rev.', 'reverend'), ('Lt.', 'lieutenant'), ('Hon.', 'honorable'), ('Sgt.', 'sergeant'),
+                       ('Capt.', 'captain'), ('Esq.', 'esquire'), ('Ltd.', 'limited'), ('Col.', 'colonel'), ('Ft.', 'fort')]]
+    for regex, replacement in _abbreviations:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def get_language_id(language):
+    if language == "en":
+        return torch.LongTensor([0])
+    elif language == "de":
+        return torch.LongTensor([1])
+    elif language == "el":
+        return torch.LongTensor([2])
+    elif language == "es":
+        return torch.LongTensor([3])
+    elif language == "fi":
+        return torch.LongTensor([4])
+    elif language == "ru":
+        return torch.LongTensor([5])
+    elif language == "hu":
+        return torch.LongTensor([6])
+    elif language == "nl":
+        return torch.LongTensor([7])
+    elif language == "fr":
+        return torch.LongTensor([8])
+    elif language == "pt":
+        return torch.LongTensor([9])
+    elif language == "pl":
+        return torch.LongTensor([10])
+    elif language == "it":
+        return torch.LongTensor([11])
+
+
+if __name__ == '__main__':
+    # test an English utterance
+    tfr_en = ArticulatoryCombinedTextFrontend(language="en")
+    print(tfr_en.string_to_tensor("This is a complex sentence, it even has a pause! But can it do this? Nice.", view=True))
+
+    tfr_en = ArticulatoryCombinedTextFrontend(language="de")
+    print(tfr_en.string_to_tensor("Alles klar, jetzt testen wir einen deutschen Satz. Ich hoffe es gibt nicht mehr viele unspezifizierte Phoneme.", view=True))

IMSToucan/Preprocessing/AudioPreprocessor.py

@@ -0,0 +1,166 @@
+import librosa
+import librosa.core as lb
+import librosa.display as lbd
+import matplotlib.pyplot as plt
+import numpy
+import numpy as np
+import pyloudnorm as pyln
+import torch
+from torchaudio.transforms import Resample
+
+
+class AudioPreprocessor:
+
+    def __init__(self, input_sr, output_sr=None, melspec_buckets=80, hop_length=256, n_fft=1024, cut_silence=False, device="cpu"):
+        """
+        The parameters are by default set up to do well
+        on a 16kHz signal. A different sampling rate may
+        require different hop_length and n_fft (e.g.
+        doubling frequency --> doubling hop_length and
+        doubling n_fft)
+        """
+        self.cut_silence = cut_silence
+        self.device = device
+        self.sr = input_sr
+        self.new_sr = output_sr
+        self.hop_length = hop_length
+        self.n_fft = n_fft
+        self.mel_buckets = melspec_buckets
+        self.meter = pyln.Meter(input_sr)
+        self.final_sr = input_sr
+        if cut_silence:
+            torch.hub._validate_not_a_forked_repo = lambda a, b, c: True  # torch 1.9 has a bug in the hub loading, this is a workaround
+            # careful: assumes 16kHz or 8kHz audio
+            self.silero_model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',
+                                                      model='silero_vad',
+                                                      force_reload=False,
+                                                      onnx=False,
+                                                      verbose=False)
+            (self.get_speech_timestamps,
+             self.save_audio,
+             self.read_audio,
+             self.VADIterator,
+             self.collect_chunks) = utils
+            self.silero_model = self.silero_model.to(self.device)
+        if output_sr is not None and output_sr != input_sr:
+            self.resample = Resample(orig_freq=input_sr, new_freq=output_sr).to(self.device)
+            self.final_sr = output_sr
+        else:
+            self.resample = lambda x: x
+
+    def cut_silence_from_audio(self, audio):
+        """
+        https://github.com/snakers4/silero-vad
+        """
+        return self.collect_chunks(self.get_speech_timestamps(audio, self.silero_model, sampling_rate=self.final_sr), audio)
+
+    def to_mono(self, x):
+        """
+        make sure we deal with a 1D array
+        """
+        if len(x.shape) == 2:
+            return lb.to_mono(numpy.transpose(x))
+        else:
+            return x
+
+    def normalize_loudness(self, audio):
+        """
+        normalize the amplitudes according to
+        their decibels, so this should turn any
+        signal with different magnitudes into
+        the same magnitude by analysing loudness
+        """
+        loudness = self.meter.integrated_loudness(audio)
+        loud_normed = pyln.normalize.loudness(audio, loudness, -30.0)
+        peak = numpy.amax(numpy.abs(loud_normed))
+        peak_normed = numpy.divide(loud_normed, peak)
+        return peak_normed
+
+    def logmelfilterbank(self, audio, sampling_rate, fmin=40, fmax=8000, eps=1e-10):
+        """
+        Compute log-Mel filterbank
+
+        one day this could be replaced by torchaudio's internal log10(melspec(audio)), but
+        for some reason it gives slightly different results, so in order not to break backwards
+        compatibility, this is kept for now. If there is ever a reason to completely re-train
+        all models, this would be a good opportunity to make the switch.
+        """
+        if isinstance(audio, torch.Tensor):
+            audio = audio.numpy()
+        # get amplitude spectrogram
+        x_stft = librosa.stft(audio, n_fft=self.n_fft, hop_length=self.hop_length, win_length=None, window="hann", pad_mode="reflect")
+        spc = np.abs(x_stft).T
+        # get mel basis
+        fmin = 0 if fmin is None else fmin
+        fmax = sampling_rate / 2 if fmax is None else fmax
+        mel_basis = librosa.filters.mel(sampling_rate, self.n_fft, self.mel_buckets, fmin, fmax)
+        # apply log and return
+        return torch.Tensor(np.log10(np.maximum(eps, np.dot(spc, mel_basis.T)))).transpose(0, 1)
+
+    def normalize_audio(self, audio):
+        """
+        one function to apply them all in an
+        order that makes sense.
+        """
+        audio = self.to_mono(audio)
+        audio = self.normalize_loudness(audio)
+        audio = torch.Tensor(audio).to(self.device)
+        audio = self.resample(audio)
+        if self.cut_silence:
+            audio = self.cut_silence_from_audio(audio)
+        return audio.to("cpu")
+
+    def visualize_cleaning(self, unclean_audio):
+        """
+        displays Mel Spectrogram of unclean audio
+        and then displays Mel Spectrogram of the
+        cleaned version.
+        """
+        fig, ax = plt.subplots(nrows=2, ncols=1)
+        unclean_audio_mono = self.to_mono(unclean_audio)
+        unclean_spec = self.audio_to_mel_spec_tensor(unclean_audio_mono, normalize=False).numpy()
+        clean_spec = self.audio_to_mel_spec_tensor(unclean_audio_mono, normalize=True).numpy()
+        lbd.specshow(unclean_spec, sr=self.sr, cmap='GnBu', y_axis='mel', ax=ax[0], x_axis='time')
+        ax[0].set(title='Uncleaned Audio')
+        ax[0].label_outer()
+        if self.new_sr is not None:
+            lbd.specshow(clean_spec, sr=self.new_sr, cmap='GnBu', y_axis='mel', ax=ax[1], x_axis='time')
+        else:
+            lbd.specshow(clean_spec, sr=self.sr, cmap='GnBu', y_axis='mel', ax=ax[1], x_axis='time')
+        ax[1].set(title='Cleaned Audio')
+        ax[1].label_outer()
+        plt.show()
+
+    def audio_to_wave_tensor(self, audio, normalize=True):
+        if normalize:
+            return self.normalize_audio(audio)
+        else:
+            if isinstance(audio, torch.Tensor):
+                return audio
+            else:
+                return torch.Tensor(audio)
+
+    def audio_to_mel_spec_tensor(self, audio, normalize=True, explicit_sampling_rate=None):
+        """
+        explicit_sampling_rate is for when
+        normalization has already been applied
+        and that included resampling. No way
+        to detect the current sr of the incoming
+        audio
+        """
+        if explicit_sampling_rate is None:
+            if normalize:
+                audio = self.normalize_audio(audio)
+                return self.logmelfilterbank(audio=audio, sampling_rate=self.final_sr)
+            return self.logmelfilterbank(audio=audio, sampling_rate=self.sr)
+        if normalize:
+            audio = self.normalize_audio(audio)
+        return self.logmelfilterbank(audio=audio, sampling_rate=explicit_sampling_rate)
+
+
+if __name__ == '__main__':
+    import soundfile
+
+    wav, sr = soundfile.read("../audios/test.wav")
+    ap = AudioPreprocessor(input_sr=sr, output_sr=16000)
+    ap.visualize_cleaning(wav)

IMSToucan/Preprocessing/ProsodicConditionExtractor.py

@@ -0,0 +1,40 @@
+import soundfile as sf
+import torch
+import torch.multiprocessing
+import torch.multiprocessing
+from numpy import trim_zeros
+from speechbrain.pretrained import EncoderClassifier
+
+from .AudioPreprocessor import AudioPreprocessor
+
+
+class ProsodicConditionExtractor:
+
+    def __init__(self, sr, device=torch.device("cpu")):
+        self.ap = AudioPreprocessor(input_sr=sr, output_sr=16000, melspec_buckets=80, hop_length=256, n_fft=1024, cut_silence=False)
+        # https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb
+        self.speaker_embedding_func_ecapa = EncoderClassifier.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb",
+                                                                           run_opts={"device": str(device)},
+                                                                           savedir="Models/SpeakerEmbedding/speechbrain_speaker_embedding_ecapa")
+        # https://huggingface.co/speechbrain/spkrec-xvect-voxceleb
+        self.speaker_embedding_func_xvector = EncoderClassifier.from_hparams(source="speechbrain/spkrec-xvect-voxceleb",
+                                                                             run_opts={"device": str(device)},
+                                                                             savedir="Models/SpeakerEmbedding/speechbrain_speaker_embedding_xvector")
+
+    def extract_condition_from_reference_wave(self, wave, already_normalized=False):
+        if already_normalized:
+            norm_wave = wave
+        else:
+            norm_wave = self.ap.audio_to_wave_tensor(normalize=True, audio=wave)
+            norm_wave = torch.tensor(trim_zeros(norm_wave.numpy()))
+        spk_emb_ecapa = self.speaker_embedding_func_ecapa.encode_batch(wavs=norm_wave.unsqueeze(0)).squeeze()
+        spk_emb_xvector = self.speaker_embedding_func_xvector.encode_batch(wavs=norm_wave.unsqueeze(0)).squeeze()
+        combined_utt_condition = torch.cat([spk_emb_ecapa.cpu(),
+                                            spk_emb_xvector.cpu()], dim=0)
+        return combined_utt_condition
+
+
+if __name__ == '__main__':
+    wave, sr = sf.read("../audios/1.wav")
+    ext = ProsodicConditionExtractor(sr=sr)
+    print(ext.extract_condition_from_reference_wave(wave=wave).shape)

IMSToucan/Preprocessing/papercup_features.py

@@ -0,0 +1,637 @@
+# Derived from  an open-source resource provided by Papercup Technologies Limited
+# Resource-Author: Marlene Staib
+# Modified by Florian Lux, 2021
+
+def generate_feature_lookup():
+    return {
+        '~': {'symbol_type': 'silence'},
+        '#': {'symbol_type': 'end of sentence'},
+        '?': {'symbol_type': 'questionmark'},
+        '!': {'symbol_type': 'exclamationmark'},
+        '.': {'symbol_type': 'fullstop'},
+        'ɜ': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'central',
+            'vowel_openness'   : 'open-mid',
+            'vowel_roundedness': 'unrounded',
+            },
+        'ɫ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'alveolar',
+            'consonant_manner': 'lateral-approximant',
+            },
+        'ə': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'central',
+            'vowel_openness'   : 'mid',
+            'vowel_roundedness': 'unrounded',
+            },
+        'ɚ': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'central',
+            'vowel_openness'   : 'mid',
+            'vowel_roundedness': 'unrounded',
+            },
+        'a': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'front',
+            'vowel_openness'   : 'open',
+            'vowel_roundedness': 'unrounded',
+            },
+        'ð': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'dental',
+            'consonant_manner': 'fricative'
+            },
+        'ɛ': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'front',
+            'vowel_openness'   : 'open-mid',
+            'vowel_roundedness': 'unrounded',
+            },
+        'ɪ': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'front_central',
+            'vowel_openness'   : 'close_close-mid',
+            'vowel_roundedness': 'unrounded',
+            },
+        'ᵻ': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'central',
+            'vowel_openness'   : 'close',
+            'vowel_roundedness': 'unrounded',
+            },
+        'ŋ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'velar',
+            'consonant_manner': 'nasal'
+            },
+        'ɔ': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'back',
+            'vowel_openness'   : 'open-mid',
+            'vowel_roundedness': 'rounded',
+            },
+        'ɒ': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'back',
+            'vowel_openness'   : 'open',
+            'vowel_roundedness': 'rounded',
+            },
+        'ɾ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'alveolar',
+            'consonant_manner': 'tap'
+            },
+        'ʃ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'unvoiced',
+            'consonant_place' : 'postalveolar',
+            'consonant_manner': 'fricative'
+            },
+        'θ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'unvoiced',
+            'consonant_place' : 'dental',
+            'consonant_manner': 'fricative'
+            },
+        'ʊ': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'central_back',
+            'vowel_openness'   : 'close_close-mid',
+            'vowel_roundedness': 'unrounded'
+            },
+        'ʌ': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'back',
+            'vowel_openness'   : 'open-mid',
+            'vowel_roundedness': 'unrounded'
+            },
+        'ʒ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'postalveolar',
+            'consonant_manner': 'fricative'
+            },
+        'æ': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'front',
+            'vowel_openness'   : 'open-mid_open',
+            'vowel_roundedness': 'unrounded'
+            },
+        'b': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'bilabial',
+            'consonant_manner': 'stop'
+            },
+        'ʔ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'unvoiced',
+            'consonant_place' : 'glottal',
+            'consonant_manner': 'stop'
+            },
+        'd': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'alveolar',
+            'consonant_manner': 'stop'
+            },
+        'e': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'front',
+            'vowel_openness'   : 'close-mid',
+            'vowel_roundedness': 'unrounded'
+            },
+        'f': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'unvoiced',
+            'consonant_place' : 'labiodental',
+            'consonant_manner': 'fricative'
+            },
+        'g': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'velar',
+            'consonant_manner': 'stop'
+            },
+        'h': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'unvoiced',
+            'consonant_place' : 'glottal',
+            'consonant_manner': 'fricative'
+            },
+        'i': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'front',
+            'vowel_openness'   : 'close',
+            'vowel_roundedness': 'unrounded'
+            },
+        'j': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'palatal',
+            'consonant_manner': 'approximant'
+            },
+        'k': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'unvoiced',
+            'consonant_place' : 'velar',
+            'consonant_manner': 'stop'
+            },
+        'l': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'alveolar',
+            'consonant_manner': 'lateral-approximant'
+            },
+        'm': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'bilabial',
+            'consonant_manner': 'nasal'
+            },
+        'n': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'alveolar',
+            'consonant_manner': 'nasal'
+            },
+        'ɳ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'palatal',
+            'consonant_manner': 'nasal'
+            },
+        'o': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'back',
+            'vowel_openness'   : 'close-mid',
+            'vowel_roundedness': 'rounded'
+            },
+        'p': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'unvoiced',
+            'consonant_place' : 'bilabial',
+            'consonant_manner': 'stop'
+            },
+        'ɡ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'velar',
+            'consonant_manner': 'stop'
+            },
+        'ɹ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'alveolar',
+            'consonant_manner': 'approximant'
+            },
+        'r': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'alveolar',
+            'consonant_manner': 'trill'
+            },
+        's': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'unvoiced',
+            'consonant_place' : 'alveolar',
+            'consonant_manner': 'fricative'
+            },
+        't': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'unvoiced',
+            'consonant_place' : 'alveolar',
+            'consonant_manner': 'stop'
+            },
+        'u': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'back',
+            'vowel_openness'   : 'close',
+            'vowel_roundedness': 'rounded',
+            },
+        'v': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'labiodental',
+            'consonant_manner': 'fricative'
+            },
+        'w': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'labial-velar',
+            'consonant_manner': 'approximant'
+            },
+        'x': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'unvoiced',
+            'consonant_place' : 'velar',
+            'consonant_manner': 'fricative'
+            },
+        'z': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'alveolar',
+            'consonant_manner': 'fricative'
+            },
+        'ʀ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'uvular',
+            'consonant_manner': 'trill'
+            },
+        'ø': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'front',
+            'vowel_openness'   : 'close-mid',
+            'vowel_roundedness': 'rounded'
+            },
+        'ç': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'unvoiced',
+            'consonant_place' : 'palatal',
+            'consonant_manner': 'fricative'
+            },
+        'ɐ': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'central',
+            'vowel_openness'   : 'open',
+            'vowel_roundedness': 'unrounded'
+            },
+        'œ': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'front',
+            'vowel_openness'   : 'open-mid',
+            'vowel_roundedness': 'rounded'
+            },
+        'y': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'front',
+            'vowel_openness'   : 'close',
+            'vowel_roundedness': 'rounded'
+            },
+        'ʏ': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'front_central',
+            'vowel_openness'   : 'close_close-mid',
+            'vowel_roundedness': 'rounded'
+            },
+        'ɑ': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'back',
+            'vowel_openness'   : 'open',
+            'vowel_roundedness': 'unrounded'
+            },
+        'c': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'unvoiced',
+            'consonant_place' : 'palatal',
+            'consonant_manner': 'stop'
+            },
+        'ɲ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'palatal',
+            'consonant_manner': 'nasal'
+            },
+        'ɣ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'velar',
+            'consonant_manner': 'fricative'
+            },
+        'ʎ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'palatal',
+            'consonant_manner': 'lateral-approximant'
+            },
+        'β': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'bilabial',
+            'consonant_manner': 'fricative'
+            },
+        'ʝ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'palatal',
+            'consonant_manner': 'fricative'
+            },
+        'ɟ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'palatal',
+            'consonant_manner': 'stop'
+            },
+        'q': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'unvoiced',
+            'consonant_place' : 'uvular',
+            'consonant_manner': 'stop'
+            },
+        'ɕ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'unvoiced',
+            'consonant_place' : 'alveolopalatal',
+            'consonant_manner': 'fricative'
+            },
+        'ʲ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'palatal',
+            'consonant_manner': 'approximant'
+            },
+        'ɭ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'palatal',  # should be retroflex, but palatal should be close enough
+            'consonant_manner': 'lateral-approximant'
+            },
+        'ɵ': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'central',
+            'vowel_openness'   : 'open-mid',
+            'vowel_roundedness': 'rounded'
+            },
+        'ʑ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'alveolopalatal',
+            'consonant_manner': 'fricative'
+            },
+        'ʋ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'labiodental',
+            'consonant_manner': 'approximant'
+            },
+        'ʁ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'voiced',
+            'consonant_place' : 'uvular',
+            'consonant_manner': 'fricative'
+            },
+        'ɨ': {
+            'symbol_type'      : 'phoneme',
+            'vowel_consonant'  : 'vowel',
+            'VUV'              : 'voiced',
+            'vowel_frontness'  : 'central',
+            'vowel_openness'   : 'close',
+            'vowel_roundedness': 'unrounded'
+            },
+        'ʂ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'unvoiced',
+            'consonant_place' : 'palatal',  # should be retroflex, but palatal should be close enough
+            'consonant_manner': 'fricative'
+            },
+        'ɬ': {
+            'symbol_type'     : 'phoneme',
+            'vowel_consonant' : 'consonant',
+            'VUV'             : 'unvoiced',
+            'consonant_place' : 'alveolar',  # should be noted it's also lateral, but should be close enough
+            'consonant_manner': 'fricative'
+            },
+        }  # REMEMBER to also add the phonemes added here to the ID lookup table in the TextFrontend as the new highest ID
+
+
+def generate_feature_table():
+    ipa_to_phonemefeats = generate_feature_lookup()
+
+    feat_types = set()
+    for ipa in ipa_to_phonemefeats:
+        if len(ipa) == 1:
+            [feat_types.add(feat) for feat in ipa_to_phonemefeats[ipa].keys()]
+
+    feat_to_val_set = dict()
+    for feat in feat_types:
+        feat_to_val_set[feat] = set()
+    for ipa in ipa_to_phonemefeats:
+        if len(ipa) == 1:
+            for feat in ipa_to_phonemefeats[ipa]:
+                feat_to_val_set[feat].add(ipa_to_phonemefeats[ipa][feat])
+
+    # print(feat_to_val_set)
+
+    value_list = set()
+    for val_set in [feat_to_val_set[feat] for feat in feat_to_val_set]:
+        for value in val_set:
+            value_list.add(value)
+    # print("{")
+    # for index, value in enumerate(list(value_list)):
+    #     print('"{}":{},'.format(value,index))
+    # print("}")
+
+    value_to_index = {
+        "dental"             : 0,
+        "postalveolar"       : 1,
+        "mid"                : 2,
+        "close-mid"          : 3,
+        "vowel"              : 4,
+        "silence"            : 5,
+        "consonant"          : 6,
+        "close"              : 7,
+        "velar"              : 8,
+        "stop"               : 9,
+        "palatal"            : 10,
+        "nasal"              : 11,
+        "glottal"            : 12,
+        "central"            : 13,
+        "back"               : 14,
+        "approximant"        : 15,
+        "uvular"             : 16,
+        "open-mid"           : 17,
+        "front_central"      : 18,
+        "front"              : 19,
+        "end of sentence"    : 20,
+        "labiodental"        : 21,
+        "close_close-mid"    : 22,
+        "labial-velar"       : 23,
+        "unvoiced"           : 24,
+        "central_back"       : 25,
+        "trill"              : 26,
+        "rounded"            : 27,
+        "open-mid_open"      : 28,
+        "tap"                : 29,
+        "alveolar"           : 30,
+        "bilabial"           : 31,
+        "phoneme"            : 32,
+        "open"               : 33,
+        "fricative"          : 34,
+        "unrounded"          : 35,
+        "lateral-approximant": 36,
+        "voiced"             : 37,
+        "questionmark"       : 38,
+        "exclamationmark"    : 39,
+        "fullstop"           : 40,
+        "alveolopalatal"     : 41
+        }
+
+    phone_to_vector = dict()
+    for ipa in ipa_to_phonemefeats:
+        if len(ipa) == 1:
+            phone_to_vector[ipa] = [0] * sum([len(values) for values in [feat_to_val_set[feat] for feat in feat_to_val_set]])
+            for feat in ipa_to_phonemefeats[ipa]:
+                if ipa_to_phonemefeats[ipa][feat] in value_to_index:
+                    phone_to_vector[ipa][value_to_index[ipa_to_phonemefeats[ipa][feat]]] = 1
+
+    for feat in feat_to_val_set:
+        for value in feat_to_val_set[feat]:
+            if value not in value_to_index:
+                print(f"Unknown feature value in featureset! {value}")
+
+    # print(f"{sum([len(values) for values in [feat_to_val_set[feat] for feat in feat_to_val_set]])} should be 42")
+
+    return phone_to_vector
+
+
+def generate_phone_to_id_lookup():
+    ipa_to_phonemefeats = generate_feature_lookup()
+    count = 0
+    phone_to_id = dict()
+    for key in sorted(list(ipa_to_phonemefeats)):  # careful: non-deterministic
+        phone_to_id[key] = count
+        count += 1
+    return phone_to_id
+
+
+if __name__ == '__main__':
+    print(generate_phone_to_id_lookup())

IMSToucan/README.md

@@ -0,0 +1,327 @@
+![image](Utility/toucan.png)
+
+IMS Toucan is a toolkit for teaching, training and using state-of-the-art Speech Synthesis models, developed at the
+**Institute for Natural Language Processing (IMS), University of Stuttgart, Germany**. Everything is pure Python and
+PyTorch based to keep it as simple and beginner-friendly, yet powerful as possible.
+
+The PyTorch Modules of [Tacotron 2](https://arxiv.org/abs/1712.05884)
+and [FastSpeech 2](https://arxiv.org/abs/2006.04558) are taken from
+[ESPnet](https://github.com/espnet/espnet), the PyTorch Modules of [HiFiGAN](https://arxiv.org/abs/2010.05646) are taken
+from the [ParallelWaveGAN repository](https://github.com/kan-bayashi/ParallelWaveGAN)
+which are also authored by the brilliant [Tomoki Hayashi](https://github.com/kan-bayashi).
+
+For a version of the toolkit that includes TransformerTTS instead of Tacotron 2 and MelGAN instead of HiFiGAN, check out
+the TransformerTTS and MelGAN branch. They are separated to keep the code clean, simple and minimal.
+
+---
+
+## Contents
+
+- [New Features](#new-features)
+- [Demonstration](#demonstration)
+- [Installation](#installation)
+    + [Basic Requirements](#basic-requirements)
+    + [Speaker Embedding](#speaker-embedding)
+    + [espeak-ng](#espeak-ng)
+- [Creating a new Pipeline](#creating-a-new-pipeline)
+    * [Build a HiFi-GAN Pipeline](#build-a-hifi-gan-pipeline)
+    * [Build a FastSpeech 2 Pipeline](#build-a-fastspeech-2-pipeline)
+- [Training a Model](#training-a-model)
+- [Creating a new InferenceInterface](#creating-a-new-inferenceinterface)
+- [Using a trained Model for Inference](#using-a-trained-model-for-inference)
+- [FAQ](#faq)
+- [Citation](#citation)
+
+---
+
+## New Features
+
+- [As shown in this paper](http://festvox.org/blizzard/bc2021/BC21_DelightfulTTS.pdf) vocoders can be used to perform
+  super-resolution and spectrogram inversion simultaneously. We added this to our HiFi-GAN vocoder. It now takes 16kHz
+  spectrograms as input, but produces 48kHz waveforms.
+- We officially introduced IMS Toucan in
+  [our contribution to the Blizzard Challenge 2021](http://festvox.org/blizzard/bc2021/BC21_IMS.pdf). Check out the
+  bottom of the readme for a bibtex entry.
+- We now use articulatory representations of phonemes as the input for all models. This allows us to easily use
+  multilingual data.
+- We provide a checkpoint trained with [model agnostic meta learning](https://arxiv.org/abs/1703.03400) from which you
+  should be able to fine-tune a model with very little data in almost any language.
+- We now use a small self-contained Aligner that is trained with CTC, inspired by
+  [this implementation](https://github.com/as-ideas/DeepForcedAligner). This allows us to get rid of the dependence on
+  autoregressive models. Tacotron 2 is thus now also no longer in this branch, but still present in other branches,
+  similar to TransformerTTS.
+
+---
+
+## Demonstration
+
+[Here are two sentences](https://drive.google.com/file/d/1ltAyR2EwAbmDo2hgkx1mvUny4FuxYmru/view?usp=sharing)
+produced by Tacotron 2 combined with HiFi-GAN, trained on
+[Nancy Krebs](https://www.cstr.ed.ac.uk/projects/blizzard/2011/lessac_blizzard2011/) using this toolkit.
+
+[Here is some speech](https://drive.google.com/file/d/1mZ1LvTlY6pJ5ZQ4UXZ9jbzB651mufBrB/view?usp=sharing)
+produced by FastSpeech 2 and MelGAN trained on [LJSpeech](https://keithito.com/LJ-Speech-Dataset/)
+using this toolkit.
+
+And [here is a sentence](https://drive.google.com/file/d/1FT49Jf0yyibwMDbsEJEO9mjwHkHRIGXc/view?usp=sharing)
+produced by TransformerTTS and MelGAN trained on [Thorsten](https://github.com/thorstenMueller/deep-learning-german-tts)
+using this toolkit.
+
+[Here is some speech](https://drive.google.com/file/d/14nPo2o1VKtWLPGF7e_0TxL8XGI3n7tAs/view?usp=sharing)
+produced by a multi-speaker FastSpeech 2 with MelGAN trained on
+[LibriTTS](https://research.google/tools/datasets/libri-tts/) using this toolkit. Fans of the videogame Portal may
+recognize who was used as the reference speaker for this utterance.
+
+[Interactive Demo of our entry to the Blizzard Challenge 2021.](https://colab.research.google.com/drive/1bRaySf8U55MRPaxqBr8huWrzCOzlxVqw)
+This is based on an older version of the toolkit though. It uses FastSpeech2 and MelGAN as vocoder and is trained on 5
+hours of Spanish.
+
+---
+
+## Installation
+
+#### Basic Requirements
+
+To install this toolkit, clone it onto the machine you want to use it on
+(should have at least one GPU if you intend to train models on that machine. For inference, you can get by without GPU).
+Navigate to the directory you have cloned. We are going to create and activate a
+[conda virtual environment](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)
+to install the basic requirements into. After creating the environment, the command you need to use to activate the
+virtual environment is displayed. The commands below show everything you need to do.
+
+```
+conda create --prefix ./toucan_conda_venv --no-default-packages python=3.8
+
+pip install --no-cache-dir -r requirements.txt
+
+pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
+```
+
+#### Speaker Embedding
+
+As [NVIDIA has shown](https://arxiv.org/pdf/2110.05798.pdf), you get better results by fine-tuning a pretrained model on
+a new speaker, rather than training a multispeaker model. We have thus dropped support for zero-shot multispeaker models
+using speaker embeddings. However we still
+use [Speechbrain's ECAPA-TDNN](https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb) for a cycle consistency loss to
+make adapting to new speakers a bit faster.
+
+In the current version of the toolkit no further action should be required. When you are using multispeaker for the
+first time, it requires an internet connection to download the pretrained models though.
+
+#### espeak-ng
+
+And finally you need to have espeak-ng installed on your system, because it is used as backend for the phonemizer. If
+you replace the phonemizer, you don't need it. On most Linux environments it will be installed already, and if it is
+not, and you have the sufficient rights, you can install it by simply running
+
+```
+apt-get install espeak-ng
+```
+
+---
+
+## Creating a new Pipeline
+
+To create a new pipeline to train a HiFiGAN vocoder, you only need a set of audio files. To create a new pipeline for a
+FastSpeech 2, you need audio files, corresponding text labels, and an already trained Aligner model to estimate the
+duration information that FastSpeech 2 needs as input. Let's go through them in order of increasing complexity.
+
+### Build a HiFi-GAN Pipeline
+
+In the directory called
+*Utility* there is a file called
+*file_lists.py*. In this file you should write a function that returns a list of all the absolute paths to each of the
+audio files in your dataset as strings.
+
+Then go to the directory
+*TrainingInterfaces/TrainingPipelines*. In there, make a copy of any existing pipeline that has HiFiGAN in its name. We
+will use this as reference and only make the necessary changes to use the new dataset. Import the function you have just
+written as
+*get_file_list*. Now look out for a variable called
+*model_save_dir*. This is the default directory that checkpoints will be saved into, unless you specify another one when
+calling the training script. Change it to whatever you like.
+
+Now you need to add your newly created pipeline to the pipeline dictionary in the file
+*run_training_pipeline.py* in the top level of the toolkit. In this file, import the
+*run* function from the pipeline you just created and give it a speaking name. Now in the
+*pipeline_dict*, add your imported function as value and use as key a shorthand that makes sense. And just like that
+you're done.
+
+### Build a FastSpeech 2 Pipeline
+
+In the directory called
+*Utility* there is a file called
+*path_to_transcript_dicts.py*. In this file you should write a function that returns a dictionary that has all the
+absolute paths to each of the audio files in your dataset as strings as the keys and the textual transcriptions of the
+corresponding audios as the values.
+
+Then go to the directory
+*TrainingInterfaces/TrainingPipelines*. In there, make a copy of any existing pipeline that has FastSpeech 2 in its
+name. We will use this copy as reference and only make the necessary changes to use the new dataset. Import the function
+you have just written as
+*build_path_to_transcript_dict*. Since the data will be processed a considerable amount, a cache will be built and saved
+as file for quick and easy restarts. So find the variable
+*cache_dir* and adapt it to your needs. The same goes for the variable
+*save_dir*, which is where the checkpoints will be saved to. This is a default value, you can overwrite it when calling
+the pipeline later using a command line argument, in case you want to fine-tune from a checkpoint and thus save into a
+different directory.
+
+In your new pipeline file, look out for the line in which the
+*acoustic_model* is loaded. Change the path to the checkpoint of an Aligner model. It can either be the one that is
+supplied with the toolkit in the download script, or one that you trained yourself. In the example pipelines, the one
+that we provide is finetuned to the dataset it is applied to before it is used to extract durations.
+
+Since we are using text here, we have to make sure that the text processing is adequate for the language. So check in
+*Preprocessing/TextFrontend* whether the TextFrontend already has a language ID (e.g. 'en' and 'de') for the language of
+your dataset. If not, you'll have to implement handling for that, but it should be pretty simple by just doing it
+analogous to what is there already. Now back in the pipeline, change the
+*lang* argument in the creation of the dataset and in the call to the train loop function to the language ID that
+matches your data.
+
+Now navigate to the implementation of the
+*train_loop* that is called in the pipeline. In this file, find the function called
+*plot_progress_spec*. This function will produce spectrogram plots during training, which is the most important way to
+monitor the progress of the training. In there, you may need to add an example sentence for the language of the data you
+are using. It should all be pretty clear from looking at it.
+
+Once this is done, we are almost done, now we just need to make it available to the
+*run_training_pipeline.py* file in the top level. In said file, import the
+*run* function from the pipeline you just created and give it a speaking name. Now in the
+*pipeline_dict*, add your imported function as value and use as key a shorthand that makes sense. And that's it.
+
+---
+
+## Training a Model
+
+Once you have a pipeline built, training is super easy. Just activate your virtual environment and run the command
+below. You might want to use something like nohup to keep it running after you log out from the server (then you should
+also add -u as option to python) and add an & to start it in the background. Also, you might want to direct the std:out
+and std:err into a file using > but all of that is just standard shell use and has nothing to do with the toolkit.
+
+```
+python run_training_pipeline.py <shorthand of the pipeline>
+```
+
+You can supply any of the following arguments, but don't have to (although for training you should definitely specify at
+least a GPU ID).
+
+```
+--gpu_id <ID of the GPU you wish to use, as displayed with nvidia-smi, default is cpu> 
+
+--resume_checkpoint <path to a checkpoint to load>
+
+--resume (if this is present, the furthest checkpoint available will be loaded automatically)
+
+--finetune (if this is present, the provided checkpoint will be fine-tuned on the data from this pipeline)
+
+--model_save_dir <path to a directory where the checkpoints should be saved>
+```
+
+After every epoch, some logs will be written to the console. If the loss becomes NaN, you'll need to use a smaller
+learning rate or more warmup steps in the arguments of the call to the training_loop in the pipeline you are running.
+
+If you get cuda out of memory errors, you need to decrease the batchsize in the arguments of the call to the
+training_loop in the pipeline you are running. Try decreasing the batchsize in small steps until you get no more out of
+cuda memory errors. Decreasing the batchsize may also require you to use a smaller learning rate. The use of GroupNorm
+should make it so that the training remains mostly stable.
+
+Speaking of plots: in the directory you specified for saving model's checkpoint files and self-explanatory visualization
+data will appear. Since the checkpoints are quite big, only the five most recent ones will be kept. Training will stop
+after 500,000 for FastSpeech 2, and after 2,500,000 steps for HiFiGAN. Depending on the machine and configuration you
+are using this will take multiple days, so verify that everything works on small tests before running the big thing. If
+you want to stop earlier, just kill the process, since everything is daemonic all the child-processes should die with
+it. In case there are some ghost-processes left behind, you can use the following command to find them and kill them
+manually.
+
+```
+fuser -v /dev/nvidia*
+```
+
+After training is complete, it is recommended to run
+*run_weight_averaging.py*. If you made no changes to the architectures and stuck to the default directory layout, it
+will automatically load any models you produced with one pipeline, average their parameters to get a slightly more
+robust model and save the result as
+*best.pt* in the same directory where all the corresponding checkpoints lie. This also compresses the file size
+significantly, so you should do this and then use the
+*best.pt* model for inference.
+
+---
+
+## Creating a new InferenceInterface
+
+To build a new
+*InferenceInterface*, which you can then use for super simple inference, we're going to use an existing one as template
+again. Make a copy of the
+*InferenceInterface*. Change the name of the class in the copy and change the paths to the models to use the trained
+models of your choice. Instantiate the model with the same hyperparameters that you used when you created it in the
+corresponding training pipeline. The last thing to check is the language that you supply to the text frontend. Make sure
+it matches what you used during training.
+
+With your newly created
+*InferenceInterface*, you can use your trained models pretty much anywhere, e.g. in other projects. All you need is the
+*Utility* directory, the
+*Layers*
+directory, the
+*Preprocessing* directory and the
+*InferenceInterfaces* directory (and of course your model checkpoint). That's all the code you need, it works
+standalone.
+
+---
+
+## Using a trained Model for Inference
+
+An
+*InferenceInterface* contains two useful methods. They are
+*read_to_file* and
+*read_aloud*.
+
+- *read_to_file* takes as input a list of strings and a filename. It will synthesize the sentences in the list and
+  concatenate them with a short pause inbetween and write them to the filepath you supply as the other argument.
+
+- *read_aloud* takes just a string, which it will then convert to speech and immediately play using the system's
+  speakers. If you set the optional argument
+  *view* to
+  *True* when calling it, it will also show a plot of the phonemes it produced, the spectrogram it came up with, and the
+  wave it created from that spectrogram. So all the representations can be seen, text to phoneme, phoneme to spectrogram
+  and finally spectrogram to wave.
+
+Those methods are used in demo code in the toolkit. In
+*run_interactive_demo.py* and
+*run_text_to_file_reader.py*, you can import
+*InferenceInterfaces* that you created and add them to the dictionary in each of the files with a shorthand that makes
+sense. In the interactive demo, you can just call the python script, then type in the shorthand when prompted and
+immediately listen to your synthesis saying whatever you put in next (be wary of out of memory errors for too long
+inputs). In the text reader demo script you have to call the function that wraps around the
+*InferenceInterface* and supply the shorthand of your choice. It should be pretty clear from looking at it.
+
+---
+
+## FAQ
+
+Here are a few points that were brought up by users:
+
+- My error message shows GPU0, even though I specified a different GPU - The way GPU selection works is that the
+  specified GPU is set as the only visible device, in order to avoid backend stuff running accidentally on different
+  GPUs. So internally the program will name the device GPU0, because it is the only GPU it can see. It is actually
+  running on the GPU you specified.
+
+---
+
+This toolkit has been written by Florian Lux (except for the pytorch modules taken
+from [ESPnet](https://github.com/espnet/espnet) and
+[ParallelWaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN), as mentioned above), so if you come across problems
+or questions, feel free to [write a mail](mailto:florian.lux@ims.uni-stuttgart.de). Also let me know if you do something
+cool with it. Thank you for reading.
+
+## Citation
+
+```
+@inproceedings{lux2021toucan,
+  title={{The IMS Toucan system for the Blizzard Challenge 2021}},
+  author={Florian Lux and Julia Koch and Antje Schweitzer and Ngoc Thang Vu},
+  year={2021},
+  booktitle={Proc. Blizzard Challenge Workshop},
+  volume={2021},
+  publisher={{Speech Synthesis SIG}}
+}
+```

IMSToucan/Utility/SpeakerVisualization.py

@@ -0,0 +1,94 @@
+import matplotlib
+import numpy
+import soundfile as sf
+from matplotlib import pyplot as plt
+from matplotlib import cm
+matplotlib.use("tkAgg")
+from sklearn.manifold import TSNE
+from sklearn.decomposition import PCA
+
+from tqdm import tqdm
+
+from Preprocessing.ProsodicConditionExtractor import ProsodicConditionExtractor
+
+
+class Visualizer:
+
+    def __init__(self, sr=48000, device="cpu"):
+        """
+        Args:
+            sr: The sampling rate of the audios you want to visualize.
+        """
+        self.tsne = TSNE(n_jobs=-1)
+        self.pca = PCA(n_components=2)
+        self.pros_cond_ext = ProsodicConditionExtractor(sr=sr, device=device)
+        self.sr = sr
+
+    def visualize_speaker_embeddings(self, label_to_filepaths, title_of_plot, save_file_path=None, include_pca=True, legend=True):
+        label_list = list()
+        embedding_list = list()
+        for label in tqdm(label_to_filepaths):
+            for filepath in tqdm(label_to_filepaths[label]):
+                wave, sr = sf.read(filepath)
+                if len(wave) / sr < 1:
+                    continue
+                if self.sr != sr:
+                    print("One of the Audios you included doesn't match the sampling rate of this visualizer object, "
+                          "creating a new condition extractor. Results will be correct, but if there are too many cases "
+                          "of changing samplingrate, this will run very slowly.")
+                    self.pros_cond_ext = ProsodicConditionExtractor(sr=sr)
+                    self.sr = sr
+                embedding_list.append(self.pros_cond_ext.extract_condition_from_reference_wave(wave).squeeze().numpy())
+                label_list.append(label)
+        embeddings_as_array = numpy.array(embedding_list)
+
+        dimensionality_reduced_embeddings_tsne = self.tsne.fit_transform(embeddings_as_array)
+        self._plot_embeddings(projected_data=dimensionality_reduced_embeddings_tsne,
+                              labels=label_list,
+                              title=title_of_plot + " t-SNE" if include_pca else title_of_plot,
+                              save_file_path=save_file_path,
+                              legend=legend)
+
+        if include_pca:
+            dimensionality_reduced_embeddings_pca = self.pca.fit_transform(embeddings_as_array)
+            self._plot_embeddings(projected_data=dimensionality_reduced_embeddings_pca,
+                                  labels=label_list,
+                                  title=title_of_plot + " PCA",
+                                  save_file_path=save_file_path,
+                                  legend=legend)
+
+    def _plot_embeddings(self, projected_data, labels, title, save_file_path, legend):
+        colors = cm.gist_rainbow(numpy.linspace(0, 1, len(set(labels))))
+        label_to_color = dict()
+        for index, label in enumerate(list(set(labels))):
+            label_to_color[label] = colors[index]
+
+        labels_to_points_x = dict()
+        labels_to_points_y = dict()
+        for label in labels:
+            labels_to_points_x[label] = list()
+            labels_to_points_y[label] = list()
+        for index, label in enumerate(labels):
+            labels_to_points_x[label].append(projected_data[index][0])
+            labels_to_points_y[label].append(projected_data[index][1])
+
+        fig, ax = plt.subplots()
+        for label in set(labels):
+            x = numpy.array(labels_to_points_x[label])
+            y = numpy.array(labels_to_points_y[label])
+            ax.scatter(x=x,
+                       y=y,
+                       c=label_to_color[label],
+                       label=label,
+                       alpha=0.9)
+        if legend:
+            ax.legend()
+        fig.tight_layout()
+        ax.axis('off')
+        fig.subplots_adjust(top=0.9, bottom=0.0, right=1.0, left=0.0)
+        ax.set_title(title)
+        if save_file_path is not None:
+            plt.savefig(save_file_path)
+        else:
+            plt.show()
+        plt.close()

IMSToucan/Utility/utils.py

@@ -0,0 +1,320 @@
+"""
+Taken from ESPNet, modified by Florian Lux
+"""
+
+import os
+from abc import ABC
+
+import torch
+
+
+def cumsum_durations(durations):
+    out = [0]
+    for duration in durations:
+        out.append(duration + out[-1])
+    centers = list()
+    for index, _ in enumerate(out):
+        if index + 1 < len(out):
+            centers.append((out[index] + out[index + 1]) / 2)
+    return out, centers
+
+
+def delete_old_checkpoints(checkpoint_dir, keep=5):
+    checkpoint_list = list()
+    for el in os.listdir(checkpoint_dir):
+        if el.endswith(".pt") and el != "best.pt":
+            checkpoint_list.append(int(el.split(".")[0].split("_")[1]))
+    if len(checkpoint_list) <= keep:
+        return
+    else:
+        checkpoint_list.sort(reverse=False)
+        checkpoints_to_delete = [os.path.join(checkpoint_dir, "checkpoint_{}.pt".format(step)) for step in checkpoint_list[:-keep]]
+        for old_checkpoint in checkpoints_to_delete:
+            os.remove(os.path.join(old_checkpoint))
+
+
+def get_most_recent_checkpoint(checkpoint_dir, verbose=True):
+    checkpoint_list = list()
+    for el in os.listdir(checkpoint_dir):
+        if el.endswith(".pt") and el != "best.pt":
+            checkpoint_list.append(int(el.split(".")[0].split("_")[1]))
+    if len(checkpoint_list) == 0:
+        print("No previous checkpoints found, cannot reload.")
+        return None
+    checkpoint_list.sort(reverse=True)
+    if verbose:
+        print("Reloading checkpoint_{}.pt".format(checkpoint_list[0]))
+    return os.path.join(checkpoint_dir, "checkpoint_{}.pt".format(checkpoint_list[0]))
+
+
+def make_pad_mask(lengths, xs=None, length_dim=-1, device=None):
+    """
+    Make mask tensor containing indices of padded part.
+
+    Args:
+        lengths (LongTensor or List): Batch of lengths (B,).
+        xs (Tensor, optional): The reference tensor.
+            If set, masks will be the same shape as this tensor.
+        length_dim (int, optional): Dimension indicator of the above tensor.
+            See the example.
+
+    Returns:
+        Tensor: Mask tensor containing indices of padded part.
+                dtype=torch.uint8 in PyTorch 1.2-
+                dtype=torch.bool in PyTorch 1.2+ (including 1.2)
+
+    """
+    if length_dim == 0:
+        raise ValueError("length_dim cannot be 0: {}".format(length_dim))
+
+    if not isinstance(lengths, list):
+        lengths = lengths.tolist()
+    bs = int(len(lengths))
+    if xs is None:
+        maxlen = int(max(lengths))
+    else:
+        maxlen = xs.size(length_dim)
+
+    if device is not None:
+        seq_range = torch.arange(0, maxlen, dtype=torch.int64, device=device)
+    else:
+        seq_range = torch.arange(0, maxlen, dtype=torch.int64)
+    seq_range_expand = seq_range.unsqueeze(0).expand(bs, maxlen)
+    seq_length_expand = seq_range_expand.new(lengths).unsqueeze(-1)
+    mask = seq_range_expand >= seq_length_expand
+
+    if xs is not None:
+        assert xs.size(0) == bs, (xs.size(0), bs)
+
+        if length_dim < 0:
+            length_dim = xs.dim() + length_dim
+        # ind = (:, None, ..., None, :, , None, ..., None)
+        ind = tuple(slice(None) if i in (0, length_dim) else None for i in range(xs.dim()))
+        mask = mask[ind].expand_as(xs).to(xs.device)
+    return mask
+
+
+def make_non_pad_mask(lengths, xs=None, length_dim=-1, device=None):
+    """
+    Make mask tensor containing indices of non-padded part.
+
+    Args:
+        lengths (LongTensor or List): Batch of lengths (B,).
+        xs (Tensor, optional): The reference tensor.
+            If set, masks will be the same shape as this tensor.
+        length_dim (int, optional): Dimension indicator of the above tensor.
+            See the example.
+
+    Returns:
+        ByteTensor: mask tensor containing indices of padded part.
+                    dtype=torch.uint8 in PyTorch 1.2-
+                    dtype=torch.bool in PyTorch 1.2+ (including 1.2)
+
+    """
+    return ~make_pad_mask(lengths, xs, length_dim, device=device)
+
+
+def initialize(model, init):
+    """
+    Initialize weights of a neural network module.
+
+    Parameters are initialized using the given method or distribution.
+
+    Args:
+        model: Target.
+        init: Method of initialization.
+    """
+
+    # weight init
+    for p in model.parameters():
+        if p.dim() > 1:
+            if init == "xavier_uniform":
+                torch.nn.init.xavier_uniform_(p.data)
+            elif init == "xavier_normal":
+                torch.nn.init.xavier_normal_(p.data)
+            elif init == "kaiming_uniform":
+                torch.nn.init.kaiming_uniform_(p.data, nonlinearity="relu")
+            elif init == "kaiming_normal":
+                torch.nn.init.kaiming_normal_(p.data, nonlinearity="relu")
+            else:
+                raise ValueError("Unknown initialization: " + init)
+    # bias init
+    for p in model.parameters():
+        if p.dim() == 1:
+            p.data.zero_()
+
+    # reset some modules with default init
+    for m in model.modules():
+        if isinstance(m, (torch.nn.Embedding, torch.nn.LayerNorm)):
+            m.reset_parameters()
+
+
+def pad_list(xs, pad_value):
+    """
+    Perform padding for the list of tensors.
+
+    Args:
+        xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)].
+        pad_value (float): Value for padding.
+
+    Returns:
+        Tensor: Padded tensor (B, Tmax, `*`).
+
+    """
+    n_batch = len(xs)
+    max_len = max(x.size(0) for x in xs)
+    pad = xs[0].new(n_batch, max_len, *xs[0].size()[1:]).fill_(pad_value)
+
+    for i in range(n_batch):
+        pad[i, : xs[i].size(0)] = xs[i]
+
+    return pad
+
+
+def subsequent_mask(size, device="cpu", dtype=torch.bool):
+    """
+    Create mask for subsequent steps (size, size).
+
+    :param int size: size of mask
+    :param str device: "cpu" or "cuda" or torch.Tensor.device
+    :param torch.dtype dtype: result dtype
+    :rtype
+    """
+    ret = torch.ones(size, size, device=device, dtype=dtype)
+    return torch.tril(ret, out=ret)
+
+
+class ScorerInterface:
+    """
+    Scorer interface for beam search.
+
+    The scorer performs scoring of the all tokens in vocabulary.
+
+    Examples:
+        * Search heuristics
+            * :class:`espnet.nets.scorers.length_bonus.LengthBonus`
+        * Decoder networks of the sequence-to-sequence models
+            * :class:`espnet.nets.pytorch_backend.nets.transformer.decoder.Decoder`
+            * :class:`espnet.nets.pytorch_backend.nets.rnn.decoders.Decoder`
+        * Neural language models
+            * :class:`espnet.nets.pytorch_backend.lm.transformer.TransformerLM`
+            * :class:`espnet.nets.pytorch_backend.lm.default.DefaultRNNLM`
+            * :class:`espnet.nets.pytorch_backend.lm.seq_rnn.SequentialRNNLM`
+
+    """
+
+    def init_state(self, x):
+        """
+        Get an initial state for decoding (optional).
+
+        Args:
+            x (torch.Tensor): The encoded feature tensor
+
+        Returns: initial state
+
+        """
+        return None
+
+    def select_state(self, state, i, new_id=None):
+        """
+        Select state with relative ids in the main beam search.
+
+        Args:
+            state: Decoder state for prefix tokens
+            i (int): Index to select a state in the main beam search
+            new_id (int): New label index to select a state if necessary
+
+        Returns:
+            state: pruned state
+
+        """
+        return None if state is None else state[i]
+
+    def score(self, y, state, x):
+        """
+        Score new token (required).
+
+        Args:
+            y (torch.Tensor): 1D torch.int64 prefix tokens.
+            state: Scorer state for prefix tokens
+            x (torch.Tensor): The encoder feature that generates ys.
+
+        Returns:
+            tuple[torch.Tensor, Any]: Tuple of
+                scores for next token that has a shape of `(n_vocab)`
+                and next state for ys
+
+        """
+        raise NotImplementedError
+
+    def final_score(self, state):
+        """
+        Score eos (optional).
+
+        Args:
+            state: Scorer state for prefix tokens
+
+        Returns:
+            float: final score
+
+        """
+        return 0.0
+
+
+class BatchScorerInterface(ScorerInterface, ABC):
+
+    def batch_init_state(self, x):
+        """
+        Get an initial state for decoding (optional).
+
+        Args:
+            x (torch.Tensor): The encoded feature tensor
+
+        Returns: initial state
+
+        """
+        return self.init_state(x)
+
+    def batch_score(self, ys, states, xs):
+        """
+        Score new token batch (required).
+
+        Args:
+            ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen).
+            states (List[Any]): Scorer states for prefix tokens.
+            xs (torch.Tensor):
+                The encoder feature that generates ys (n_batch, xlen, n_feat).
+
+        Returns:
+            tuple[torch.Tensor, List[Any]]: Tuple of
+                batchfied scores for next token with shape of `(n_batch, n_vocab)`
+                and next state list for ys.
+
+        """
+        scores = list()
+        outstates = list()
+        for i, (y, state, x) in enumerate(zip(ys, states, xs)):
+            score, outstate = self.score(y, state, x)
+            outstates.append(outstate)
+            scores.append(score)
+        scores = torch.cat(scores, 0).view(ys.shape[0], -1)
+        return scores, outstates
+
+
+def to_device(m, x):
+    """Send tensor into the device of the module.
+    Args:
+        m (torch.nn.Module): Torch module.
+        x (Tensor): Torch tensor.
+    Returns:
+        Tensor: Torch tensor located in the same place as torch module.
+    """
+    if isinstance(m, torch.nn.Module):
+        device = next(m.parameters()).device
+    elif isinstance(m, torch.Tensor):
+        device = m.device
+    else:
+        raise TypeError(
+            "Expected torch.nn.Module or torch.tensor, " f"bot got: {type(m)}"
+            )
+    return x.to(device)

IMSToucan/requirements.txt

@@ -0,0 +1,83 @@
+appdirs~=1.4.4
+attrs~=21.2.0
+audioread~=2.1.9
+auraloss~=0.2.1
+certifi~=2021.5.30
+cffi~=1.14.6
+charset-normalizer~=2.0.4
+clldutils~=3.9.0
+colorama~=0.4.4
+colorlog~=6.4.1
+commonmark~=0.9.1
+csvw~=1.11.0
+cycler~=0.10.0
+Cython~=0.29.24
+decorator~=5.0.9
+editdistance~=0.5.3
+emoji~=1.4.2
+filelock~=3.0.12
+ftfy~=6.0.3
+future~=0.18.2
+gdown~=3.13.0
+graphviz~=0.17
+huggingface-hub~=0.0.16
+HyperPyYAML~=1.0.0
+idna~=3.2
+isodate~=0.6.0
+jiwer~=2.2.1
+joblib~=1.0.1
+kiwisolver~=1.3.2
+librosa~=0.8.1
+llvmlite~=0.37.0
+matplotlib~=3.4.3
+munkres~=1.1.4
+numba~=0.54.0
+numpy~=1.20.3
+packaging~=21.0
+panphon~=0.19
+pedalboard~=0.3.11
+phonemizer~=2.2.2
+Pillow~=8.3.2
+pooch~=1.5.1
+pycparser~=2.20
+Pygments~=2.10.0
+pyloudnorm~=0.1.0
+pyparsing~=2.4.7
+pysndfx~=0.3.6
+PySocks~=1.7.1
+python-dateutil~=2.8.2
+python-Levenshtein~=0.12.2
+pyworld~=0.3.0
+PyYAML~=5.4.1
+regex~=2021.8.28
+requests~=2.26.0
+resampy~=0.2.2
+rfc3986~=1.5.0
+rich~=9.10.0
+ruamel.yaml~=0.17.16
+ruamel.yaml.clib~=0.2.6
+scikit-learn~=0.24.2
+scipy~=1.7.1
+segments~=2.2.0
+sentencepiece~=0.1.96
+six~=1.16.0
+sklearn~=0.0
+sounddevice~=0.4.2
+SoundFile~=0.10.3.post1
+speechbrain~=0.5.10
+tabulate~=0.8.9
+threadpoolctl~=2.2.0
+torch~=1.10.1
+torch-complex~=0.2.1
+torchaudio~=0.10.1
+torchvision~=0.11.2
+torchviz~=0.0.2
+tqdm~=4.62.2
+typing-extensions~=3.10.0.2
+unicodecsv~=0.14.1
+Unidecode~=1.2.0
+unsilence~=1.0.8
+uritemplate~=3.0.1
+urllib3~=1.26.6
+wcwidth~=0.2.5
+wincertstore~=0.2