File size: 6,857 Bytes
08d5f37
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
import torch
from synthesizer import audio
from synthesizer.hparams import hparams
from synthesizer.models.tacotron import Tacotron
from synthesizer.utils.symbols import symbols
from synthesizer.utils.text import text_to_sequence
from vocoder.display import simple_table
from pathlib import Path
from typing import Union, List
import numpy as np
import librosa


class Synthesizer:
    sample_rate = hparams.sample_rate
    hparams = hparams

    def __init__(self, model_fpath: Path, verbose=True):
        """

        The model isn't instantiated and loaded in memory until needed or until load() is called.



        :param model_fpath: path to the trained model file

        :param verbose: if False, prints less information when using the model

        """
        self.model_fpath = model_fpath
        self.verbose = verbose

        # Check for GPU
        if torch.cuda.is_available():
            self.device = torch.device("cuda")
        else:
            self.device = torch.device("cpu")
        if self.verbose:
            print("Synthesizer using device:", self.device)

        # Tacotron model will be instantiated later on first use.
        self._model = None

    def is_loaded(self):
        """

        Whether the model is loaded in memory.

        """
        return self._model is not None

    def load(self):
        """

        Instantiates and loads the model given the weights file that was passed in the constructor.

        """
        self._model = Tacotron(embed_dims=hparams.tts_embed_dims,
                               num_chars=len(symbols),
                               encoder_dims=hparams.tts_encoder_dims,
                               decoder_dims=hparams.tts_decoder_dims,
                               n_mels=hparams.num_mels,
                               fft_bins=hparams.num_mels,
                               postnet_dims=hparams.tts_postnet_dims,
                               encoder_K=hparams.tts_encoder_K,
                               lstm_dims=hparams.tts_lstm_dims,
                               postnet_K=hparams.tts_postnet_K,
                               num_highways=hparams.tts_num_highways,
                               dropout=hparams.tts_dropout,
                               stop_threshold=hparams.tts_stop_threshold,
                               speaker_embedding_size=hparams.speaker_embedding_size).to(self.device)

        self._model.load(self.model_fpath)
        self._model.eval()

        if self.verbose:
            print("Loaded synthesizer \"%s\" trained to step %d" % (self.model_fpath.name, self._model.state_dict()["step"]))

    def synthesize_spectrograms(self, texts: List[str],

                                embeddings: Union[np.ndarray, List[np.ndarray]],

                                return_alignments=False):
        """

        Synthesizes mel spectrograms from texts and speaker embeddings.



        :param texts: a list of N text prompts to be synthesized

        :param embeddings: a numpy array or list of speaker embeddings of shape (N, 256)

        :param return_alignments: if True, a matrix representing the alignments between the

        characters

        and each decoder output step will be returned for each spectrogram

        :return: a list of N melspectrograms as numpy arrays of shape (80, Mi), where Mi is the

        sequence length of spectrogram i, and possibly the alignments.

        """
        # Load the model on the first request.
        if not self.is_loaded():
            self.load()

        # Preprocess text inputs
        inputs = [text_to_sequence(text.strip(), hparams.tts_cleaner_names) for text in texts]
        if not isinstance(embeddings, list):
            embeddings = [embeddings]

        # Batch inputs
        batched_inputs = [inputs[i:i+hparams.synthesis_batch_size]
                             for i in range(0, len(inputs), hparams.synthesis_batch_size)]
        batched_embeds = [embeddings[i:i+hparams.synthesis_batch_size]
                             for i in range(0, len(embeddings), hparams.synthesis_batch_size)]

        specs = []
        for i, batch in enumerate(batched_inputs, 1):
            if self.verbose:
                print(f"\n| Generating {i}/{len(batched_inputs)}")

            # Pad texts so they are all the same length
            text_lens = [len(text) for text in batch]
            max_text_len = max(text_lens)
            chars = [pad1d(text, max_text_len) for text in batch]
            chars = np.stack(chars)

            # Stack speaker embeddings into 2D array for batch processing
            speaker_embeds = np.stack(batched_embeds[i-1])

            # Convert to tensor
            chars = torch.tensor(chars).long().to(self.device)
            speaker_embeddings = torch.tensor(speaker_embeds).float().to(self.device)

            # Inference
            _, mels, alignments = self._model.generate(chars, speaker_embeddings)
            mels = mels.detach().cpu().numpy()
            for m in mels:
                # Trim silence from end of each spectrogram
                while np.max(m[:, -1]) < hparams.tts_stop_threshold:
                    m = m[:, :-1]
                specs.append(m)

        if self.verbose:
            print("\n\nDone.\n")
        return (specs, alignments) if return_alignments else specs

    @staticmethod
    def load_preprocess_wav(fpath):
        """

        Loads and preprocesses an audio file under the same conditions the audio files were used to

        train the synthesizer.

        """
        wav = librosa.load(str(fpath), hparams.sample_rate)[0]
        if hparams.rescale:
            wav = wav / np.abs(wav).max() * hparams.rescaling_max
        return wav

    @staticmethod
    def make_spectrogram(fpath_or_wav: Union[str, Path, np.ndarray]):
        """

        Creates a mel spectrogram from an audio file in the same manner as the mel spectrograms that

        were fed to the synthesizer when training.

        """
        if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path):
            wav = Synthesizer.load_preprocess_wav(fpath_or_wav)
        else:
            wav = fpath_or_wav

        mel_spectrogram = audio.melspectrogram(wav, hparams).astype(np.float32)
        return mel_spectrogram

    @staticmethod
    def griffin_lim(mel):
        """

        Inverts a mel spectrogram using Griffin-Lim. The mel spectrogram is expected to have been built

        with the same parameters present in hparams.py.

        """
        return audio.inv_mel_spectrogram(mel, hparams)


def pad1d(x, max_len, pad_value=0):
    return np.pad(x, (0, max_len - len(x)), mode="constant", constant_values=pad_value)