InferenceInterfaces/ToucanTTSInterface.py

import itertools
import os
import warnings

import matplotlib.pyplot as plt
import pyloudnorm
import sounddevice
import soundfile
import torch
with warnings.catch_warnings():
    warnings.simplefilter("ignore")
    from speechbrain.pretrained import EncoderClassifier
    from torchaudio.transforms import Resample

from Architectures.ToucanTTS.InferenceToucanTTS import ToucanTTS
from Architectures.Vocoder.HiFiGAN_Generator import HiFiGAN
from Preprocessing.AudioPreprocessor import AudioPreprocessor
from Preprocessing.TextFrontend import ArticulatoryCombinedTextFrontend
from Preprocessing.TextFrontend import get_language_id
from Utility.storage_config import MODELS_DIR
from Utility.utils import cumsum_durations
from Utility.utils import float2pcm


class ToucanTTSInterface(torch.nn.Module):

    def __init__(self,
                 device="cpu",  # device that everything computes on. If a cuda device is available, this can speed things up by an order of magnitude.
                 tts_model_path=os.path.join(MODELS_DIR, f"ToucanTTS_Meta", "best.pt"),  # path to the ToucanTTS checkpoint or just a shorthand if run standalone
                 vocoder_model_path=os.path.join(MODELS_DIR, f"Vocoder", "best.pt"),  # path to the Vocoder checkpoint
                 language="eng",  # initial language of the model, can be changed later with the setter methods
                 enhance=None  # legacy argument
                 ):
        super().__init__()
        self.device = device
        if not tts_model_path.endswith(".pt"):
            # default to shorthand system
            tts_model_path = os.path.join(MODELS_DIR, f"ToucanTTS_{tts_model_path}", "best.pt")

        ################################
        #   build text to phone        #
        ################################
        self.text2phone = ArticulatoryCombinedTextFrontend(language=language, add_silence_to_end=True)

        #####################################
        #   load phone to features model    #
        #####################################
        checkpoint = torch.load(tts_model_path, map_location='cpu')
        self.phone2mel = ToucanTTS(weights=checkpoint["model"], config=checkpoint["config"])
        with torch.no_grad():
            self.phone2mel.store_inverse_all()  # this also removes weight norm
        self.phone2mel = self.phone2mel.to(torch.device(device))

        ######################################
        #  load features to style models     #
        ######################################
        self.speaker_embedding_func_ecapa = EncoderClassifier.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb",
                                                                           run_opts={"device": str(device)},
                                                                           savedir=os.path.join(MODELS_DIR, "Embedding", "speechbrain_speaker_embedding_ecapa"))

        ################################
        #  load mel to wave model      #
        ################################
        vocoder_checkpoint = torch.load(vocoder_model_path, map_location="cpu")
        self.vocoder = HiFiGAN()
        self.vocoder.load_state_dict(vocoder_checkpoint)
        self.vocoder = self.vocoder.to(device).eval()
        self.vocoder.remove_weight_norm()
        self.meter = pyloudnorm.Meter(24000)

        ################################
        #  set defaults                #
        ################################
        self.default_utterance_embedding = checkpoint["default_emb"].to(self.device)
        self.ap = AudioPreprocessor(input_sr=100, output_sr=16000, device=device)
        self.phone2mel.eval()
        self.vocoder.eval()
        self.lang_id = get_language_id(language)
        self.to(torch.device(device))
        self.eval()

    def set_utterance_embedding(self, path_to_reference_audio="", embedding=None):
        if embedding is not None:
            self.default_utterance_embedding = embedding.squeeze().to(self.device)
            return
        if type(path_to_reference_audio) != list:
            path_to_reference_audio = [path_to_reference_audio]

        if len(path_to_reference_audio) > 0:
            for path in path_to_reference_audio:
                assert os.path.exists(path)
            speaker_embs = list()
            for path in path_to_reference_audio:
                wave, sr = soundfile.read(path)
                wave = Resample(orig_freq=sr, new_freq=16000).to(self.device)(torch.tensor(wave, device=self.device, dtype=torch.float32))
                speaker_embedding = self.speaker_embedding_func_ecapa.encode_batch(wavs=wave.to(self.device).unsqueeze(0)).squeeze()
                speaker_embs.append(speaker_embedding)
            self.default_utterance_embedding = sum(speaker_embs) / len(speaker_embs)

    def set_language(self, lang_id):
        """
        The id parameter actually refers to the shorthand. This has become ambiguous with the introduction of the actual language IDs
        """
        self.set_phonemizer_language(lang_id=lang_id)
        self.set_accent_language(lang_id=lang_id)

    def set_phonemizer_language(self, lang_id):
        self.text2phone.change_lang(language=lang_id, add_silence_to_end=True)

    def set_accent_language(self, lang_id):
        if lang_id in ['ajp', 'ajt', 'lak', 'lno', 'nul', 'pii', 'plj', 'slq', 'smd', 'snb', 'tpw', 'wya', 'zua', 'en-us', 'en-sc', 'fr-be', 'fr-sw', 'pt-br', 'spa-lat', 'vi-ctr', 'vi-so']:
            if lang_id == 'vi-so' or lang_id == 'vi-ctr':
                lang_id = 'vie'
            elif lang_id == 'spa-lat':
                lang_id = 'spa'
            elif lang_id == 'pt-br':
                lang_id = 'por'
            elif lang_id == 'fr-sw' or lang_id == 'fr-be':
                lang_id = 'fra'
            elif lang_id == 'en-sc' or lang_id == 'en-us':
                lang_id = 'eng'
            else:
                # no clue where these others are even coming from, they are not in ISO 639-2
                lang_id = 'eng'

        self.lang_id = get_language_id(lang_id).to(self.device)

    def forward(self,
                text,
                view=False,
                duration_scaling_factor=1.0,
                pitch_variance_scale=1.0,
                energy_variance_scale=1.0,
                pause_duration_scaling_factor=1.0,
                durations=None,
                pitch=None,
                energy=None,
                input_is_phones=False,
                return_plot_as_filepath=False,
                loudness_in_db=-24.0,
                glow_sampling_temperature=0.2):
        """
        duration_scaling_factor: reasonable values are 0.8 < scale < 1.2.
                                     1.0 means no scaling happens, higher values increase durations for the whole
                                     utterance, lower values decrease durations for the whole utterance.
        pitch_variance_scale: reasonable values are 0.6 < scale < 1.4.
                                  1.0 means no scaling happens, higher values increase variance of the pitch curve,
                                  lower values decrease variance of the pitch curve.
        energy_variance_scale: reasonable values are 0.6 < scale < 1.4.
                                   1.0 means no scaling happens, higher values increase variance of the energy curve,
                                   lower values decrease variance of the energy curve.
        """
        with torch.inference_mode():
            phones = self.text2phone.string_to_tensor(text, input_phonemes=input_is_phones).to(torch.device(self.device))
            mel, durations, pitch, energy = self.phone2mel(phones,
                                                           return_duration_pitch_energy=True,
                                                           utterance_embedding=self.default_utterance_embedding.to(self.device),
                                                           durations=durations,
                                                           pitch=pitch,
                                                           energy=energy,
                                                           lang_id=self.lang_id.to(self.device),
                                                           duration_scaling_factor=duration_scaling_factor,
                                                           pitch_variance_scale=pitch_variance_scale,
                                                           energy_variance_scale=energy_variance_scale,
                                                           pause_duration_scaling_factor=pause_duration_scaling_factor,
                                                           glow_sampling_temperature=glow_sampling_temperature)

            wave, _, _ = self.vocoder(mel.unsqueeze(0))
            wave = wave.squeeze().cpu()
        wave = wave.numpy()
        sr = 24000
        try:
            loudness = self.meter.integrated_loudness(wave)
            wave = pyloudnorm.normalize.loudness(wave, loudness, loudness_in_db)
        except ValueError:
            # if the audio is too short, a value error will arise
            pass

        if view or return_plot_as_filepath:
            fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(9, 5))

            ax.imshow(mel.cpu().numpy(), origin="lower", cmap='GnBu')
            ax.yaxis.set_visible(False)
            duration_splits, label_positions = cumsum_durations(durations.cpu().numpy())
            ax.xaxis.grid(True, which='minor')
            ax.set_xticks(label_positions, minor=False)
            if input_is_phones:
                phones = text.replace(" ", "|")
            else:
                phones = self.text2phone.get_phone_string(text, for_plot_labels=True)
            ax.set_xticklabels(phones)
            word_boundaries = list()
            for label_index, phone in enumerate(phones):
                if phone == "|":
                    word_boundaries.append(label_positions[label_index])

            try:
                prev_word_boundary = 0
                word_label_positions = list()
                for word_boundary in word_boundaries:
                    word_label_positions.append((word_boundary + prev_word_boundary) / 2)
                    prev_word_boundary = word_boundary
                word_label_positions.append((duration_splits[-1] + prev_word_boundary) / 2)

                secondary_ax = ax.secondary_xaxis('bottom')
                secondary_ax.tick_params(axis="x", direction="out", pad=24)
                secondary_ax.set_xticks(word_label_positions, minor=False)
                secondary_ax.set_xticklabels(text.split())
                secondary_ax.tick_params(axis='x', colors='orange')
                secondary_ax.xaxis.label.set_color('orange')
            except ValueError:
                ax.set_title(text)
            except IndexError:
                ax.set_title(text)

            ax.vlines(x=duration_splits, colors="green", linestyles="solid", ymin=0, ymax=120, linewidth=0.5)
            ax.vlines(x=word_boundaries, colors="orange", linestyles="solid", ymin=0, ymax=120, linewidth=1.0)
            plt.subplots_adjust(left=0.02, bottom=0.2, right=0.98, top=.9, wspace=0.0, hspace=0.0)
            ax.set_aspect("auto")

            if return_plot_as_filepath:
                plt.savefig("tmp.png")
                return wave, sr, "tmp.png"

        return wave, sr

    def read_to_file(self,
                     text_list,
                     file_location,
                     duration_scaling_factor=1.0,
                     pitch_variance_scale=1.0,
                     energy_variance_scale=1.0,
                     pause_duration_scaling_factor=1.0,
                     silent=False,
                     dur_list=None,
                     pitch_list=None,
                     energy_list=None,
                     glow_sampling_temperature=0.2):
        """
        Args:
            silent: Whether to be verbose about the process
            text_list: A list of strings to be read
            file_location: The path and name of the file it should be saved to
            energy_list: list of energy tensors to be used for the texts
            pitch_list: list of pitch tensors to be used for the texts
            dur_list: list of duration tensors to be used for the texts
            duration_scaling_factor: reasonable values are 0.8 < scale < 1.2.
                                     1.0 means no scaling happens, higher values increase durations for the whole
                                     utterance, lower values decrease durations for the whole utterance.
            pitch_variance_scale: reasonable values are 0.6 < scale < 1.4.
                                  1.0 means no scaling happens, higher values increase variance of the pitch curve,
                                  lower values decrease variance of the pitch curve.
            energy_variance_scale: reasonable values are 0.6 < scale < 1.4.
                                   1.0 means no scaling happens, higher values increase variance of the energy curve,
                                   lower values decrease variance of the energy curve.
        """
        if not dur_list:
            dur_list = []
        if not pitch_list:
            pitch_list = []
        if not energy_list:
            energy_list = []
        silence = torch.zeros([14300])
        wav = silence.clone()
        for (text, durations, pitch, energy) in itertools.zip_longest(text_list, dur_list, pitch_list, energy_list):
            if text.strip() != "":
                if not silent:
                    print("Now synthesizing: {}".format(text))
                spoken_sentence, sr = self(text,
                                           durations=durations.to(self.device) if durations is not None else None,
                                           pitch=pitch.to(self.device) if pitch is not None else None,
                                           energy=energy.to(self.device) if energy is not None else None,
                                           duration_scaling_factor=duration_scaling_factor,
                                           pitch_variance_scale=pitch_variance_scale,
                                           energy_variance_scale=energy_variance_scale,
                                           pause_duration_scaling_factor=pause_duration_scaling_factor,
                                           glow_sampling_temperature=glow_sampling_temperature)
                spoken_sentence = torch.tensor(spoken_sentence).cpu()
                wav = torch.cat((wav, spoken_sentence, silence), 0)
        soundfile.write(file=file_location, data=float2pcm(wav), samplerate=sr, subtype="PCM_16")

    def read_aloud(self,
                   text,
                   view=False,
                   duration_scaling_factor=1.0,
                   pitch_variance_scale=1.0,
                   energy_variance_scale=1.0,
                   blocking=False,
                   glow_sampling_temperature=0.2):
        if text.strip() == "":
            return
        wav, sr = self(text,
                       view,
                       duration_scaling_factor=duration_scaling_factor,
                       pitch_variance_scale=pitch_variance_scale,
                       energy_variance_scale=energy_variance_scale,
                       glow_sampling_temperature=glow_sampling_temperature)
        silence = torch.zeros([sr // 2])
        wav = torch.cat((silence, torch.tensor(wav), silence), 0).numpy()
        sounddevice.play(float2pcm(wav), samplerate=sr)
        if view:
            plt.show()
        if blocking:
            sounddevice.wait()