import os import librosa.display as lbd import matplotlib.pyplot as plt import soundfile import torch from InferenceInterfaces.InferenceArchitectures.InferenceFastSpeech2 import FastSpeech2 from InferenceInterfaces.InferenceArchitectures.InferenceHiFiGAN import HiFiGANGenerator from Preprocessing.ArticulatoryCombinedTextFrontend import ArticulatoryCombinedTextFrontend from Preprocessing.ArticulatoryCombinedTextFrontend import get_language_id from Preprocessing.ProsodicConditionExtractor import ProsodicConditionExtractor class Meta_FastSpeech2(torch.nn.Module): def __init__(self, device="cpu"): super().__init__() model_name = "Meta" language = "de" self.device = device self.text2phone = ArticulatoryCombinedTextFrontend(language=language, add_silence_to_end=True) checkpoint = torch.load(os.path.join("Models", f"FastSpeech2_{model_name}", "best.pt"), map_location='cpu') self.phone2mel = FastSpeech2(weights=checkpoint["model"]).to(torch.device(device)) self.mel2wav = HiFiGANGenerator(path_to_weights=os.path.join("Models", "HiFiGAN_combined", "best.pt")).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 set_utterance_embedding(self, path_to_reference_audio): wave, sr = soundfile.read(path_to_reference_audio) self.default_utterance_embedding = ProsodicConditionExtractor(sr=sr).extract_condition_from_reference_wave(wave).to(self.device) 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.text2phone = ArticulatoryCombinedTextFrontend(language=lang_id, add_silence_to_end=True, silent=True) self.lang_id = get_language_id(lang_id).to(self.device) def forward(self, text, view=False, durations=None, pitch=None, energy=None, phones = False): with torch.inference_mode(): if phones is False: phones = self.text2phone.string_to_tensor(text).to(torch.device(self.device)) else: phones = self.text2phone.string_to_tensor(text, input_phonemes=True).to(torch.device(self.device)) mel, durations, pitch, energy = self.phone2mel(phones, return_duration_pitch_energy=True, utterance_embedding=self.default_utterance_embedding, durations=durations, pitch=pitch, energy=energy, lang_id=self.lang_id) 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