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| 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 | |