import os import torch from InferenceInterfaces.ToucanTTSInterface import ToucanTTSInterface from Modules.ControllabilityGAN.GAN import GanWrapper from Utility.storage_config import MODELS_DIR class ControllableInterface: def __init__(self, gpu_id="cpu", available_artificial_voices=1000): if gpu_id == "cpu": os.environ["CUDA_VISIBLE_DEVICES"] = "" else: os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = f"{gpu_id}" self.device = "cuda" if gpu_id != "cpu" else "cpu" self.model = ToucanTTSInterface(device=self.device, tts_model_path="Meta") self.wgan = GanWrapper(os.path.join(MODELS_DIR, "Embedding", "embedding_gan.pt"), device=self.device) self.generated_speaker_embeds = list() self.available_artificial_voices = available_artificial_voices self.current_language = "" self.current_accent = "" def read(self, prompt, reference_audio, language, accent, voice_seed, prosody_creativity, duration_scaling_factor, pause_duration_scaling_factor, pitch_variance_scale, energy_variance_scale, emb_slider_1, emb_slider_2, emb_slider_3, emb_slider_4, emb_slider_5, emb_slider_6, loudness_in_db ): if self.current_language != language: self.model.set_phonemizer_language(language) print(f"switched phonemizer language to {language}") self.current_language = language if self.current_accent != accent: self.model.set_accent_language(accent) print(f"switched accent language to {accent}") self.current_accent = accent if reference_audio is None: self.wgan.set_latent(voice_seed) controllability_vector = torch.tensor([emb_slider_1, emb_slider_2, emb_slider_3, emb_slider_4, emb_slider_5, emb_slider_6], dtype=torch.float32) embedding = self.wgan.modify_embed(controllability_vector) self.model.set_utterance_embedding(embedding=embedding) else: self.model.set_utterance_embedding(reference_audio) phones = self.model.text2phone.get_phone_string(prompt) if len(phones) > 1800: if language == "deu": prompt = "Deine Eingabe war zu lang. Bitte versuche es entweder mit einem kürzeren Text oder teile ihn in mehrere Teile auf." elif language == "ell": prompt = "Η εισήγησή σας ήταν πολύ μεγάλη. Παρακαλώ δοκιμάστε είτε ένα μικρότερο κείμενο είτε χωρίστε το σε διάφορα μέρη." elif language == "spa": prompt = "Su entrada es demasiado larga. Por favor, intente un texto más corto o divídalo en varias partes." elif language == "fin": prompt = "Vastauksesi oli liian pitkä. Kokeile joko lyhyempää tekstiä tai jaa se useampaan osaan." elif language == "rus": prompt = "Ваш текст слишком длинный. Пожалуйста, попробуйте либо сократить текст, либо разделить его на несколько частей." elif language == "hun": prompt = "Túl hosszú volt a bevitele. Kérjük, próbáljon meg rövidebb szöveget írni, vagy ossza több részre." elif language == "nld": prompt = "Uw input was te lang. Probeer een kortere tekst of splits het in verschillende delen." elif language == "fra": prompt = "Votre saisie était trop longue. Veuillez essayer un texte plus court ou le diviser en plusieurs parties." elif language == 'pol': prompt = "Twój wpis był zbyt długi. Spróbuj skrócić tekst lub podzielić go na kilka części." elif language == 'por': prompt = "O seu contributo foi demasiado longo. Por favor, tente um texto mais curto ou divida-o em várias partes." elif language == 'ita': prompt = "Il tuo input era troppo lungo. Per favore, prova un testo più corto o dividilo in più parti." elif language == 'cmn': prompt = "你的输入太长了。请尝试使用较短的文本或将其拆分为多个部分。" elif language == 'vie': prompt = "Đầu vào của bạn quá dài. Vui lòng thử một văn bản ngắn hơn hoặc chia nó thành nhiều phần." else: prompt = "Your input was too long. Please try either a shorter text or split it into several parts." if self.current_language != "eng": self.model.set_phonemizer_language("eng") self.current_language = "eng" if self.current_accent != "eng": self.model.set_accent_language("eng") self.current_accent = "eng" print(prompt + "\n\n") wav, sr, fig = self.model(prompt, input_is_phones=False, 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, return_plot_as_filepath=True, prosody_creativity=prosody_creativity, loudness_in_db=loudness_in_db) return sr, wav, fig