import os import gradio as gr import numpy as np import torch from Utility.utils import emotion from InferenceInterfaces.Meta_FastSpeech2 import Meta_FastSpeech2 def float2pcm(sig, dtype="int16"): sig = np.asarray(sig) if sig.dtype.kind != "f": raise TypeError("'sig' must be a float array") dtype = np.dtype(dtype) if dtype.kind not in "iu": raise TypeError("'dtype' must be an integer type") i = np.iinfo(dtype) abs_max = 2 ** (i.bits - 1) offset = i.min + abs_max return (sig * abs_max + offset).clip(i.min, i.max).astype(dtype) class TTS_Interface: def __init__(self): self.device = "cuda" if torch.cuda.is_available() else "cpu" self.model = Meta_FastSpeech2(device=self.device) self.current_speaker = "M_Angry" self.current_language = "English" self.current_accent = "English" self.language_id_lookup = { "English": "en", "German": "de", "Greek": "el", "Spanish": "es", "Finnish": "fi", "Russian": "ru", "Hungarian": "hu", "Dutch": "nl", "French": "fr", "Polish": "pl", "Portuguese": "pt", "Italian": "it", } self.speaker_path_lookup = { "M_Angry": "voices/nate_angry.mp3", "M_Sad": "output1.wav", "M_Cheerful": "voices/nate_cheerful.mp3", "M_Excited": "voices/nate_excited.mp3", "M_Friendly": "voices/robert-friendly.mp3", "M_Hopeful": "voices/robert-hopeful.mp3", "M_Normal": "voices/robert-normal.mp3", "M_Shouting": "output1.wav", "M_Terrified": "voices/nate_terrified.mp3", "M_Unfriendly": "voices/robert-unfriendly.mp3", "M_Whispering": "voices/robert-whispering.mp3", "F_Angry": "voices/cleo-angry.mp3", "F_Sad": "voices/cleo-sad.mp3", "F_Cheerful": "voices/cleo-cheerful.mp3", "F_Excited": "voices/cleo-excited.mp3", "F_Friendly": "voices/cleo-friendly.mp3", "F_Hopeful": "voices/cleo-hopeful.mp3", "F_Normal": "voices/cleo-normal.mp3", "F_Shouting": "voices/cleo-shouting.mp3", "F_Terrified": "voices/cleo-terrified.mp3", "F_Unfriendly": "voices/cleo-unfriendly.mp3", "F_Whispering": "voices/cleo-whispering.mp3", } self.model.set_utterance_embedding( self.speaker_path_lookup[self.current_speaker] ) def read(self, prompt, language, accent, speaker, gender): language = language.split()[0] accent = accent.split()[0] if self.current_language != language: self.model.set_phonemizer_language(self.language_id_lookup[language]) self.current_language = language if self.current_accent != accent: self.model.set_accent_language(self.language_id_lookup[accent]) self.current_accent = accent speaker = (gender[0]) + "_" + speaker if self.current_speaker != speaker: self.model.set_utterance_embedding(self.speaker_path_lookup[speaker]) self.current_speaker = speaker phones = self.model.text2phone.get_phone_string(prompt) if len(phones) > 1800: if language == "English": prompt = "Your input was too long. Please try either a shorter text or split it into several parts." elif language == "German": prompt = "Deine Eingabe war zu lang. Bitte versuche es entweder mit einem kürzeren Text oder teile ihn in mehrere Teile auf." elif language == "Greek": prompt = "Η εισήγησή σας ήταν πολύ μεγάλη. Παρακαλώ δοκιμάστε είτε ένα μικρότερο κείμενο είτε χωρίστε το σε διάφορα μέρη." elif language == "Spanish": prompt = "Su entrada es demasiado larga. Por favor, intente un texto más corto o divídalo en varias partes." elif language == "Finnish": prompt = "Vastauksesi oli liian pitkä. Kokeile joko lyhyempää tekstiä tai jaa se useampaan osaan." elif language == "Russian": prompt = "Ваш текст слишком длинный. Пожалуйста, попробуйте либо сократить текст, либо разделить его на несколько частей." elif language == "Hungarian": prompt = "Túl hosszú volt a bevitele. Kérjük, próbáljon meg rövidebb szöveget írni, vagy ossza több részre." elif language == "Dutch": prompt = "Uw input was te lang. Probeer een kortere tekst of splits het in verschillende delen." elif language == "French": prompt = "Votre saisie était trop longue. Veuillez essayer un texte plus court ou le diviser en plusieurs parties." elif language == "Polish": prompt = "Twój wpis był zbyt długi. Spróbuj skrócić tekst lub podzielić go na kilka części." elif language == "Portuguese": prompt = "O seu contributo foi demasiado longo. Por favor, tente um texto mais curto ou divida-o em várias partes." elif language == "Italian": prompt = "Il tuo input era troppo lungo. Per favore, prova un testo più corto o dividilo in più parti." phones = self.model.text2phone.get_phone_string(prompt) wav = self.model(phones) wav = emotion(wav, self.current_speaker) return 48000, float2pcm(wav.cpu().numpy()) meta_model = TTS_Interface() article = "

" iface = gr.Interface( fn=meta_model.read, inputs=[ gr.inputs.Textbox( lines=2, placeholder="write what you want the synthesis to read here... \n(to prevent out of memory errors, too long inputs get replaced with a placeholder)", label="Text input", ), gr.inputs.Dropdown( [ "English Text", "German Text", "Greek Text", "Spanish Text", "Finnish Text", "Russian Text", "Hungarian Text", "Dutch Text", "French Text", "Polish Text", "Portuguese Text", "Italian Text", ], type="value", default="English Text", label="Select the Language of the Text", ), gr.inputs.Dropdown( [ "English Accent", "German Accent", "Greek Accent", "Spanish Accent", "Finnish Accent", "Russian Accent", "Hungarian Accent", "Dutch Accent", "French Accent", "Polish Accent", "Portuguese Accent", "Italian Accent", ], type="value", default="English Accent", label="Select the Accent of the Speaker", ), gr.inputs.Dropdown( [ "Angry", "Sad", "Cheerful", "Excited", "Friendly", "Hopeful", "Normal", "Shouting", "Terrified", "Unfriendly", "Whispering", ], type="value", default="Angry", label="Select the Voice of the Speaker", ), gr.inputs.Dropdown( ["Male", "Female"], type="value", default="Male", label="Select the gender" ), ], outputs=gr.outputs.Audio(type="numpy", label=None), layout="vertical", title="", theme="default", allow_flagging="never", allow_screenshot=False, article=article, ) iface.launch(server_name="0.0.0.0", enable_queue=True)