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import os |
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import gradio as gr |
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import numpy as np |
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import torch |
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from InferenceInterfaces.Meta_FastSpeech2 import Meta_FastSpeech2 |
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os.system("pip uninstall -y gradio") |
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os.system("pip install gradio==2.7.5.2") |
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def float2pcm(sig, dtype='int16'): |
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""" |
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https://gist.github.com/HudsonHuang/fbdf8e9af7993fe2a91620d3fb86a182 |
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""" |
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sig = np.asarray(sig) |
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if sig.dtype.kind != 'f': |
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raise TypeError("'sig' must be a float array") |
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dtype = np.dtype(dtype) |
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if dtype.kind not in 'iu': |
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raise TypeError("'dtype' must be an integer type") |
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i = np.iinfo(dtype) |
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abs_max = 2 ** (i.bits - 1) |
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offset = i.min + abs_max |
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return (sig * abs_max + offset).clip(i.min, i.max).astype(dtype) |
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class TTS_Interface: |
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def __init__(self): |
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self.device = "cuda" if torch.cuda.is_available() else "cpu" |
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self.model = Meta_FastSpeech2(device=self.device) |
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self.current_speaker = "English Speaker's Voice" |
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self.current_language = "English" |
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self.current_accent = "English" |
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self.language_id_lookup = { |
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"English" : "en", |
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"German" : "de", |
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"Greek" : "el", |
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"Spanish" : "es", |
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"Finnish" : "fi", |
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"Russian" : "ru", |
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"Hungarian" : "hu", |
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"Dutch" : "nl", |
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"French" : "fr", |
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'Polish' : "pl", |
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'Portuguese': "pt", |
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'Italian' : "it", |
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} |
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self.speaker_path_lookup = { |
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"English Speaker's Voice" : "reference_audios/english.wav", |
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"German Speaker's Voice" : "reference_audios/german.wav", |
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"Greek Speaker's Voice" : "reference_audios/greek.wav", |
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"Spanish Speaker's Voice" : "reference_audios/spanish.wav", |
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"Finnish Speaker's Voice" : "reference_audios/finnish.wav", |
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"Russian Speaker's Voice" : "reference_audios/russian.wav", |
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"Hungarian Speaker's Voice" : "reference_audios/hungarian.wav", |
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"Dutch Speaker's Voice" : "reference_audios/dutch.wav", |
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"French Speaker's Voice" : "reference_audios/french.wav", |
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"Polish Speaker's Voice" : "reference_audios/polish.flac", |
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"Portuguese Speaker's Voice": "reference_audios/portuguese.flac", |
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"Italian Speaker's Voice" : "reference_audios/italian.flac", |
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} |
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self.model.set_utterance_embedding(self.speaker_path_lookup[self.current_speaker]) |
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def read(self, prompt, language, accent, speaker): |
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language = language.split()[0] |
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accent = accent.split()[0] |
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if self.current_language != language: |
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self.model.set_phonemizer_language(self.language_id_lookup[language]) |
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self.current_language = language |
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if self.current_accent != accent: |
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self.model.set_accent_language(self.language_id_lookup[accent]) |
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self.current_accent = accent |
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if self.current_speaker != speaker: |
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self.model.set_utterance_embedding(self.speaker_path_lookup[speaker]) |
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self.current_speaker = speaker |
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phones = self.model.text2phone.get_phone_string(prompt) |
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if len(phones) > 1800: |
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if language == "English": |
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prompt = "Your input was too long. Please try either a shorter text or split it into several parts." |
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elif language == "German": |
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prompt = "Deine Eingabe war zu lang. Bitte versuche es entweder mit einem kürzeren Text oder teile ihn in mehrere Teile auf." |
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elif language == "Greek": |
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prompt = "Η εισήγησή σας ήταν πολύ μεγάλη. Παρακαλώ δοκιμάστε είτε ένα μικρότερο κείμενο είτε χωρίστε το σε διάφορα μέρη." |
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elif language == "Spanish": |
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prompt = "Su entrada es demasiado larga. Por favor, intente un texto más corto o divídalo en varias partes." |
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elif language == "Finnish": |
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prompt = "Vastauksesi oli liian pitkä. Kokeile joko lyhyempää tekstiä tai jaa se useampaan osaan." |
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elif language == "Russian": |
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prompt = "Ваш текст слишком длинный. Пожалуйста, попробуйте либо сократить текст, либо разделить его на несколько частей." |
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elif language == "Hungarian": |
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prompt = "Túl hosszú volt a bevitele. Kérjük, próbáljon meg rövidebb szöveget írni, vagy ossza több részre." |
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elif language == "Dutch": |
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prompt = "Uw input was te lang. Probeer een kortere tekst of splits het in verschillende delen." |
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elif language == "French": |
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prompt = "Votre saisie était trop longue. Veuillez essayer un texte plus court ou le diviser en plusieurs parties." |
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elif language == 'Polish': |
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prompt = "Twój wpis był zbyt długi. Spróbuj skrócić tekst lub podzielić go na kilka części." |
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elif language == 'Portuguese': |
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prompt = "O seu contributo foi demasiado longo. Por favor, tente um texto mais curto ou divida-o em várias partes." |
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elif language == 'Italian': |
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prompt = "Il tuo input era troppo lungo. Per favore, prova un testo più corto o dividilo in più parti." |
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phones = self.model.text2phone.get_phone_string(prompt) |
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wav = self.model(phones) |
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return 48000, float2pcm(wav.cpu().numpy()) |
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meta_model = TTS_Interface() |
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iface = gr.Interface(fn=meta_model.read, |
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inputs=[gr.inputs.Textbox(lines=2, |
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placeholder="write what you want the synthesis to read here...", |
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label="Text input"), |
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gr.inputs.Dropdown(['English Text', |
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'German Text', |
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'Greek Text', |
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'Spanish Text', |
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'Finnish Text', |
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'Russian Text', |
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'Hungarian Text', |
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'Dutch Text', |
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'French Text', |
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'Polish Text', |
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'Portuguese Text', |
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'Italian Text'], type="value", default='English Text', label="Select the Language of the Text"), |
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gr.inputs.Dropdown(['English Accent', |
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'German Accent', |
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'Greek Accent', |
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'Spanish Accent', |
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'Finnish Accent', |
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'Russian Accent', |
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'Hungarian Accent', |
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'Dutch Accent', |
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'French Accent', |
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'Polish Accent', |
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'Portuguese Accent', |
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'Italian Accent'], type="value", default='English Accent', label="Select the Accent of the Speaker"), |
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gr.inputs.Dropdown(["English Speaker's Voice", |
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"German Speaker's Voice", |
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"Greek Speaker's Voice", |
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"Spanish Speaker's Voice", |
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"Finnish Speaker's Voice", |
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"Russian Speaker's Voice", |
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"Hungarian Speaker's Voice", |
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"Dutch Speaker's Voice", |
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"French Speaker's Voice", |
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"Polish Speaker's Voice", |
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"Portuguese Speaker's Voice", |
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"Italian Speaker's Voice"], type="value", default="English Speaker's Voice", label="Select the Voice of the Speaker")], |
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outputs=gr.outputs.Audio(type="numpy", label=None), |
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layout="vertical", |
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title="Anonymous ACL Submission", |
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theme="default", |
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allow_flagging="never", |
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allow_screenshot=False, |
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article="") |
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iface.launch(enable_queue=True) |
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