File size: 9,008 Bytes
0c54e86 c5dbd2e 0c54e86 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 |
import os
import gradio as gr
import numpy as np
import torch
from InferenceInterfaces.Meta_FastSpeech2 import Meta_FastSpeech2
os.system("pip uninstall -y gradio")
os.system("pip install gradio==2.7.5.2")
def float2pcm(sig, dtype='int16'):
"""
https://gist.github.com/HudsonHuang/fbdf8e9af7993fe2a91620d3fb86a182
"""
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 = "English Speaker's Voice"
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 = {
"English Speaker's Voice" : "reference_audios/english.wav",
"German Speaker's Voice" : "reference_audios/german.wav",
"Greek Speaker's Voice" : "reference_audios/greek.wav",
"Spanish Speaker's Voice" : "reference_audios/spanish.wav",
"Finnish Speaker's Voice" : "reference_audios/finnish.wav",
"Russian Speaker's Voice" : "reference_audios/russian.wav",
"Hungarian Speaker's Voice" : "reference_audios/hungarian.wav",
"Dutch Speaker's Voice" : "reference_audios/dutch.wav",
"French Speaker's Voice" : "reference_audios/french.wav",
"Polish Speaker's Voice" : "reference_audios/polish.flac",
"Portuguese Speaker's Voice": "reference_audios/portuguese.flac",
"Italian Speaker's Voice" : "reference_audios/italian.flac",
}
self.model.set_utterance_embedding(self.speaker_path_lookup[self.current_speaker])
def read(self, prompt, language, accent, speaker):
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
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)
return 48000, float2pcm(wav.cpu().numpy())
meta_model = TTS_Interface()
iface = gr.Interface(fn=meta_model.read,
inputs=[gr.inputs.Textbox(lines=2,
placeholder="write what you want the synthesis to read here...",
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(["English Speaker's Voice",
"German Speaker's Voice",
"Greek Speaker's Voice",
"Spanish Speaker's Voice",
"Finnish Speaker's Voice",
"Russian Speaker's Voice",
"Hungarian Speaker's Voice",
"Dutch Speaker's Voice",
"French Speaker's Voice",
"Polish Speaker's Voice",
"Portuguese Speaker's Voice",
"Italian Speaker's Voice"], type="value", default="English Speaker's Voice", label="Select the Voice of the Speaker")],
outputs=gr.outputs.Audio(type="numpy", label=None),
layout="vertical",
title="Anonymous ACL Submission",
theme="default",
allow_flagging="never",
allow_screenshot=False,
article="")
iface.launch(enable_queue=True)
|