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import gradio as gr
import numpy as np
import torch
from InferenceInterfaces.Meta_FastSpeech2 import Meta_FastSpeech2
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)
def read(self, prompt, language):
language_id_lookup = {
"English" : "en",
"German" : "de",
"Greek" : "el",
"Spanish" : "es",
"Finnish" : "fi",
"Russian" : "ru",
"Hungarian": "hu",
"Dutch" : "nl",
"French" : "fr"
}
self.model.set_language(language_id_lookup[language])
wav = self.model(prompt)
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=" "),
gr.inputs.Dropdown(['English',
'German',
'Greek',
'Spanish',
'Finnish',
'Russian',
'Hungarian',
'Dutch',
'French'], type="value", default='English', label="Language Selection")],
outputs=gr.outputs.Audio(type="numpy", label=None),
layout="vertical",
title="IMS Toucan Multilingual Multispeaker Demo",
thumbnail="Utility/toucan.png",
theme="default",
allow_flagging="never",
allow_screenshot=False)
iface.launch()
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