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import os
from run_model_downloader import download_models
if not os.path.exists("Models/ToucanTTS_Meta/best.pt"):
download_models()
import gradio as gr
from Preprocessing.multilinguality.SimilaritySolver import load_json_from_path
from Utility.utils import float2pcm
import os
import torch
from Architectures.ControllabilityGAN.GAN import GanWrapper
from InferenceInterfaces.ToucanTTSInterface import ToucanTTSInterface
from Utility.storage_config import MODELS_DIR
class ControllableInterface(torch.nn.Module):
def __init__(self, available_artificial_voices=1000):
super().__init__()
self.model = ToucanTTSInterface(device="cuda", tts_model_path="Meta", language="eng")
self.wgan = GanWrapper(os.path.join(MODELS_DIR, "Embedding", "embedding_gan.pt"), device="cuda")
self.generated_speaker_embeds = list()
self.available_artificial_voices = available_artificial_voices
self.current_language = ""
self.current_accent = ""
def read(self,
prompt,
language,
accent,
voice_seed,
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 = ToucanTTSInterface(device="cpu", tts_model_path="Meta", language=language)
self.current_language = language
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)
phones = self.model.text2phone.get_phone_string(prompt)
if len(phones) > 1800:
return
print(prompt)
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,
loudness_in_db=loudness_in_db)
return sr, wav, fig
title = "Controllable Text-to-Speech for over 7000 Languages"
article = "Check out the IMS Toucan TTS Toolkit at https://github.com/DigitalPhonetics/IMS-Toucan"
available_artificial_voices = 1000
path_to_iso_list = "Preprocessing/multilinguality/iso_to_fullname.json"
iso_to_name = load_json_from_path(path_to_iso_list)
text_selection = [f"{iso_to_name[iso_code]} Text ({iso_code})" for iso_code in iso_to_name]
controllable_ui = ControllableInterface(available_artificial_voices=available_artificial_voices)
def read(prompt,
language,
voice_seed,
duration_scaling_factor,
pitch_variance_scale,
energy_variance_scale,
emb1,
emb2
):
with torch.no_grad():
sr, wav, fig = controllable_ui.read(prompt,
language.split(" ")[-1].split("(")[1].split(")")[0],
language.split(" ")[-1].split("(")[1].split(")")[0],
voice_seed,
duration_scaling_factor,
1.,
pitch_variance_scale,
energy_variance_scale,
emb1,
emb2,
0.,
0.,
0.,
0.,
-24.)
return (sr, float2pcm(wav)), fig
iface = gr.Interface(fn=read,
inputs=[gr.Textbox(lines=2,
placeholder="write what you want the synthesis to read here...",
value="The woods are lovely, dark and deep, but I have promises to keep, and miles to go, before I sleep.",
label="Text input"),
gr.Dropdown(text_selection,
type="value",
value='English Text (eng)',
label="Select the Language of the Text (type on your keyboard to find it quickly)"),
gr.Slider(minimum=0, maximum=available_artificial_voices, step=1,
value=279,
label="Random Seed for the artificial Voice"),
gr.Slider(minimum=0.7, maximum=1.3, step=0.1, value=1.0, label="Duration Scale"),
gr.Slider(minimum=0.5, maximum=1.5, step=0.1, value=1.0, label="Pitch Variance Scale"),
gr.Slider(minimum=0.5, maximum=1.5, step=0.1, value=1.0, label="Energy Variance Scale"),
gr.Slider(minimum=-10.0, maximum=10.0, step=0.1, value=0.0, label="Femininity / Masculinity"),
gr.Slider(minimum=-10.0, maximum=10.0, step=0.1, value=0.0, label="Voice Depth")
],
outputs=[gr.Audio(type="numpy", label="Speech"),
gr.Image(label="Visualization")],
title=title,
theme="default",
allow_flagging="never",
article=article)
iface.launch()
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