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import gradio as gr
import numpy as np
from model.DiffSynthSampler import DiffSynthSampler
from tools import safe_int, read_wav_to_numpy
from webUI.natural_language_guided.utils import latent_representation_to_Gradio_image, \
encodeBatch2GradioOutput_STFT, add_instrument
from webUI.natural_language_guided_4.utils import resize_image_to_aspect_ratio
def get_instruments_module(gradioWebUI, virtual_instruments_state):
with gr.Tab("intruments"):
gr.Markdown("Use neural networks to select random sounds using your favorite instrument!")
with gr.Row(variant="panel"):
with gr.Column(scale=1):
input_text = gr.Textbox(label="input")
@gr.render(inputs=input_text)
def show_split(text):
textboxes = []
if len(text) == 0:
gr.Markdown("## No Input Provided")
else:
for letter in text:
textboxes.append(gr.Textbox(letter, interactive=True))
def merge(*splitted_texts):
out = ""
for t in splitted_texts:
out += t
return out
submit_botton.click(merge, inputs=textboxes, outputs=merged_textbox)
submit_botton = gr.Button("submit")
merged_textbox = gr.Textbox(placeholder="placeholder", interactive=False)
with gr.Column(scale=1):
@gr.render(inputs=virtual_instruments_state)
def check_instruments(virtual_instruments_dict):
virtual_instruments = virtual_instruments_dict["virtual_instruments"]
instrument_names = list(virtual_instruments.keys())
instrument_dropdown = gr.Dropdown(
instrument_names, label="instrument", info="info placeholder"
)
def select_instrument(instrument):
print(f"instrument: {instrument}")
sr, signal = virtual_instruments[instrument]["signal"]
return {selected_instrument_audio: (sr, signal)}
instrument_dropdown.select(select_instrument, inputs=instrument_dropdown,
outputs=selected_instrument_audio)
selected_instrument_audio = gr.Audio(type="numpy", label="Play", scale=1, interactive=False)
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