code: def ui_full(launch_kwargs):
      with gr.Blocks() as interface:
          gr.Markdown(
              """
              # MAGNeT
              This is your private demo for [MAGNeT](https://github.com/facebookresearch/audiocraft),
              A fast text-to-music model, consists of a single, non-autoregressive transformer.
              presented at: ["Masked Audio Generation using a Single Non-Autoregressive Transformer"] (https://huggingface.co/papers/2401.04577)
              """
          )
          with gr.Row():
              with gr.Column():
                  with gr.Row():
                      text = gr.Text(
                          label="Input Text",
                          value="80s electronic track with melodic synthesizers, catchy beat and groovy bass",
                          interactive=True,
                      )
                  with gr.Row():
                      submit = gr.Button("Submit")
                      # Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
                      _ = gr.Button("Interrupt").click(
                          fn=interrupt, queue=False
                      )
                  with gr.Row():
                      model = gr.Radio(
                          [
                              "facebook/magnet-small-10secs",
                              "facebook/magnet-medium-10secs",
                              "facebook/magnet-small-30secs",
                              "facebook/magnet-medium-30secs",
                              "facebook/audio-magnet-small",
                              "facebook/audio-magnet-medium",
                          ],
                          label="Model",
                          value="facebook/magnet-small-10secs",
                          interactive=True,
                      )
                      model_path = gr.Text(
                          label="Model Path (custom models)"
                      )
                  with gr.Row():
                      span_score = gr.Radio(
                          ["max-nonoverlap", PROD_STRIDE_1],
                          label="Span Scoring",
                          value=PROD_STRIDE_1,
                          interactive=True,
                      )
                  with gr.Row():
                      decoding_steps1 = gr.Number(
                          label="Decoding Steps (stage 1)",
                          value=20,
                          interactive=True,
                      )
                      decoding_steps2 = gr.Number(
                          label="Decoding Steps (stage 2)",
                          value=10,
                          interactive=True,
                      )
                      decoding_steps3 = gr.Number(
                          label="Decoding Steps (stage 3)",
                          value=10,
                          interactive=True,
                      )
                      decoding_steps4 = gr.Number(
                          label="Decoding Steps (stage 4)",
                          value=10,
                          interactive=True,
                      )
                  with gr.Row():
                      temperature = gr.Number(
                          label="Temperature",
                          value=3.0,
                          step=0.25,
                          minimum=0,
                          interactive=True,
                      )
                      topp = gr.Number(
                          label="Top-p",
                          value=0.9,
                          step=0.1,
                          minimum=0,
                          maximum=1,
                          interactive=True,
                      )
                      max_cfg_coef = gr.Number(
                          label="Max CFG coefficient",
                          value=10.0,
                          minimum=0,
                          interactive=True,
                      )
                      min_cfg_coef = gr.Number(
                          label="Min CFG coefficient",
                          value=1.0,
                          minimum=0,
                          interactive=True,
                      )
              with gr.Column():
                  output = gr.Video(
                      label="Generated Audio - variation 1"
                  )
                  audio_outputs = [
                      gr.Audio(
                          label=f"Generated Audio - variation {i+1}",
                          type="filepath",
                      )
                      for i in range(N_REPEATS)
                  ]
          submit.click(
              fn=predict_full,
              inputs=[
                  model,
                  model_path,
                  text,
                  temperature,
                  topp,
                  max_cfg_coef,
                  min_cfg_coef,
                  decoding_steps1,
                  decoding_steps2,
                  decoding_steps3,
                  decoding_steps4,
                  span_score,
              ],
              outputs=[output] + [o for o in audio_outputs],
          )
          gr.Examples(
              fn=predict_full,
              examples=[
                  [
                      "80s electronic track with melodic synthesizers, catchy beat and groovy bass",
                      "facebook/magnet-small-10secs",
                      20,
                      3.0,
                      0.9,
                      10.0,
                  ],
                  [
                      "80s electronic track with melodic synthesizers, catchy beat and groovy bass. 170 bpm",
                      "facebook/magnet-small-10secs",
                      20,
                      3.0,
                      0.9,
                      10.0,
                  ],
                  [
                      "Earthy tones, environmentally conscious, ukulele-infused, harmonic, breezy, easygoing, organic instrumentation, gentle grooves",
                      "facebook/magnet-medium-10secs",
                      20,
                      3.0,
                      0.9,
                      10.0,
                  ],
                  [
                      "Funky groove with electric piano playing blue chords rhythmically",
                      "facebook/magnet-medium-10secs",
                      20,
                      3.0,
                      0.9,
                      10.0,
                  ],
                  [
                      "Rock with saturated guitars, a heavy bass line and crazy drum break and fills.",
                      "facebook/magnet-small-30secs",
                      60,
                      3.0,
                      0.9,
                      10.0,
                  ],
                  [
                      "A grand orchestral arrangement with thunderous percussion, epic brass fanfares, and soaring strings, creating a cinematic atmosphere fit for a heroic battle",
                      "facebook/magnet-medium-30secs",
                      60,
                      3.0,
                      0.9,
                      10.0,
                  ],
                  [
                      "Seagulls squawking as ocean waves crash while wind blows heavily into a microphone.",
                      "facebook/audio-magnet-small",
                      20,
                      3.5,
                      0.8,
                      20.0,
                  ],
                  [
                      "A toilet flushing as music is playing and a man is singing in the distance.",
                      "facebook/audio-magnet-medium",
                      20,
                      3.5,
                      0.8,
                      20.0,
                  ],
              ],
              inputs=[
                  text,
                  model,
                  decoding_steps1,
                  temperature,
                  topp,
                  max_cfg_coef,
              ],
              outputs=[output],
          )
  
          gr.Markdown(
              """
              ### More details
              
              #### Music Generation
              "magnet" models will generate a short music extract based on the textual description you provided.
              These models can generate either 10 seconds or 30 seconds of music.
              These models were trained with descriptions from a stock music catalog. Descriptions that will work best
              should include some level of details on the instruments present, along with some intended use case
              (e.g. adding "perfect for a commercial" can somehow help).
  
              We present 4 model variants:
              1. facebook/magnet-small-10secs - a 300M non-autoregressive transformer capable of generating 10-second music conditioned
                  on text.
              2. facebook/magnet-medium-10secs - 1.5B parameters, 10 seconds audio.
              3. facebook/magnet-small-30secs - 300M parameters, 30 seconds audio.
              4. facebook/magnet-medium-30secs - 1.5B parameters, 30 seconds audio.
          
              #### Sound-Effect Generation
              "audio-magnet" models will generate a 10-second sound effect based on the description you provide. 
  
              These models were trained on the following data sources: a subset of AudioSet (Gemmeke et al., 2017), 
              [BBC sound effects](https://sound-effects.bbcrewind.co.uk/), AudioCaps (Kim et al., 2019), 
              Clotho v2 (Drossos et al., 2020), VGG-Sound (Chen et al., 2020), FSD50K (Fonseca et al., 2021), 
              [Free To Use Sounds](https://www.freetousesounds.com/all-in-one-bundle/), [Sonniss Game Effects](https://sonniss.com/gameaudiogdc), 
              [WeSoundEffects](https://wesoundeffects.com/we-sound-effects-bundle-2020/), 
              [Paramount Motion - Odeon Cinematic Sound Effects](https://www.paramountmotion.com/odeon-sound-effects).
  
              We present 2 model variants:
              1. facebook/audio-magnet-small - 10 second sound effect generation, 300M parameters.
              2. facebook/audio-magnet-medium - 10 second sound effect generation, 1.5B parameters.
  
              See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft/blob/main/docs/MAGNET.md)
              for more details.
              """
          )
  
          interface.queue().launch(**launch_kwargs)
  
imports: import argparse
  from concurrent.futures import ProcessPoolExecutor
  import logging
  import os
  from pathlib import Path
  import subprocess
  import sys
  from tempfile import NamedTemporaryFile
  import time
  import typing
  import warnings
  import gradio
  from audiocraft.data.audio import audio_write
  from audiocraft.models import MAGNeT
label_desc: Use image node_id 8 for calling a global function with example usage: ui_full(launch_kwargs) without return types