Update app.py

app.py

@@ -6,11 +6,10 @@
 
 # Updated to account for UI changes from https://github.com/rkfg/audiocraft/blob/long/app.py
 # also released under the MIT license.
-import os
-command = "pip install --upgrade pip; pip3 install --trusted-host pypi.python.org --no-cache-dir -r req.txt"
-os.system(command)
+
 import argparse
 from concurrent.futures import ProcessPoolExecutor
+import os
 from pathlib import Path
 import subprocess as sp
 from tempfile import NamedTemporaryFile
@@ -252,28 +251,23 @@ def ui_full(launch_kwargs):
  gr.Markdown(
  """
  ### More details
-
  The model will generate a short music extract based on the description you provided.
  The model can generate up to 30 seconds of audio in one pass. It is now possible
  to extend the generation by feeding back the end of the previous chunk of audio.
  This can take a long time, and the model might lose consistency. The model might also
  decide at arbitrary positions that the song ends.
-
  **WARNING:** Choosing long durations will take a long time to generate (2min might take ~10min).
  An overlap of 12 seconds is kept with the previously generated chunk, and 18 "new" seconds
  are generated each time.
-
  We present 4 model variations:
  1. Melody -- a music generation model capable of generating music condition
  on text and melody inputs. **Note**, you can also use text only.
  2. Small -- a 300M transformer decoder conditioned on text only.
  3. Medium -- a 1.5B transformer decoder conditioned on text only.
  4. Large -- a 3.3B transformer decoder conditioned on text only (might OOM for the longest sequences.)
-
  When using `melody`, ou can optionaly provide a reference audio from
  which a broad melody will be extracted. The model will then try to follow both
  the description and melody provided.
-
  You can also use your own GPU or a Google Colab by following the instructions on our repo.
  See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
  for more details.
@@ -288,7 +282,6 @@ def ui_batched(launch_kwargs):
  gr.Markdown(
  """
  # MusicGen
-
  This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
  a simple and controllable model for music generation
  presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
@@ -345,14 +338,11 @@ def ui_batched(launch_kwargs):
  )
  gr.Markdown("""
  ### More details
-
  The model will generate 12 seconds of audio based on the description you provided.
  You can optionaly provide a reference audio from which a broad melody will be extracted.
  The model will then try to follow both the description and melody provided.
  All samples are generated with the `melody` model.
-
  You can also use your own GPU or a Google Colab by following the instructions on our repo.
-
  See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
  for more details.
  """)
@@ -405,4 +395,4 @@ if __name__ == "__main__":
  if IS_BATCHED:
  ui_batched(launch_kwargs)
  else:
- ui_full(launch_kwargs)
+ ui_full(launch_kwargs)