fffiloni commited on
Commit
8f8affb
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1 Parent(s): 91b23fa

Update app.py

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Files changed (1) hide show
  1. app.py +16 -16
app.py CHANGED
@@ -24,21 +24,21 @@ def load_model(version):
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  return MusicGen.get_pretrained(version)
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- def predict(model, uploaded_image, melody, duration, topk, topp, temperature, cfg_coef):
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  text = img_to_text(uploaded_image, 'best', 4, fn_index=1)[0]
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  global MODEL
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  topk = int(topk)
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- if MODEL is None or MODEL.name != model:
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- MODEL = load_model(model)
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  if duration > MODEL.lm.cfg.dataset.segment_duration:
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  raise gr.Error("MusicGen currently supports durations of up to 30 seconds!")
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  MODEL.set_generation_params(
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  use_sampling=True,
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- top_k=topk,
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- top_p=topp,
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- temperature=temperature,
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- cfg_coef=cfg_coef,
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  duration=duration,
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  )
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@@ -84,18 +84,18 @@ with gr.Blocks() as demo:
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  melody = gr.Audio(source="upload", type="numpy", label="Melody Condition (optional)", interactive=True)
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  with gr.Row():
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  submit = gr.Button("Submit")
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- with gr.Row():
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- model = gr.Radio(["melody", "medium", "small", "large"], label="Model", value="melody", interactive=True)
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  with gr.Row():
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  duration = gr.Slider(minimum=1, maximum=30, value=10, label="Duration", interactive=True)
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- with gr.Row():
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- topk = gr.Number(label="Top-k", value=250, interactive=True)
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- topp = gr.Number(label="Top-p", value=0, interactive=True)
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- temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
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- cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
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  with gr.Column():
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  output = gr.Audio(label="Generated Music")
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- submit.click(predict, inputs=[model, uploaded_image, melody, duration, topk, topp, temperature, cfg_coef], outputs=[output])
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  gr.Markdown(
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  """
@@ -104,7 +104,7 @@ with gr.Blocks() as demo:
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  The model will generate a short music extract based on the image you provided.
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  You can generate up to 30 seconds of audio.
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- We present 4 model variations:
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  1. Melody -- a music generation model capable of generating music condition on text and melody inputs. **Note**, you can also use text only.
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  2. Small -- a 300M transformer decoder conditioned on text only.
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  3. Medium -- a 1.5B transformer decoder conditioned on text only.
 
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  return MusicGen.get_pretrained(version)
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+ def predict(uploaded_image, melody, duration):
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  text = img_to_text(uploaded_image, 'best', 4, fn_index=1)[0]
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  global MODEL
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  topk = int(topk)
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+ if MODEL is None or MODEL.name != "melody":
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+ MODEL = load_model("melody")
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  if duration > MODEL.lm.cfg.dataset.segment_duration:
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  raise gr.Error("MusicGen currently supports durations of up to 30 seconds!")
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  MODEL.set_generation_params(
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  use_sampling=True,
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+ top_k=250,
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+ top_p=0,
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+ temperature=1.0,
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+ cfg_coef=3.0,
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  duration=duration,
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  )
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  melody = gr.Audio(source="upload", type="numpy", label="Melody Condition (optional)", interactive=True)
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  with gr.Row():
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  submit = gr.Button("Submit")
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+ #with gr.Row():
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+ # model = gr.Radio(["melody", "medium", "small", "large"], label="Model", value="melody", interactive=True)
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  with gr.Row():
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  duration = gr.Slider(minimum=1, maximum=30, value=10, label="Duration", interactive=True)
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+ #with gr.Row():
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+ # topk = gr.Number(label="Top-k", value=250, interactive=True)
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+ # topp = gr.Number(label="Top-p", value=0, interactive=True)
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+ # temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
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+ # cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
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  with gr.Column():
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  output = gr.Audio(label="Generated Music")
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+ submit.click(predict, inputs=[uploaded_image, melody, duration, topk, topp, temperature, cfg_coef], outputs=[output])
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  gr.Markdown(
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  """
 
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  The model will generate a short music extract based on the image you provided.
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  You can generate up to 30 seconds of audio.
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+ This demo is set to use only the Melody model
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  1. Melody -- a music generation model capable of generating music condition on text and melody inputs. **Note**, you can also use text only.
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  2. Small -- a 300M transformer decoder conditioned on text only.
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  3. Medium -- a 1.5B transformer decoder conditioned on text only.