Update app.py

app.py

@@ -6,6 +6,8 @@ from tempfile import _TemporaryFileWrapper
 import torch
 import torchaudio
 
+SAMPLE_RATE=16000
+
 if torch.cuda.is_available():
  device = "cuda"
  torch_dtype = torch.float16
@@ -21,10 +23,12 @@ pipe = pipe.to(device)
 generator = torch.Generator(device)
 
 
-def predict(midi_file=None, prompt="", negative_prompt="", audio_length_in_s=5, random_seed=0, controlnet_conditioning_scale=1, num_inference_steps=20, guidance_scale=2.5, guess_mode=False):
+def predict(midi_file=None, prompt="", negative_prompt="", audio_length_in_s=10, random_seed=0, controlnet_conditioning_scale=1, num_inference_steps=20, guidance_scale=2.5, guess_mode=False):
  if isinstance(midi_file, _TemporaryFileWrapper):
  midi_file = midi_file.name
  midi = PrettyMIDI(midi_file)
+ midi_synth = midi.synthesize(fs=SAMPLE_RATE)[:int(SAMPLE_RATE*audio_length_in_s)]
+ midi_synth = midi_synth.reshape(midi_synth.shape[0], 1)
  audio = pipe(
  prompt,
  negative_prompt=negative_prompt,
@@ -36,7 +40,8 @@ def predict(midi_file=None, prompt="", negative_prompt="", audio_length_in_s=5,
  generator=generator.manual_seed(int(random_seed)),
  guidance_scale=float(guidance_scale),
  )
- return (16000, audio.audios.T)
+ return (SAMPLE_RATE, midi_synth), (SAMPLE_RATE, audio.audios.T)
+ 
 
 with gr.Blocks(title="🎹 MIDI-AudioLDM", theme=gr.themes.Base(text_size=gr.themes.sizes.text_md, font=[gr.themes.GoogleFont("Nunito Sans")])) as demo:
  gr.HTML(
@@ -52,7 +57,8 @@ with gr.Blocks(title="🎹 MIDI-AudioLDM", theme=gr.themes.Base(text_size=gr.the
  midi = gr.File(label="midi file", file_types=[".mid"])
  prompt = gr.Textbox(label="prompt", info="Enter a descriptive text prompt to guide the audio generation.")
  with gr.Column(variant='panel'):
- audio = gr.Audio(label="audio")
+ synth = gr.Audio(label="synthesized audio")
+ audio = gr.Audio(label="generated audio")
  with gr.Accordion("Advanced Settings", open=False):
  duration = gr.Slider(0, 30, value=10, step=2.5, label="duration", info="Modify the duration in seconds of the output audio file.")
  inf = gr.Slider(0, 50, value=20, step=1, label="inference steps", info="Edit the number of denoising steps. A larger number usually leads to higher quality but slower results.")
@@ -62,7 +68,7 @@ with gr.Blocks(title="🎹 MIDI-AudioLDM", theme=gr.themes.Base(text_size=gr.the
  cond = gr.Slider(0.0, 1.0, value=1.0, step=0.1, label="conditioning scale", info="Choose a value between 0 and 1. The larger the more it will take the conditioning into account. Lower values are recommended for more creative prompts.")
  guess = gr.Checkbox(label="guess mode", info="Optionally select guess mode. If so, the model will try to recognize the content of the MIDI without the need of a text prompt.")
  btn = gr.Button("Generate")
- btn.click(predict, inputs=[midi, prompt, neg_prompt, duration, seed, cond, inf, guidance_scale, guess], outputs=[audio])
+ btn.click(predict, inputs=[midi, prompt, neg_prompt, duration, seed, cond, inf, guidance_scale, guess], outputs=[synth, audio])
  gr.Examples(examples=[["S00.mid", "piano", "", 10, 25, 1.0, 20, 2.5, False], ["S00.mid", "violin", "", 10, 25, 1.0, 20, 2.5, False], ["S00.mid", "woman singing, studio recording", "noise", 10, 25, 1.0, 20, 2.5, False], ["S00.mid", "jazz band, clean", "noise", 10, 25, 0.8, 20, 2.5, False], ["S00.mid", "choir", "noise, percussion", 10, 25, 0.7, 20, 2.5, False]], inputs=[midi, prompt, neg_prompt, duration, seed, cond, inf, guidance_scale, guess], fn=predict, outputs=audio, cache_examples=True)
  
 demo.launch()