Update app.py

app.py

@@ -354,14 +354,6 @@ def add_batch(batch_number, final_composition, generated_batches, block_lines):
         final_composition.extend(generated_batches[batch_number])
         midi_fname, midi_score = save_midi(final_composition)
         block_lines.append(midi_score[-1][1] / 1000 if final_composition else 0)
-        TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(
-            midi_score,
-            output_signature='Orpheus Music Transformer',
-            output_file_name=midi_fname,
-            track_name='Project Los Angeles',
-            list_of_MIDI_patches=[0]*16,
-            verbose=False
-        )
         midi_plot = TMIDIX.plot_ms_SONG(
             midi_score,
             plot_title='Orpheus Music Transformer Composition',
@@ -416,7 +408,7 @@ with gr.Blocks() as demo:
 
     gr.Markdown("<h1 style='text-align: left; margin-bottom: 1rem'>Orpheus Music Transformer</h1>")
     gr.Markdown("<h1 style='text-align: left; margin-bottom: 1rem'>SOTA 8k multi-instrumental music transformer trained on 2.31M+ high-quality MIDIs</h1>")
-    gr.Markdown("<h1 style='text-align: left; margin-bottom: 1rem'>This is a WIP preview. Please check back for final release soon.</h1>")
+    gr.Markdown("<h1 style='text-align: left; margin-bottom: 1rem'>This is a WIP preview. Please check back soon for a final release!</h1>")
     gr.HTML("""
         Check out <a href="https://huggingface.co/datasets/projectlosangeles/Godzilla-MIDI-Dataset">Godzilla MIDI Dataset</a> on Hugging Face
         <p>
@@ -431,7 +423,7 @@ with gr.Blocks() as demo:
     gr.Markdown("""
     - **Efficient Architecture with RoPE**: Compact and very fast 479M full attention autoregressive transformer with RoPE.
     - **Extended Sequence Length**: 8k tokens that comfortably fit most music compositions and facilitate long-term music structure generation.
-    - **Premium Training Data**: Exclusively trained on high-quality MIDIs from the Godzilla MIDI dataset.
+    - **Premium Training Data**: Trained solely on the highest-quality MIDIs from the Godzilla MIDI dataset.
     - **Optimized MIDI Encoding**: Extremely efficient MIDI representation using only 3 tokens per note and 7 tokens per tri-chord.
     - **Distinct Encoding Order**: Features a unique duration/velocity last MIDI encoding order for refined musical expression.
     - **Full-Range Instrumental Learning**: True full-range MIDI instruments encoding enabling the model to learn each instrument separately.
@@ -443,7 +435,7 @@ with gr.Blocks() as demo:
     generated_batches = gr.State([])
     block_lines = gr.State([])
 
-    gr.Markdown("## Upload seed MIDI or click 'Generate' for a random output")
+    gr.Markdown("## Upload seed MIDI or click 'Generate' for random output")
     input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"])
     input_midi.upload(reset, [final_composition, generated_batches, block_lines],
                       [final_composition, generated_batches, block_lines])