Update README.md

README.md

@@ -1 +1,68 @@
-https://github.com/xingjianll/symbolic-music-generation/
+https://github.com/xingjianll/symbolic-music-generation/
+
+
+```python
+from miditok import PerTok, TokenizerConfig
+from transformers import AutoModelForCausalLM, GenerationConfig
+import torch
+import io
+import mido
+import rtmidi
+
+# Load tokenizer config and tokenizer
+config = TokenizerConfig(
+    num_velocities=8,
+    use_velocities=True,
+    use_chords=False,
+    use_rests=True,
+    use_tempos=True,
+    use_time_signatures=False,
+    use_sustain_pedals=False,
+    use_pitch_bends=False,
+    use_pitch_intervals=False,
+    use_programs=False,
+    use_pitchdrum_tokens=False,
+    ticks_per_quarter=320,
+    use_microtiming=False,
+    max_microtiming_shift=0.125
+)
+tokenizer = PerTok(config)
+tokenizer.from_pretrained("xingjianll/midi-tokenizer")
+
+# Load model and generation config
+model = AutoModelForCausalLM.from_pretrained("xingjianll/midi-gpt2")
+gen_config = GenerationConfig.from_pretrained("xingjianll/midi-gpt2")
+model.eval()
+
+# Generate token sequence
+with torch.no_grad():
+    input_ids = torch.tensor([[tokenizer["BOS_None"]]], dtype=torch.long)
+    output = model.generate(
+        input_ids=input_ids,
+        generation_config=gen_config
+    )
+
+# Decode tokens to MIDI bytes
+generated_ids = output[0].tolist()
+midi_bytes = tokenizer.decode([generated_ids]).dumps_midi()
+midi_file = io.BytesIO(midi_bytes)
+
+# Optionally save to disk
+# with open("output.mid", "wb") as f:
+#     f.write(midi_bytes)
+
+# Setup MIDI output to GarageBand
+midiout = rtmidi.MidiOut()
+available_ports = midiout.get_ports()
+print("Available MIDI ports:", available_ports)
+
+# Choose GarageBand's virtual port
+port = mido.open_output('GarageBand Virtual In')
+
+# Play MIDI via GarageBand
+midi = mido.MidiFile(file=midi_file)
+for msg in midi.play():
+    print(msg)
+    port.send(msg)
+
+```