#====================================================================
# https://huggingface.co/spaces/asigalov61/Orpheus-Music-Transformer
#====================================================================
"""
Orpheus Music Transformer Gradio App - Single Model, Simplified Version
SOTA 8k multi-instrumental music transformer trained on 2.31M+ high-quality MIDIs
Using one large optimized model which was trained for 4 full epochs"
"""
import os
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
import time as reqtime
import datetime
from pytz import timezone
import torch
import matplotlib.pyplot as plt
import gradio as gr
import spaces
from huggingface_hub import hf_hub_download
import TMIDIX
from midi_to_colab_audio import midi_to_colab_audio
from x_transformer_2_3_1 import TransformerWrapper, AutoregressiveWrapper, Decoder, top_p
import random
# -----------------------------
# CONFIGURATION & GLOBALS
# -----------------------------
SEP = '=' * 70
PDT = timezone('US/Pacific')
MODEL_CHECKPOINT = 'Orpheus_Music_Transformer_Large_Trained_Model_31087_steps_0.6878_loss_0.7889_acc.pth'
SOUNDFONT_PATH = 'SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2'
NUM_OUT_BATCHES = 10
PREVIEW_LENGTH = 120 # in tokens
# -----------------------------
# PRINT START-UP INFO
# -----------------------------
def print_sep():
print(SEP)
print_sep()
print("Orpheus Music Transformer Gradio App")
print_sep()
print("Loading modules...")
# -----------------------------
# ENVIRONMENT & PyTorch Settings
# -----------------------------
os.environ['USE_FLASH_ATTENTION'] = '1'
torch.set_float32_matmul_precision('high')
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
torch.backends.cuda.enable_mem_efficient_sdp(True)
torch.backends.cuda.enable_math_sdp(True)
torch.backends.cuda.enable_flash_sdp(True)
torch.backends.cuda.enable_cudnn_sdp(True)
print_sep()
print("PyTorch version:", torch.__version__)
print("Done loading modules!")
print_sep()
# -----------------------------
# MODEL INITIALIZATION
# -----------------------------
print_sep()
print("Instantiating model...")
device_type = 'cuda'
dtype = 'bfloat16'
ptdtype = {'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]
ctx = torch.amp.autocast(device_type=device_type, dtype=ptdtype)
SEQ_LEN = 8192
PAD_IDX = 18819
model = TransformerWrapper(
num_tokens=PAD_IDX + 1,
max_seq_len=SEQ_LEN,
attn_layers=Decoder(
dim=2048,
depth=16,
heads=16,
rotary_pos_emb=True,
attn_flash=True
)
)
model = AutoregressiveWrapper(model, ignore_index=PAD_IDX, pad_value=PAD_IDX)
print_sep()
print("Loading model checkpoint...")
checkpoint = hf_hub_download(
repo_id='asigalov61/Orpheus-Music-Transformer',
filename=MODEL_CHECKPOINT
)
model.cuda()
model = torch.compile(model)
model.load_state_dict(torch.load(checkpoint, map_location='cuda', weights_only=True))
model.eval()
print_sep()
print("Done!")
print("Model will use", dtype, "precision...")
print_sep()
# -----------------------------
# HELPER FUNCTIONS
# -----------------------------
def render_midi_output(final_composition):
"""Generate MIDI score, plot, and audio from final composition."""
fname, midi_score = save_midi(final_composition)
time_val = midi_score[-1][1] / 1000 # seconds marker from last note
midi_plot = TMIDIX.plot_ms_SONG(
midi_score,
plot_title='Orpheus Music Transformer Composition',
block_lines_times_list=[],
return_plt=True
)
midi_audio = midi_to_colab_audio(
fname + '.mid',
soundfont_path=SOUNDFONT_PATH,
sample_rate=16000,
output_for_gradio=True
)
return (16000, midi_audio), midi_plot, fname + '.mid', time_val
# -----------------------------
# MIDI PROCESSING FUNCTIONS
# -----------------------------
def load_midi(input_midi,
apply_sustains=True,
remove_duplicate_pitches=True,
remove_overlapping_durations=True
):
"""Process the input MIDI file and create a token sequence."""
raw_score = TMIDIX.midi2single_track_ms_score(input_midi.name)
escore_notes = TMIDIX.advanced_score_processor(raw_score,
return_enhanced_score_notes=True,
apply_sustain=apply_sustains
)
if escore_notes:
escore_notes = TMIDIX.augment_enhanced_score_notes(escore_notes[0],
sort_drums_last=True
)
if remove_duplicate_pitches:
escore_notes = TMIDIX.remove_duplicate_pitches_from_escore_notes(escore_notes)
if remove_overlapping_durations:
escore_notes = TMIDIX.fix_escore_notes_durations(escore_notes,
min_notes_gap=0
)
dscore = TMIDIX.delta_score_notes(escore_notes)
dcscore = TMIDIX.chordify_score([d[1:] for d in dscore])
melody_chords = [18816]
#=======================================================
# MAIN PROCESSING CYCLE
#=======================================================
for i, c in enumerate(dcscore):
delta_time = c[0][0]
melody_chords.append(delta_time)
for e in c:
#=======================================================
# Durations
dur = max(1, min(255, e[1]))
# Patches
pat = max(0, min(128, e[5]))
# Pitches
ptc = max(1, min(127, e[3]))
# Velocities
# Calculating octo-velocity
vel = max(8, min(127, e[4]))
velocity = round(vel / 15)-1
#=======================================================
# FINAL NOTE SEQ
#=======================================================
# Writing final note
pat_ptc = (128 * pat) + ptc
dur_vel = (8 * dur) + velocity
melody_chords.extend([pat_ptc+256, dur_vel+16768])
return melody_chords
else:
return [18816]
def save_midi(tokens):
"""Convert token sequence back to a MIDI score and write it using TMIDIX.
"""
time = 0
dur = 1
vel = 90
pitch = 60
channel = 0
patch = 0
patches = [-1] * 16
channels = [0] * 16
channels[9] = 1
song_f = []
for ss in tokens:
if 0 <= ss < 256:
time += ss * 16
if 256 <= ss < 16768:
patch = (ss-256) // 128
if patch < 128:
if patch not in patches:
if 0 in channels:
cha = channels.index(0)
channels[cha] = 1
else:
cha = 15
patches[cha] = patch
channel = patches.index(patch)
else:
channel = patches.index(patch)
if patch == 128:
channel = 9
pitch = (ss-256) % 128
if 16768 <= ss < 18816:
dur = ((ss-16768) // 8) * 16
vel = (((ss-16768) % 8)+1) * 15
song_f.append(['note', time, dur, channel, pitch, vel, patch])
song_f = TMIDIX.remove_duplicate_pitches_from_escore_notes(song_f)
song_f = TMIDIX.fix_escore_notes_durations(song_f,
min_notes_gap=0
)
output_score, patches, overflow_patches = TMIDIX.patch_enhanced_score_notes(song_f)
fname = f"Orpheus-Music-Transformer-Composition"
TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(
output_score,
output_signature='Orpheus Music Transformer',
output_file_name=fname,
track_name='Project Los Angeles',
list_of_MIDI_patches=patches,
verbose=False
)
return fname, output_score
# -----------------------------
# MUSIC GENERATION FUNCTION (Combined)
# -----------------------------
@spaces.GPU
def generate_music(prime, num_gen_tokens, num_gen_batches, model_temperature, model_top_p):
"""Generate music tokens given prime tokens and parameters."""
if len(prime) >= 6656:
prime = [18816] + prime[-6656:]
inputs = prime
print("Generating...")
inp = torch.LongTensor([inputs] * num_gen_batches).cuda()
if model_top_p < 1:
with ctx:
out = model.generate(
inp,
num_gen_tokens,
filter_logits_fn=top_p,
filter_kwargs={'thres': model_top_p},
temperature=model_temperature,
eos_token=18818,
return_prime=False,
verbose=False
)
else:
with ctx:
out = model.generate(
inp,
num_gen_tokens,
temperature=model_temperature,
eos_token=18818,
return_prime=False,
verbose=False
)
print("Done!")
print_sep()
return out.tolist()
def generate_music_and_state(input_midi,
apply_sustains,
remove_duplicate_pitches,
remove_overlapping_durations,
prime_instruments,
num_prime_tokens,
num_gen_tokens,
model_temperature,
model_top_p,
add_drums,
add_outro,
final_composition,
generated_batches,
block_lines
):
"""
Generate tokens using the model, update the composition state, and prepare outputs.
This function combines seed loading, token generation, and UI output packaging.
"""
print_sep()
print("Request start time:", datetime.datetime.now(PDT).strftime("%Y-%m-%d %H:%M:%S"))
start_time = reqtime.time()
print_sep()
if input_midi is not None:
fn = os.path.basename(input_midi.name)
fn1 = fn.split('.')[0]
print('Input file name:', fn)
print('Apply sustains:', apply_sustains)
print('Remove duplicate pitches:', remove_duplicate_pitches)
print('Remove overlapping duriations', remove_overlapping_durations)
print('Prime instruments:', prime_instruments)
print('Num prime tokens:', num_prime_tokens)
print('Num gen tokens:', num_gen_tokens)
print('Model temp:', model_temperature)
print('Model top p:', model_top_p)
print('Add drums:', add_drums)
print('Add outro:', add_outro)
print_sep()
# Load seed from MIDI if there is no existing composition.
if not final_composition and input_midi is not None:
final_composition = load_midi(input_midi,
apply_sustains=apply_sustains,
remove_duplicate_pitches=remove_duplicate_pitches,
remove_overlapping_durations=remove_overlapping_durations
)
if num_prime_tokens < 6656:
final_composition = final_composition[:num_prime_tokens]
midi_fname, midi_score = save_midi(final_composition)
# Use the last note's time as a marker.
block_lines.append(midi_score[-1][1] / 1000 if final_composition else 0)
if not final_composition and input_midi is None:
final_composition = [18816, 0]
for i, instr in enumerate(prime_instruments):
instr_num = patch2number[instr]
final_composition.append((128*instr_num)+(72-(i*12))+256)
final_composition.append((8*16)+5+16768)
if final_composition:
if add_outro:
final_composition.append(18817) # Outro token
if add_drums:
drum_pitches = random.sample([35, 36, 41, 43, 45], k=1)
for dp in drum_pitches:
final_composition.extend([(128*128)+dp+256]) # Drum patch/pitch token
print_sep()
print('Composition has', len(final_composition), 'tokens')
print_sep()
batched_gen_tokens = generate_music(final_composition, num_gen_tokens,
NUM_OUT_BATCHES, model_temperature, model_top_p)
output_batches = []
for i, tokens in enumerate(batched_gen_tokens):
preview_tokens = final_composition[-PREVIEW_LENGTH:]
midi_fname, midi_score = save_midi(preview_tokens + tokens)
plot_kwargs = {'plot_title': f'Batch # {i}', 'return_plt': True}
if len(final_composition) > PREVIEW_LENGTH:
plot_kwargs['preview_length_in_notes'] = len([t for t in preview_tokens if 256 <= t < 16768])
midi_plot = TMIDIX.plot_ms_SONG(midi_score, **plot_kwargs)
midi_audio = midi_to_colab_audio(midi_fname + '.mid',
soundfont_path=SOUNDFONT_PATH,
sample_rate=16000,
output_for_gradio=True)
output_batches.append([(16000, midi_audio), midi_plot, tokens])
# Update generated_batches (for use by add/remove functions)
generated_batches = batched_gen_tokens
# Flatten outputs: states then audio and plots for each batch.
outputs_flat = []
for batch in output_batches:
outputs_flat.extend([batch[0], batch[1]])
print("Request end time:", datetime.datetime.now(PDT).strftime("%Y-%m-%d %H:%M:%S"))
print_sep()
end_time = reqtime.time()
execution_time = end_time - start_time
print(f"Request execution time: {execution_time} seconds")
print_sep()
return [final_composition, generated_batches, block_lines] + outputs_flat
# -----------------------------
# BATCH HANDLING FUNCTIONS
# -----------------------------
def add_batch(batch_number, final_composition, generated_batches, block_lines):
"""Add tokens from the specified batch to the final composition and update outputs."""
if generated_batches:
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)
midi_plot = TMIDIX.plot_ms_SONG(
midi_score,
plot_title='Orpheus Music Transformer Composition',
block_lines_times_list=block_lines[:-1],
return_plt=True
)
midi_audio = midi_to_colab_audio(midi_fname + '.mid',
soundfont_path=SOUNDFONT_PATH,
sample_rate=16000,
output_for_gradio=True)
print("Added batch #", batch_number)
print_sep()
return (16000, midi_audio), midi_plot, midi_fname + '.mid', final_composition, generated_batches, block_lines
else:
return None, None, None, [], [], []
def remove_batch(batch_number, num_tokens, final_composition, generated_batches, block_lines):
"""Remove tokens from the final composition and update outputs."""
if final_composition and len(final_composition) > num_tokens:
final_composition = final_composition[:-num_tokens]
if block_lines:
block_lines.pop()
midi_fname, midi_score = save_midi(final_composition)
midi_plot = TMIDIX.plot_ms_SONG(
midi_score,
plot_title='Orpheus Music Transformer Composition',
block_lines_times_list=block_lines[:-1],
return_plt=True
)
midi_audio = midi_to_colab_audio(midi_fname + '.mid',
soundfont_path=SOUNDFONT_PATH,
sample_rate=16000,
output_for_gradio=True)
print("Removed batch #", batch_number)
print_sep()
return (16000, midi_audio), midi_plot, midi_fname + '.mid', final_composition, generated_batches, block_lines
else:
return None, None, None, [], [], []
def clear():
"""Clear outputs and reset state."""
print_sep()
print('Clear batch...')
print_sep()
return None, None, None, [], []
def reset(final_composition=[], generated_batches=[], block_lines=[]):
"""Reset composition state."""
print_sep()
print('Reset composition...')
print_sep()
return [], [], []
patch2number = {v: k for k, v in TMIDIX.Number2patch.items()}
patch2number['Drums'] = 128
# -----------------------------
# GRADIO INTERFACE SETUP
# -----------------------------
with gr.Blocks() as demo:
gr.Markdown("Orpheus Music Transformer
")
gr.Markdown("SOTA 8k multi-instrumental music transformer trained on 2.31M+ high-quality MIDIs
")
gr.Markdown("๐ฅ[2026]๐ฅ Now featuring large optimized model!
")
gr.HTML("""
Check out Godzilla MIDI Dataset on Hugging Face
for faster execution and endless generation!
""")
gr.HTML("""
""")
gr.Markdown("## Key Features")
gr.Markdown("""
- **Efficient Architecture with RoPE**: Large optimized 748M 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**: 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.
- **Natural Composition Endings**: Outro tokens that help generate smooth and natural musical conclusions.
""")
gr.Markdown(
"""
## If you enjoyed Orpheus Music Transformer, please star and duplicate. It helps a lot! ๐ค
### [โญ Star this Space](https://huggingface.co/spaces/asigalov61/Orpheus-Music-Transformer)
### [๐ Duplicate this Space](https://huggingface.co/spaces/asigalov61/Orpheus-Music-Transformer?duplicate=true)
### [โญ Star models repo](https://huggingface.co/asigalov61/Orpheus-Music-Transformer)
"""
)
# Global state variables for composition
final_composition = gr.State([])
generated_batches = gr.State([])
block_lines = gr.State([])
gr.Markdown("## Upload seed MIDI or click 'Generate' for random output")
gr.Markdown("### PLEASE NOTE:")
gr.Markdown("* Orpheus Music Transformer is a primarily music continuation/co-composition model!")
gr.Markdown("* The model works best if given some music context to work with")
gr.Markdown("* Random generation from SOS token/embeddings may not always produce good results")
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])
apply_sustains = gr.Checkbox(value=True, label="Apply sustains (if present)")
remove_duplicate_pitches = gr.Checkbox(value=True, label="Remove duplicate pitches (if present)")
remove_overlapping_durations = gr.Checkbox(value=True, label="Trim overlapping durations (if present)")
gr.Markdown("## Generation options")
prime_instruments = gr.Dropdown(label="Prime instruments (select up to 5)", choices=list(patch2number.keys()),
multiselect=True, max_choices=5, type="value",
info="Instruments are asigned from top to bottom in order of selection. Custom MIDI overrides prime instruments."
)
prime_instruments.input(reset, [final_composition, generated_batches, block_lines],
[final_composition, generated_batches, block_lines])
num_prime_tokens = gr.Slider(16, 6656, value=6656, step=1, label="Number of prime tokens")
num_gen_tokens = gr.Slider(16, 1024, value=512, step=1, label="Number of tokens to generate")
model_temperature = gr.Slider(0.1, 1, value=0.9, step=0.01, label="Model temperature")
model_top_p = gr.Slider(0.1, 1.0, value=0.96, step=0.01, label="Model sampling top p value")
add_drums = gr.Checkbox(value=False, label="Add drums")
add_outro = gr.Checkbox(value=False, label="Add an outro")
generate_btn = gr.Button("Generate", variant="primary")
gr.Markdown("## Batch Previews")
outputs = [final_composition, generated_batches, block_lines]
# Two outputs (audio and plot) for each batch
for i in range(NUM_OUT_BATCHES):
with gr.Tab(f"Batch # {i}"):
audio_output = gr.Audio(label=f"Batch # {i} MIDI Audio", format="mp3")
plot_output = gr.Plot(label=f"Batch # {i} MIDI Plot")
outputs.extend([audio_output, plot_output])
generate_btn.click(
generate_music_and_state,
[input_midi,
apply_sustains,
remove_duplicate_pitches,
remove_overlapping_durations,
prime_instruments,
num_prime_tokens,
num_gen_tokens,
model_temperature,
model_top_p,
add_drums,
add_outro,
final_composition,
generated_batches,
block_lines
],
outputs
)
gr.Markdown("## Add/Remove Batch")
batch_number = gr.Slider(0, NUM_OUT_BATCHES - 1, value=0, step=1, label="Batch number to add/remove")
add_btn = gr.Button("Add batch", variant="primary")
remove_btn = gr.Button("Remove batch", variant="stop")
clear_btn = gr.ClearButton()
final_audio_output = gr.Audio(label="Final MIDI audio", format="mp3")
final_plot_output = gr.Plot(label="Final MIDI plot")
final_file_output = gr.File(label="Final MIDI file")
add_btn.click(
add_batch,
[batch_number, final_composition, generated_batches, block_lines],
[final_audio_output, final_plot_output, final_file_output, final_composition, generated_batches, block_lines]
)
remove_btn.click(
remove_batch,
[batch_number, num_gen_tokens, final_composition, generated_batches, block_lines],
[final_audio_output, final_plot_output, final_file_output, final_composition, generated_batches, block_lines]
)
clear_btn.click(clear, inputs=None,
outputs=[final_audio_output, final_plot_output, final_file_output, final_composition, block_lines])
demo.launch(mcp_server=True)