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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "ePWjo4hLkSZh"
},
"source": [
"# Orpheus Auto-Continuation Generator Notebook (ver. 3.0)\n",
"\n",
"***\n",
"\n",
"Powered by tegridy-tools: https://github.com/asigalov61/tegridy-tools\n",
"\n",
"***\n",
"\n",
"#### Project Los Angeles\n",
"\n",
"#### Tegridy Code 2026\n",
"\n",
"***"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "y1H5U8iiAIgD"
},
"source": [
"# Setup Environment"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "8Dt7FYceaCKF",
"scrolled": true
},
"outputs": [],
"source": [
"# Install all dependencies (run only once per session)\n",
"\n",
"!git clone https://github.com/asigalov61/tegridy-tools\n",
"!pip install tqdm\n",
"!pip install ipywidgets\n",
"\n",
"!pip install einops\n",
"!pip install einx\n",
"!pip install scikit-learn\n",
"!pip install torch-summary\n",
"\n",
"!pip install huggingface_hub"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import Modules"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "Lqp3urZyaDAp",
"scrolled": true
},
"outputs": [],
"source": [
"# Import all needed modules\n",
"\n",
"print('=' * 70)\n",
"print('Loading needed modules. Please wait...')\n",
"\n",
"import os\n",
"\n",
"os.environ[\"HF_XET_HIGH_PERFORMANCE\"] = \"1\"\n",
"\n",
"from tqdm import tqdm\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"print('=' * 70)\n",
"print('Loading TMIDIX module...')\n",
"\n",
"%cd ~/tegridy-tools/tegridy-tools/\n",
"\n",
"import TMIDIX\n",
"\n",
"%cd ~/tegridy-tools/tegridy-tools/X-Transformer/\n",
"\n",
"from x_transformer_2_3_1 import TransformerWrapper, Decoder, AutoregressiveWrapper, top_p\n",
"from x_transformer_2_3_1 import build_cls_model, cls_predict\n",
"\n",
"%cd ~\n",
"\n",
"import torch\n",
"from torch.amp import autocast\n",
"\n",
"from torchsummary import summary\n",
"\n",
"from huggingface_hub import hf_hub_download\n",
"\n",
"print('=' * 70)\n",
"print('Done!')\n",
"print('Enjoy! :)')\n",
"print('=' * 70)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PcEkAnhyAIgL"
},
"source": [
"# Download and Init Models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download Orpheus Classifier Models"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"hf_hub_download(repo_id='asigalov61/Orpheus-Music-Transformer',\n",
" filename='Orpheus_Music_Transformer_Classifier_Trained_Model_23670_steps_0.1837_loss_0.9207_acc.pth',\n",
" local_dir='./Models/',\n",
" )\n",
"\n",
"hf_hub_download(repo_id='asigalov61/Orpheus-Music-Transformer',\n",
" filename='Orpheus_Music_Transformer_Classifier_Trained_Model_6698_steps_0.0894_loss_0.9654_acc.pth',\n",
" local_dir='./Models/',\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Init Orpheus Classifier Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model_to_load = 'Original' # Or \"Alternative\" (more efficient, trained on a larger training corpus)\n",
"\n",
"print('=' * 70)\n",
"print('Building model...')\n",
"if model_to_load == 'Original': \n",
" \n",
" cls_model = build_cls_model()\n",
" \n",
" full_path_to_trained_model = \"./Models/Orpheus_Music_Transformer_Classifier_Trained_Model_23670_steps_0.1837_loss_0.9207_acc.pth\"\n",
"\n",
"else:\n",
" cls_model = build_cls_model(use_cls_token=False,\n",
" average_pool_embed=True,\n",
" rotary_pos_emb=True\n",
" )\n",
" \n",
" full_path_to_trained_model = \"./Models/Orpheus_Music_Transformer_Classifier_Trained_Model_6698_steps_0.0894_loss_0.9654_acc.pth\"\n",
"\n",
"\n",
"print('=' * 70)\n",
"print('Loading model...')\n",
"\n",
"cls_model.load_state_dict(torch.load(full_path_to_trained_model))\n",
"cls_model.cuda()\n",
"cls_model.eval()\n",
"\n",
"summary(cls_model)\n",
"\n",
"print('=' * 70)\n",
"print('Done!')\n",
"print('=' * 70)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download Orpheus Large Base Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"hf_hub_download(repo_id='asigalov61/Orpheus-Music-Transformer',\n",
" filename='Orpheus_Music_Transformer_Large_Trained_Model_43860_steps_0.6682_loss_0.8054_acc.pth',\n",
" local_dir='./Models/',\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Init Orpheus Large Base Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"SEQ_LEN = 8192\n",
"PAD_IDX = 18819\n",
"\n",
"model = TransformerWrapper(\n",
" num_tokens = PAD_IDX+1,\n",
" max_seq_len = SEQ_LEN,\n",
" attn_layers = Decoder(dim = 2048,\n",
" depth = 16,\n",
" heads = 16,\n",
" rotary_pos_emb = True,\n",
" attn_flash = True\n",
" )\n",
" )\n",
"\n",
"model = AutoregressiveWrapper(model, ignore_index = PAD_IDX, pad_value=PAD_IDX)\n",
"\n",
"print('=' * 70)\n",
"print('Loading model checkpoint...')\n",
"\n",
"model_path = './Models/Orpheus_Music_Transformer_Large_Trained_Model_43860_steps_0.6682_loss_0.8054_acc.pth'\n",
"\n",
"model.load_state_dict(torch.load(model_path))\n",
"\n",
"print('=' * 70)\n",
"\n",
"model.cuda()\n",
"model.eval()\n",
"\n",
"model = torch.compile(model)\n",
"\n",
"print('Done!')\n",
"\n",
"summary(model)\n",
"\n",
"dtype = torch.bfloat16\n",
"\n",
"ctx = autocast(device_type='cuda', dtype=dtype)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load source MIDI"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"midi_file = './tegridy-tools/tegridy-tools/seed-intro.mid'\n",
"\n",
"print('=' * 70)\n",
"print('Loading MIDI File:', midi_file)\n",
"print('=' * 70)\n",
"\n",
"raw_score = TMIDIX.midi2single_track_ms_score(midi_file)\n",
"\n",
"escore_notes = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True, apply_sustain=True)\n",
"\n",
"escore_notes = TMIDIX.augment_enhanced_score_notes(escore_notes[0], sort_drums_last=True)\n",
"\n",
"escore_notes = TMIDIX.remove_duplicate_pitches_from_escore_notes(escore_notes)\n",
"\n",
"escore_notes = TMIDIX.fix_escore_notes_durations(escore_notes, min_notes_gap=0)\n",
"\n",
"dscore = TMIDIX.delta_score_notes(escore_notes)\n",
"\n",
"dcscore = TMIDIX.chordify_score([d[1:] for d in dscore])\n",
"\n",
"melody_chords = [18816]\n",
"\n",
"#=======================================================\n",
"# MAIN PROCESSING CYCLE\n",
"#=======================================================\n",
"\n",
"for i, c in enumerate(dcscore):\n",
"\n",
" # Delta start-times\n",
" delta_time = c[0][0]\n",
" melody_chords.append(delta_time)\n",
"\n",
" for e in c:\n",
" \n",
" #=======================================================\n",
" \n",
" # Durations\n",
" dur = max(1, min(255, e[1]))\n",
"\n",
" # Patches\n",
" pat = max(0, min(128, e[5]))\n",
" \n",
" # Pitches\n",
" ptc = max(1, min(127, e[3]))\n",
" \n",
" # Velocities\n",
" # Calculating octo-velocity\n",
" \n",
" vel = max(8, min(127, e[4]))\n",
" velocity = round(vel / 15)-1\n",
" \n",
" #=======================================================\n",
" # FINAL NOTE SEQ\n",
" #=======================================================\n",
" \n",
" # Writing final note\n",
" pat_ptc = (128 * pat) + ptc \n",
" dur_vel = (8 * dur) + velocity\n",
"\n",
" melody_chords.extend([pat_ptc+256, dur_vel+16768]) # 18816\n",
"\n",
"print('Done!')\n",
"print('=' * 70)\n",
"print('Composition has', len(melody_chords), 'tokens')\n",
"print('=' * 70)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Generate"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"num_gen_chunk = 4 # Number of continuation chunks to generate\n",
"batch_size = 12 # More is better (max it out)\n",
"temperature = 0.95\n",
"top_p_value = 0.96\n",
"\n",
"#===================================================================\n",
"\n",
"song = melody_chords[:1024]\n",
"\n",
"torch.cuda.empty_cache()\n",
"\n",
"for i in tqdm(range(num_gen_chunk)):\n",
"\n",
" x = torch.LongTensor([song] * batch_size).cuda()\n",
" \n",
" with ctx:\n",
" out = model.generate(x,\n",
" 512,\n",
" temperature=temperature,\n",
" filter_logits_fn=top_p,\n",
" filter_kwargs={'thres': top_p_value},\n",
" return_prime=True,\n",
" verbose=False)\n",
" \n",
" outs = out.tolist()\n",
"\n",
" outputs = []\n",
"\n",
" for o in outs:\n",
" if 18818 not in o and 18817 not in o:\n",
" times = [oo for oo in o if oo < 256]\n",
" if all(True if oo < 128 else False for oo in times):\n",
" outputs.append(o) \n",
" \n",
" cls_outputs = []\n",
" \n",
" for o in outputs:\n",
" cls_outputs.append(o[-1024:])\n",
" \n",
" preds, probs = cls_predict(cls_model, cls_outputs)\n",
"\n",
" max_prob = max(probs)\n",
"\n",
" print(sorted(probs, reverse=True))\n",
" \n",
" best_idx = probs.index(max_prob)\n",
" \n",
" best_output = outputs[best_idx][-512:]\n",
"\n",
" song.extend(best_output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Convert to MIDI"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('Sample INTs', song[:15])\n",
"\n",
"if len(song) != 0:\n",
"\n",
" song_f = []\n",
" \n",
" time = 0\n",
" dur = 1\n",
" vel = 90\n",
" pitch = 60\n",
" channel = 0\n",
" patch = 0\n",
"\n",
" patches = [-1] * 16\n",
"\n",
" channels = [0] * 16\n",
" channels[9] = 1\n",
"\n",
" for ss in song:\n",
"\n",
" if 0 <= ss < 256:\n",
"\n",
" time += ss * 16\n",
"\n",
" if 256 <= ss < 16768:\n",
"\n",
" patch = (ss-256) // 128\n",
"\n",
" if patch < 128:\n",
"\n",
" if patch not in patches:\n",
" if 0 in channels:\n",
" cha = channels.index(0)\n",
" channels[cha] = 1\n",
" else:\n",
" cha = 15\n",
"\n",
" patches[cha] = patch\n",
" channel = patches.index(patch)\n",
" else:\n",
" channel = patches.index(patch)\n",
"\n",
" if patch == 128:\n",
" channel = 9\n",
"\n",
" pitch = (ss-256) % 128\n",
"\n",
"\n",
" if 16768 <= ss < 18816:\n",
"\n",
" dur = ((ss-16768) // 8) * 16\n",
" vel = (((ss-16768) % 8)+1) * 15\n",
"\n",
" song_f.append(['note', time, dur, channel, pitch, vel, patch])\n",
"\n",
"patches = [0 if x==-1 else x for x in patches]\n",
"\n",
"output_score, patches, overflow_patches = TMIDIX.patch_enhanced_score_notes(song_f)\n",
"\n",
"detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(output_score,\n",
" output_signature = 'Orpheus Music Transformer',\n",
" output_file_name = './Orpheus-Music-Transformer-Composition',\n",
" track_name='Project Los Angeles',\n",
" list_of_MIDI_patches=patches\n",
" )\n",
"\n",
"print('Done!')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Congrats! You did it! :)"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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