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{
"cells": [
{
"cell_type": "markdown",
"id": "4821055b-c45c-4a9f-8196-2a9d09df6c39",
"metadata": {},
"source": [
"# Orpheus Auto-Continuations Generator (ver. 1.0)\n",
"\n",
"***\n",
"\n",
"Powered by tegridy-tools: https://github.com/asigalov61/tegridy-tools\n",
"\n",
"***\n",
"\n",
"WARNING: This complete implementation is a functioning model of the Artificial Intelligence. Please excercise great humility, care, and respect. https://www.nscai.gov/\n",
"\n",
"***\n",
"\n",
"#### Project Los Angeles\n",
"\n",
"#### Tegridy Code 2025\n",
"\n",
"***"
]
},
{
"cell_type": "markdown",
"id": "a6e2249a-6b57-4193-830d-7772c29b6f38",
"metadata": {},
"source": [
"# Setup environment"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1de7766b-1df0-4281-9322-650068da2a2d",
"metadata": {},
"outputs": [],
"source": [
"!git clone --depth 1 https://github.com/asigalov61/tegridy-tools"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7e9de3f7-4a3d-41d0-a6b6-1bc8fc98fa6e",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"!pip install huggingface_hub\n",
"!pip install hf-transfer\n",
"\n",
"!pip install ipywidgets\n",
"!pip install tqdm\n",
"\n",
"!pip install einx\n",
"!pip install einops\n",
"!pip install torch-summary\n",
"!pip install scikit-learn\n",
"!pip install matplotlib"
]
},
{
"cell_type": "markdown",
"id": "68799e16-da90-4f1b-97c8-813bd5df665e",
"metadata": {},
"source": [
"# Import modules"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6073f1b3-edca-49b1-bfed-2029a9efda35",
"metadata": {},
"outputs": [],
"source": [
"# Load modules and make data dir\n",
"\n",
"print('Loading modules...')\n",
"\n",
"import os\n",
"\n",
"os.environ[\"HF_HUB_ENABLE_HF_TRANSFER\"] = \"1\"\n",
"\n",
"import pickle\n",
"import random\n",
"import tqdm\n",
"\n",
"!set USE_FLASH_ATTENTION=1\n",
"os.environ['USE_FLASH_ATTENTION'] = '1'\n",
"\n",
"import torch\n",
"import numpy as np\n",
"\n",
"from torchsummary import summary\n",
"from sklearn.metrics.pairwise import cosine_similarity\n",
"\n",
"%cd /home/ubuntu/tegridy-tools/tegridy-tools/\n",
"\n",
"import TMIDIX\n",
"\n",
"%cd /home/ubuntu/tegridy-tools/tegridy-tools/X-Transformer\n",
"\n",
"from x_transformer_2_3_1 import *\n",
"\n",
"torch.set_float32_matmul_precision('high')\n",
"torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul\n",
"torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn\n",
"torch.backends.cuda.enable_flash_sdp(True)\n",
"torch.backends.cuda.enable_cudnn_sdp(False)\n",
"\n",
"!set USE_FLASH_ATTENTION=1\n",
"\n",
"%cd /home/ubuntu/\n",
"\n",
"import random\n",
"\n",
"from huggingface_hub import hf_hub_download\n",
"\n",
"print('Done')\n",
"\n",
"print('Torch version:', torch.__version__)"
]
},
{
"cell_type": "markdown",
"id": "f94d805e-ac8a-400b-9e9c-a6ff572c4b80",
"metadata": {},
"source": [
"# Download Orpheus model and Orpheus embeddings dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2f8d75d4-982b-4a60-a234-afc71aa6dd84",
"metadata": {},
"outputs": [],
"source": [
"print('=' * 70)\n",
"print('Donwloading Orpheus Music Transformer model...')\n",
"print('=' * 70)\n",
"\n",
"model_file = hf_hub_download(repo_id='asigalov61/Orpheus-Music-Transformer',\n",
" filename='Orpheus_Music_Transformer_Trained_Model_128497_steps_0.6934_loss_0.7927_acc.pth',\n",
" local_dir='/home/ubuntu/Models/',\n",
" )\n",
"\n",
"\n",
"print('=' * 70)\n",
"print('Donwloading Orpheus embeddings dataset...')\n",
"print('=' * 70)\n",
"\n",
"emb_file = hf_hub_download(repo_id='asigalov61/Orpheus-Music-Transformer',\n",
" filename='orpheus_data/1765807_Orpheus_Training_Data_Reference_MP_Embeddings_CC_BY_NC_SA.npy',\n",
" local_dir='/home/ubuntu/Models/',\n",
" )\n",
"\n",
"print('=' * 70)\n",
"print('Done!')\n",
"print('=' * 70)"
]
},
{
"cell_type": "markdown",
"id": "30147799-9bc5-4acd-8352-d5fe309bd844",
"metadata": {},
"source": [
"# Load model and embeddings"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d95d650e-a6b6-4fca-bc3d-75bd1c042c06",
"metadata": {},
"outputs": [],
"source": [
"#=================================================================\n",
"\n",
"def get_embeddings(inputs):\n",
" \n",
" with ctx:\n",
" with torch.no_grad():\n",
" out = model(inputs, return_outputs=True)\n",
" \n",
" cache = out[3]\n",
"\n",
" hidden = cache.layer_hiddens[-1]\n",
" \n",
" mean_pool = torch.mean(hidden, dim=1)\n",
" \n",
" return mean_pool.cpu().detach().numpy()\n",
"\n",
"#=================================================================\n",
"\n",
"exists_ratio = lambda sub, main, ratio: sum(x in set(main) for x in sub) / len(sub) >= ratio\n",
"\n",
"#=================================================================\n",
"\n",
"print('=' * 70)\n",
"print('Loading Orpheus Music Transformer model...')\n",
"print('=' * 70)\n",
"\n",
"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 = 8,\n",
" heads = 32,\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.load_state_dict(torch.load(model_file, weights_only=True))\n",
"\n",
"print('=' * 70)\n",
"\n",
"model.cuda()\n",
"model.eval()\n",
"\n",
"print('Done!')\n",
"\n",
"summary(model)\n",
"\n",
"dtype = torch.bfloat16\n",
"\n",
"ctx = torch.amp.autocast(device_type='cuda', dtype=dtype)\n",
"\n",
"#=================================================================\n",
"\n",
"print('=' * 70)\n",
"print('Loading Orpheus embeddings dataset...')\n",
"print('=' * 70)\n",
"\n",
"embeddings = np.load(emb_file)\n",
"\n",
"print('=' * 70)\n",
"print('Done!')\n",
"print('=' * 70)"
]
},
{
"cell_type": "markdown",
"id": "e3a4cd32-4680-4d35-b46e-0022369715b7",
"metadata": {},
"source": [
"# Create IO dirs"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d27279a0-2892-4aca-a074-1ebe3e82bb94",
"metadata": {},
"outputs": [],
"source": [
"print('=' * 70) \n",
"print('Creating IO dirs...')\n",
"\n",
"input_midis_dir = '/home/ubuntu/Input MIDIs/'\n",
"output_midis_dir = '/home/ubuntu/Output MIDIs/'\n",
"\n",
"midi_files_list = []\n",
"\n",
"os.makedirs(input_midis_dir, exist_ok=True)\n",
"os.makedirs(output_midis_dir, exist_ok=True)\n",
"\n",
"print('Done!')\n",
"print('=' * 70) "
]
},
{
"cell_type": "markdown",
"id": "03e505df-42bc-4246-8b11-1e98e7b2515a",
"metadata": {},
"source": [
"# Create MIDIs files list"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "829f6093-67f5-4655-83c7-8af36ef60079",
"metadata": {},
"outputs": [],
"source": [
"print('=' * 70)\n",
"print('Creating MIDI files list...')\n",
"print('=' * 70) \n",
"\n",
"midi_files_list = TMIDIX.create_files_list([input_midis_dir])\n",
"print('=' * 70) "
]
},
{
"cell_type": "markdown",
"id": "e2e041a5-5564-41d1-a2b2-0cc92dc713ae",
"metadata": {},
"source": [
"# Generate"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cb9a7864-028b-468f-9fda-72abe84d6edc",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"print('=' * 70) \n",
"print('Orpheus Auto-Continuations Generator')\n",
"print('=' * 70)\n",
"\n",
"#=========================================================================\n",
"# Generation options\n",
"#=========================================================================\n",
"\n",
"# Primary generation options\n",
"num_prime_tokens = 1024\n",
"num_songs_per_midi = 4\n",
"num_gen_chunks = 12\n",
"max_num_tries = 4\n",
"\n",
"# Model sampling options\n",
"num_gen_tokens = 512\n",
"batch_size = 32\n",
"temperature = 1.0\n",
"top_p_value = 0.96\n",
"num_mem_tokens = 7168 # up to 12 chunks\n",
"use_prime_embeddings = False\n",
"\n",
"# Advanced options\n",
"max_tok_rep_ratio = 0.95\n",
"num_rep_window_toks = 1024\n",
"num_emb_tokens = 1024\n",
"\n",
"# Aux options\n",
"score_var = 0.05\n",
"batch_size_step = 4\n",
"\n",
"#=========================================================================\n",
"\n",
"if not midi_files_list:\n",
" \n",
" print('=' * 70)\n",
" print('Generating prime tokens...')\n",
" print('=' * 70)\n",
"\n",
" x = torch.LongTensor([[18816, 0]] * batch_size).cuda()\n",
"\n",
" with ctx:\n",
" out = model.generate(x,\n",
" num_prime_tokens,\n",
" temperature=temperature,\n",
" filter_logits_fn=top_p,\n",
" filter_kwargs={'thres': top_p_value},\n",
" return_prime=True,\n",
" verbose=True)\n",
"\n",
" y = out.tolist()\n",
" \n",
" inp = torch.LongTensor(y).cuda()\n",
" \n",
" prime_embs = get_embeddings(inp)\n",
" \n",
" scores = cosine_similarity(embeddings, prime_embs).max(axis=0)\n",
"\n",
" scores = [o for o in scores if o != max(scores)]\n",
"\n",
" max_score = max(scores)\n",
"\n",
" max_score_idx = scores.index(max_score)\n",
" melody_chords = y[max_score_idx]\n",
"\n",
" midi_fname = 'Improvisation'\n",
" midi_files_list.append(midi_fname)\n",
" \n",
" print('=' * 70)\n",
" print('Done!')\n",
" print('=' * 70)\n",
" print('Generating songs for \"Improvisation\"')\n",
" print('=' * 70)\n",
" \n",
"#=========================================================================\n",
"\n",
"for midi_file in midi_files_list:\n",
"\n",
" if midi_file != 'Improvisation':\n",
"\n",
" midi_fname = os.path.splitext(os.path.basename(midi_file))[0]\n",
" \n",
" print('=' * 70)\n",
" print('Generating songs for MIDI file \"' + midi_fname + '\"')\n",
" print('-' * 70) \n",
" \n",
" #==============================================================================\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_time = c[0][0]\n",
" \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",
" #==============================================================================\n",
"\n",
" print('Total number of input tokens:', len(melody_chords))\n",
" print('=' * 70)\n",
"\n",
" #==============================================================================\n",
"\n",
" song_number = 0\n",
" \n",
" while song_number < num_songs_per_midi:\n",
"\n",
" print('Generating song #', song_number+1, '/', num_songs_per_midi)\n",
" print('=' * 70)\n",
" \n",
" song = melody_chords[:num_prime_tokens][-num_mem_tokens:]\n",
" \n",
" inp = torch.LongTensor([song]).cuda()\n",
" \n",
" prime_embs = get_embeddings(inp)\n",
" \n",
" start_score = cosine_similarity(embeddings, prime_embs).max(axis=0)[0]\n",
"\n",
" b_size = batch_size\n",
" stop = False\n",
" \n",
" for i in tqdm.tqdm(range(num_gen_chunks)):\n",
" \n",
" max_score = -1\n",
" num_tries = 0\n",
" \n",
" if i > 7:\n",
" bsize = b_size - batch_size_step\n",
" \n",
" while max_score < start_score - score_var:\n",
" \n",
" output = []\n",
" output_scores = []\n",
" \n",
" x = torch.LongTensor([song[-num_mem_tokens:]] * b_size).cuda()\n",
" \n",
" with ctx:\n",
" out = model.generate(x,\n",
" num_gen_tokens,\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",
" y = out.tolist()\n",
" \n",
" for yy in y:\n",
" if 18817 not in yy and 18818 not in yy and not exists_ratio(yy[-num_gen_tokens:], \n",
" song[-num_rep_window_toks:], \n",
" max_tok_rep_ratio\n",
" ):\n",
" output.append(yy[-num_emb_tokens:])\n",
" \n",
" if output:\n",
" \n",
" inp = torch.LongTensor(output).cuda()\n",
" \n",
" embs = get_embeddings(inp)\n",
"\n",
" if use_prime_embeddings:\n",
" scores = cosine_similarity(prime_embs, embs).max(axis=0) \n",
"\n",
" else:\n",
" scores = cosine_similarity(embeddings, embs).max(axis=0)\n",
" \n",
" output_scores.extend(scores)\n",
" \n",
" scores = [o for o in output_scores if o != max(output_scores)]\n",
" \n",
" if not scores:\n",
" max_score = -1\n",
" num_tries += 1\n",
" \n",
" if num_tries == max_num_tries:\n",
" stop = True\n",
" break\n",
" \n",
" if i > max_num_tries:\n",
" song = song[:-num_gen_tokens]\n",
" \n",
" else:\n",
" max_score = max(scores)\n",
" \n",
" else:\n",
" num_tries += 1\n",
" \n",
" if num_tries == max_num_tries:\n",
" stop = True\n",
" break\n",
" \n",
" if i > max_num_tries:\n",
" song = song[:-num_gen_tokens]\n",
" \n",
" if stop:\n",
" break\n",
" \n",
" max_score_idx = output_scores.index(max_score)\n",
" max_score_chunk = output[max_score_idx]\n",
" \n",
" song.extend(max_score_chunk[-num_gen_tokens:])\n",
"\n",
" #==============================================================================\n",
" \n",
" if i > num_gen_chunks // 2:\n",
"\n",
" print('=' * 70)\n",
" print('Saving song...')\n",
" print('=' * 70)\n",
" \n",
" print('Sample INTs', song[:15])\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",
" output_dir = os.path.join(output_midis_dir, midi_fname)\n",
"\n",
" os.makedirs(output_dir, exist_ok=True)\n",
" \n",
" detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(output_score,\n",
" output_signature = 'Orpheus Music Transformer',\n",
" output_file_name = output_dir + '/Orpheus-Music-Transformer-Composition_'+str(song_number+1).zfill(3),\n",
" track_name='Project Los Angeles',\n",
" list_of_MIDI_patches=patches\n",
" )\n",
"\n",
" song_number += 1\n",
" \n",
" print('=' * 70)\n",
" print('Done!')\n",
" print('=' * 70)"
]
},
{
"cell_type": "markdown",
"id": "f892ac8a-9f5f-462d-b3b9-d4be1f78b31d",
"metadata": {},
"source": [
"# Congrats! You did it ! :)"
]
}
],
"metadata": {
"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": 5
}
|