{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "DWLOSBkp0A2U"
},
"source": [
"# GPT-2 for music - By Dr. Tristan Behrens\n",
"\n",
"This notebook shows you how to generate music with GPT-2\n",
"\n",
"--- \n",
"\n",
"## Find me online\n",
"\n",
"- https://www.linkedin.com/in/dr-tristan-behrens-734967a2/\n",
"- https://twitter.com/DrTBehrens\n",
"- https://github.com/AI-Guru\n",
"- https://huggingface.co/TristanBehrens\n",
"- https://huggingface.co/ai-guru\n",
"\n",
"\n",
"---\n",
"\n",
"## Install depencencies.\n",
"\n",
"The following cell sets up fluidsynth and pyfluidsynth on colaboratory."
]
},
{
"cell_type": "code",
"source": [
"if \"google.colab\" in str(get_ipython()):\n",
" print(\"Installing dependencies...\")\n",
" !apt-get update -qq && apt-get install -qq libfluidsynth2 build-essential libasound2-dev libjack-dev\n",
" !pip install -qU pyfluidsynth"
],
"metadata": {
"id": "k1a8sd2KZCz9"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "6J_AnhV8D5p6"
},
"outputs": [],
"source": [
"!pip install transformers\n",
"!pip install note_seq"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RzhHhFll0JVl"
},
"source": [
"## Load the tokenizer and the model from 🤗 Hub."
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"os.environ[\"PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION\"] = \"python\""
],
"metadata": {
"id": "zGupj_vuZ9f2"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "g3ih12FMD7bs"
},
"outputs": [],
"source": [
"from transformers import AutoTokenizer, AutoModelForCausalLM\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(\"ai-guru/lakhclean_mmmtrack_4bars_d-2048\")\n",
"model = AutoModelForCausalLM.from_pretrained(\"ai-guru/lakhclean_mmmtrack_4bars_d-2048\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YfHXFugA0WdI"
},
"source": [
"## Convert the generated tokens to music that you can listen to.\n",
"\n",
"This uses note_seq, which is something like MIDI coming from Google Magenta. You could even use it to load and save MIDI files. Check their repo if you want to learn more.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "L3QMj8NyEBqs"
},
"outputs": [],
"source": [
"import note_seq\n",
"\n",
"NOTE_LENGTH_16TH_120BPM = 0.25 * 60 / 120\n",
"BAR_LENGTH_120BPM = 4.0 * 60 / 120\n",
"\n",
"def token_sequence_to_note_sequence(token_sequence, use_program=True, use_drums=True, instrument_mapper=None, only_piano=False):\n",
"\n",
" if isinstance(token_sequence, str):\n",
" token_sequence = token_sequence.split()\n",
"\n",
" note_sequence = empty_note_sequence()\n",
"\n",
" # Render all notes.\n",
" current_program = 1\n",
" current_is_drum = False\n",
" current_instrument = 0\n",
" track_count = 0\n",
" for token_index, token in enumerate(token_sequence):\n",
"\n",
" if token == \"PIECE_START\":\n",
" pass\n",
" elif token == \"PIECE_END\":\n",
" print(\"The end.\")\n",
" break\n",
" elif token == \"TRACK_START\":\n",
" current_bar_index = 0\n",
" track_count += 1\n",
" pass\n",
" elif token == \"TRACK_END\":\n",
" pass\n",
" elif token == \"KEYS_START\":\n",
" pass\n",
" elif token == \"KEYS_END\":\n",
" pass\n",
" elif token.startswith(\"KEY=\"):\n",
" pass\n",
" elif token.startswith(\"INST\"):\n",
" instrument = token.split(\"=\")[-1]\n",
" if instrument != \"DRUMS\" and use_program:\n",
" if instrument_mapper is not None:\n",
" if instrument in instrument_mapper:\n",
" instrument = instrument_mapper[instrument]\n",
" current_program = int(instrument)\n",
" current_instrument = track_count\n",
" current_is_drum = False\n",
" if instrument == \"DRUMS\" and use_drums:\n",
" current_instrument = 0\n",
" current_program = 0\n",
" current_is_drum = True\n",
" elif token == \"BAR_START\":\n",
" current_time = current_bar_index * BAR_LENGTH_120BPM\n",
" current_notes = {}\n",
" elif token == \"BAR_END\":\n",
" current_bar_index += 1\n",
" pass\n",
" elif token.startswith(\"NOTE_ON\"):\n",
" pitch = int(token.split(\"=\")[-1])\n",
" note = note_sequence.notes.add()\n",
" note.start_time = current_time\n",
" note.end_time = current_time + 4 * NOTE_LENGTH_16TH_120BPM\n",
" note.pitch = pitch\n",
" note.instrument = current_instrument\n",
" note.program = current_program\n",
" note.velocity = 80\n",
" note.is_drum = current_is_drum\n",
" current_notes[pitch] = note\n",
" elif token.startswith(\"NOTE_OFF\"):\n",
" pitch = int(token.split(\"=\")[-1])\n",
" if pitch in current_notes:\n",
" note = current_notes[pitch]\n",
" note.end_time = current_time\n",
" elif token.startswith(\"TIME_DELTA\"):\n",
" delta = float(token.split(\"=\")[-1]) * NOTE_LENGTH_16TH_120BPM\n",
" current_time += delta\n",
" elif token.startswith(\"DENSITY=\"):\n",
" pass\n",
" elif token == \"[PAD]\":\n",
" pass\n",
" else:\n",
" #print(f\"Ignored token {token}.\")\n",
" pass\n",
"\n",
" # Make the instruments right.\n",
" instruments_drums = []\n",
" for note in note_sequence.notes:\n",
" pair = [note.program, note.is_drum]\n",
" if pair not in instruments_drums:\n",
" instruments_drums += [pair]\n",
" note.instrument = instruments_drums.index(pair)\n",
"\n",
" if only_piano:\n",
" for note in note_sequence.notes:\n",
" if not note.is_drum:\n",
" note.instrument = 0\n",
" note.program = 0\n",
"\n",
" return note_sequence\n",
"\n",
"def empty_note_sequence(qpm=120.0, total_time=0.0):\n",
" note_sequence = note_seq.protobuf.music_pb2.NoteSequence()\n",
" note_sequence.tempos.add().qpm = qpm\n",
" note_sequence.ticks_per_quarter = note_seq.constants.STANDARD_PPQ\n",
" note_sequence.total_time = total_time\n",
" return note_sequence"
]
},
{
"cell_type": "markdown",
"source": [
"## Generate music\n",
"\n",
"This will generate one track of music and render it. "
],
"metadata": {
"id": "4kr2dECziaFA"
}
},
{
"cell_type": "code",
"source": [
"generated_sequence = \"PIECE_START\""
],
"metadata": {
"id": "cUg1DrlygzgT"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Note: Run the following cell multiple times to generate more tracks."
],
"metadata": {
"id": "SinUPIHyimr5"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZYpukydNESDF"
},
"outputs": [],
"source": [
"# Encode the conditioning tokens.\n",
"input_ids = tokenizer.encode(generated_sequence, return_tensors=\"pt\")\n",
"#print(input_ids)\n",
"\n",
"# Generate more tokens.\n",
"eos_token_id = tokenizer.encode(\"TRACK_END\")[0]\n",
"temperature = 1.0\n",
"generated_ids = model.generate(\n",
" input_ids, \n",
" max_length=2048,\n",
" do_sample=True,\n",
" temperature=temperature,\n",
" eos_token_id=eos_token_id,\n",
")\n",
"generated_sequence = tokenizer.decode(generated_ids[0])\n",
"print(generated_sequence)\n",
"\n",
"note_sequence = token_sequence_to_note_sequence(generated_sequence)\n",
"\n",
"synth = note_seq.fluidsynth\n",
"note_seq.plot_sequence(note_sequence)\n",
"note_seq.play_sequence(note_sequence, synth)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "d1x6HeF90kkO"
},
"source": [
"# Thank you!"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
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"display_name": "Python 3 (ipykernel)",
"language": "python",
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