316 lines
10 KiB
Plaintext
316 lines
10 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "DWLOSBkp0A2U"
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},
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"source": [
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"# GPT-2 for music - By Dr. Tristan Behrens\n",
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"\n",
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"This notebook shows you how to generate music with GPT-2\n",
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"\n",
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"--- \n",
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"\n",
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"## Find me online\n",
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"\n",
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"- https://www.linkedin.com/in/dr-tristan-behrens-734967a2/\n",
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"- https://twitter.com/DrTBehrens\n",
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"- https://github.com/AI-Guru\n",
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"- https://huggingface.co/TristanBehrens\n",
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"- https://huggingface.co/ai-guru\n",
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"\n",
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"\n",
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"---\n",
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"\n",
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"## Install depencencies.\n",
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"\n",
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"The following cell sets up fluidsynth and pyfluidsynth on colaboratory."
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]
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},
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{
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"cell_type": "code",
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"source": [
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"if \"google.colab\" in str(get_ipython()):\n",
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" print(\"Installing dependencies...\")\n",
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" !apt-get update -qq && apt-get install -qq libfluidsynth2 build-essential libasound2-dev libjack-dev\n",
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" !pip install -qU pyfluidsynth"
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],
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"metadata": {
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"id": "k1a8sd2KZCz9"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "6J_AnhV8D5p6"
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},
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"outputs": [],
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"source": [
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"!pip install transformers\n",
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"!pip install note_seq"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "RzhHhFll0JVl"
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},
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"source": [
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"## Load the tokenizer and the model from 🤗 Hub."
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]
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},
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{
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"cell_type": "code",
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"source": [
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"import os\n",
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"os.environ[\"PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION\"] = \"python\""
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],
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"metadata": {
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"id": "zGupj_vuZ9f2"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "g3ih12FMD7bs"
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},
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"outputs": [],
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"source": [
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"from transformers import AutoTokenizer, AutoModelForCausalLM\n",
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"\n",
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"tokenizer = AutoTokenizer.from_pretrained(\"ai-guru/lakhclean_mmmtrack_4bars_d-2048\")\n",
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"model = AutoModelForCausalLM.from_pretrained(\"ai-guru/lakhclean_mmmtrack_4bars_d-2048\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "YfHXFugA0WdI"
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},
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"source": [
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"## Convert the generated tokens to music that you can listen to.\n",
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"\n",
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"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"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "L3QMj8NyEBqs"
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},
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"outputs": [],
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"source": [
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"import note_seq\n",
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"\n",
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"NOTE_LENGTH_16TH_120BPM = 0.25 * 60 / 120\n",
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"BAR_LENGTH_120BPM = 4.0 * 60 / 120\n",
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"\n",
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"def token_sequence_to_note_sequence(token_sequence, use_program=True, use_drums=True, instrument_mapper=None, only_piano=False):\n",
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"\n",
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" if isinstance(token_sequence, str):\n",
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" token_sequence = token_sequence.split()\n",
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"\n",
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" note_sequence = empty_note_sequence()\n",
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"\n",
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" # Render all notes.\n",
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" current_program = 1\n",
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" current_is_drum = False\n",
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" current_instrument = 0\n",
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" track_count = 0\n",
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" for token_index, token in enumerate(token_sequence):\n",
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"\n",
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" if token == \"PIECE_START\":\n",
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" pass\n",
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" elif token == \"PIECE_END\":\n",
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" print(\"The end.\")\n",
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" break\n",
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" elif token == \"TRACK_START\":\n",
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" current_bar_index = 0\n",
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" track_count += 1\n",
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" pass\n",
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" elif token == \"TRACK_END\":\n",
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" pass\n",
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" elif token == \"KEYS_START\":\n",
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" pass\n",
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" elif token == \"KEYS_END\":\n",
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" pass\n",
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" elif token.startswith(\"KEY=\"):\n",
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" pass\n",
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" elif token.startswith(\"INST\"):\n",
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" instrument = token.split(\"=\")[-1]\n",
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" if instrument != \"DRUMS\" and use_program:\n",
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" if instrument_mapper is not None:\n",
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" if instrument in instrument_mapper:\n",
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" instrument = instrument_mapper[instrument]\n",
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" current_program = int(instrument)\n",
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" current_instrument = track_count\n",
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" current_is_drum = False\n",
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" if instrument == \"DRUMS\" and use_drums:\n",
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" current_instrument = 0\n",
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" current_program = 0\n",
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" current_is_drum = True\n",
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" elif token == \"BAR_START\":\n",
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" current_time = current_bar_index * BAR_LENGTH_120BPM\n",
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" current_notes = {}\n",
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" elif token == \"BAR_END\":\n",
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" current_bar_index += 1\n",
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" pass\n",
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" elif token.startswith(\"NOTE_ON\"):\n",
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" pitch = int(token.split(\"=\")[-1])\n",
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" note = note_sequence.notes.add()\n",
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" note.start_time = current_time\n",
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" note.end_time = current_time + 4 * NOTE_LENGTH_16TH_120BPM\n",
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" note.pitch = pitch\n",
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" note.instrument = current_instrument\n",
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" note.program = current_program\n",
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" note.velocity = 80\n",
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" note.is_drum = current_is_drum\n",
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" current_notes[pitch] = note\n",
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" elif token.startswith(\"NOTE_OFF\"):\n",
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" pitch = int(token.split(\"=\")[-1])\n",
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" if pitch in current_notes:\n",
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" note = current_notes[pitch]\n",
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" note.end_time = current_time\n",
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" elif token.startswith(\"TIME_DELTA\"):\n",
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" delta = float(token.split(\"=\")[-1]) * NOTE_LENGTH_16TH_120BPM\n",
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" current_time += delta\n",
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" elif token.startswith(\"DENSITY=\"):\n",
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" pass\n",
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" elif token == \"[PAD]\":\n",
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" pass\n",
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" else:\n",
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" #print(f\"Ignored token {token}.\")\n",
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" pass\n",
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"\n",
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" # Make the instruments right.\n",
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" instruments_drums = []\n",
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" for note in note_sequence.notes:\n",
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" pair = [note.program, note.is_drum]\n",
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" if pair not in instruments_drums:\n",
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" instruments_drums += [pair]\n",
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" note.instrument = instruments_drums.index(pair)\n",
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"\n",
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" if only_piano:\n",
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" for note in note_sequence.notes:\n",
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" if not note.is_drum:\n",
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" note.instrument = 0\n",
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" note.program = 0\n",
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"\n",
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" return note_sequence\n",
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"\n",
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"def empty_note_sequence(qpm=120.0, total_time=0.0):\n",
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" note_sequence = note_seq.protobuf.music_pb2.NoteSequence()\n",
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" note_sequence.tempos.add().qpm = qpm\n",
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" note_sequence.ticks_per_quarter = note_seq.constants.STANDARD_PPQ\n",
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" note_sequence.total_time = total_time\n",
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" return note_sequence"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Generate music\n",
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"\n",
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"This will generate one track of music and render it. "
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],
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"metadata": {
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"id": "4kr2dECziaFA"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"generated_sequence = \"PIECE_START\""
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],
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"metadata": {
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"id": "cUg1DrlygzgT"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"Note: Run the following cell multiple times to generate more tracks."
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],
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"metadata": {
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"id": "SinUPIHyimr5"
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "ZYpukydNESDF"
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},
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"outputs": [],
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"source": [
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"# Encode the conditioning tokens.\n",
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"input_ids = tokenizer.encode(generated_sequence, return_tensors=\"pt\")\n",
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"#print(input_ids)\n",
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"\n",
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"# Generate more tokens.\n",
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"eos_token_id = tokenizer.encode(\"TRACK_END\")[0]\n",
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"temperature = 1.0\n",
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"generated_ids = model.generate(\n",
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" input_ids, \n",
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" max_length=2048,\n",
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" do_sample=True,\n",
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" temperature=temperature,\n",
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" eos_token_id=eos_token_id,\n",
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")\n",
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"generated_sequence = tokenizer.decode(generated_ids[0])\n",
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"print(generated_sequence)\n",
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"\n",
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"note_sequence = token_sequence_to_note_sequence(generated_sequence)\n",
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"\n",
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"synth = note_seq.fluidsynth\n",
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"note_seq.plot_sequence(note_sequence)\n",
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"note_seq.play_sequence(note_sequence, synth)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "d1x6HeF90kkO"
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},
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"source": [
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"# Thank you!"
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]
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}
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],
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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},
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"accelerator": "GPU",
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"gpuClass": "standard"
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},
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"nbformat": 4,
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"nbformat_minor": 0
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} |