Upload lakhclean_gpt2_generation.ipynb

lakhclean_gpt2_generation.ipynb

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+{
+  "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": []
+    },
+    "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": "3.9.7"
+    },
+    "accelerator": "GPU",
+    "gpuClass": "standard"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}