{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "6RYtdNIcPqHG" }, "source": [ "# MusicGen\n", "Welcome to MusicGen's demo jupyter notebook. Here you will find a series of self-contained examples of how to use MusicGen in different settings.\n", "\n", "First, we start by initializing MusicGen, you can choose a model from the following selection:\n", "1. `small` - 300M transformer decoder.\n", "2. `medium` - 1.5B transformer decoder.\n", "3. `melody` - 1.5B transformer decoder also supporting melody conditioning.\n", "4. `large` - 3.3B transformer decoder.\n", "\n", "We will use the `small` variant for the purpose of this demonstration." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "ruqumwjNVDje", "outputId": "b88a4ada-d18d-4686-9252-43d3386f4765" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting git+https://github.com/facebookresearch/audiocraft.git\n", " Cloning https://github.com/facebookresearch/audiocraft.git to /tmp/pip-req-build-02ky6lic\n", " Running command git clone --filter=blob:none 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satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich<14.0.0,>=10.11.0->typer<0.10.0,>=0.3.0->spacy>=3.6.1->audiocraft==1.3.0a1) (3.0.0)\n", "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich<14.0.0,>=10.11.0->typer<0.10.0,>=0.3.0->spacy>=3.6.1->audiocraft==1.3.0a1) (2.16.1)\n", "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx->gradio->audiocraft==1.3.0a1) (1.2.0)\n", "Requirement already satisfied: cloudpickle>=1.2.1 in /usr/local/lib/python3.10/dist-packages (from submitit->dora-search->flashy>=0.0.1->audiocraft==1.3.0a1) (2.2.1)\n", "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich<14.0.0,>=10.11.0->typer<0.10.0,>=0.3.0->spacy>=3.6.1->audiocraft==1.3.0a1) (0.1.2)\n", "Building wheels for collected packages: audiocraft, flashy, antlr4-python3-runtime, demucs, julius, encodec, docopt, dora-search, ffmpy, treetable\n", " Building wheel for audiocraft (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for audiocraft: filename=audiocraft-1.3.0a1-py3-none-any.whl size=264806 sha256=b3562e4184c8850515156688513e2f5c460c1559a85d07b4d8aa2001c34d5a0e\n", " Stored in directory: /tmp/pip-ephem-wheel-cache-n0ow3ybc/wheels/e2/4e/a8/93cdfda3b8e18998e7330772fa774fe5e14097e228b8dfc1ee\n", " Building wheel for flashy (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for flashy: filename=flashy-0.0.2-py3-none-any.whl size=34524 sha256=1d1cdab2277fe41e01ef5a8cd00512318d166caabfd5707ef4eb00013040af90\n", " Stored in directory: /root/.cache/pip/wheels/07/bd/3d/16c6bc059203299f37b6014643b739afb7f6d1be13a94fc2f7\n", " Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144554 sha256=0d543efdea65d05cd6252bb7838bc6d804f8e326dccb6e8166428357df8e110e\n", " Stored in directory: /root/.cache/pip/wheels/12/93/dd/1f6a127edc45659556564c5730f6d4e300888f4bca2d4c5a88\n", " Building wheel for demucs (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for demucs: filename=demucs-4.0.1-py3-none-any.whl size=78391 sha256=6ebd462e8fbfb9213d11360d12b92627cb10d1d2d8238963db0dc88cae9e61c5\n", " Stored in directory: /root/.cache/pip/wheels/2a/65/a1/6cc0e525a84375af3b09823b3326b0ece53c4e68302c054548\n", " Building wheel for julius (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for julius: filename=julius-0.2.7-py3-none-any.whl size=21870 sha256=5ca7a08ccc2e25892376e1c9289bd672306a75e8bd664b2113e5a82331d08b26\n", " Stored in directory: /root/.cache/pip/wheels/b9/b2/05/f883527ffcb7f2ead5438a2c23439aa0c881eaa9a4c80256f4\n", " Building wheel for encodec (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for encodec: filename=encodec-0.1.1-py3-none-any.whl size=45759 sha256=53ae5fa67e816ffd4ee5b0f7f8c31374bdc4afd8fae91036247c949bf1f2b669\n", " Stored in directory: /root/.cache/pip/wheels/fc/36/cb/81af8b985a5f5e0815312d5e52b41263237af07b977e6bcbf3\n", " Building wheel for docopt (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for docopt: filename=docopt-0.6.2-py2.py3-none-any.whl size=13706 sha256=087cf15bd7bfbd4097cc94d33f4d88eaf7c1322c937a098d5bb1585dba275de7\n", " Stored in directory: /root/.cache/pip/wheels/fc/ab/d4/5da2067ac95b36618c629a5f93f809425700506f72c9732fac\n", " Building wheel for dora-search (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for dora-search: filename=dora_search-0.1.12-py3-none-any.whl size=75092 sha256=26fbce833360501be6f158e74d2f6f3efd27b974f54463775ed4c1d9d8977fbf\n", " Stored in directory: /root/.cache/pip/wheels/b1/c2/c0/bea5cc405497284d584b958f293ef32c23bad42ae5e44d973c\n", " Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for ffmpy: filename=ffmpy-0.3.1-py3-none-any.whl size=5579 sha256=0f4c282bf456b7cd533cec5cd9247c12271dbf04799d3f33564e367c2c4221ed\n", " Stored in directory: /root/.cache/pip/wheels/01/a6/d1/1c0828c304a4283b2c1639a09ad86f83d7c487ef34c6b4a1bf\n", " Building wheel for treetable (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for treetable: filename=treetable-0.2.5-py3-none-any.whl size=7333 sha256=391d7da558b9c0e8ca54b6b8ce97a032d819115ff46984921eb103e2fe2e2bff\n", " Stored in directory: /root/.cache/pip/wheels/72/55/0e/91c3655bdb162446f8a7cd477579397544454a63ae7c599c0c\n", "Successfully built audiocraft flashy antlr4-python3-runtime demucs julius encodec docopt dora-search ffmpy treetable\n", "Installing collected packages: pydub, lameenc, ffmpy, docopt, antlr4-python3-runtime, websockets, typing-extensions, treetable, tomlkit, shellingham, semantic-version, ruff, retrying, python-multipart, orjson, omegaconf, num2words, h11, einops, colorlog, colorama, av, annotated-types, aiofiles, uvicorn, submitit, starlette, pydantic-core, lightning-utilities, hydra-core, httpcore, xformers, torchmetrics, pydantic, julius, hydra_colorlog, httpx, dora-search, openunmix, gradio-client, flashy, fastapi, encodec, gradio, demucs, audiocraft\n", " Attempting uninstall: typing-extensions\n", " Found existing installation: typing_extensions 4.5.0\n", " Uninstalling typing_extensions-4.5.0:\n", " Successfully uninstalled typing_extensions-4.5.0\n", " Attempting uninstall: pydantic\n", " Found existing installation: pydantic 1.10.14\n", " Uninstalling pydantic-1.10.14:\n", " Successfully uninstalled pydantic-1.10.14\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "lida 0.0.10 requires kaleido, which is not installed.\n", "llmx 0.0.15a0 requires cohere, which is not installed.\n", "llmx 0.0.15a0 requires openai, which is not installed.\n", "llmx 0.0.15a0 requires tiktoken, which is not installed.\n", "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed aiofiles-23.2.1 annotated-types-0.6.0 antlr4-python3-runtime-4.9.3 audiocraft-1.3.0a1 av-11.0.0 colorama-0.4.6 colorlog-6.8.2 demucs-4.0.1 docopt-0.6.2 dora-search-0.1.12 einops-0.7.0 encodec-0.1.1 fastapi-0.109.0 ffmpy-0.3.1 flashy-0.0.2 gradio-4.16.0 gradio-client-0.8.1 h11-0.14.0 httpcore-1.0.2 httpx-0.26.0 hydra-core-1.3.2 hydra_colorlog-1.2.0 julius-0.2.7 lameenc-1.7.0 lightning-utilities-0.10.1 num2words-0.5.13 omegaconf-2.3.0 openunmix-1.2.1 orjson-3.9.12 pydantic-2.6.0 pydantic-core-2.16.1 pydub-0.25.1 python-multipart-0.0.6 retrying-1.3.4 ruff-0.1.15 semantic-version-2.10.0 shellingham-1.5.4 starlette-0.35.1 submitit-1.5.1 tomlkit-0.12.0 torchmetrics-1.3.0.post0 treetable-0.2.5 typing-extensions-4.9.0 uvicorn-0.27.0.post1 websockets-11.0.3 xformers-0.0.22.post7\n" ] }, { "output_type": "display_data", "data": { "application/vnd.colab-display-data+json": { "pip_warning": { "packages": [ "pydevd_plugins" ] } } }, "metadata": {} } ], "source": [ "!pip install git+https://github.com/facebookresearch/audiocraft.git\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-V6BUR77yJSF", "outputId": "bc4163fa-8cf7-4c50-ea25-b43aaf7a3677" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: audiocraft in /usr/local/lib/python3.10/dist-packages (1.3.0a1)\n", "Requirement already satisfied: av in /usr/local/lib/python3.10/dist-packages (from audiocraft) 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"source": [ "%pip install audiocraft" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 403, "referenced_widgets": [ "34e3996b7fed4fe698bf4c1df8a8c891", "8a7a62fb387c41f0ad3bfa09b85be604", "bad3c711af334fc297e9f1dd7a139ea6", "eb3848ba8316438bb68ee834050e4c01", "646e0f7997f9459a9c9bbef7485a8494", "9d370e0819014530b2768dbc5e1c5cb0", "d9bf070d6d2a4096994131f656187e19", "bf2630379ac74050a967ba2d71e4ffc4", "c989c9fda73f43ec997f1904255f3429", "acf2fb5ebc6c406b8f20e3d9b492f3e6", "b08d0915809143fb8f0e02edc4e45a67", "a6d6200e194a4e01bbc28dfdb35b4038", "33f98bfe19034a64a6cdcf47b8d5737f", "b6d778d1bfdd4cf082e8f98452b91005", "829dbbde113d43fd97d1a104bafd0274", "2d50e8d5433f49faab8bcdf712c799aa", "b5f3447b5b3940c5b5a362e79e8b5d57", "59485d32a1754586becac31c32aef39e", "2781506189eb4a0d96274d0d011ce842", "ced7be95c0334bf0b7c55f9c7d30541e", "e4e0971243d84069a0a3ab984f080101", "2b7dbc9afc4d43aa845ff7bebb32ce85", "2fe80d3ab424428d824cece1f0d21a3c", "27b0838e0c9f48a486f7b976e911c140", "78e4f3341b21488ba22ba42c6192ad01", "7701031f15de4f64a220aaf9dffab97f", "2e6c31eda63d41e88c921564aac2ab8f", "60b0e553cfd2464091ae9b5309f4b0ac", "f3ae2a19380247e1b80ca4be301cc173", "3d6bd71ff92240e9bf385b90c96355e0", "0fcc620375254740afce144a4deac368", "0791646bcd734d9bbeba967e9b05f3ad", "eccef267cfc14e6a993e89afcc8cfee0", "b5742cacf27c48bbbf0197b745015dd9", "89bef4379da84190950ffeaf71995504", "ee8234b2153541eebf8b16e603722475", "449e607ad57b4d109e1d421077d689b0", "9cfd740587a3491c8ea94dca3d764064", "1064697851664b76812ef0a85c3b3d37", "2c35c7e2859541bc9f488e082fa1ca05", "1597f2d824584334ab876203324b2267", "4b0407dc7e3b42b585bd2dfcf424d834", "d93e5c46262f48a4b811ad1846e49e38", "38e03817280f485795cfd66b434a156b", "df939aaba0ee45a1bf0cc90e4e51afef", "6901dfc69aff4bcd9866b114ebc86208", "e32c1aeead2946cd9b0e438449e2f067", "aa759f581c634ad88a752ce88fe15f23", "7e25574501654162a1578d25293653f8", "c2bd7ae58330402eb88e20f4b2685930", "6f4e8f06a6354b70a944554b17139b53", "ff0a5f464dcb4d918766020fd1f832d3", "a6ddfaf3bbbf431ba8ffc707c2f5cd99", "a2b5a3852f274f2585c7415058ff410a", "f64295042da84133b290e2238f350b49", "a5ded6000a2c48188bebe9083aefb147", "8af839e161b547cb8078a0ce5e8adf51", "e2b0c290d8f54294a01cb1edcc593e86", "7bd9844c2e884dd2b7f46f2e5dcd0a28", "40a233392f1347d59956e9d1ea2750f3", "8ddeb028efa74959b56207f4c4798196", "7aee0648f1574538afd65457d551832f", "9669ae80cad74369b6ffcf6f42d11a5a", "ac2a2c3dc57c4e0ea267b99c8529e34f", "0ca9fdd8756b4f028f86e31342646a01", "8a75a8eb691b4693bd815a06efbea9a0" ] }, "id": "dvoo41vmPqH4", "outputId": "084cbbd4-268d-4a56-c17c-7debc72e056d" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/audiocraft/models/musicgen.py:80: UserWarning: MusicGen pretrained model relying on deprecated checkpoint mapping. Please use full pre-trained id instead: facebook/musicgen-small\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "state_dict.bin: 0%| | 0.00/841M [00:00" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {} } ], "source": [ "from audiocraft.utils.notebook import display_audio\n", "\n", "output = model.generate_unconditional(num_samples=2, progress=True)\n", "display_audio(output, sample_rate=32000)" ] }, { "cell_type": "markdown", "metadata": { "id": "6uTwpUC_PqID" }, "source": [ "### Music Continuation" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "-pak_U3bPqIE" }, "outputs": [], "source": [ "import math\n", "import torchaudio\n", "import torch\n", "from audiocraft.utils.notebook import display_audio\n", "\n", "def get_bip_bip(bip_duration=0.125, frequency=440,\n", " duration=0.5, sample_rate=32000, device=\"cuda\"):\n", " \"\"\"Generates a series of bip bip at the given frequency.\"\"\"\n", " t = torch.arange(\n", " int(duration * sample_rate), device=\"cuda\", dtype=torch.float) / sample_rate\n", " wav = torch.cos(2 * math.pi * 440 * t)[None]\n", " tp = (t % (2 * bip_duration)) / (2 * bip_duration)\n", " envelope = (tp >= 0.5).float()\n", " return wav * envelope\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 133 }, "id": "V0cTcQtNPqIG", "outputId": "85cd4c85-1dc2-4344-8141-66d09038c96e" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {} } ], "source": [ "# Here we use a synthetic signal to prompt both the tonality and the BPM\n", "# of the generated audio.\n", "res = model.generate_continuation(\n", " get_bip_bip(0.125).expand(2, -1, -1),\n", " 32000, ['Jazz jazz and only jazz',\n", " 'Heartful EDM with beautiful synths and chords'],\n", " progress=True)\n", "display_audio(res, 32000)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1G4GANGhoWjh", "outputId": "e4643e53-186e-4a68-dc76-5ac271a94910" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 75 }, "id": "XT2aCBENPqIH", "outputId": "2449ea69-043d-4d13-81b4-1ae68cfb0c16" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {} } ], "source": [ "# You can also use any audio from a file. Make sure to trim the file if it is too long!\n", "prompt_waveform, prompt_sr = torchaudio.load(\"/content/drive/MyDrive/Colab Notebooks/audio_output/dataset_example_electro_2.mp3\")\n", "prompt_duration = 2\n", "prompt_waveform = prompt_waveform[..., :int(prompt_duration * prompt_sr)]\n", "output = model.generate_continuation(prompt_waveform, prompt_sample_rate=prompt_sr, progress=True)\n", "display_audio(output, sample_rate=32000)" ] }, { "cell_type": "markdown", "metadata": { "id": "s9KlzVjGPqII" }, "source": [ "### Text-conditional Generation" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 133 }, "id": "40laFxmlPqIJ", "outputId": "373f5d85-988a-479a-b386-073873cde5ab" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {} } ], "source": [ "from audiocraft.utils.notebook import display_audio\n", "\n", "output = model.generate(\n", " descriptions=[\n", " 'a funky house with 80s hip hop vibes',\n", " '90s rock song with loud guitars and heavy drums',\n", " ],\n", " progress=True\n", ")\n", "display_audio(output, sample_rate=32000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Q0_6VA_gEYn4" }, "outputs": [], "source": [ "# !pip install audiocraft" ] }, { "cell_type": "markdown", "metadata": { "id": "dcCotW5APqIK" }, "source": [ "### Melody-conditional Generation" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 321, "referenced_widgets": [ "d352d99cc7f64e82a82a05dc07e799b5", "5cdaef937c434c46917b12cb9d2eba76", "11536d37acb144268a1a59e2b9787953", "8336ba16a8db449bbbd54d2dc9f8cfae", "3793f7f0379447a1bcc832d09753aa82", "a6f523fd76e745f5b3535e679516c16e", "e11b2b72b46041ba8008d52dff6987aa", "b6d488e8592c43309054c8d848d6e161", "3b5cea66455444469bcde0e830bf9d92", "ae5d710dc79048d5af5de978740f912a", "f7e328427ab842408233c02a835a9a5a", "a204e6fe6ae04f44ae857716e69c534f", "4413766c6ff64704920737f8d63c06d1", "10807c76a1a141ae8cb11d57ac152d02", "2cfc075784994e1facedb324fcf96327", "d0c7e3a974184d4e8e2376528c7aad93", "6714d36896da44bdb6a8b21954ec1564", "04c411675bee49c4948e77b4e77adf8b", "d4cec84a8b0b4cafa946712be8d3015e", "523e56b310df428c9e5d766488b3f57f", "60049ddc6485402abea061b2c082182d", "b621d6e3425842888c1d82366ab35774" ] }, "id": "Iz447cD_PqIK", "outputId": "2c1b5c0e-c2d8-4e87-e21b-79fdd4f05bf8" }, "outputs": [ { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/audiocraft/models/musicgen.py:80: UserWarning: MusicGen pretrained model relying on deprecated checkpoint mapping. Please use full pre-trained id instead: facebook/musicgen-melody\n", " warnings.warn(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d352d99cc7f64e82a82a05dc07e799b5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "state_dict.bin: 0%| | 0.00/2.77G [00:00" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {} } ], "source": [ "import torchaudio\n", "from audiocraft.utils.notebook import display_audio\n", "\n", "model = MusicGen.get_pretrained('melody')\n", "model.set_generation_params(duration=8)\n", "\n", "melody_waveform, sr = torchaudio.load(\"/content/drive/MyDrive/Colab Notebooks/audio_output/dataset_example_electro_2.mp3\")\n", "melody_waveform = melody_waveform.unsqueeze(0).repeat(2, 1, 1)\n", "output = model.generate_with_chroma(\n", " descriptions=[\n", " '80s pop track with bassy drums and synth',\n", " '90s rock song with loud guitars and heavy drums',\n", " ],\n", " melody_wavs=melody_waveform,\n", " melody_sample_rate=sr,\n", " progress=True\n", ")\n", "display_audio(output, sample_rate=32000)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "id": "SOykZTDKXeri" }, "outputs": [], "source": [ "import locale\n", "locale.getpreferredencoding = lambda: \"UTF-8\"" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uXtcCQC9fYHk", "outputId": "2fdd26d8-6ccc-493d-fbb7-5b3216551464" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: torch==2.1.0 in /usr/local/lib/python3.10/dist-packages (2.1.0+cu121)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch==2.1.0) (3.13.1)\n", "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch==2.1.0) (4.9.0)\n", "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch==2.1.0) (1.12)\n", "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch==2.1.0) (3.2.1)\n", "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch==2.1.0) (3.1.3)\n", "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch==2.1.0) (2023.6.0)\n", "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch==2.1.0) (2.1.0)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch==2.1.0) (2.1.4)\n", "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch==2.1.0) (1.3.0)\n" ] } ], "source": [ "# Install PyTorch\n", "!pip install 'torch==2.1.0'" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wmE7y42QXWEj", "outputId": "0773436d-44d8-4bfe-833c-1d28ca4c7797" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Reading package lists... Done\n", "Building dependency tree... Done\n", "Reading state information... Done\n", "ffmpeg is already the newest version (7:4.4.2-0ubuntu0.22.04.1).\n", "0 upgraded, 0 newly installed, 0 to remove and 31 not upgraded.\n" ] } ], "source": [ "!sudo apt-get install ffmpeg" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "somcDKgspQDm", "outputId": "1be46670-21ee-440c-c3c9-61e81526f019" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.4/8.4 MB\u001b[0m \u001b[31m26.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m196.4/196.4 kB\u001b[0m \u001b[31m25.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.8/4.8 MB\u001b[0m \u001b[31m54.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m82.1/82.1 kB\u001b[0m \u001b[31m11.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h" ] } ], "source": [ "! pip install streamlit -q" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Kpqgv6bhckrt", "outputId": "ddc469a2-f554-44d5-b9d8-7db3fc8cb3b4" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "34.16.174.223\n" ] } ], "source": [ "!wget -q -O - ipv4.icanhazip.com" ] }, { "cell_type": "code", "source": [ "%%writefile app.py\n", "import streamlit as st\n", "import torch\n", "import torchaudio\n", "from audiocraft.models import MusicGen\n", "import os\n", "import numpy as np\n", "import base64\n", "\n", "genres = [\"Pop\", \"Rock\", \"Jazz\", \"Electronic\", \"Hip-Hop\", \"Classical\", \"Lofi\", \"Chillpop\"]\n", "\n", "@st.cache_resource()\n", "def load_model():\n", " model = MusicGen.get_pretrained('facebook/musicgen-small')\n", " return model\n", "\n", "def generate_music_tensors(descriptions, duration: int):\n", " model = load_model()\n", "\n", " model.set_generation_params(\n", " use_sampling=True,\n", " top_k=250,\n", " duration=duration\n", " )\n", "\n", " with st.spinner(\"Generating Music...\"):\n", " output = model.generate(\n", " descriptions=descriptions,\n", " progress=True,\n", " return_tokens=True\n", " )\n", "\n", " st.success(\"Music Generation Complete!\")\n", " return output\n", "\n", "\n", "def save_audio(samples: torch.Tensor):\n", " sample_rate = 30000\n", " save_path = \"/content/drive/MyDrive/Colab Notebooks/audio_output\"\n", " assert samples.dim() == 2 or samples.dim() == 3\n", "\n", " samples = samples.detach().cpu()\n", " if samples.dim() == 2:\n", " samples = samples[None, ...]\n", "\n", " for idx, audio in enumerate(samples):\n", " audio_path = os.path.join(save_path, f\"audio_{idx}.wav\")\n", " torchaudio.save(audio_path, audio, sample_rate)\n", "\n", "def get_binary_file_downloader_html(bin_file, file_label='File'):\n", " with open(bin_file, 'rb') as f:\n", " data = f.read()\n", " bin_str = base64.b64encode(data).decode()\n", " href = f'Download {file_label}'\n", " return href\n", "\n", "st.set_page_config(\n", " page_icon= \"musical_note\",\n", " page_title= \"Music Gen\"\n", ")\n", "\n", "def main():\n", " with st.sidebar:\n", " st.header(\"\"\"⚙️Generate Music ⚙️\"\"\",divider=\"rainbow\")\n", " st.text(\"\")\n", " st.subheader(\"1. Enter your music description.......\")\n", " bpm = st.number_input(\"Enter Speed in BPM\", min_value=60)\n", "\n", " text_area = st.text_area('Ex : 80s rock song with guitar and drums')\n", " st.text('')\n", " # Dropdown for genres\n", " selected_genre = st.selectbox(\"Select Genre\", genres)\n", "\n", " st.subheader(\"2. Select time duration (In Seconds)\")\n", " time_slider = st.slider(\"Select time duration (In Seconds)\", 0, 60, 10)\n", "\n", " st.title(\"\"\"🎵 Song Lab AI 🎵\"\"\")\n", " st.text('')\n", " left_co,right_co = st.columns(2)\n", " left_co.write(\"\"\"Music Generation through a prompt\"\"\")\n", " left_co.write((\"\"\"PS : First generation may take some time .......\"\"\"))\n", "\n", " if st.sidebar.button('Generate !'):\n", " with left_co:\n", " st.text('')\n", " st.text('')\n", " st.text('')\n", " st.text('')\n", " st.text('')\n", " st.text('')\n", " st.subheader(\"Generated Music\")\n", "\n", " # Generate audio\n", " descriptions = [f\"{text_area} {selected_genre} {bpm} BPM\" for _ in range(5)] # Adjust the batch size (5 in this case)\n", " music_tensors = generate_music_tensors(descriptions, time_slider)\n", "\n", " # Only play the full audio for index 0\n", " idx = 0\n", " music_tensor = music_tensors[idx]\n", " save_music_file = save_audio(music_tensor)\n", " audio_filepath = f'/content/drive/MyDrive/Colab Notebooks/audio_output/audio_{idx}.wav'\n", " audio_file = open(audio_filepath, 'rb')\n", " audio_bytes = audio_file.read()\n", "\n", " # Play the full audio\n", " st.audio(audio_bytes, format='audio/wav')\n", " st.markdown(get_binary_file_downloader_html(audio_filepath, f'Audio_{idx}'), unsafe_allow_html=True)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " main()\n", "\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XAYcdheDwgRX", "outputId": "c8fb40ff-d8ea-42de-9b10-edd8d529c2da" }, "execution_count": 52, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Overwriting app.py\n" ] } ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "X9e_G3BhpnkH", "outputId": "c27fdda8-58d0-46e9-80e0-92cdd7c78535" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[..................] / rollbackFailedOptional: verb npm-session 3670fb27ba980f2\u001b[0m\u001b[K\n", "Collecting usage statistics. To deactivate, set browser.gatherUsageStats to False.\n", "\u001b[0m\n", "\u001b[0m\n", "\u001b[34m\u001b[1m You can now view your Streamlit app in your browser.\u001b[0m\n", "\u001b[0m\n", "\u001b[34m Network URL: \u001b[0m\u001b[1mhttp://172.28.0.12:8501\u001b[0m\n", "\u001b[34m External URL: \u001b[0m\u001b[1mhttp://34.16.174.223:8501\u001b[0m\n", "\u001b[0m\n", "\u001b[K\u001b[?25hnpx: installed 22 in 2.151s\n", "your url is: https://silly-sloths-guess.loca.lt\n", "2024-01-31 05:34:06.593717: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "2024-01-31 05:34:06.593772: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "2024-01-31 05:34:06.595191: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", "2024-01-31 05:34:08.219565: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", "/usr/local/lib/python3.10/dist-packages/streamlit/watcher/local_sources_watcher.py:193: UserWarning: Torchaudio's I/O functions now support par-call bakcend dispatch. Importing backend implementation directly is no longer guaranteed to work. Please use `backend` keyword with load/save/info function, instead of calling the udnerlying implementation directly.\n", " lambda m: [p for p in m.__path__._path],\n", "/usr/local/lib/python3.10/dist-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\n", " warnings.warn(\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\")\n", "/usr/local/lib/python3.10/dist-packages/streamlit/watcher/local_sources_watcher.py:193: UserWarning: Torchaudio's I/O functions now support par-call bakcend dispatch. Importing backend implementation directly is no longer guaranteed to work. Please use `backend` keyword with load/save/info function, instead of calling the udnerlying implementation directly.\n", " lambda m: [p for p in m.__path__._path],\n", "2024-01-31 05:37:53.816 Uncaught app exception\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.10/dist-packages/streamlit/runtime/scriptrunner/script_runner.py\", line 535, in _run_script\n", " exec(code, module.__dict__)\n", " File \"/content/app.py\", line 110, in \n", " main()\n", " File \"/content/app.py\", line 94, in main\n", " music_tensors = generate_music_tensors(descriptions, time_slider)\n", " File \"/content/app.py\", line 26, in generate_music_tensors\n", " output = model.generate(\n", " File \"/usr/local/lib/python3.10/dist-packages/audiocraft/models/genmodel.py\", line 163, in generate\n", " return self.generate_audio(tokens), tokens\n", " File \"/usr/local/lib/python3.10/dist-packages/audiocraft/models/genmodel.py\", line 266, in generate_audio\n", " gen_audio = self.compression_model.decode(gen_tokens, None)\n", " File \"/usr/local/lib/python3.10/dist-packages/audiocraft/models/encodec.py\", line 252, in decode\n", " out = self.decoder(emb)\n", " File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\n", " return self._call_impl(*args, **kwargs)\n", " File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\n", " return forward_call(*args, **kwargs)\n", " File \"/usr/local/lib/python3.10/dist-packages/audiocraft/modules/seanet.py\", line 257, in forward\n", " y = self.model(z)\n", " File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\n", " return self._call_impl(*args, **kwargs)\n", " File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\n", " return forward_call(*args, **kwargs)\n", " File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py\", line 215, in forward\n", " input = module(input)\n", " File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\n", " return self._call_impl(*args, **kwargs)\n", " File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\n", " return forward_call(*args, **kwargs)\n", " File \"/usr/local/lib/python3.10/dist-packages/audiocraft/modules/seanet.py\", line 60, in forward\n", " return self.shortcut(x) + self.block(x)\n", " File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\n", " return self._call_impl(*args, **kwargs)\n", " File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\n", " return forward_call(*args, **kwargs)\n", " File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py\", line 215, in forward\n", " input = module(input)\n", " File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\n", " return self._call_impl(*args, **kwargs)\n", " File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\n", " return forward_call(*args, **kwargs)\n", " File \"/usr/local/lib/python3.10/dist-packages/audiocraft/modules/conv.py\", line 200, in forward\n", " x = pad1d(x, (padding_left, padding_right + extra_padding), mode=self.pad_mode)\n", " File \"/usr/local/lib/python3.10/dist-packages/audiocraft/modules/conv.py\", line 84, in pad1d\n", " padded = F.pad(x, paddings, mode, value)\n", "torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.29 GiB. GPU 0 has a total capacty of 14.75 GiB of which 773.06 MiB is free. Process 9239 has 7.73 GiB memory in use. Process 371175 has 6.26 GiB memory in use. Of the allocated memory 6.09 GiB is allocated by PyTorch, and 40.12 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF\n", "\u001b[34m Stopping...\u001b[0m\n", "^C\n" ] } ], "source": [ "!streamlit run app.py & npx localtunnel --port 8501" ] } ], "metadata": { "accelerator": "GPU", "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3", "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" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "34e3996b7fed4fe698bf4c1df8a8c891": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", 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