{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "ytvvaR-fBhqJ" }, "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. `facebook/musicgen-small` - 300M transformer decoder.\n", "2. `facebook/musicgen-medium` - 1.5B transformer decoder.\n", "3. `facebook/musicgen-melody` - 1.5B transformer decoder also supporting melody conditioning.\n", "4. `facebook/musicgen-large` - 3.3B transformer decoder.\n", "\n", "We will use the `facebook/musicgen-small` variant for the purpose of this demonstration." ] }, { "cell_type": "code", "source": [ "!pip install git+https://github.com/facebookresearch/audiocraft.git" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "jkxqYX2GCUiB", "outputId": "b8616d58-42ff-48b7-d84d-5310a556c086" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting 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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=827bc72ff8d1f59b60cd1c568d58dd092ba3cd08c83bd5b0d6fb2392e42bdd7a\n", " Stored in directory: /tmp/pip-ephem-wheel-cache-3iksznnv/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 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\u001b[?25l\u001b[?25hdone\n", " Created wheel for dora-search: filename=dora_search-0.1.12-py3-none-any.whl size=75092 sha256=9fd10263873baba8ebffe7f9b90a1efe2cac54cd79c13149c60cdf7fe293635f\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-0.3.2-py3-none-any.whl size=5584 sha256=948c9334e52460d894d5fbafb8fe4d9086efd87b10f7c68dc36fd7b63c0ddc4e\n", " Stored in directory: /root/.cache/pip/wheels/bd/65/9a/671fc6dcde07d4418df0c592f8df512b26d7a0029c2a23dd81\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=7ca6a892c6dce5ed26ce357851addd4792f217f71cf1d68f16964605c9037fcf\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, treetable, tomlkit, submitit, shellingham, semantic-version, ruff, retrying, python-multipart, orjson, omegaconf, num2words, lightning-utilities, h11, einops, colorlog, colorama, av, aiofiles, uvicorn, starlette, hydra-core, httpcore, xformers, torchmetrics, julius, hydra_colorlog, httpx, fastapi, dora-search, openunmix, gradio-client, flashy, encodec, gradio, demucs, audiocraft\n", "Successfully installed aiofiles-23.2.1 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-0.109.2 ffmpy-0.3.2 flashy-0.0.2 gradio-4.19.1 gradio-client-0.10.0 h11-0.14.0 httpcore-1.0.4 httpx-0.27.0 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-3.9.14 pydub-0.25.1 python-multipart-0.0.9 retrying-1.3.4 ruff-0.2.2 semantic-version-2.10.0 shellingham-1.5.4 starlette-0.36.3 submitit-1.5.1 tomlkit-0.12.0 torchmetrics-1.3.1 treetable-0.2.5 uvicorn-0.27.1 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": {} } ] }, { "cell_type": "code", "source": [ "%pip install audiocraft" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NnaKBrjSDOVx", "outputId": "5e7287bc-e78a-42f4-908a-f9425e901ff6" }, "execution_count": 2, "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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(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) (0.1.2)\n" ] } ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "8Rq67mxLBhqN", "colab": { "base_uri": "https://localhost:8080/", "height": 368, "referenced_widgets": [ "e24876140e414b2d8b11b895717ae35b", "7b090df25f664506aa732a264d9dac2d", "82bc7e77dd1747809eb8704b3b21ed76", "04fd12bff8fc40dd8dea3d5960d4b769", "55604b51259a47ec9c25d5a1806d43a9", "f4247fb2cd9c48dba500b41c93acb202", "154d4b1b3ec246e8bae78a343025dbaf", "5bd4e7e026744e9c9af5817589dcad2f", "31cf251d70b24aabbed6d63c0db9dd54", "e081fc92e4a2434da87b6fe5c4019553", "847075cc360e496ca036b0998f42dd78", "fc078de7eb53405682166181682fa2b2", "01599587ff8c456ea5ade967dbe76caa", "998ce2daae2445b79564c907ec14f13d", "e43d84b00210441088741c638a9ef426", "8b15b237a47c4d43a270de9b2d1fb98f", "6163856bd6264220a440cabc710ed3f8", "cfa8c34348674eb4bd289250d0873a2f", "66dbee42ca184d718789ea643767906d", "c1b2ae6419aa45b9986797d50aa337ac", "1514a0490b384d94b77d05ffaee0dd7d", "a40dcba09dd74bb3afd9ea01c955f86d", "d0d787d93d64450ca466eebee6e4ca61", "bd46fb23a4b44a9786a6bdc8ac96f942", "5b813b456c3344a2abd86d20638dc201", "abaefd61eb2f49c5b90d356d1ce0cb5c", "e31f66d701004286a487eca92359568e", "ed41053146de4bb59ef49515a08e8e1d", "3fd8c6f6411e47449ff16f9fd0afad7e", "ed97ff31483e41c5b386964e5f0fe4d3", "6cfe497d117d42849c4b06e0da262af1", "9dac94855d2746ac9701a2e80408262e", "8ac858d3a347472793d7de415cd44282", "74b042fe3e4d4d57bdd7d4a65847c301", "10d8e7b6b41f444db2a29c26bfb078e3", "c1fc26a6ac784687ac30ddddd0cd0ef0", "3a7e86b3cfef4971a1063928b06e78f8", "8b404214fa124db5ab2ac8a3ec219197", "6881f491f860419bbda30305d2bd03d8", "2e134dd48d7245b1a91ba43feb3b525b", "ce3a847d8aae4c0588b0eba97827dd03", "bd8d773c8a7c40f5ab9f66769d227dfe", "4305d185eedb447685131b0aa999e2e9", "eae336ec45a34fd0b4979187bbca7bfe", "618ee20c4f1c471f80014a95d9bcacd7", "371fb032343a4ee1ba11632e69315beb", "a4cb6b07a3a047e3a3936b65f1e98d76", "dadda1d3761e4413b1700f926841b72b", "88dd2e936d5f4541b3f79181fb041162", "67324ca3dd6d4c76af3e300fb72f3641", "d8576d45720a4b2ba9ede1166b97b84a", "f81b6d72032a4e76af508c7f4d9ee718", "230d71503dfb40aa9d4a1c29ba598162", "acd00f9f22714320a9ae7854617703ec", "b00e56d1ff8e418582223c1fe24aab20", "d96af440c3804128b17a8d2e5f6907ff", "90f11fe3e0774e8e86c8bf30b95b7324", "4df61ff95d92441f9e089afe4f73da52", "a2f0364d9ac34668aaac1c1bfd836248", "cfd7deaed28448119c070c2b08815a79", "d6dde083f98a4deb9b8698adb77f9dfb", "821eb554678f403abd4f6f7dfd65a7dc", "84323819b109401385c3773b8fcf4d8b", "64b01a48ef904761a28d4396fca0b692", "5b723ff4d09d473aa38c22621cba7519", "824cc1cb39904fc29ffd44a9cfe42e0c" ] }, "outputId": "ac152a6a-a6e5-4f04-8e65-ddfac18c1cc3" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/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= 0.5).float()\n", " return wav * envelope" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "id": "xn5CxkD6Bhqe" }, "outputs": [], "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", "source": [ "import locale\n", "locale.getpreferredencoding = lambda: \"UTF-8\"" ], "metadata": { "id": "OE3YXu3qyUl6" }, "execution_count": 33, "outputs": [] }, { "cell_type": "code", "source": [ "!sudo apt-get install ffmpeg" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "noo9Pqg4yUyN", "outputId": "307c7413-56da-4042-c17c-fd988ee5604f" }, "execution_count": 34, "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 35 not upgraded.\n" ] } ] }, { "cell_type": "code", "source": [ "! pip install streamlit -q" ], "metadata": { "id": "bsA3Wof5yY3j" }, "execution_count": 35, "outputs": [] }, { "cell_type": "code", "source": [ "!wget -q -O - ipv4.icanhazip.com" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VdCMg6RUybck", "outputId": "83213ae9-f669-491d-98d8-ec0481b025e2" }, "execution_count": 36, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "35.247.172.216\n" ] } ] }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8bR3g1Sf1VN4", "outputId": "5f6db288-264f-458e-83b1-a4871bc4da6c" }, "execution_count": 37, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" ] } ] }, { "cell_type": "code", "source": [ "# %%writefile app.py\n", "import streamlit as st\n", "import torch\n", "import os\n", "import base64\n", "import torchaudio\n", "import numpy as np\n", "from audiocraft.models import MusicGen\n", "\n", "genres = [\"Pop\", \"Rock\", \"Jazz\", \"Electronic\", \"Hip-Hop\", \"Classical\",\n", " \"Lofi\", \"Chillpop\",\"Country\",\"R&G\", \"Folk\",\"EDM\", \"Disco\", \"House\", \"Techno\",]\n", "\n", "@st.cache_resource()\n", "def load_model(model_name):\n", " model = MusicGen.get_pretrained(model_name)\n", " return model\n", "\n", "def generate_music_tensors(description, duration: int, batch_size=1, models=None):\n", " outputs = {}\n", " for model_name, model in models.items():\n", " model.set_generation_params(\n", " use_sampling=True,\n", " top_k=250,\n", " duration=duration\n", " )\n", "\n", " with st.spinner(f\"Generating Music with {model_name}...\"):\n", " output = model.generate(\n", " descriptions=description,\n", " progress=True,\n", " return_tokens=True\n", " )\n", " outputs[model_name] = output\n", "\n", " st.success(\"Music Generation Complete!\")\n", " return outputs\n", "\n", "def save_audio(samples: torch.Tensor, filename):\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\"{filename}_{idx}.wav\")\n", " torchaudio.save(audio_path, audio, sample_rate)\n", " return audio_path\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", " st.title(\"🎧AI Composer Medium-Model 🎧\")\n", "\n", " st.subheader(\"Generate Music\")\n", " st.write(\"Craft your perfect melody! Fill in the blanks below to create your music masterpiece:\")\n", "\n", " bpm = st.number_input(\"Enter Speed in BPM\", min_value=60)\n", " text_area = st.text_area('Example: 80s rock song with guitar and drums')\n", " selected_genre = st.selectbox(\"Select Genre\", genres)\n", " time_slider = st.slider(\"Select time duration (In Seconds)\", 0, 30, 10)\n", "\n", " mood = st.selectbox(\"Select Mood\", [\"Happy\", \"Sad\", \"Angry\", \"Relaxed\", \"Energetic\"])\n", " instrument = st.selectbox(\"Select Instrument\", [\"Piano\", \"Guitar\", \"Flute\", \"Violin\", \"Drums\"])\n", " tempo = st.selectbox(\"Select Tempo\", [\"Slow\", \"Moderate\", \"Fast\"])\n", " melody = st.text_input(\"Enter Melody or Chord Progression\", \"e.g., C D:min G:7 C, Twinkle Twinkle Little Star\")\n", "\n", " models = {\n", " 'Medium': load_model('facebook/musicgen-medium'),\n", " 'Large': load_model('facebook/musicgen-large'),\n", " # Add more models here as needed\n", " }\n", "\n", " if st.button('Let\\'s Generate 🎶'):\n", " st.text('\\n\\n')\n", " st.subheader(\"Generated Music\")\n", "\n", " description = f\"{text_area} {selected_genre} {bpm} BPM {mood} {instrument} {tempo} {melody}\"\n", " music_outputs = generate_music_tensors(description, time_slider, batch_size=2, models=models)\n", "\n", " for model_name, output in music_outputs.items():\n", " idx = 0 # Assuming you want to access the first audio file for each model\n", " audio_filepath = save_audio(output, f'audio_{model_name}_{idx}')\n", " audio_file = open(audio_filepath, 'rb')\n", " audio_bytes = audio_file.read()\n", "\n", " st.audio(audio_bytes, format='audio/wav', label=f'{model_name} Model')\n", " st.markdown(get_binary_file_downloader_html(audio_filepath, f'Audio_{model_name}_{idx}'), unsafe_allow_html=True)\n", "\n", "if __name__ == \"__main__\":\n", " main()\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nHJe4H7Cx9b7", "outputId": "b1fe2d00-eb04-4e37-8de7-beecf8a8714b" }, "execution_count": 38, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Overwriting app.py\n" ] } ] }, { "cell_type": "code", "source": [ "!streamlit run app.py & npx localtunnel --port 8501" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "l_T44WyzyiNH", "outputId": "1e16c9d5-e689-4f31-ba7a-1473c760ffb1" }, "execution_count": 39, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[##................] \\ fetchMetadata: sill resolveWithNewModule color-convert@2\u001b[0m\u001b[K\n", "Collecting usage statistics. To deactivate, set browser.gatherUsageStats to False.\n", "\u001b[0m\n", "\u001b[K\u001b[?25hnpx: installed 22 in 3.164s\n", "your url is: https://slick-memes-enjoy.loca.lt\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://35.247.172.216:8501\u001b[0m\n", "\u001b[0m\n", "2024-02-22 08:41:30.010651: 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", "2024-02-22 08:41:30.010713: 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", "2024-02-22 08:41:30.018009: 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", "2024-02-22 08:41:32.200793: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", "^C\n" ] } ] } ], "metadata": { "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": "3.9.16" }, "vscode": { "interpreter": { "hash": "b02c911f9b3627d505ea4a19966a915ef21f28afb50dbf6b2115072d27c69103" } }, "colab": { "provenance": [], "gpuType": "T4" }, "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { "e24876140e414b2d8b11b895717ae35b": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], 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