{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "ruqumwjNVDje", "outputId": "1eee8f16-1e86-4fc2-9cbd-6ed2bdfbf00e" }, "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-yo2z_2yb\n", " Running command git clone --filter=blob:none --quiet https://github.com/facebookresearch/audiocraft.git /tmp/pip-req-build-yo2z_2yb\n", " Resolved https://github.com/facebookresearch/audiocraft.git to commit 69fea8b290ad1b4b40d28f92d1dfc0ab01dbab85\n", " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "Collecting av (from audiocraft==1.3.0a1)\n", " Downloading av-11.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (32.9 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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\u001b[?25l\u001b[?25hdone\n", " Created wheel for demucs: filename=demucs-4.0.1-py3-none-any.whl size=78391 sha256=7d1d1b7243f2f251a56c2eb332cac5e0212a3f0702a402360eab528beeae250a\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=0533f1d279daa9ce6249147780b8c4b7ef98548e37d6cec677c74269e4597477\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=a5f1ae50c9935d024cd197d35786d9242db77be44ec88c1e979ab65804e5ce71\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=7b8a8ca2458035e7f8aa3a3bec25336dccc429c634126fa9e2f9ba59dd191dfd\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=3584d6c6c3e01894dd04ab649d8539c53c1fe67d8d99a4ad5a1d30f679d69cf8\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=6ccecfd7692f8800cff98a2e5e8b11ec049132b061755f8330c53b78d6053d00\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=216ae6fedb6029e9bb97b09a7c702cb92c082be3e658b8065b36aa7ce9b12b3d\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", "\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.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully 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-4.18.0 gradio-client-0.10.0 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-3.9.13 pydub-0.25.1 python-multipart-0.0.9 retrying-1.3.4 ruff-0.2.1 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": {} } ], "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": "e2a744f7-49bc-478b-ff4f-906125485d93" }, "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) (11.0.0)\n", "Requirement already satisfied: einops in /usr/local/lib/python3.10/dist-packages (from audiocraft) (0.7.0)\n", "Requirement already satisfied: flashy>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from audiocraft) (0.0.2)\n", "Requirement already satisfied: hydra-core>=1.1 in /usr/local/lib/python3.10/dist-packages (from audiocraft) (1.3.2)\n", "Requirement already satisfied: hydra-colorlog in 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(from audiocraft) (0.20.3)\n", "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from audiocraft) (4.66.1)\n", "Requirement already satisfied: transformers>=4.31.0 in /usr/local/lib/python3.10/dist-packages (from audiocraft) (4.35.2)\n", "Requirement already satisfied: xformers<0.0.23 in /usr/local/lib/python3.10/dist-packages (from audiocraft) (0.0.22.post7)\n", "Requirement already satisfied: demucs in /usr/local/lib/python3.10/dist-packages (from audiocraft) (4.0.1)\n", "Requirement already satisfied: librosa in /usr/local/lib/python3.10/dist-packages (from audiocraft) (0.10.1)\n", "Requirement already satisfied: gradio in /usr/local/lib/python3.10/dist-packages (from audiocraft) (4.18.0)\n", "Requirement already satisfied: torchmetrics in /usr/local/lib/python3.10/dist-packages (from audiocraft) (1.3.1)\n", "Requirement already satisfied: encodec in /usr/local/lib/python3.10/dist-packages (from audiocraft) (0.1.1)\n", "Requirement already satisfied: 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/usr/local/lib/python3.10/dist-packages (from anyio->httpx->gradio->audiocraft) (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) (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) (0.1.2)\n" ] } ], "source": [ "%pip install audiocraft" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 368, "referenced_widgets": [ "a3848c6f40944f95892e5f7f441df7b8", "da40093204b94d6ea6afa17d8ec0ead2", "fe35c6b1c47d44bd82ccee620a688be8", "6b1aa5d196664fc6a317d4d98e9ddbe1", "167bc1dde7d04d85b2280dd8da0aab52", "82d119df44bc436c8c753960f7f8666d", "c259a67e851e42899befdee4eb202b33", "204befdae8c94fe698305703cd7a9275", "7fe6e6785be0496088184a4a32129fc1", "55a43c276140428e81223dea778ec7c6", "844b4e3c142e4525bf50bec0f79c8c5d", "8fed6c9c9b95427a800e3db96e246d3c", "da1f32369d6a4233b55a35c770d3bcba", "528569b7b26e4e9d99c36bfb53c8c3d0", "d8eed176fa3f413b99c28eb4d9b750ff", "dbdad97a67494587905078e656cdb8e0", "82653774d2564aa98192627f1b236631", "1f0ddeb53ee94529bf9ee214a7501372", "789ffbd15c06497791e872c58433e687", "c20c9d9091dd47f49cf91757429476f0", "7ef659cced97493186d0ee765d09511a", "966025cd1d5a40c89fb57abbf879dedf", "f64058691e464d7da98b54a11fb90870", "d7678345ffc04808a81946c69df8888e", "f90802f2a1984198be87a18089b8ea3e", "c2abb1cdfc124c84ab055c91e422a1b8", "78d4d4481be242b7a78fde93f629d735", "c42d7f2f8bb7408a846a7c81ff1e6328", "70702754f51f4ef29f2a3b8c2697ac66", "f9e7762bdcae439eb3df8e99d1517abf", "37e7ce2735f946e687942512bd6a0258", "ee811ebce7ad4ad6a5ebfd360ff1c213", "b9f733fb6b5e41a3bffc2c80baba9ba6", "3621907aff904518943d5d940bc97f96", "6573d4edf3d5449e9ffd15a7c837fa1c", "f6ec9807b22740079e631f25b1caf862", "252dbf0c4b324d74b43079ec4bc95b83", "5ba5ab074ec648d6bbd582dfe481b2db", "bde3bf90eb844fe68e96730ed5f369b1", "9dc97d66b068427897914053e5564961", "e255e541cfea436e9eed8fea53946694", "fd29e0d209f4493a887daf4d913c3c16", "162095642848418387785bfc5c253445", "105487bda7044a7c85510cb2c0cdbd76", "505beb184d1a46e8b645f475488fa1b1", "df528d66c33649e09132113938cec9e9", "e6e0729ff6084d2cb0e168812075faa8", "57482542d44a4d36a86a434c9e6e22fa", "34e450e64aee4aeb9f1abc54f589b14f", "503c682a741e4b57ab3ea440e931c026", "a8aae2ede06c47b7a522a9829a670cc2", "124d5afc8ec54ee8a1d1eaf20bb36ab6", "1748b352055f4da2b0fc104b467543b7", "b7947a0fbce84061a698a62fa092179e", "dc79bba0feac447491382a24a84c175b", "d6a170aa50804516a63674c0c4abaa4b", "84764585d84148648d08b5d7846b1ec5", "6104738c81a44478b21a25fe289ca783", "46f9f258e2c9440c9e6557176d273537", "c7ee46c44c8a40b79eea660d28d5171d", "a739962992d54d4fbf4cee24752fadc9", "4fc4419719ed44b5b5476cd5da78fcda", "fa929058e3e44911a1b4119a9bd5ea8a", "1a6af510e0834860b2d86fef871326bf", "71c1ac03348b42ecaefa53a2ec143301", "5ff3526993a8447ba065f7f057e0d706" ] }, "id": "dvoo41vmPqH4", "outputId": "a633da5e-e8b4-4b76-e5b4-0d319970ba56" }, "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": [ "compression_state_dict.bin: 0%| | 0.00/236M [00:00= 0.5).float()\n", "# return wav * envelope\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "id": "V0cTcQtNPqIG" }, "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", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1G4GANGhoWjh", "outputId": "2ea90dcd-f3a1-40c2-ffd5-dfcbbcaa133a" }, "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": 9, "metadata": { "id": "XT2aCBENPqIH" }, "outputs": [], "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": 10, "metadata": { "id": "40laFxmlPqIJ" }, "outputs": [], "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": 11, "metadata": { "id": "Q0_6VA_gEYn4" }, "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" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "4iNKZqtuxfy0" }, "outputs": [], "source": [ "# # Here we use a synthetic signal to prompt the generated audio.\n", "# res = model.generate_continuation(\n", "# get_bip_bip(0.125).expand(2, -1, -1),\n", "# 16000, ['Whistling with wind blowing',\n", "# 'Typing on a typewriter'],\n", "# progress=True)\n", "# display_audio(res, 32000)" ] }, { "cell_type": "markdown", "metadata": { "id": "dcCotW5APqIK" }, "source": [ "### Melody-conditional Generation" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "Iz447cD_PqIK" }, "outputs": [], "source": [ "# import math\n", "# import torchaudio\n", "# import torch\n", "# from audiocraft.utils.notebook import display_audio\n", "\n", "# model = audiogen.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": 14, "metadata": { "id": "SOykZTDKXeri" }, "outputs": [], "source": [ "import locale\n", "locale.getpreferredencoding = lambda: \"UTF-8\"" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uXtcCQC9fYHk", "outputId": "91f6ad62-b2a8-4ec2-a74a-c3add16fc54f" }, "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) (2.1.5)\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": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wmE7y42QXWEj", "outputId": "8e5631eb-3e80-4ee0-f3b8-0444858b46cc" }, "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 32 not upgraded.\n" ] } ], "source": [ "!sudo apt-get install ffmpeg" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "somcDKgspQDm", "outputId": "47e16184-6928-49ce-a8ea-b3f2ae4320d9" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.4/8.4 MB\u001b[0m \u001b[31m25.9 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[31m20.3 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[31m58.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m83.0/83.0 kB\u001b[0m \u001b[31m11.0 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[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h" ] } ], "source": [ "! pip install streamlit -q" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Kpqgv6bhckrt", "outputId": "1c4264ea-46fa-453f-ef2b-c8f77848214e" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "34.82.127.138\n" ] } ], "source": [ "!wget -q -O - ipv4.icanhazip.com" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "id": "XAYcdheDwgRX", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "6e4a4431-ae5c-4ed1-b960-fd278d656590" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Overwriting app.py\n" ] } ], "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\",\n", " \"Lofi\", \"Chillpop\",\"Country\",\"R&G\", \"Folk\",\"Heavy Metal\",\n", " \"EDM\", \"Soil\", \"Funk\",\"Reggae\", \"Disco\", \"Punk Rock\", \"House\",\n", " \"Techno\",\"Indie Rock\", \"Grunge\", \"Ambient\",\"Gospel\", \"Latin Music\",\"Grime\" ,\"Trap\", \"Psychedelic Rock\" ]\n", "\n", "@st.cache_resource()\n", "def load_model():\n", " model = MusicGen.get_pretrained('facebook/musicgen-melody')\n", " return model\n", "\n", "def generate_music_tensors(descriptions, duration: int):\n", " model = load_model()\n", " # model = load_model().to('cpu')\n", "\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, 10, 10)\n", " # time_slider = st.slider(\"Select time duration (In Minutes)\", 0,300,10, step=1)\n", "\n", "\n", " st.title(\"\"\"🎵 Song Lab AI Melody-Model 🎵\"\"\")\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.text('\\n\\n')\n", " st.subheader(\"Generated Music\")\n", "\n", " # Generate audio\n", " # descriptions = [f\"{text_area} {selected_genre} {bpm} BPM\" for _ in range(5)]\n", " descriptions = [f\"{text_area} {selected_genre} {bpm} BPM\" for _ in range(1)] # Change the batch size to 1\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" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "id": "X9e_G3BhpnkH", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "40467bcc-8def-4d5d-8333-2446d26eec7a" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[?25l[..................] / rollbackFailedOptional: verb npm-session e83d6a093faf638\u001b[0m\u001b[K\r[..................] / rollbackFailedOptional: verb npm-session e83d6a093faf638\u001b[0m\u001b[K\r[..................] / rollbackFailedOptional: verb npm-session e83d6a093faf638\u001b[0m\u001b[K\r\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://34.82.127.138:8501\u001b[0m\n", "\u001b[0m\n", "\u001b[K\u001b[?25hnpx: installed 22 in 2.396s\n", "your url is: https://giant-lizards-guess.loca.lt\n", "2024-02-13 14:27:27.865655: 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", "2024-02-13 14:27:27.865716: 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", "2024-02-13 14:27:27.867134: 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", "2024-02-13 14:27:28.939770: 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", "state_dict.bin: 100% 2.77G/2.77G [00:25<00:00, 109MB/s]\n", "Downloading: \"https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/955717e8-8726e21a.th\" to /root/.cache/torch/hub/checkpoints/955717e8-8726e21a.th\n", "100% 80.2M/80.2M [00:00<00:00, 118MB/s]\n", "compression_state_dict.bin: 100% 236M/236M [00:01<00:00, 168MB/s]\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", "\u001b[34m Stopping...\u001b[0m\n", "\u001b[0m^C\n" ] } ], "source": [ "!streamlit run app.py & npx localtunnel --port 8501" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "n-GKyPNVyA-i" }, "outputs": [], "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "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": { "a3848c6f40944f95892e5f7f441df7b8": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": 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