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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=9930af8bffb82e02587e995f1809e3259eacd3e1ac57f46958a3f17bd6691df6\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-ouswsnza/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=ea32fae19fde0bb334a1c262acbeb19cbd5b56525974c6a83ba877f69e5c8437\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=b381d425a4252d797faa2f78ef54a510763b1de21d17d85da80305e6be5e3951\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=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 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"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": { + 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