diff --git "a/checkpoint (1).ipynb" "b/checkpoint (1).ipynb" new file mode 100644--- /dev/null +++ "b/checkpoint (1).ipynb" @@ -0,0 +1,5404 @@ +{ + "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", 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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) (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 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(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": { + 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