Delete demos_musicgen_demo.ipynb

demos_musicgen_demo.ipynb

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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "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",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from audiocraft.models import MusicGen\n",
-    "from audiocraft.models import MultiBandDiffusion\n",
-    "\n",
-    "USE_DIFFUSION_DECODER = False\n",
-    "# Using small model, better results would be obtained with `medium` or `large`.\n",
-    "model = MusicGen.get_pretrained('facebook/musicgen-small')\n",
-    "if USE_DIFFUSION_DECODER:\n",
-    "    mbd = MultiBandDiffusion.get_mbd_musicgen()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, let us configure the generation parameters. Specifically, you can control the following:\n",
-    "* `use_sampling` (bool, optional): use sampling if True, else do argmax decoding. Defaults to True.\n",
-    "* `top_k` (int, optional): top_k used for sampling. Defaults to 250.\n",
-    "* `top_p` (float, optional): top_p used for sampling, when set to 0 top_k is used. Defaults to 0.0.\n",
-    "* `temperature` (float, optional): softmax temperature parameter. Defaults to 1.0.\n",
-    "* `duration` (float, optional): duration of the generated waveform. Defaults to 30.0.\n",
-    "* `cfg_coef` (float, optional): coefficient used for classifier free guidance. Defaults to 3.0.\n",
-    "\n",
-    "When left unchanged, MusicGen will revert to its default parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.set_generation_params(\n",
-    "    use_sampling=True,\n",
-    "    top_k=250,\n",
-    "    duration=30\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we can go ahead and start generating music using one of the following modes:\n",
-    "* Unconditional samples using `model.generate_unconditional`\n",
-    "* Music continuation using `model.generate_continuation`\n",
-    "* Text-conditional samples using `model.generate`\n",
-    "* Melody-conditional samples using `model.generate_with_chroma`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Music Continuation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "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": null,
-   "metadata": {},
-   "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": null,
-   "metadata": {},
-   "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(\"../assets/bach.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, return_tokens=True)\n",
-    "display_audio(output[0], sample_rate=32000)\n",
-    "if USE_DIFFUSION_DECODER:\n",
-    "    out_diffusion = mbd.tokens_to_wav(output[1])\n",
-    "    display_audio(out_diffusion, sample_rate=32000)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Text-conditional Generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from audiocraft.utils.notebook import display_audio\n",
-    "\n",
-    "output = model.generate(\n",
-    "    descriptions=[\n",
-    "        #'80s pop track with bassy drums and synth',\n",
-    "        #'90s rock song with loud guitars and heavy drums',\n",
-    "        #'Progressive rock drum and bass solo',\n",
-    "        #'Punk Rock song with loud drum and power guitar',\n",
-    "        #'Bluesy guitar instrumental with soulful licks and a driving rhythm section',\n",
-    "        #'Jazz Funk song with slap bass and powerful saxophone',\n",
-    "        'drum and bass beat with intense percussions'\n",
-    "    ],\n",
-    "    progress=True, return_tokens=True\n",
-    ")\n",
-    "display_audio(output[0], sample_rate=32000)\n",
-    "if USE_DIFFUSION_DECODER:\n",
-    "    out_diffusion = mbd.tokens_to_wav(output[1])\n",
-    "    display_audio(out_diffusion, sample_rate=32000)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Melody-conditional Generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torchaudio\n",
-    "from audiocraft.utils.notebook import display_audio\n",
-    "\n",
-    "model = MusicGen.get_pretrained('facebook/musicgen-melody')\n",
-    "model.set_generation_params(duration=8)\n",
-    "\n",
-    "melody_waveform, sr = torchaudio.load(\"../assets/bach.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, return_tokens=True\n",
-    ")\n",
-    "display_audio(output[0], sample_rate=32000)\n",
-    "if USE_DIFFUSION_DECODER:\n",
-    "    out_diffusion = mbd.tokens_to_wav(output[1])\n",
-    "    display_audio(out_diffusion, sample_rate=32000)"
-   ]
-  }
- ],
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-   "display_name": "Python 3 (ipykernel)",
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-   "pygments_lexer": "ipython3",
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-  "vscode": {
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