File size: 7,051 Bytes
c7e6202 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 |
{
"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. `small` - 300M transformer decoder.\n",
"2. `medium` - 1.5B transformer decoder.\n",
"3. `melody` - 1.5B transformer decoder also supporting melody conditioning.\n",
"4. `large` - 3.3B transformer decoder.\n",
"\n",
"We will use the `small` variant for the purpose of this demonstration."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from audiocraft.models import MusicGen\n",
"\n",
"# Using small model, better results would be obtained with `medium` or `large`.\n",
"model = MusicGen.get_pretrained('small')"
]
},
{
"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=5\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": [
"### Unconditional Generation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from audiocraft.utils.notebook import display_audio\n",
"\n",
"output = model.generate_unconditional(num_samples=2, progress=True)\n",
"display_audio(output, sample_rate=32000)"
]
},
{
"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\n"
]
},
{
"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)\n",
"display_audio(output, 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",
" ],\n",
" progress=True\n",
")\n",
"display_audio(output, 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('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\n",
")\n",
"display_audio(output, sample_rate=32000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"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"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|