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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",
"import torch\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-large')\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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