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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# AudioGen\n",
"Welcome to AudioGen's demo jupyter notebook. Here you will find a series of self-contained examples of how to use AudioGen in different settings.\n",
"\n",
"First, we start by initializing AudioGen. For now, we provide only a medium sized model for AudioGen: `facebook/audiogen-medium` - 1.5B transformer decoder. \n",
"\n",
"**Important note:** This variant is different from the original AudioGen model presented at [\"AudioGen: Textually-guided audio generation\"](https://arxiv.org/abs/2209.15352) as the model architecture is similar to MusicGen with a smaller frame rate and multiple streams of tokens, allowing to reduce generation time."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from audiocraft.models import AudioGen\n",
"\n",
"model = AudioGen.get_pretrained('facebook/audiogen-medium')"
]
},
{
"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 10.0.\n",
"* `cfg_coef` (float, optional): coefficient used for classifier free guidance. Defaults to 3.0.\n",
"\n",
"When left unchanged, AudioGen 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 sound using one of the following modes:\n",
"* Audio continuation using `model.generate_continuation`\n",
"* Text-conditional samples using `model.generate`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Audio 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=16000, 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 * frequency * 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 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, 16000)"
]
},
{
"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/sirens_and_a_humming_engine_approach_and_pass.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=16000)"
]
},
{
"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",
" 'Subway train blowing its horn',\n",
" 'A cat meowing',\n",
" ],\n",
" progress=True\n",
")\n",
"display_audio(output, sample_rate=16000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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