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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# JASCO\n",
"Welcome to JASCO's demo jupyter notebook. \n",
"Here you will find a self-contained example of how to use JASCO for temporally controlled music generation.\n",
"\n",
"You can choose a model from the following selection:\n",
"1. facebook/jasco-chords-drums-400M - 10s music generation conditioned on text, chords and drums, 400M parameters\n",
"2. facebook/jasco-chords-drums-1B - 10s music generation conditioned on text, chords and drums, 1B parameters\n",
"3. facebook/jasco-chords-drums-melody-400M - 10s music generation conditioned on text, chords, drums and melody, 400M parameters\n",
"4. facebook/jasco-chords-drums-melody-1B - 10s music generation conditioned on text, chords, drums and melody, 1B parameters\n",
"\n",
"First, we start by initializing the JASCO model:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os \n",
"from audiocraft.models import JASCO\n",
"\n",
"model = JASCO.get_pretrained('facebook/jasco-chords-drums-melody-400M', chords_mapping_path='../assets/chord_to_index_mapping.pkl')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, let us configure the generation parameters. Specifically, you can control the following:\n",
"* `cfg_coef_all` (float, optional): Coefficient used for classifier free guidance - fully conditional term. \n",
" Defaults to 5.0.\n",
"* `cfg_coef_txt` (float, optional): Coefficient used for classifier free guidance - additional text conditional term. \n",
" Defaults to 0.0.\n",
"\n",
"When left unchanged, JASCO will revert to its default parameters."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.set_generation_params(\n",
" cfg_coef_all=0.0,\n",
" cfg_coef_txt=5.0\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we can go ahead and start generating music given textual prompts."
]
},
{
"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",
"# set textual prompt\n",
"text = \"Funky groove with electric piano playing blue chords rhythmically\"\n",
"\n",
"# run the model\n",
"print(\"Generating...\") \n",
"output = model.generate(descriptions=[text], progress=True)\n",
"\n",
"# display the result\n",
"print(f\"Text: {text}\\n\")\n",
"display_audio(output, sample_rate=model.compression_model.sample_rate)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can start adding temporal controls! We begin with conditioning on chord progressions:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Chords-conditional Generation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.set_generation_params(\n",
" cfg_coef_all=1.5,\n",
" cfg_coef_txt=3.0\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from audiocraft.utils.notebook import display_audio\n",
"\n",
"# set textual prompt\n",
"text = \"Strings, woodwind, orchestral, symphony.\"\n",
"\n",
"# define chord progression\n",
"chords = [('C', 0.0), ('D', 2.0), ('F', 4.0), ('Ab', 6.0), ('Bb', 7.0), ('C', 8.0)]\n",
"\n",
"# run the model\n",
"print(\"Generating...\")\n",
"output = model.generate_music(descriptions=[text], chords=chords, progress=True)\n",
"\n",
"# display the result\n",
"print(f'Text: {text}')\n",
"print(f'Chord progression: {chords}')\n",
"display_audio(output, sample_rate=model.compression_model.sample_rate)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we can condition the generation on drum tracks:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Drums-conditional Generation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torchaudio\n",
"from audiocraft.utils.notebook import display_audio\n",
"\n",
"\n",
"# load drum prompt\n",
"drums_waveform, sr = torchaudio.load(\"../assets/sep_drums_1.mp3\")\n",
"\n",
"# set textual prompt \n",
"text = \"distortion guitars, heavy rock, catchy beat\"\n",
"\n",
"# run the model\n",
"print(\"Generating...\")\n",
"output = model.generate_music(\n",
" descriptions=[text],\n",
" drums_wav=drums_waveform,\n",
" drums_sample_rate=sr,\n",
" progress=True\n",
")\n",
"\n",
"# display the result\n",
"print('drum prompt:')\n",
"display_audio(drums_waveform, sample_rate=sr)\n",
"print(f'Text: {text}')\n",
"display_audio(output, sample_rate=model.compression_model.sample_rate)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also combine multiple temporal controls! Let's move on to generating with both chords and drums conditioning:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Drums + Chords conditioning"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torchaudio\n",
"from audiocraft.utils.notebook import display_audio\n",
"\n",
"\n",
"# load drum prompt\n",
"drums_waveform, sr = torchaudio.load(\"../assets/sep_drums_1.mp3\")\n",
"\n",
"# set textual prompt \n",
"text = \"string quartet, orchestral, dramatic\"\n",
"\n",
"# define chord progression\n",
"chords = [('C', 0.0), ('D', 2.0), ('F', 4.0), ('Ab', 6.0), ('Bb', 7.0), ('C', 8.0)]\n",
"\n",
"# run the model\n",
"print(\"Generating...\")\n",
"output = model.generate_music(\n",
" descriptions=[text],\n",
" drums_wav=drums_waveform,\n",
" drums_sample_rate=sr,\n",
" chords=chords,\n",
" progress=True\n",
")\n",
"\n",
"# display the result\n",
"print('drum prompt:')\n",
"display_audio(drums_waveform, sample_rate=sr)\n",
"print(f'Chord progression: {chords}')\n",
"print(f'Text: {text}')\n",
"display_audio(output, sample_rate=model.compression_model.sample_rate)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Melody + Drums + Chords conditioning - inference example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from demucs import pretrained\n",
"from demucs.apply import apply_model\n",
"from demucs.audio import convert_audio\n",
"import torch\n",
"from audiocraft.utils.notebook import display_audio\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# --------------------------\n",
"# First, choose file to load\n",
"# --------------------------\n",
"fnames = ['salience_1', 'salience_2']\n",
"chords = [\n",
" [('N', 0.0), ('Eb7', 1.088000000), ('C#', 4.352000000), ('D', 4.864000000), ('Dm7', 6.720000000), ('G7', 8.256000000), ('Am7b5/G', 9.152000000)], # for salience 1\n",
" [('N', 0.0), ('C', 0.320000000), ('Dm7', 3.456000000), ('Am', 4.608000000), ('F', 8.320000000), ('C', 9.216000000)] # for salience 2\n",
"]\n",
"file_idx = 0 # either 0 or 1\n",
"\n",
"\n",
"# ------------------------------------\n",
"# display audio, melody map and chords\n",
"# ------------------------------------\n",
"def plot_chromagram(tensor):\n",
" # Check if tensor is a PyTorch tensor\n",
" if not torch.is_tensor(tensor):\n",
" raise ValueError('Input should be a PyTorch tensor')\n",
" tensor = tensor.numpy().T # C, T\n",
" plt.figure(figsize=(20, 20))\n",
" plt.imshow(tensor, cmap='binary', interpolation='nearest', origin='lower')\n",
" plt.show()\n",
"\n",
"# load salience and display the corresponding wav\n",
"melody_prompt_wav, melody_prompt_sr = torchaudio.load(f\"../assets/{fnames[file_idx]}.wav\")\n",
"print(\"Source melody:\")\n",
"display_audio(melody_prompt_wav, sample_rate=melody_prompt_sr)\n",
"melody = torch.load(f\"../assets/{fnames[file_idx]}.th\", weights_only=True)\n",
"plot_chromagram(melody)\n",
"print(\"Chords:\")\n",
"print(chords[file_idx])\n",
"\n",
"# --------------------------------------------------\n",
"# use demucs to seperate the drums stem from src mix\n",
"# --------------------------------------------------\n",
"def _get_drums_stem(wav: torch.Tensor, sample_rate: int) -> torch.Tensor:\n",
" \"\"\"Get parts of the wav that holds the drums, extracting the main stems from the wav.\"\"\"\n",
" demucs_model = pretrained.get_model('htdemucs').to('cuda')\n",
" wav = convert_audio(\n",
" wav, sample_rate, demucs_model.samplerate, demucs_model.audio_channels) # type: ignore\n",
" stems = apply_model(demucs_model, wav.cuda().unsqueeze(0), device='cuda').squeeze(0)\n",
" drum_stem = stems[demucs_model.sources.index('drums')] # extract relevant stems for drums conditioning\n",
" return convert_audio(drum_stem.cpu(), demucs_model.samplerate, sample_rate, 1) # type: ignore\n",
"drums_wav = _get_drums_stem(melody_prompt_wav, melody_prompt_sr)\n",
"print(\"Separated drums:\")\n",
"display_audio(drums_wav, sample_rate=melody_prompt_sr)\n",
"\n",
"# ----------------------------------\n",
"# Generate using the loaded controls\n",
"# ----------------------------------\n",
"# these are free-form texts written randomly\n",
"texts = [\n",
" '90s rock with heavy drums and hammond',\n",
" '80s pop with groovy synth bass and drum machine',\n",
" 'folk song with leading accordion',\n",
"]\n",
"\n",
"print(\"Generating...\")\n",
"# replacing dynammic solver with simple euler solver\n",
"model.set_generation_params(cfg_coef_all=1.5, cfg_coef_txt=2.5, euler=True, euler_steps=50) # manually set with euler solver\n",
"output = model.generate_music(\n",
" descriptions=texts,\n",
" chords=chords[file_idx],\n",
" drums_wav=drums_wav,\n",
" drums_sample_rate=melody_prompt_sr,\n",
" melody_salience_matrix=melody.permute(1, 0),\n",
" progress=True\n",
")\n",
"display_audio(output, sample_rate=model.compression_model.sample_rate)"
]
}
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