# huggingface space exclusive import os # print("installing pyharp") # os.system('pip install "pyharp@git+https://github.com/audacitorch/pyharp.git"') # print("installing madmom") os.system('pip install cython') os.system('pip install madmom') from pathlib import Path from typing import Tuple import yaml import tempfile import uuid import shutil from dataclasses import dataclass, asdict import numpy as np import audiotools as at import argbind import torch import gradio as gr from vampnet.interface import Interface from vampnet import mask as pmask from pyharp import ModelCard, build_endpoint # loader = AudioLoader() # AudioLoader = argbind.bind(at.data.datasets.AudioLoader) conf = argbind.parse_args() from torch_pitch_shift import pitch_shift, get_fast_shifts def shift_pitch(signal, interval: int): signal.samples = pitch_shift( signal.samples, shift=interval, sample_rate=signal.sample_rate ) return signal def load_interface(): interface = Interface( coarse_ckpt="./models/vampnet/coarse.pth", coarse2fine_ckpt="./models/vampnet/c2f.pth", codec_ckpt="./models/vampnet/codec.pth", wavebeat_ckpt="./models/wavebeat.pth", device="cuda" if torch.cuda.is_available() else "cpu", ) return interface interface = load_interface() OUT_DIR = Path("gradio-outputs") OUT_DIR.mkdir(exist_ok=True, parents=True) def load_audio(file): print(file) filepath = file.name sig = at.AudioSignal.salient_excerpt( filepath, duration=interface.coarse.chunk_size_s ) sig = interface.preprocess(sig) out_dir = OUT_DIR / "tmp" / str(uuid.uuid4()) out_dir.mkdir(parents=True, exist_ok=True) sig.write(out_dir / "input.wav") return sig.path_to_file def load_example_audio(): return "./assets/example.wav" def _vamp(data, return_mask=False): # remove any old files in the output directory (from previous runs) shutil.rmtree(OUT_DIR) OUT_DIR.mkdir() out_dir = OUT_DIR / str(uuid.uuid4()) out_dir.mkdir() sig = at.AudioSignal(data[input_audio]) sig = interface.preprocess(sig) if data[pitch_shift_amt] != 0: sig = shift_pitch(sig, data[pitch_shift_amt]) z = interface.encode(sig) ncc = data[n_conditioning_codebooks] # build the mask mask = pmask.linear_random(z, data[rand_mask_intensity]) mask = pmask.mask_and( mask, pmask.inpaint( z, interface.s2t(data[prefix_s]), interface.s2t(data[suffix_s]) ) ) mask = pmask.mask_and( mask, pmask.periodic_mask( z, data[periodic_p], data[periodic_w], random_roll=True ) ) if data[onset_mask_width] > 0: mask = pmask.mask_or( mask, pmask.onset_mask(sig, z, interface, width=data[onset_mask_width]) ) if data[beat_mask_width] > 0: beat_mask = interface.make_beat_mask( sig, after_beat_s=(data[beat_mask_width]/1000), mask_upbeats=not data[beat_mask_downbeats], ) mask = pmask.mask_and(mask, beat_mask) # these should be the last two mask ops mask = pmask.dropout(mask, data[dropout]) mask = pmask.codebook_unmask(mask, ncc) print(f"dropout {data[dropout]}") print(f"masktemp {data[masktemp]}") print(f"sampletemp {data[sampletemp]}") print(f"top_p {data[top_p]}") print(f"prefix_s {data[prefix_s]}") print(f"suffix_s {data[suffix_s]}") print(f"rand_mask_intensity {data[rand_mask_intensity]}") print(f"num_steps {data[num_steps]}") print(f"periodic_p {data[periodic_p]}") print(f"periodic_w {data[periodic_w]}") print(f"n_conditioning_codebooks {data[n_conditioning_codebooks]}") print(f"use_coarse2fine {data[use_coarse2fine]}") print(f"onset_mask_width {data[onset_mask_width]}") print(f"beat_mask_width {data[beat_mask_width]}") print(f"beat_mask_downbeats {data[beat_mask_downbeats]}") print(f"stretch_factor {data[stretch_factor]}") print(f"seed {data[seed]}") print(f"pitch_shift_amt {data[pitch_shift_amt]}") print(f"sample_cutoff {data[sample_cutoff]}") _top_p = data[top_p] if data[top_p] > 0 else None # save the mask as a txt file np.savetxt(out_dir / "mask.txt", mask[:,0,:].long().cpu().numpy()) _seed = data[seed] if data[seed] > 0 else None zv, mask_z = interface.coarse_vamp( z, mask=mask, sampling_steps=data[num_steps], mask_temperature=data[masktemp]*10, sampling_temperature=data[sampletemp], return_mask=True, typical_filtering=data[typical_filtering], typical_mass=data[typical_mass], typical_min_tokens=data[typical_min_tokens], top_p=_top_p, gen_fn=interface.coarse.generate, seed=_seed, sample_cutoff=data[sample_cutoff], ) if use_coarse2fine: zv = interface.coarse_to_fine( zv, mask_temperature=data[masktemp]*10, sampling_temperature=data[sampletemp], mask=mask, sampling_steps=data[num_steps], sample_cutoff=data[sample_cutoff], seed=_seed, ) sig = interface.to_signal(zv).cpu() print("done") sig.write(out_dir / "output.wav") if return_mask: mask = interface.to_signal(mask_z).cpu() mask.write(out_dir / "mask.wav") return sig.path_to_file, mask.path_to_file else: return sig.path_to_file def vamp(data): return _vamp(data, return_mask=True) def api_vamp(data): return _vamp(data, return_mask=False) def save_vamp(data): out_dir = OUT_DIR / "saved" / str(uuid.uuid4()) out_dir.mkdir(parents=True, exist_ok=True) sig_in = at.AudioSignal(data[input_audio]) sig_out = at.AudioSignal(data[output_audio]) sig_in.write(out_dir / "input.wav") sig_out.write(out_dir / "output.wav") _data = { "masktemp": data[masktemp], "sampletemp": data[sampletemp], "top_p": data[top_p], "prefix_s": data[prefix_s], "suffix_s": data[suffix_s], "rand_mask_intensity": data[rand_mask_intensity], "num_steps": data[num_steps], "notes": data[notes_text], "periodic_period": data[periodic_p], "periodic_width": data[periodic_w], "n_conditioning_codebooks": data[n_conditioning_codebooks], "use_coarse2fine": data[use_coarse2fine], "stretch_factor": data[stretch_factor], "seed": data[seed], "samplecutoff": data[sample_cutoff], } # save with yaml with open(out_dir / "data.yaml", "w") as f: yaml.dump(_data, f) import zipfile zip_path = out_dir.with_suffix(".zip") with zipfile.ZipFile(zip_path, "w") as zf: for file in out_dir.iterdir(): zf.write(file, file.name) return f"saved! your save code is {out_dir.stem}", zip_path def harp_vamp(_input_audio, _beat_mask_width, _sampletemp): out_dir = OUT_DIR / str(uuid.uuid4()) out_dir.mkdir() sig = at.AudioSignal(_input_audio) sig = interface.preprocess(sig) z = interface.encode(sig) # build the mask mask = pmask.linear_random(z, 1.0) if _beat_mask_width > 0: beat_mask = interface.make_beat_mask( sig, after_beat_s=(_beat_mask_width/1000), ) mask = pmask.mask_and(mask, beat_mask) # save the mask as a txt file zv, mask_z = interface.coarse_vamp( z, mask=mask, sampling_temperature=_sampletemp, return_mask=True, gen_fn=interface.coarse.generate, ) zv = interface.coarse_to_fine( zv, sampling_temperature=_sampletemp, mask=mask, ) sig = interface.to_signal(zv).cpu() print("done") sig.write(out_dir / "output.wav") return sig.path_to_file with gr.Blocks() as demo: with gr.Row(): with gr.Column(): gr.Markdown("# VampNet Audio Vamping") gr.Markdown("""## Description: This is a demo of the VampNet, a generative audio model that transforms the input audio based on the chosen settings. You can control the extent and nature of variation with a set of manual controls and presets. Use this interface to experiment with different mask settings and explore the audio outputs. """) gr.Markdown(""" ## Instructions: 1. You can start by uploading some audio, or by loading the example audio. 2. Choose a preset for the vamp operation, or manually adjust the controls to customize the mask settings. 3. Click the "generate (vamp)!!!" button to apply the vamp operation. Listen to the output audio. 4. Optionally, you can add some notes and save the result. 5. You can also use the output as the new input and continue experimenting! """) with gr.Row(): with gr.Column(): manual_audio_upload = gr.File( label=f"upload some audio (will be randomly trimmed to max of {interface.coarse.chunk_size_s:.2f}s)", file_types=["audio"] ) load_example_audio_button = gr.Button("or load example audio") input_audio = gr.Audio( label="input audio", interactive=False, type="filepath", ) audio_mask = gr.Audio( label="audio mask (listen to this to hear the mask hints)", interactive=False, type="filepath", ) # connect widgets load_example_audio_button.click( fn=load_example_audio, inputs=[], outputs=[ input_audio] ) manual_audio_upload.change( fn=load_audio, inputs=[manual_audio_upload], outputs=[ input_audio] ) # mask settings with gr.Column(): presets = { "unconditional": { "periodic_p": 0, "onset_mask_width": 0, "beat_mask_width": 0, "beat_mask_downbeats": False, }, "slight periodic variation": { "periodic_p": 5, "onset_mask_width": 5, "beat_mask_width": 0, "beat_mask_downbeats": False, }, "moderate periodic variation": { "periodic_p": 13, "onset_mask_width": 5, "beat_mask_width": 0, "beat_mask_downbeats": False, }, "strong periodic variation": { "periodic_p": 17, "onset_mask_width": 5, "beat_mask_width": 0, "beat_mask_downbeats": False, }, "very strong periodic variation": { "periodic_p": 21, "onset_mask_width": 5, "beat_mask_width": 0, "beat_mask_downbeats": False, }, "beat-driven variation": { "periodic_p": 0, "onset_mask_width": 0, "beat_mask_width": 50, "beat_mask_downbeats": False, }, "beat-driven variation (downbeats only)": { "periodic_p": 0, "onset_mask_width": 0, "beat_mask_width": 50, "beat_mask_downbeats": True, }, "beat-driven variation (downbeats only, strong)": { "periodic_p": 0, "onset_mask_width": 0, "beat_mask_width": 20, "beat_mask_downbeats": True, }, } preset = gr.Dropdown( label="preset", choices=list(presets.keys()), value="strong periodic variation", ) load_preset_button = gr.Button("load_preset") with gr.Accordion("manual controls", open=True): periodic_p = gr.Slider( label="periodic prompt (0 - unconditional, 2 - lots of hints, 8 - a couple of hints, 16 - occasional hint, 32 - very occasional hint, etc)", minimum=0, maximum=128, step=1, value=3, ) onset_mask_width = gr.Slider( label="onset mask width (multiplies with the periodic mask, 1 step ~= 10milliseconds) ", minimum=0, maximum=100, step=1, value=5, ) beat_mask_width = gr.Slider( label="beat prompt (ms)", minimum=0, maximum=200, value=0, ) beat_mask_downbeats = gr.Checkbox( label="beat mask downbeats only?", value=False ) with gr.Accordion("extras ", open=False): pitch_shift_amt = gr.Slider( label="pitch shift amount (semitones)", minimum=-12, maximum=12, step=1, value=0, ) rand_mask_intensity = gr.Slider( label="random mask intensity. (If this is less than 1, scatters prompts throughout the audio, should be between 0.9 and 1.0)", minimum=0.0, maximum=1.0, value=1.0 ) periodic_w = gr.Slider( label="periodic prompt width (steps, 1 step ~= 10milliseconds)", minimum=1, maximum=20, step=1, value=1, ) n_conditioning_codebooks = gr.Number( label="number of conditioning codebooks. probably 0", value=0, precision=0, ) stretch_factor = gr.Slider( label="time stretch factor", minimum=0, maximum=64, step=1, value=1, ) preset_outputs = { periodic_p, onset_mask_width, beat_mask_width, beat_mask_downbeats, } def load_preset(_preset): return tuple(presets[_preset].values()) load_preset_button.click( fn=load_preset, inputs=[preset], outputs=preset_outputs ) with gr.Accordion("prefix/suffix prompts", open=False): prefix_s = gr.Slider( label="prefix hint length (seconds)", minimum=0.0, maximum=10.0, value=0.0 ) suffix_s = gr.Slider( label="suffix hint length (seconds)", minimum=0.0, maximum=10.0, value=0.0 ) masktemp = gr.Slider( label="mask temperature", minimum=0.0, maximum=100.0, value=1.5 ) sampletemp = gr.Slider( label="sample temperature", minimum=0.1, maximum=10.0, value=1.0, step=0.001 ) with gr.Accordion("sampling settings", open=False): top_p = gr.Slider( label="top p (0.0 = off)", minimum=0.0, maximum=1.0, value=0.0 ) typical_filtering = gr.Checkbox( label="typical filtering ", value=False ) typical_mass = gr.Slider( label="typical mass (should probably stay between 0.1 and 0.5)", minimum=0.01, maximum=0.99, value=0.15 ) typical_min_tokens = gr.Slider( label="typical min tokens (should probably stay between 1 and 256)", minimum=1, maximum=256, step=1, value=64 ) sample_cutoff = gr.Slider( label="sample cutoff", minimum=0.0, maximum=1.0, value=0.5, step=0.01 ) use_coarse2fine = gr.Checkbox( label="use coarse2fine", value=True, visible=False ) num_steps = gr.Slider( label="number of steps (should normally be between 12 and 36)", minimum=1, maximum=128, step=1, value=36 ) dropout = gr.Slider( label="mask dropout", minimum=0.0, maximum=1.0, step=0.01, value=0.0 ) seed = gr.Number( label="seed (0 for random)", value=0, precision=0, ) # mask settings with gr.Column(): # lora_choice = gr.Dropdown( # label="lora choice", # choices=list(loras.keys()), # value=LORA_NONE, # visible=False # ) vamp_button = gr.Button("generate (vamp)!!!") output_audio = gr.Audio( label="output audio", interactive=False, type="filepath" ) notes_text = gr.Textbox( label="type any notes about the generated audio here", value="", interactive=True ) save_button = gr.Button("save vamp") download_file = gr.File( label="vamp to download will appear here", interactive=False ) use_as_input_button = gr.Button("use output as input") thank_you = gr.Markdown("") _inputs = { input_audio, num_steps, masktemp, sampletemp, top_p, prefix_s, suffix_s, rand_mask_intensity, periodic_p, periodic_w, n_conditioning_codebooks, dropout, use_coarse2fine, stretch_factor, onset_mask_width, typical_filtering, typical_mass, typical_min_tokens, beat_mask_width, beat_mask_downbeats, seed, # lora_choice, pitch_shift_amt, sample_cutoff } # connect widgets vamp_button.click( fn=vamp, inputs=_inputs, outputs=[output_audio, audio_mask], ) api_vamp_button = gr.Button("api vamp", visible=False) api_vamp_button.click( fn=api_vamp, inputs=_inputs, outputs=[output_audio], api_name="vamp" ) use_as_input_button.click( fn=lambda x: x, inputs=[output_audio], outputs=[input_audio] ) save_button.click( fn=save_vamp, inputs=_inputs | {notes_text, output_audio}, outputs=[thank_you, download_file] ) # harp stuff harp_inputs = [ input_audio, beat_mask_width, sampletemp, ] build_endpoint( inputs=harp_inputs, output=output_audio, process_fn=harp_vamp, card=ModelCard( name="vampnet", description="Generate variations on music input, based on small prompts around the beat.", author="Hugo Flores García", tags=["music", "generative"] ), visible=False ) demo.launch()