app.py

import spaces
from pathlib import Path
import yaml
import time
import uuid

import numpy as np
import audiotools as at
import argbind
import shutil
import torch
from datetime import datetime

import gradio as gr
from vampnet.interface import Interface, signal_concat
from vampnet import mask as pmask


device = "cuda" if torch.cuda.is_available() else "cpu"

interface = Interface.default()
 
def to_output(sig):
 return sig.sample_rate, sig.cpu().detach().numpy()[0][0]

MAX_DURATION_S = 10
def load_audio(file):
 print(file)
 if isinstance(file, str):
 filepath = file
 elif isinstance(file, tuple):
 # not a file
 sr, samples = file
 samples = samples / np.iinfo(samples.dtype).max
 return sr, samples
 else:
 filepath = file.name
 sig = at.AudioSignal.salient_excerpt(
 filepath, duration=MAX_DURATION_S
 )
 sig = at.AudioSignal(filepath)
 return to_output(sig)


def load_example_audio():
 return load_audio("./assets/example.wav")

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


@spaces.GPU
def _vamp(
 seed, input_audio, model_choice, 
 pitch_shift_amt, periodic_p, 
 n_mask_codebooks, periodic_w, onset_mask_width, 
 dropout, sampletemp, typical_filtering, 
 typical_mass, typical_min_tokens, top_p, 
 sample_cutoff, stretch_factor, api=False
 ):

 t0 = time.time()
 interface.to("cuda" if torch.cuda.is_available() else "cpu")
 print(f"using device {interface.device}")
 _seed = seed if seed > 0 else None
 if _seed is None:
 _seed = int(torch.randint(0, 2**32, (1,)).item())
 at.util.seed(_seed)

 sr, input_audio = input_audio
 input_audio = input_audio / np.iinfo(input_audio.dtype).max
 
 sig = at.AudioSignal(input_audio, sr)

 # reload the model if necessary
 interface.load_finetuned(model_choice)

 if pitch_shift_amt != 0:
 sig = shift_pitch(sig, pitch_shift_amt)

 codes = interface.encode(sig)

 mask = interface.build_mask(
 codes, sig,
 rand_mask_intensity=1.0,
 prefix_s=0.0,
 suffix_s=0.0,
 periodic_prompt=int(periodic_p),
 periodic_prompt_width=periodic_w,
 onset_mask_width=onset_mask_width,
 _dropout=dropout,
 upper_codebook_mask=int(n_mask_codebooks), 
 )


 # save the mask as a txt file
 interface.set_chunk_size(10.0)
 codes, mask = interface.vamp(
 codes, mask,
 batch_size=1 if api else 1,
 feedback_steps=1,
 time_stretch_factor=stretch_factor,
 return_mask=True,
 temperature=sampletemp,
 typical_filtering=typical_filtering, 
 typical_mass=typical_mass, 
 typical_min_tokens=typical_min_tokens, 
 top_p=None,
 seed=_seed,
 sample_cutoff=1.0,
 )
 print(f"vamp took {time.time() - t0} seconds")

 sig = interface.decode(codes)

 return to_output(sig)

def vamp(data):
 return _vamp(
 seed=data[seed], 
 input_audio=data[input_audio],
 model_choice=data[model_choice],
 pitch_shift_amt=data[pitch_shift_amt],
 periodic_p=data[periodic_p],
 n_mask_codebooks=data[n_mask_codebooks],
 periodic_w=data[periodic_w],
 onset_mask_width=data[onset_mask_width],
 dropout=data[dropout],
 sampletemp=data[sampletemp],
 typical_filtering=data[typical_filtering],
 typical_mass=data[typical_mass],
 typical_min_tokens=data[typical_min_tokens],
 top_p=data[top_p],
 sample_cutoff=data[sample_cutoff],
 stretch_factor=data[stretch_factor],
 api=False, 
 )

def api_vamp(data):
 return _vamp(
 seed=data[seed], 
 input_audio=data[input_audio],
 model_choice=data[model_choice],
 pitch_shift_amt=data[pitch_shift_amt],
 periodic_p=data[periodic_p],
 n_mask_codebooks=data[n_mask_codebooks],
 periodic_w=data[periodic_w],
 onset_mask_width=data[onset_mask_width],
 dropout=data[dropout],
 sampletemp=data[sampletemp],
 typical_filtering=data[typical_filtering],
 typical_mass=data[typical_mass],
 typical_min_tokens=data[typical_min_tokens],
 top_p=data[top_p],
 sample_cutoff=data[sample_cutoff],
 stretch_factor=data[stretch_factor],
 api=True, 
 )


 
 

with gr.Blocks() as demo:
 with gr.Row():
 with gr.Column():
 manual_audio_upload = gr.File(
 label=f"upload some audio (will be randomly trimmed to max of 100s)",
 file_types=["audio"]
 )
 load_example_audio_button = gr.Button("or load example audio")

 input_audio = gr.Audio(
 label="input audio",
 interactive=False, 
 type="numpy",
 )

 audio_mask = gr.Audio(
 label="audio mask (listen to this to hear the mask hints)",
 interactive=False, 
 type="numpy",
 )

 # 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():
 with gr.Accordion("manual controls", open=True):
 periodic_p = gr.Slider(
 label="periodic prompt",
 minimum=0,
 maximum=13, 
 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=0, visible=False
 )

 n_mask_codebooks = gr.Slider(
 label="compression prompt ",
 value=3,
 minimum=1, 
 maximum=14,
 step=1,
 )
 
 maskimg = gr.Image(
 label="mask image",
 interactive=False,
 type="filepath"
 )

 with gr.Accordion("extras ", open=False):
 pitch_shift_amt = gr.Slider(
 label="pitch shift amount (semitones)",
 minimum=-12,
 maximum=12,
 step=1,
 value=0,
 )

 stretch_factor = gr.Slider(
 label="time stretch factor",
 minimum=0,
 maximum=8, 
 step=1,
 value=1, 
 )

 periodic_w = gr.Slider(
 label="periodic prompt width (steps, 1 step ~= 10milliseconds)",
 minimum=1,
 maximum=20,
 step=1,
 value=1,
 )


 with gr.Accordion("sampling settings", open=False):
 sampletemp = gr.Slider(
 label="sample temperature",
 minimum=0.1,
 maximum=10.0,
 value=1.0, 
 step=0.001
 )
 
 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=True
 )
 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=0.9,
 value=1.0, 
 step=0.01
 )


 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():

 model_choice = gr.Dropdown(
 label="model choice", 
 choices=list(interface.available_models()),
 value="default", 
 visible=True
 )


 vamp_button = gr.Button("generate (vamp)!!!")


 audio_outs = []
 use_as_input_btns = []
 for i in range(1):
 with gr.Column():
 audio_outs.append(gr.Audio(
 label=f"output audio {i+1}",
 interactive=False,
 type="numpy"
 ))
 use_as_input_btns.append(
 gr.Button(f"use as input (feedback)")
 )

 thank_you = gr.Markdown("")

 # download all the outputs
 # download = gr.File(type="filepath", label="download outputs")


 _inputs = {
 input_audio, 
 sampletemp,
 top_p,
 periodic_p, periodic_w,
 dropout,
 stretch_factor, 
 onset_mask_width, 
 typical_filtering,
 typical_mass,
 typical_min_tokens,
 seed, 
 model_choice,
 n_mask_codebooks,
 pitch_shift_amt, 
 sample_cutoff, 
 }
 
 # connect widgets
 vamp_button.click(
 fn=vamp,
 inputs=_inputs,
 outputs=[audio_outs[0]], 
 )

 api_vamp_button = gr.Button("api vamp", visible=True)
 api_vamp_button.click(
 fn=api_vamp,
 inputs=_inputs, 
 outputs=[audio_outs[0]], 
 api_name="vamp"
 )

 for i, btn in enumerate(use_as_input_btns):
 btn.click(
 fn=load_audio,
 inputs=[audio_outs[i]],
 outputs=[input_audio]
 )

try:
 demo.queue()
 demo.launch(share=True)
except KeyboardInterrupt:
 shutil.rmtree("gradio-outputs", ignore_errors=True)
 raise