app.py

import random

import gradio as gr
import torch
from huggingface_hub import hf_hub_download

from model.music_transformer import MusicTransformer
from processor import decode_midi, encode_midi
from utilities.constants import TOKEN_END, TOKEN_PAD, TORCH_LABEL_TYPE
from utilities.device import get_device, use_cuda

REPO_ID = "Launchpad/lofi-bytes"
FILENAME = "weights_maestro_finetuned.pickle"

SEQUENCE_START = 0
OUTPUT_PATH = "./output_midi"
RPR = True
# TARGET_SEQ_LENGTH = 1023
TARGET_SEQ_LENGTH = 512
NUM_PRIME = 65
MAX_SEQUENCE = 2048
N_LAYERS = 6
NUM_HEADS = 8
D_MODEL = 512
DIM_FEEDFORWARD = 1024
BEAM = 0
FORCE_CPU = False
ALLOWED_EXTENSIONS = {'mid'}
UPLOAD_FOLDER = './uploaded_midis'

generated_midi = None

use_cuda(True)

model = MusicTransformer(
    n_layers=N_LAYERS,
    num_heads=NUM_HEADS,
    d_model=D_MODEL,
    dim_feedforward=DIM_FEEDFORWARD,
    max_sequence=MAX_SEQUENCE,
    rpr=RPR
).to(get_device())

state_dict = torch.load(
    hf_hub_download(repo_id=REPO_ID, filename=FILENAME),
    map_location=get_device()
)

model.load_state_dict(state_dict)

def generate(input_midi):

    raw_mid = encode_midi(input_midi)
    if(len(raw_mid) == 0):
        return

    primer, _  = process_midi(raw_mid, NUM_PRIME, random_seq=False)
    primer = torch.tensor(primer, dtype=TORCH_LABEL_TYPE, device=get_device())

    # saves a pretty_midi at file_path
    # decode_midi(primer[:NUM_PRIME].cpu().numpy(), file_path=f_path)
    decode_midi(primer[:NUM_PRIME].cpu().numpy())

    # GENERATION
    model.eval()
    with torch.set_grad_enabled(False):

        # NOTE: model.generate() returns a MIDI stored as an ARRAY given a primer
        beam_seq = model.generate(primer[:NUM_PRIME], TARGET_SEQ_LENGTH, beam=BEAM)

        file_path = "output.mid"

        # NOTE: function decode_midi() returns an actual MIDI of class pretty_midi.PrettyMIDI
        decoded_midi = decode_midi(beam_seq[0].cpu().numpy(), file_path=file_path)

        # THIS SHOULD BE EITHER decoded_midi OR beam_seq
        # TODO: decoded_midi is actual pretty_midi MIDI file, beam_seq is just an array representing a MIDI
        # decoded_midi stores more information about instruments and stuff
        return file_path

def process_midi(raw_mid, max_seq, random_seq):
    """
    ----------
    Author: Damon Gwinn
    ----------
    Takes in pre-processed raw midi and returns the input and target. Can use a random sequence or
    go from the start based on random_seq.
    ----------
    """

    x   = torch.full((max_seq, ), TOKEN_PAD, dtype=TORCH_LABEL_TYPE, device=get_device())
    tgt = torch.full((max_seq, ), TOKEN_PAD, dtype=TORCH_LABEL_TYPE, device=get_device())

    raw_len     = len(raw_mid)
    full_seq    = max_seq + 1 # Performing seq2seq

    if(raw_len == 0):
        return x, tgt

    if(raw_len < full_seq):
        x[:raw_len]         = raw_mid
        tgt[:raw_len-1]     = raw_mid[1:]
        tgt[raw_len]        = TOKEN_END
    else:
        # Randomly selecting a range
        if(random_seq):
            end_range = raw_len - full_seq
            start = random.randint(SEQUENCE_START, end_range)

        # Always taking from the start to as far as we can
        else:
            start = SEQUENCE_START

        end = start + full_seq

        data = raw_mid[start:end]

        x = data[:max_seq]
        tgt = data[1:full_seq]

    return x, tgt


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column(scale=1):
            gr.Image(
                "https://www.ocf.berkeley.edu/~launchpad/media/uploads/project_logos/410912267_278779401866686_2517511436172822307_n_0iVwDxI.png",
                elem_id="logo-img",
                show_label=False,
                show_share_button=False,
                show_download_button=False,
                show_fullscreen_button=False,
            )

        with gr.Column(scale=3):
            gr.Markdown("""lofi-bytes is a [Launchpad](https://launchpad.studentorg.berkeley.edu/) project (Spring 2023) that generates lofi tracks from input MIDI stamples using a MusicTransformer model.
                        <br/><br/>
                        **Model**: [lofi-bytes](https://huggingface.co/Launchpad/lofi-bytes)
                        <br/>
                        **Project Leader**: Alicia Wang
                        <br/>
                        **Members**: Alena Chao, Eric Liu, Zane Mogannam, Chloe Wong, Iris Zhou
                        <br/>
                        **Advisors**: Vincent Lim, Winston Liu
                        <br/>
                        """
                        )
    gr.Interface(
        fn=generate,
        inputs=gr.File(),
        outputs=gr.File(),
        examples=["uploaded_midis/ghibli_castle_in_the_sky.mid", "uploaded_midis/am_i_blue_jazz.mid"]
    )

if __name__ == '__main__':
    demo.launch(share=True, show_error=True)