app.py

# -*- coding: utf-8 -*-
"""loop_ downloadOnly_MidiMusicGenApp.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1gFFJW56DAbeLYqKWqi6jZN_einOQBf4j
"""

import gradio as gr
import torch
import gc
from transformers import GPT2LMHeadModel
from miditokenizer import MIDITokenizer
from genprocessor import GENProcessor, generated_tokens_to_midi
from midi2audio import FluidSynth
from pydub import AudioSegment
import tempfile
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"

# Load model and tokenizer

torch.serialization.add_safe_globals([set])
torch.serialization.add_safe_globals([GPT2LMHeadModel])
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = torch.load('model_complete_18epochs.pkl',map_location=device, weights_only=False)
tokenizer = MIDITokenizer()
processor = GENProcessor()
model.eval()

class State:
    def __init__(self):
        self.generated_text = None  # Store the text representation of the music

state = State()

#functions to adjust timing & combine generated song parts
def adjust_midi_timing(midi_data, start_time=0):
   """Adjust MIDI timing with optional start time. Prevent large gaps based on ticks_per_beat."""
   try:
       # Keep tempo track separate
       tempo_track = midi_data['tracks'][0]
       ticks_per_beat = midi_data['metadata']['ticks_per_beat']

       # Calculate thresholds based on ticks_per_beat
       gap_threshold = ticks_per_beat * 2
       small_increment = ticks_per_beat // 8  # Eighth note

       # Get all other events and sort by time
       all_events = []
       for track in midi_data['tracks'][1:]:
           all_events.extend(track)
       all_events.sort(key=lambda x: x['time'])

       # Find sequential times, ignoring large gaps
       sequential_events = []
       current_time = all_events[0]['time'] if all_events else 0

       for event in all_events:
           if event['time'] - current_time > gap_threshold:
               event['time'] = current_time + small_increment
           current_time = event['time']
           sequential_events.append(event)

       # Find first non-zero time
       first_time = min((event['time'] for event in sequential_events if event['time'] != 0), default=0)

       adjusted_data = {'metadata': midi_data['metadata'], 'tracks': [tempo_track]}

       # Adjust all events
       adjusted_track = []
       for event in sequential_events:
           adjusted_event = event.copy()
           if event['time'] != 0:
               adjusted_event['time'] = (event['time'] - first_time) + start_time
           else:
               adjusted_event['time'] = start_time
           adjusted_track.append(adjusted_event)

       adjusted_data['tracks'].append(adjusted_track)
       return adjusted_data

   except Exception as e:
       print(f"Error adjusting MIDI timing: {str(e)}")
       return midi_data

def combine_tracks(first, continued):
    """Combine two generated sequences into a single song"""

    processor = GENProcessor()

    # Check if first input is already decoded or needs decoding
    if isinstance(first, str):
        gendecoded = processor.decode_midi_file(first)
    else:
        gendecoded = first
    first_midi = adjust_midi_timing(gendecoded, start_time=0)

    # Get last time from the single note track
    last_time = max(event['time'] for event in first_midi['tracks'][1])

    # adjust timing of second midi
    second_midi = adjust_midi_timing(processor.decode_midi_file(continued), start_time=last_time)

    # Combine into a single song
    full_song = {
        'metadata': first_midi['metadata'],
        'tracks': [
            first_midi['tracks'][0],  # Keep tempo track
            first_midi['tracks'][1] + second_midi['tracks'][1]  # Combine note tracks
        ]
    }
    return full_song

def extract_context(text_or_midi, num_events=3):
    """get metadata, composer, and last few events from generated text to create new prompt to continue sequence"""
    result_metadata = None
    if isinstance(text_or_midi, dict):
        # Extract metadata from MIDI structure
        ticks = text_or_midi['metadata']['ticks_per_beat']
        numerator = text_or_midi['tracks'][0][1]['numerator']
        composer = text_or_midi.get('composer', 'Bach') #default to Bach if missing

        # Get last events from track
        all_events = text_or_midi['tracks'][1]
        sorted_events = sorted(all_events, key=lambda x: x['time'])
        last_events = sorted_events[-num_events:]

        # Format metadata string
        result_metadata = (f"<|START_METADATA|> <|composer_{composer}|><metadata> "
                         f"ticks_per_beat={ticks} <|START_TRACK|> "
                         f"tempo=500000 <time_signature> time=0 numerator={numerator} denominator=4")

        # Format context string from last events
        context = " ".join(f"<{event['type']}> time={event['time']} channel={event['channel']} " +
                         (f"note={event['note']} velocity={event['velocity']}" if 'note' in event else
                          f"control={event['control']} value={event['value']}")
                         for event in last_events)

        last_time = last_events[-1]['time'] if last_events else 0

    else:  # Input is string
        if "<|START_METADATA|>" in text_or_midi:
            composer_match = re.search(r"<\|composer_([^|]+)\|>", text_or_midi)
            ticks_match = re.search(r"ticks[_]?(?:per_)?beat=(\d+)", text_or_midi)
            time_sig_match = re.search(r"numerator=(\d+)", text_or_midi)

            if composer_match and ticks_match:
                composer = composer_match.group(1)
                numerator = 4
                if time_sig_match:
                    try:
                        num = int(time_sig_match.group(1))
                        numerator = min(4, max(2, num))
                    except ValueError:
                        pass

                result_metadata = (f"<|START_METADATA|> <|composer_{composer}|><metadata> "
                                 f"ticks_per_beat={max(75, int(ticks_match.group(1)))} <|START_TRACK|> "
                                 f"tempo=500000 <time_signature> time=0 numerator={numerator} denominator=4")

        # Get last N complete events and timestamp
        events = []
        matches = re.finditer(r"<(note_on|control_change|note_off)>.*?(value=\d+|velocity=\d+)", text_or_midi)
        events = list(matches)[-num_events:]
        context = " ".join(event.group(0) for event in events)

        last_time = None
        time_match = re.search(r"time=(\d+)", context)
        if time_match:
            last_time = int(time_match.group(1))

    return result_metadata, context, last_time

def continue_sequence(generated_text, num_loops=1):
   """continue the sequence, extract info from previous sequence to create prompt, append to prevous sequence"""
   full_song = generated_text

   for i in range(num_loops):
       print(f"Generating loop {i+1}/{num_loops}")
       metadata, context, last_time = extract_context(full_song)
       print(metadata+context)
       continued = generate_music(metadata+context)
       full_song = combine_tracks(full_song, continued)

   return full_song

#Functions to generate music

def generate_music(prompt):
    """Generate music based on a given prompt."""
    # Tokenize
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token

    inputs = tokenizer(
        prompt,
        return_tensors="pt",
        padding=True,
        truncation=True,
        add_special_tokens=True
    )

    # Generate
    output_sequences = model.generate(
        input_ids=inputs["input_ids"].to(model.device),
        attention_mask=inputs["attention_mask"].to(model.device),
        max_length=1024,
        do_sample=True,
        temperature=0.6, #adjust creativity
        top_k=30,
        top_p=0.90,
        pad_token_id=tokenizer.eos_token_id,
        eos_token_id=tokenizer.eos_token_id,
    )

    # Decode the generated sequence
    generated_text = tokenizer.decode(output_sequences[0])

    return generated_text

def generate_wrapper(composer):
   try:
       # Clear memory before generation
       gc.collect()
       if torch.cuda.is_available():
           torch.cuda.empty_cache()
       # Format the prompt with the selected composer
       prompt = f"<|START_METADATA|> <|composer_{composer}|><metadata> ticks_per_beat="
       generated_text = generate_music(prompt)
       midi_data = adjust_midi_timing(processor.decode_midi_file(generated_text))
       state.generated_text = midi_data
       # Create temp file for MIDI
       with tempfile.NamedTemporaryFile(suffix='.mid', delete=False) as tmp:
           generated_tokens_to_midi(midi_data, tmp.name)
           return tmp.name, gr.update(visible=True)

   finally:
       # Clear memory after generation
       gc.collect()
       if torch.cuda.is_available():
           torch.cuda.empty_cache()

def continue_wrapper():
    """Wrapper for continue_sequence"""
    if state.generated_text is not None:
        # Continue the sequence using your existing function
        extended_text = continue_sequence(state.generated_text, num_loops=8)
        state.generated_text = extended_text  # Update the stored text

         # Create temp file for MIDI
    with tempfile.NamedTemporaryFile(suffix='.mid', delete=False) as tmp:
        generated_tokens_to_midi(extended_text, tmp.name)
        return tmp.name

    return None

with gr.Blocks() as iface:
    gr.Markdown("""
    # MAI: MIDI AI Music Generation Model
    Select a composer whose musical style you'd like to emulate. Generate an original sequence inspired by that composer's unique sound.
    It should take a few minutes. Once it's ready, you can download the audio file.

    If you like the opening, you can continue the sequence and make your song longer, repeat, or try again. It may take a few minutes to continue the sequence.
    """)

    with gr.Column():
        composer_input = gr.Dropdown(
            choices=["Bach", "Chopin"],
            label="Select Composer",
            value="Bach"
        )

        generate_btn = gr.Button("Generate Music")
        output_file = gr.File(label="Generated MIDI File")
        continue_btn = gr.Button("Continue Sequence (add 10 seconds)", visible=False)

        #generate_btn.click(
        #    fn=generate_wrapper,
        #    inputs=composer_input,
        #    outputs=[output_file, continue_btn]
        #)
        generate_btn.click(
            lambda: (None, gr.update(visible=False)),
            None,
            [output_file, continue_btn],
            queue=False
        ).success(
            generate_wrapper,
            inputs=composer_input,
            outputs=[output_file, continue_btn]
        )

        continue_btn.click(
            fn=continue_wrapper,
            outputs=output_file
        )

iface.launch()