app.py

import os.path

import time as reqtime
import datetime
from pytz import timezone

import torch

import spaces
import gradio as gr

import random
import tqdm

from midi_to_colab_audio import midi_to_colab_audio
import TMIDIX

import matplotlib.pyplot as plt

from inference import PianoTranscription
from config import sample_rate
from utilities import load_audio

in_space = os.getenv("SYSTEM") == "spaces"
         
# =================================================================================================
                       
@spaces.GPU
def TranscribePianoAudio(input_audio):
    print('=' * 70)
    print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
    start_time = reqtime.time()
    print('=' * 70)

    f = input_audio.name
    fn = os.path.basename(f)
    fn1 = fn.split('.')[0]

    print('-' * 70)
    print('Input file name:', fn)
    print('-' * 70)
    print('Loading audio...')
    
    # Load audio
    (audio, _) = load_audio(f, sr=sample_rate, mono=True)
    print('Done!')
    print('-' * 70)
    print('Loading transcriptor..')
          
    # Transcriptor
    transcriptor = PianoTranscription(device='cuda')    # 'cuda' | 'cpu'
    print('Done!')
    print('-' * 70)
    print('Transcribing...')
    
    transcribed_dict = transcriptor.transcribe(audio, fn+'.mid')
    print('Done!')
    print('-' * 70)

    #===============================================================================
    raw_score = TMIDIX.midi2single_track_ms_score(fn+'.mid')
    
    #===============================================================================
    # Enhanced score notes
    
    escore = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True)[0]
    
    #==================================================================

    print('=' * 70)
    print('Number of transcribed notes:', len(escore))
    print('Sample trascribed MIDI events', escore[:5])
    print('=' * 70)
    print('Done!')
    print('=' * 70)
    
    #===============================================================================
    print('Rendering results...')
    
    patches = [0] * 16
   
    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(escore,
                                                              output_signature = 'ByteDance Solo Piano Audio to MIDI Transcription',
                                                              output_file_name = fn1,
                                                              track_name='Project Los Angeles',
                                                              list_of_MIDI_patches=patches
                                                              )
    print('=' * 70)
    new_fn = fn1+'.mid'
            
    
    audio = midi_to_colab_audio(new_fn, 
                        soundfont_path=soundfont,
                        sample_rate=16000,
                        volume_scale=10,
                        output_for_gradio=True
                        )
    
    print('Done!')
    print('=' * 70)

    #========================================================

    output_midi_title = str(fn1)
    output_midi_summary = str(song_f[:3])
    output_midi = str(new_fn)
    output_audio = (16000, audio)
    
    output_plot = TMIDIX.plot_ms_SONG(song_f, plot_title=output_midi, return_plt=True)

    print('Output MIDI file name:', output_midi)
    print('Output MIDI title:', output_midi_title)
    print('Output MIDI summary:', output_midi_summary)
    print('=' * 70) 
    

    #========================================================
    
    print('-' * 70)
    print('Req end time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
    print('-' * 70)
    print('Req execution time:', (reqtime.time() - start_time), 'sec')

    return output_midi_title, output_midi_summary, output_midi, output_audio, output_plot

# =================================================================================================

if __name__ == "__main__":
    
    PDT = timezone('US/Pacific')
    
    print('=' * 70)
    print('App start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
    print('=' * 70)

    soundfont = "SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2"
   
    app = gr.Blocks()
    with app:
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>ByteDance Solo Piano Audio to MIDI Transcription</h1>")
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Transcribe any Solo Piano WAV or MP3 audio to MIDI</h1>")
        gr.Markdown(
            "![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.ByteDance-Solo-Piano-Adio-to-MIDI-Transcription&style=flat)\n\n"
            "This is a ByteDance Solo Piano Audio to MIDI Transcription Model\n\n"
            "Check out [ByteDance Solo Piano Audio to MIDI Transcription](https://github.com/asigalov61/piano_transcription_inference) on GitHub!\n\n"
            "[Open In Colab]"
            "(https://colab.research.google.com/github/asigalov61/tegridy-tools/blob/main/tegridy-tools/notebooks/ByteDance_Piano_Transcription.ipynb)"
            " for faster execution and endless transcription"
        )
        gr.Markdown("## Upload your Solo Piano WAV or MP3 audio or select a sample example audio file")
        
        input_audio = gr.File(label="Input Solo Piano WAV or MP3 Audio File", file_types=[".wav", ".mp3"])
        
        run_btn = gr.Button("transcribe", variant="primary")

        gr.Markdown("## Generation results")

        output_midi_title = gr.Textbox(label="Output MIDI title")
        output_midi_summary = gr.Textbox(label="Output MIDI summary")
        output_audio = gr.Audio(label="Output MIDI audio", format="wav", elem_id="midi_audio")
        output_plot = gr.Plot(label="Output MIDI score plot")
        output_midi = gr.File(label="Output MIDI file", file_types=[".mid"])


        run_event = run_btn.click(TranscribePianoAudio, [input_audio],
                                  [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot])

        gr.Examples(
            ["cut_liszt.mp3"
            ],
            [input_audio],
            [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot],
            TranscribePianoAudio,
            cache_examples=True,
        )
        
        app.queue().launch()