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import gradio as gr
import note_seq
import numpy as np
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TristanBehrens/js-fakes-4bars")
model = AutoModelForCausalLM.from_pretrained("TristanBehrens/js-fakes-4bars")

NOTE_LENGTH_16TH_120BPM = 0.25 * 60 / 120
BAR_LENGTH_120BPM = 4.0 * 60 / 120
SAMPLE_RATE=44100

def token_sequence_to_note_sequence(token_sequence, use_program=True, use_drums=True, instrument_mapper=None, only_piano=False):
    if isinstance(token_sequence, str):
        token_sequence = token_sequence.split()
    note_sequence = empty_note_sequence()

    # Render all notes.
    current_program = 1
    current_is_drum = False
    current_instrument = 0
    track_count = 0
    for token_index, token in enumerate(token_sequence):

        if token == "PIECE_START":
            pass
        elif token == "PIECE_END":
            print("The end.")
            break
        elif token == "TRACK_START":
            current_bar_index = 0
            track_count += 1
            pass
        elif token == "TRACK_END":
            pass
        elif token == "KEYS_START":
            pass
        elif token == "KEYS_END":
            pass
        elif token.startswith("KEY="):
            pass
        elif token.startswith("INST"):
            instrument = token.split("=")[-1]
            if instrument != "DRUMS" and use_program:
                if instrument_mapper is not None:
                    if instrument in instrument_mapper:
                        instrument = instrument_mapper[instrument]
                current_program = int(instrument)
                current_instrument = track_count
                current_is_drum = False
            if instrument == "DRUMS" and use_drums:
                current_instrument = 0
                current_program = 0
                current_is_drum = True
        elif token == "BAR_START":
            current_time = current_bar_index * BAR_LENGTH_120BPM
            current_notes = {}
        elif token == "BAR_END":
            current_bar_index += 1
            pass
        elif token.startswith("NOTE_ON"):
            pitch = int(token.split("=")[-1])
            note = note_sequence.notes.add()
            note.start_time = current_time
            note.end_time = current_time + 4 * NOTE_LENGTH_16TH_120BPM
            note.pitch = pitch
            note.instrument = current_instrument
            note.program = current_program
            note.velocity = 80
            note.is_drum = current_is_drum
            current_notes[pitch] = note
        elif token.startswith("NOTE_OFF"):
            pitch = int(token.split("=")[-1])
            if pitch in current_notes:
                note = current_notes[pitch]
                note.end_time = current_time
        elif token.startswith("TIME_DELTA"):
            delta = float(token.split("=")[-1]) * NOTE_LENGTH_16TH_120BPM
            current_time += delta
        elif token.startswith("DENSITY="):
            pass
        elif token == "[PAD]":
            pass
        else:
            #print(f"Ignored token {token}.")
            pass

    # Make the instruments right.
    instruments_drums = []
    for note in note_sequence.notes:
        pair = [note.program, note.is_drum]
        if pair not in instruments_drums:
            instruments_drums += [pair]
        note.instrument = instruments_drums.index(pair)

    if only_piano:
        for note in note_sequence.notes:
            if not note.is_drum:
                note.instrument = 0
                note.program = 0

    return note_sequence

def empty_note_sequence(qpm=120.0, total_time=0.0):
    note_sequence = note_seq.protobuf.music_pb2.NoteSequence()
    note_sequence.tempos.add().qpm = qpm
    note_sequence.ticks_per_quarter = note_seq.constants.STANDARD_PPQ
    note_sequence.total_time = total_time
    return note_sequence

def process(text):
  input_ids = tokenizer.encode(text, return_tensors="pt")
  generated_ids = model.generate(input_ids, max_length=500)
  generated_sequence = tokenizer.decode(generated_ids[0])

  # Convert text of notes to audio
  note_sequence = token_sequence_to_note_sequence(generated_sequence)
  synth = note_seq.midi_synth.synthesize
  array_of_floats = synth(note_sequence, sample_rate=SAMPLE_RATE)
  note_plot = note_seq.plot_sequence(note_sequence, False)
  array_of_floats /=1.414
  array_of_floats *= 32767
  int16_data = array_of_floats.astype(np.int16)
  return SAMPLE_RATE, int16_data
  
title = "Music generation with GPT-2"

iface = gr.Interface(
  fn=process, 
  inputs=[gr.inputs.Textbox(default="PIECE_START")],
  outputs=['audio'],
  title=title,
  examples=[["PIECE_START"], ["PIECE_START STYLE=JSFAKES GENRE=JSFAKES TRACK_START INST=48 BAR_START NOTE_ON=61"]],
  article="This demo is inspired in the notebook from https://huggingface.co/TristanBehrens/js-fakes-4bars"
)

iface.launch(debug=True)