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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) |