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""" Code by Nathan Fradet https://github.com/Natooz, reworked by Adam Łukawski https://github.com/sunsetsobserver """
from copy import deepcopy
from pathlib import Path
from random import shuffle
from torch import Tensor, argmax
from torch.utils.data import DataLoader
from torch.cuda import is_available as cuda_available, is_bf16_supported
from torch.backends.mps import is_available as mps_available
from transformers import AutoModelForCausalLM, MistralConfig, Trainer, TrainingArguments, GenerationConfig, AutoTokenizer, AutoModel
from transformers.trainer_utils import set_seed
from evaluate import load as load_metric
from miditok import REMI, TokenizerConfig
from miditok.pytorch_data import DatasetTok, DataCollator
from tqdm import tqdm
# Seed
set_seed(777)
# Creates the tokenizer
tokenizer = AutoTokenizer.from_pretrained("tokenizer")
# Trains the tokenizer with Byte Pair Encoding (BPE) to build the vocabulary, here 10k tokens
midi_paths = list(Path('Maestro').glob('**/*.mid')) + list(Path('Maestro').glob('**/*.midi'))
# Split MIDI paths in train/valid/test sets
total_num_files = len(midi_paths)
num_files_valid = round(total_num_files * 0.2)
num_files_test = round(total_num_files * 0.1)
shuffle(midi_paths)
midi_paths_test = midi_paths[num_files_valid:num_files_valid + num_files_test]
# Loads tokens and create data collator
kwargs_dataset = {"min_seq_len": 256, "max_seq_len": 1024, "tokenizer": tokenizer}
dataset_test = DatasetTok(midi_paths_test, **kwargs_dataset)
collator = DataCollator(
tokenizer["PAD_None"], tokenizer["BOS_None"], tokenizer["EOS_None"]
)
# Creates model using the correct configuration
model = AutoModel.from_pretrained("./runs/model.safetensors")
collator = DataCollator(tokenizer["PAD_None"], tokenizer["BOS_None"], tokenizer["EOS_None"], copy_inputs_as_labels=True)
(gen_results_path := Path('gen_res')).mkdir(parents=True, exist_ok=True)
generation_config = GenerationConfig(
max_new_tokens=512, # extends samples by 512 tokens
num_beams=1, # no beam search
do_sample=True, # but sample instead
temperature=0.9,
top_k=15,
top_p=0.95,
epsilon_cutoff=3e-4,
eta_cutoff=1e-3,
pad_token_id=tokenizer.padding_token_id,
)
# Here the sequences are padded to the left, so that the last token along the time dimension
# is always the last token of each seq, allowing to efficiently generate by batch
collator.pad_on_left = True
collator.eos_token = None
dataloader_test = DataLoader(dataset_test, batch_size=16, collate_fn=collator)
model.eval()
count = 0
for batch in tqdm(dataloader_test, desc='Testing model / Generating results'): # (N,T)
res = model.generate(
inputs=batch["input_ids"].to(model.device),
attention_mask=batch["attention_mask"].to(model.device),
generation_config=generation_config) # (N,T)
# Saves the generated music, as MIDI files and tokens (json)
for prompt, continuation in zip(batch["input_ids"], res):
generated = continuation[len(prompt):]
midi = tokenizer.tokens_to_midi([deepcopy(generated.tolist())])
tokens = [generated, prompt, continuation] # list compr. as seqs of dif. lengths
tokens = [seq.tolist() for seq in tokens]
for tok_seq in tokens[1:]:
_midi = tokenizer.tokens_to_midi([deepcopy(tok_seq)])
midi.instruments.append(_midi.instruments[0])
midi.instruments[0].name = f'Continuation of original sample ({len(generated)} tokens)'
midi.instruments[1].name = f'Original sample ({len(prompt)} tokens)'
midi.instruments[2].name = f'Original sample and continuation'
midi.dump_midi(gen_results_path / f'{count}.mid')
tokenizer.save_tokens(tokens, gen_results_path / f'{count}.json')
count += 1 |