Add generate only continuation

gen_res/0.json

@@ -1 +0,0 @@
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generate_on_one_track.py

@@ -0,0 +1,105 @@
+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, MistralForCausalLM
+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
+
+# Our tokenizer's configuration
+PITCH_RANGE = (21, 109)
+BEAT_RES = {(0, 1): 8, (1, 2): 4, (2, 4): 2, (4, 8): 1}
+NUM_VELOCITIES = 24
+SPECIAL_TOKENS = ["PAD", "MASK", "BOS", "EOS"]
+USE_CHORDS = False
+USE_RESTS = False
+USE_TEMPOS = True
+USE_TIME_SIGNATURE = False
+USE_PROGRAMS = False
+NUM_TEMPOS = 32
+TEMPO_RANGE = (50, 200)  # (min_tempo, max_tempo)
+TOKENIZER_PARAMS = {
+    "pitch_range": PITCH_RANGE,
+    "beat_res": BEAT_RES,
+    "num_velocities": NUM_VELOCITIES,
+    "special_tokens": SPECIAL_TOKENS,
+    "use_chords": USE_CHORDS,
+    "use_rests": USE_RESTS,
+    "use_tempos": USE_TEMPOS,
+    "use_time_signatures": USE_TIME_SIGNATURE,
+    "use_programs": USE_PROGRAMS,
+    "num_tempos": NUM_TEMPOS,
+    "tempo_range": TEMPO_RANGE,
+}
+config = TokenizerConfig(**TOKENIZER_PARAMS)
+
+# Seed
+set_seed(777)
+
+# Creates the tokenizer
+tokenizer = REMI.from_pretrained("sunsetsobserver/MIDI")
+
+midi_paths = list(Path('input').glob('**/*.mid')) + list(Path('input').glob('**/*.midi'))
+
+""" list(Path('Maestro').glob('**/*.mid')) + list(Path('Maestro').glob('**/*.midi')) """
+
+# Loads tokens and create data collator
+kwargs_dataset = {"min_seq_len": 10, "max_seq_len": 1024, "tokenizer": tokenizer}
+dataset_test = DatasetTok(midi_paths, **kwargs_dataset)
+collator = DataCollator(
+    tokenizer["PAD_None"], tokenizer["BOS_None"], tokenizer["EOS_None"]
+)
+
+# Creates model using the correct configuration
+model = MistralForCausalLM.from_pretrained("./runs")
+
+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,
+)
+
+# 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=1, 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):
+        # Generate the MIDI for the entire sequence (prompt + continuation)
+        midi = tokenizer.tokens_to_midi([deepcopy(continuation.tolist())])
+
+        # Set the track name to indicate it includes both the original and the continuation
+        midi.tracks[0].name = f'Original sample and continuation ({len(continuation)} tokens)'
+
+        # Dump the MIDI file for the combined prompt and continuation
+        midi.dump_midi(gen_results_path / f'{count}.mid')
+
+        # Optionally, save the tokens for the combined sequence
+        tokens = [continuation.tolist()]  # This time, only saving the combined sequence
+        tokenizer.save_tokens(tokens, gen_results_path / f'{count}.json')
+
+        count += 1