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3b71bd4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | # from transformers import Trainer
# import wandb
# import note_seq
# from utils import token_sequence_to_note_sequence
# # first create a custom trainer to log prediction distribution
# SAMPLE_RATE = 44100
# class CustomTrainer(Trainer):
# def __init__(self, *args, **kwargs):
# super().__init__(*args, **kwargs)
# def evaluation_loop(
# self,
# dataloader,
# description,
# prediction_loss_only=None,
# ignore_keys=None,
# metric_key_prefix="eval",
# ):
# # call super class method to get the eval outputs
# eval_output = super().evaluation_loop(
# dataloader,
# description,
# prediction_loss_only,
# ignore_keys,
# metric_key_prefix,
# )
# # log the prediction distribution using `wandb.Histogram` method.
# if wandb.run is not None:
# input_ids = self.tokenizer.encode(
# "PIECE_START",
# return_tensors="pt",
# ).cuda()
# # Generate more tokens.
# voice1_generated_ids = self.model.generate(
# input_ids,
# max_new_tokens=512,
# do_sample=True,
# temperature=0.75,
# eos_token_id=self.tokenizer.encode("TRACK_END")[0],
# )
# voice2_generated_ids = self.model.generate(
# voice1_generated_ids,
# max_new_tokens=512,
# do_sample=True,
# temperature=0.75,
# eos_token_id=self.tokenizer.encode("TRACK_END")[0],
# )
# voice3_generated_ids = self.model.generate(
# voice2_generated_ids,
# max_new_tokens=512,
# do_sample=True,
# temperature=0.75,
# eos_token_id=self.tokenizer.encode("TRACK_END")[0],
# )
# voice4_generated_ids = self.model.generate(
# voice3_generated_ids,
# max_new_tokens=512,
# do_sample=True,
# temperature=0.75,
# eos_token_id=self.tokenizer.encode("TRACK_END")[0],
# )
# token_sequence = self.tokenizer.decode(voice4_generated_ids[0])
# note_sequence = token_sequence_to_note_sequence(token_sequence)
# synth = note_seq.fluidsynth
# array_of_floats = synth(note_sequence, sample_rate=SAMPLE_RATE)
# int16_data = note_seq.audio_io.float_samples_to_int16(array_of_floats)
# wandb.log({"Generated_audio": wandb.Audio(int16_data, SAMPLE_RATE)})
# return eval_output
import torch
from typing import Tuple
from transformers import AutoTokenizer, AutoModelForCausalLM
# Initialize the model and tokenizer variables as None
tokenizer = None
model = None
def get_model_and_tokenizer() -> Tuple[AutoModelForCausalLM, AutoTokenizer]:
"""
Returns the preloaded model and tokenizer. If they haven't been loaded before, loads them.
Returns:
tuple: A tuple containing the preloaded model and tokenizer.
"""
global model, tokenizer
if model is None or tokenizer is None:
# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained("juancopi81/lmd_8bars_tokenizer")
model = AutoModelForCausalLM.from_pretrained(
"juancopi81/lmd-8bars-2048-epochs40_v4"
)
# Move model to device
model = model.to(device)
return model, tokenizer |