Update audioldm_train/conditional_models.py

audioldm_train/conditional_models.py

@@ -4,8 +4,8 @@ sys.path.append("src")
 import torch
 import logging
 import torch.nn as nn
-from audioldm_train.modules.clap.open_clip import create_model
-from audioldm_train.modules.clap.training.data import get_audio_features
+from qa_mdt.audioldm_train.modules.clap.open_clip import create_model
+from qa_mdt.audioldm_train.modules.clap.training.data import get_audio_features
 
 import torchaudio
 from transformers import (
@@ -15,18 +15,18 @@ from transformers import (
     MT5EncoderModel,
 )
 import torch.nn.functional as F
-from audioldm_train.modules.audiomae.AudioMAE import Vanilla_AudioMAE
-from audioldm_train.modules.phoneme_encoder.encoder import TextEncoder
+from qa_mdt.audioldm_train.modules.audiomae.AudioMAE import Vanilla_AudioMAE
+from qa_mdt.audioldm_train.modules.phoneme_encoder.encoder import TextEncoder
 
 from transformers import SpeechT5Processor, AutoTokenizer, GPT2Model, GPT2Tokenizer
 from transformers.models.speecht5.modeling_speecht5 import SpeechT5EncoderWithTextPrenet
 
-from audioldm_train.modules.audiomae.sequence_gen.model import CLAP2AudioMAE
-from audioldm_train.modules.audiomae.sequence_gen.sequence_input import (
+from qa_mdt.audioldm_train.modules.audiomae.sequence_gen.model import CLAP2AudioMAE
+from qa_mdt.audioldm_train.modules.audiomae.sequence_gen.sequence_input import (
     Sequence2AudioMAE,
 )
 import numpy as np
-from audioldm_train.modules.audiomae.sequence_gen.model import Prenet
+from qa_mdt.audioldm_train.modules.audiomae.sequence_gen.model import Prenet
 import json 
 with open('offset_pretrained_checkpoints.json', 'r') as config_file:
     config_data = json.load(config_file)