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69c07f8 550f5fe 69c07f8 550f5fe 69c07f8 550f5fe 69c07f8 550f5fe 69c07f8 | 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 | import torch
import torch.nn as nn
from models.CLAP.open_clip import create_model
from transformers import RobertaTokenizer
# removed: import random, torchaudio, get_audio_features — only used by _get_audio_embed (audio modality), not needed for text inference
class CLAP_Encoder(nn.Module):
def __init__(
self,
pretrained_path='checkpoint/music_speech_audioset_epoch_15_esc_89.98.pt',
amodel = "HTSAT-base",
):
super().__init__()
self.device = "cpu"
self.precision = "fp32"
self.amodel = amodel # or 'PANN-14'
self.tmodel = "roberta" # the best text encoder in our training
self.enable_fusion = False # False if you do not want to use the fusion model
self.fusion_type = "aff_2d"
self.pretrained = pretrained_path
# removed: self.sampling_rate — only used by _get_audio_embed
self.tokenize = RobertaTokenizer.from_pretrained("roberta-base")
self.model, self.model_cfg = create_model(
self.amodel,
self.tmodel,
self.pretrained,
precision=self.precision,
device=self.device,
enable_fusion=self.enable_fusion,
fusion_type=self.fusion_type,
)
for p in self.model.parameters():
p.requires_grad = False
self.model.eval()
self.encoder_type = 'CLAP'
# removed: batch_to_list, _get_audio_embed — audio modality not used in inference
def _get_text_embed(self, batch):
double_batch = False
if len(batch) == 1:
batch = batch * 2
double_batch = True
with torch.no_grad():
# the 'fusion' truncate mode can be changed to 'rand_trunc' if run in unfusion mode
text_data = self.tokenizer(batch)
embed = self.model.get_text_embedding(text_data)
if double_batch:
embed = embed[0].unsqueeze(0)
return embed.detach()
def get_query_embed(self, modality, audio=None, text=None, use_text_ratio=0.5, device=None):
# removed: audio and hybird modality branches — only text modality used in inference
if modality == 'text':
embed = self._get_text_embed(text)
else:
raise NotImplementedError("Please check flag 'training_modality'.")
return embed.float()
def tokenizer(self, text):
result = self.tokenize(
text,
padding="max_length",
truncation=True,
max_length=512,
return_tensors="pt",
)
return {k: v.squeeze(0) for k, v in result.items()}
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