import sys sys.path.append( "/mnt/fast/nobackup/users/hl01486/projects/contrastive_pretraining/CLAP/src" ) import os import torch import librosa from open_clip import create_model from training.data import get_audio_features from training.data import int16_to_float32, float32_to_int16 from transformers import RobertaTokenizer tokenize = RobertaTokenizer.from_pretrained("roberta-base") def tokenizer(text): result = tokenize( text, padding="max_length", truncation=True, max_length=77, return_tensors="pt", ) return {k: v.squeeze(0) for k, v in result.items()} PRETRAINED_PATH = "/mnt/fast/nobackup/users/hl01486/projects/contrastive_pretraining/CLAP/assets/checkpoints/epoch_top_0_audioset_no_fusion.pt" WAVE_48k_PATH = "/mnt/fast/nobackup/users/hl01486/projects/contrastive_pretraining/CLAP/assets/audio/machine.wav" def infer_text(): device = "cuda:0" if torch.cuda.is_available() else "cpu" precision = "fp32" amodel = "HTSAT-tiny" # or 'PANN-14' tmodel = "roberta" # the best text encoder in our training enable_fusion = False # False if you do not want to use the fusion model fusion_type = "aff_2d" pretrained = PRETRAINED_PATH model, model_cfg = create_model( amodel, tmodel, pretrained, precision=precision, device=device, enable_fusion=enable_fusion, fusion_type=fusion_type, ) # load the text, can be a list (i.e. batch size) text_data = ["I love the contrastive learning", "I love the pretrain model"] # tokenize for roberta, if you want to tokenize for another text encoder, please refer to data.py#L43-90 text_data = tokenizer(text_data) text_embed = model.get_text_embedding(text_data) print(text_embed.size()) def infer_audio(): device = "cuda:0" if torch.cuda.is_available() else "cpu" precision = "fp32" amodel = "HTSAT-tiny" # or 'PANN-14' tmodel = "roberta" # the best text encoder in our training enable_fusion = False # False if you do not want to use the fusion model fusion_type = "aff_2d" pretrained = PRETRAINED_PATH model, model_cfg = create_model( amodel, tmodel, pretrained, precision=precision, device=device, enable_fusion=enable_fusion, fusion_type=fusion_type, ) # load the waveform of the shape (T,), should resample to 48000 audio_waveform, sr = librosa.load(WAVE_48k_PATH, sr=48000) # quantize audio_waveform = int16_to_float32(float32_to_int16(audio_waveform)) audio_waveform = torch.from_numpy(audio_waveform).float() audio_dict = {} # the 'fusion' truncate mode can be changed to 'rand_trunc' if run in unfusion mode import ipdb ipdb.set_trace() audio_dict = get_audio_features( audio_dict, audio_waveform, 480000, data_truncating="fusion", data_filling="repeatpad", audio_cfg=model_cfg["audio_cfg"], ) # can send a list to the model, to process many audio tracks in one time (i.e. batch size) audio_embed = model.get_audio_embedding([audio_dict]) print(audio_embed.size()) import ipdb ipdb.set_trace() if __name__ == "__main__": infer_text() infer_audio()