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"""
Contrastive Language-Audio Pretraining Model from LAION
--------------------------------------------------------
Paper: https://arxiv.org/abs/2211.06687
Authors (equal contributions): Ke Chen, Yusong Wu, Tianyu Zhang, Yuchen Hui
Support: LAION
"""
import os
import torch
import librosa
from clap_module import create_model
from training.data import get_audio_features
from training.data import int16_to_float32, float32_to_int16
from transformers import RobertaTokenizer
import wget
from clap_module.factory import load_state_dict
class CLAP_Module(torch.nn.Module):
def __init__(self, enable_fusion=False, device=None, amodel='HTSAT-tiny', tmodel='roberta') -> None:
"""Initialize CLAP Model
Parameters
----------
enable_fusion: bool
if true, it will create the fusion clap model, otherwise non-fusion clap model (default: false)
device: str
if None, it will automatically detect the device (gpu or cpu)
amodel: str
audio encoder architecture, default: HTSAT-tiny
tmodel: str
text encoder architecture, default: roberta
"""
super(CLAP_Module, self).__init__()
if device is None:
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
precision = 'fp32'
if enable_fusion:
fusion_type = 'aff_2d'
model, model_cfg = create_model(
amodel,
tmodel,
precision=precision,
device=device,
enable_fusion=enable_fusion,
fusion_type=fusion_type
)
else:
model, model_cfg = create_model(
amodel,
tmodel,
precision=precision,
device=device,
enable_fusion=enable_fusion
)
self.enable_fusion = enable_fusion
self.model = model
self.model_cfg = model_cfg
self.tokenize = RobertaTokenizer.from_pretrained('roberta-base')
def tokenizer(self, text):
result = self.tokenize(
text,
padding="max_length",
truncation=True,
max_length=77,
return_tensors="pt",
)
return result
def load_ckpt(self, ckpt = None, model_id = -1, verbose = True):
"""Load the pretrained checkpoint of CLAP model
Parameters
----------
ckpt: str
if ckpt is specified, the model will load this ckpt, otherwise the model will download the ckpt from zenodo. \n
For fusion model, it will download the 630k+audioset fusion model (id=3). For non-fusion model, it will download the 630k+audioset model (id=1).
model_id:
if model_id is specified, you can download our best ckpt, as:
id = 0 --> 630k non-fusion ckpt \n
id = 1 --> 630k+audioset non-fusion ckpt \n
id = 2 --> 630k fusion ckpt \n
id = 3 --> 630k+audioset fusion ckpt \n
Note that if your model is specied as non-fusion model but you download a fusion model ckpt, you will face an error.
"""
download_link = 'https://huggingface.co/lukewys/laion_clap/resolve/main/'
download_names = [
'630k-best.pt',
'630k-audioset-best.pt',
'630k-fusion-best.pt',
'630k-audioset-fusion-best.pt'
]
if ckpt is not None:
print(f'Load the specified checkpoint {ckpt} from users.')
else:
print(f'Load our best checkpoint in the paper.')
if model_id == -1:
model_id = 3 if self.enable_fusion else 1
package_dir = os.path.dirname(os.path.realpath(__file__))
weight_file_name = download_names[model_id]
ckpt = os.path.join(package_dir, weight_file_name)
if os.path.exists(ckpt):
print(f'The checkpoint is already downloaded')
else:
print('Downloading laion_clap weight files...')
ckpt = wget.download(download_link + weight_file_name, os.path.dirname(ckpt))
print('Download completed!')
print('Load Checkpoint...')
ckpt = load_state_dict(ckpt, skip_params=True)
self.model.load_state_dict(ckpt)
if verbose:
param_names = [n for n, p in self.model.named_parameters()]
for n in param_names:
print(n, "\t", "Loaded" if n in ckpt else "Unloaded")
def get_audio_embedding_from_filelist(self, x, use_tensor=False):
"""get audio embeddings from the audio file list
Parameters
----------
x: List[str] (N,):
an audio file list to extract features, audio files can have different lengths (as we have the feature fusion machanism)
use_tensor: boolean:
if True, it will return the torch tensor, preserving the gradient (default: False).
Returns
----------
audio_embed : numpy.darray | torch.Tensor (N,D):
audio embeddings that extracted from audio files
"""
self.model.eval()
audio_input = []
for f in x:
# load the waveform of the shape (T,), should resample to 48000
audio_waveform, _ = librosa.load(f, sr=48000)
# quantize
audio_waveform = int16_to_float32(float32_to_int16(audio_waveform))
audio_waveform = torch.from_numpy(audio_waveform).float()
temp_dict = {}
temp_dict = get_audio_features(
temp_dict, audio_waveform, 480000,
data_truncating='fusion' if self.enable_fusion else 'rand_trunc',
data_filling='repeatpad',
audio_cfg=self.model_cfg['audio_cfg'],
require_grad=audio_waveform.requires_grad
)
audio_input.append(temp_dict)
audio_embed = self.model.get_audio_embedding(audio_input)
if not use_tensor:
audio_embed = audio_embed.detach().cpu().numpy()
return audio_embed
def get_audio_embedding_from_data(self, x, use_tensor=False):
"""get audio embeddings from the audio data
Parameters
----------
x: np.darray | torch.Tensor (N,T):
audio data, must be mono audio tracks.
use_tensor: boolean:
if True, x should be the tensor input and the output will be the tesnor, preserving the gradient (default: False).
Note that if 'use tensor' is set to True, it will not do the quantize of the audio waveform (otherwise the gradient will not be preserved).
Returns
----------
audio embed: numpy.darray | torch.Tensor (N,D):
audio embeddings that extracted from audio files
"""
self.model.eval()
audio_input = []
for audio_waveform in x:
# quantize
if not use_tensor:
audio_waveform = int16_to_float32(float32_to_int16(audio_waveform))
audio_waveform = torch.from_numpy(audio_waveform).float()
temp_dict = {}
temp_dict = get_audio_features(
temp_dict, audio_waveform, 480000,
data_truncating='fusion' if self.enable_fusion else 'rand_trunc',
data_filling='repeatpad',
audio_cfg=self.model_cfg['audio_cfg'],
require_grad=audio_waveform.requires_grad
)
audio_input.append(temp_dict)
audio_embed = self.model.get_audio_embedding(audio_input)
if not use_tensor:
audio_embed = audio_embed.detach().cpu().numpy()
return audio_embed
def get_text_embedding(self, x, tokenizer = None, use_tensor = False):
"""get text embeddings from texts
Parameters
----------
x: List[str] (N,):
text list
tokenizer: func:
the tokenizer function, if not provided (None), will use the default Roberta tokenizer.
use_tensor: boolean:
if True, the output will be the tesnor, preserving the gradient (default: False).
Returns
----------
text_embed : numpy.darray | torch.Tensor (N,D):
text embeddings that extracted from texts
"""
self.model.eval()
if tokenizer is not None:
text_input = tokenizer(x)
else:
text_input = self.tokenizer(x)
text_embed = self.model.get_text_embedding(text_input)
if not use_tensor:
text_embed = text_embed.detach().cpu().numpy()
return text_embed
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