[U] Bump version and deploy to pypi

Remove torchvision dependency and solve pytorch vulnerability

.gitignore

@@ -348,3 +348,9 @@ MigrationBackup/
 
 # Ionide (cross platform F# VS Code tools) working folder
 .ionide/
+dist/
+.DS_Store
+._*
+
+venv/
+root_path/

README.md

@@ -1,71 +1,98 @@
+###### [Overview](#CLAP) | [Setup](#Setup) | [CLAP weights](#CLAP-weights) | [Usage](#Usage) | [Examples](#Examples) | [Citation](#Citation)
+
 # CLAP
 
-CLAP (Contrastive Language-Audio Pretraining) is a neural network model that learns acoustic concepts from natural language supervision. It achieves SoTA in “Zero-Shot” classification, Audio-Text & Text-Audio Retrieval, and in some datasets when finetuned.
+CLAP (Contrastive Language-Audio Pretraining) is a model that learns acoustic concepts from natural language supervision and enables “Zero-Shot” inference. The model has been extensively evaluated in 26 audio downstream tasks achieving SoTA in several of them including classification, retrieval, and captioning.
 
-<img width="832" alt="clap_diagram_v3" src="https://user-images.githubusercontent.com/26778834/199842089-39ef6a2e-8abb-4338-bdfe-680abab70f53.png">
+<img width="832" alt="clap_diagrams" src="docs/clap2_diagram.png">
 
 ## Setup
 
-You are required to just install the dependencies: `pip install -r requirements.txt` using Python 3 to get started.
-
-If you have [conda](https://www.anaconda.com) installed, you can run the following: 
+First, install python 3.8 or higher (3.11 recommended). Then, install CLAP using either of the following:
 
 ```shell
-git clone https://github.com/microsoft/CLAP.git && \
-cd CLAP && \
-conda create -n clap python=3.8 && \
-conda activate clap && \
-pip install -r requirements.txt
+# Install pypi pacakge
+pip install msclap
+
+# Or Install latest (unstable) git source
+pip install git+https://github.com/microsoft/CLAP.git
 ```
 
 ## CLAP weights
-Request CLAP weights: [Pretrained Model \[Zenodo\]](https://zenodo.org/record/7312125#.Y22vecvMIQ9)
+CLAP weights are downloaded automatically (choose between versions _2022_, _2023_, and _clapcap_), but are also available at: [Zenodo](https://zenodo.org/record/8378278) or [HuggingFace](https://huggingface.co/microsoft/msclap)
 
+_clapcap_ is the audio captioning model that uses the 2023 encoders.
 
 ## Usage
 
-Please take a look at `src/examples` for usage examples. 
-
-- Load model
+- Zero-Shot Classification and Retrieval
 ```python
-from src import CLAP 
+from msclap import CLAP
 
-clap_model = CLAP("<PATH TO WEIGHTS>", use_cuda=False)
-```
+# Load model (Choose between versions '2022' or '2023')
+# The model weight will be downloaded automatically if `model_fp` is not specified
+clap_model = CLAP(version = '2023', use_cuda=False)
 
-- Extract text embeddings
-```python
+# Extract text embeddings
 text_embeddings = clap_model.get_text_embeddings(class_labels: List[str])
-```
 
-- Extract audio embeddings
-```python
+# Extract audio embeddings
 audio_embeddings = clap_model.get_audio_embeddings(file_paths: List[str])
+
+# Compute similarity between audio and text embeddings 
+similarities = clap_model.compute_similarity(audio_embeddings, text_embeddings)
 ```
 
-- Compute similarity 
+- Audio Captioning
 ```python
-sim = clap_model.compute_similarity(audio_embeddings, text_embeddings)
+from msclap import CLAP
+
+# Load model (Choose version 'clapcap')
+clap_model = CLAP(version = 'clapcap', use_cuda=False)
+
+# Generate audio captions
+captions = clap_model.generate_caption(file_paths: List[str])
 ```
 
 ## Examples
-To run zero-shot evaluation on the ESC50 dataset or a single audio file from ESC50, check `CLAP\src\`. For zero-shot evaluation on the ESC50 dataset:
+Take a look at [examples](./examples/) for usage examples. 
+
+To run Zero-Shot Classification on the ESC50 dataset try the following:
+
 ```bash
-> cd src && python zero_shot_classification.py
+> cd examples && python zero_shot_classification.py
 ```
-Output
+Output (version 2023)
 ```bash
-ESC50 Accuracy: 82.6%
+ESC50 Accuracy: 93.9%
 ```
 
 ## Citation
-https://arxiv.org/pdf/2206.04769.pdf
+
+Kindly cite our work if you find it useful.
+
+[CLAP: Learning Audio Concepts from Natural Language Supervision](https://ieeexplore.ieee.org/abstract/document/10095889)
 ```
-@article{elizalde2022clap,
-  title={Clap: Learning audio concepts from natural language supervision},
-  author={Elizalde, Benjamin and Deshmukh, Soham and Ismail, Mahmoud Al and Wang, Huaming},
-  journal={arXiv preprint arXiv:2206.04769},
-  year={2022}
+@inproceedings{CLAP2022,
+  title={Clap learning audio concepts from natural language supervision},
+  author={Elizalde, Benjamin and Deshmukh, Soham and Al Ismail, Mahmoud and Wang, Huaming},
+  booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+  pages={1--5},
+  year={2023},
+  organization={IEEE}
+}
+```
+
+[Natural Language Supervision for General-Purpose Audio Representations](https://arxiv.org/abs/2309.05767)
+```
+@misc{CLAP2023,
+      title={Natural Language Supervision for General-Purpose Audio Representations}, 
+      author={Benjamin Elizalde and Soham Deshmukh and Huaming Wang},
+      year={2023},
+      eprint={2309.05767},
+      archivePrefix={arXiv},
+      primaryClass={cs.SD},
+      url={https://arxiv.org/abs/2309.05767}
 }
 ```
 

examples/audio_captioning.py

@@ -0,0 +1,24 @@
+"""
+This is an example using CLAPCAP for audio captioning.
+"""
+from msclap import CLAP
+
+# Load and initialize CLAP
+clap_model = CLAP(version = 'clapcap', use_cuda=False)
+
+#Load audio files
+audio_files = ['audio_file']
+
+# Generate captions for the recording
+captions = clap_model.generate_caption(audio_files, resample=True, beam_size=5, entry_length=67, temperature=0.01)
+
+# Print the result
+for i in range(len(audio_files)):
+    print(f"Audio file: {audio_files[i]} \n")
+    print(f"Generated caption: {captions[i]} \n")
+
+"""
+The output (the exact caption may vary):
+
+The birds are singing in the trees.
+"""

examples/esc50_dataset.py

@@ -1,6 +1,6 @@
 from torch.utils.data import Dataset
-from torchvision.datasets.utils import download_url
 from tqdm import tqdm
+from pathlib import Path
 import pandas as pd
 import os
 import torch.nn as nn
@@ -24,7 +24,7 @@ class AudioDataset(Dataset):
 
 class ESC50(AudioDataset):
     base_folder = 'ESC-50-master'
-    url = "https://github.com/karolpiczak/ESC-50/archive/refs/heads/master.zip"
+    url = "https://github.com/karoldvl/ESC-50/archive/master.zip"
     filename = "ESC-50-master.zip"
     num_files_in_dir = 2000
     audio_dir = 'audio'
@@ -74,9 +74,29 @@ class ESC50(AudioDataset):
         return len(self.audio_paths)
 
     def download(self):
-        download_url(self.url, self.root, self.filename)
+        # Download file using requests
+        import requests
+        file = Path(self.root) / self.filename
+        if file.is_file():
+            return
+        
+        r = requests.get(self.url, stream=True)
 
-        # extract file
+        # To prevent partial downloads, download to a temp file first
+        tmp = file.with_suffix('.tmp')
+        tmp.parent.mkdir(parents=True, exist_ok=True)
+        with open(tmp, 'wb') as f:
+            pbar = tqdm(unit=" MB", bar_format=f'{file.name}: {{rate_noinv_fmt}}')
+
+            for chunk in r.iter_content(chunk_size=1024):
+                if chunk:
+                    pbar.update(len(chunk) / 1024 / 1024)
+                    f.write(chunk)
+                    
+        # move temp file to correct location
+        tmp.rename(file)
+        
+        # # extract file
         from zipfile import ZipFile
         with ZipFile(os.path.join(self.root, self.filename), 'r') as zip:
-            zip.extractall(path=self.root)
+            zip.extractall(path=self.root)

examples/zero_shot_classification.py

@@ -1,9 +1,9 @@
 """
-This is an example using CLAP to perform zeroshot 
+This is an example using CLAP to perform zeroshot
     classification on ESC50 (https://github.com/karolpiczak/ESC-50).
 """
 
-from CLAPWrapper import CLAPWrapper
+from msclap import CLAP
 from esc50_dataset import ESC50
 import torch.nn.functional as F
 import numpy as np
@@ -11,15 +11,13 @@ from tqdm import tqdm
 from sklearn.metrics import accuracy_score
 
 # Load dataset
-dataset = ESC50(root="data_path", download=False)
-prompt = 'this is a sound of '
+root_path = "root_path" # Folder with ESC-50-master/
+dataset = ESC50(root=root_path, download=True) #If download=False code assumes base_folder='ESC-50-master' in esc50_dataset.py
+prompt = 'this is the sound of '
 y = [prompt + x for x in dataset.classes]
 
-
 # Load and initialize CLAP
-weights_path = "weights_path"
-clap_model = CLAPWrapper(weights_path, use_cuda=False)
-
+clap_model = CLAP(version = '2023', use_cuda=False)
 
 # Computing text embeddings
 text_embeddings = clap_model.get_text_embeddings(y)
@@ -34,6 +32,7 @@ for i in tqdm(range(len(dataset))):
     y_preds.append(y_pred)
     y_labels.append(one_hot_target.detach().cpu().numpy())
 
+
 y_labels, y_preds = np.concatenate(y_labels, axis=0), np.concatenate(y_preds, axis=0)
 acc = accuracy_score(np.argmax(y_labels, axis=1), np.argmax(y_preds, axis=1))
 print('ESC50 Accuracy {}'.format(acc))
@@ -41,6 +40,6 @@ print('ESC50 Accuracy {}'.format(acc))
 """
 The output:
 
-ESC50 Accuracy: 82.6%
+ESC50 Accuracy: 93.9%
 
 """

examples/zero_shot_predictions.py

@@ -0,0 +1,50 @@
+"""
+This is an example using CLAP for zero-shot inference.
+"""
+from msclap import CLAP
+import torch.nn.functional as F
+
+# Define classes for zero-shot
+# Should be in lower case and can be more than one word
+classes = ['coughing','sneezing','drinking sipping', 'breathing', 'brushing teeth']
+ground_truth = ['coughing']
+# Add prompt
+prompt = 'this is a sound of '
+class_prompts = [prompt + x for x in classes]
+#Load audio files
+audio_files = ['audio_file']
+
+# Load and initialize CLAP
+# Setting use_cuda = True will load the model on a GPU using CUDA
+clap_model = CLAP(version = '2023', use_cuda=False)
+
+# compute text embeddings from natural text
+text_embeddings = clap_model.get_text_embeddings(class_prompts)
+
+# compute the audio embeddings from an audio file
+audio_embeddings = clap_model.get_audio_embeddings(audio_files, resample=True)
+
+# compute the similarity between audio_embeddings and text_embeddings
+similarity = clap_model.compute_similarity(audio_embeddings, text_embeddings)
+
+similarity = F.softmax(similarity, dim=1)
+values, indices = similarity[0].topk(5)
+
+# Print the results
+print("Ground Truth: {}".format(ground_truth))
+print("Top predictions:\n")
+for value, index in zip(values, indices):
+    print(f"{classes[index]:>16s}: {100 * value.item():.2f}%")
+
+"""
+The output (the exact numbers may vary):
+
+Ground Truth: coughing
+Top predictions:
+
+        coughing: 98.55%
+        sneezing: 1.24%
+drinking sipping: 0.15%
+       breathing: 0.02%
+  brushing teeth: 0.01%
+"""

msclap/CLAPWrapper.py

@@ -0,0 +1,419 @@
+from __future__ import annotations
+
+from pathlib import Path
+import warnings
+warnings.filterwarnings("ignore")
+import random
+import torchaudio
+import collections
+import re
+import numpy as np
+from transformers import AutoTokenizer, logging
+from .models.clap import CLAP
+from .models.mapper import get_clapcap
+import math
+import torchaudio.transforms as T
+import os
+import torch
+import argparse
+import yaml
+import sys
+from huggingface_hub.file_download import hf_hub_download
+logging.set_verbosity_error()
+
+
+class CLAPWrapper():
+    """
+    A class for interfacing CLAP model.  
+    """
+    model_repo = "microsoft/msclap"
+    model_name = {
+        '2022': 'CLAP_weights_2022.pth',
+        '2023': 'CLAP_weights_2023.pth',
+        'clapcap': 'clapcap_weights_2023.pth'
+    }
+
+    def __init__(self, model_fp: Path | str | None = None, version: str = '2023', use_cuda=False):
+        # Check if version is supported
+        self.supported_versions = self.model_name.keys()
+        if version not in self.supported_versions:
+            raise ValueError(f"The version {version} is not supported. The supported versions are {str(self.supported_versions)}")
+
+        self.np_str_obj_array_pattern = re.compile(r'[SaUO]')
+        self.file_path = os.path.realpath(__file__)
+        self.default_collate_err_msg_format = (
+            "default_collate: batch must contain tensors, numpy arrays, numbers, "
+            "dicts or lists; found {}")
+        self.config_as_str = (Path(__file__).parent / f"configs/config_{version}.yml").read_text()
+
+        # Automatically download model if not provided
+        if not model_fp:
+            model_fp = hf_hub_download(self.model_repo, self.model_name[version])
+            
+        self.model_fp = model_fp
+        self.use_cuda = use_cuda
+        if 'clapcap' in  version:
+            self.clapcap, self.tokenizer, self.args = self.load_clapcap()
+        else:
+            self.clap, self.tokenizer, self.args = self.load_clap()
+    
+    def read_config_as_args(self,config_path,args=None,is_config_str=False):
+        return_dict = {}
+
+        if config_path is not None:
+            if is_config_str:
+                yml_config = yaml.load(config_path, Loader=yaml.FullLoader)
+            else:
+                with open(config_path, "r") as f:
+                    yml_config = yaml.load(f, Loader=yaml.FullLoader)
+
+            if args != None:
+                for k, v in yml_config.items():
+                    if k in args.__dict__:
+                        args.__dict__[k] = v
+                    else:
+                        sys.stderr.write("Ignored unknown parameter {} in yaml.\n".format(k))
+            else:
+                for k, v in yml_config.items():
+                    return_dict[k] = v
+
+        args = args if args != None else return_dict
+        return argparse.Namespace(**args)
+
+    def load_clap(self):
+        r"""Load CLAP model with args from config file"""
+
+        args = self.read_config_as_args(self.config_as_str, is_config_str=True)
+
+        if 'roberta' in args.text_model or 'clip' in args.text_model or 'gpt' in args.text_model:
+            self.token_keys = ['input_ids', 'attention_mask']
+        elif 'bert' in args.text_model:
+            self.token_keys = ['input_ids', 'token_type_ids', 'attention_mask']
+
+        clap = CLAP(
+            audioenc_name=args.audioenc_name,
+            sample_rate=args.sampling_rate,
+            window_size=args.window_size,
+            hop_size=args.hop_size,
+            mel_bins=args.mel_bins,
+            fmin=args.fmin,
+            fmax=args.fmax,
+            classes_num=args.num_classes,
+            out_emb=args.out_emb,
+            text_model=args.text_model,
+            transformer_embed_dim=args.transformer_embed_dim,
+            d_proj=args.d_proj
+        )
+
+        # Load pretrained weights for model
+        model_state_dict = torch.load(self.model_fp, map_location=torch.device('cpu'))['model']
+
+        # We unwrap the DDP model and save. If the model is not unwrapped and saved, then the model needs to unwrapped before `load_state_dict`: 
+        # Reference link: https://discuss.pytorch.org/t/how-to-load-dataparallel-model-which-trained-using-multiple-gpus/146005
+        clap.load_state_dict(model_state_dict, strict=False)
+
+        clap.eval()  # set clap in eval mode
+        tokenizer = AutoTokenizer.from_pretrained(args.text_model)
+        if 'gpt' in args.text_model:
+            tokenizer.add_special_tokens({'pad_token': '!'})
+
+        if self.use_cuda and torch.cuda.is_available():
+            clap = clap.cuda()
+
+        return clap, tokenizer, args
+    
+    def load_clapcap(self):
+        r"""Load CLAP model with args from config file"""
+
+        args = self.read_config_as_args(self.config_as_str, is_config_str=True)
+        args.prefix_dim = args.d_proj
+        text_model = args.text_model
+        args.text_model = args.text_decoder
+        args.cross_attention = True if 'cross' in args.clapcap_model.lower() else False
+
+        if 'roberta' in args.text_model or 'clip' in args.text_model or 'gpt' in args.text_model:
+            self.token_keys = ['input_ids', 'attention_mask']
+        elif 'bert' in args.text_model:
+            self.token_keys = ['input_ids', 'token_type_ids', 'attention_mask']
+
+        clap = CLAP(
+            audioenc_name=args.audioenc_name,
+            sample_rate=args.sampling_rate,
+            window_size=args.window_size,
+            hop_size=args.hop_size,
+            mel_bins=args.mel_bins,
+            fmin=args.fmin,
+            fmax=args.fmax,
+            classes_num=args.num_classes,
+            out_emb=args.out_emb,
+            text_model=text_model,
+            transformer_embed_dim=args.transformer_embed_dim,
+            d_proj=args.d_proj
+        )
+
+        clapcap = get_clapcap(args.clapcap_model)(clap, args.text_decoder, args.prefix_length, args.prefix_length_clip, args.prefix_dim,
+                 args.num_layers, args.normalize_prefix, args.mapping_type, True, True)
+
+        model_state_dict = torch.load(self.model_fp, map_location=torch.device('cpu'))['model']
+        clapcap.load_state_dict(model_state_dict, strict=False)
+
+        clapcap.eval()  # set clap in eval mode
+        tokenizer = AutoTokenizer.from_pretrained(args.text_model)
+        if 'gpt' in args.text_model:
+            tokenizer.add_special_tokens({'pad_token': '!'})
+
+        if self.use_cuda and torch.cuda.is_available():
+            clapcap = clapcap.cuda()
+
+        return clapcap, tokenizer, args
+
+    def default_collate(self, batch):
+        r"""Puts each data field into a tensor with outer dimension batch size"""
+        elem = batch[0]
+        elem_type = type(elem)
+        if isinstance(elem, torch.Tensor):
+            out = None
+            if torch.utils.data.get_worker_info() is not None:
+                # If we're in a background process, concatenate directly into a
+                # shared memory tensor to avoid an extra copy
+                numel = sum([x.numel() for x in batch])
+                storage = elem.storage()._new_shared(numel)
+                out = elem.new(storage)
+            return torch.stack(batch, 0, out=out)
+        elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
+                and elem_type.__name__ != 'string_':
+            if elem_type.__name__ == 'ndarray' or elem_type.__name__ == 'memmap':
+                # array of string classes and object
+                if self.np_str_obj_array_pattern.search(elem.dtype.str) is not None:
+                    raise TypeError(
+                        self.default_collate_err_msg_format.format(elem.dtype))
+
+                return self.default_collate([torch.as_tensor(b) for b in batch])
+            elif elem.shape == ():  # scalars
+                return torch.as_tensor(batch)
+        elif isinstance(elem, float):
+            return torch.tensor(batch, dtype=torch.float64)
+        elif isinstance(elem, int):
+            return torch.tensor(batch)
+        elif isinstance(elem, str):
+            return batch
+        elif isinstance(elem, collections.abc.Mapping):
+            return {key: self.default_collate([d[key] for d in batch]) for key in elem}
+        elif isinstance(elem, tuple) and hasattr(elem, '_fields'):  # namedtuple
+            return elem_type(*(self.default_collate(samples) for samples in zip(*batch)))
+        elif isinstance(elem, collections.abc.Sequence):
+            # check to make sure that the elements in batch have consistent size
+            it = iter(batch)
+            elem_size = len(next(it))
+            if not all(len(elem) == elem_size for elem in it):
+                raise RuntimeError(
+                    'each element in list of batch should be of equal size')
+            transposed = zip(*batch)
+            return [self.default_collate(samples) for samples in transposed]
+
+        raise TypeError(self.default_collate_err_msg_format.format(elem_type))
+    
+    def read_audio(self, audio_path, resample=True):
+        r"""Loads audio file or array and returns a torch tensor"""
+        # Randomly sample a segment of audio_duration from the clip or pad to match duration
+        audio_time_series, sample_rate = torchaudio.load(audio_path)
+        
+        resample_rate = self.args.sampling_rate
+        if resample and resample_rate != sample_rate:
+            resampler = T.Resample(sample_rate, resample_rate)
+            audio_time_series = resampler(audio_time_series)
+        return audio_time_series, resample_rate
+
+    def load_audio_into_tensor(self, audio_path, audio_duration, resample=False):
+        r"""Loads audio file and returns raw audio."""
+        # Randomly sample a segment of audio_duration from the clip or pad to match duration
+        audio_time_series, sample_rate = self.read_audio(audio_path, resample=resample)
+        audio_time_series = audio_time_series.reshape(-1)
+
+        # audio_time_series is shorter than predefined audio duration,
+        # so audio_time_series is extended
+        if audio_duration*sample_rate >= audio_time_series.shape[0]:
+            repeat_factor = int(np.ceil((audio_duration*sample_rate) /
+                                        audio_time_series.shape[0]))
+            # Repeat audio_time_series by repeat_factor to match audio_duration
+            audio_time_series = audio_time_series.repeat(repeat_factor)
+            # remove excess part of audio_time_series
+            audio_time_series = audio_time_series[0:audio_duration*sample_rate]
+        else:
+            # audio_time_series is longer than predefined audio duration,
+            # so audio_time_series is trimmed
+            start_index = random.randrange(
+                audio_time_series.shape[0] - audio_duration*sample_rate)
+            audio_time_series = audio_time_series[start_index:start_index +
+                                                  audio_duration*sample_rate]
+        return torch.FloatTensor(audio_time_series)
+
+    def preprocess_audio(self, audio_files, resample):
+        r"""Load list of audio files and return raw audio"""
+        audio_tensors = []
+        for audio_file in audio_files:
+            audio_tensor = self.load_audio_into_tensor(
+                audio_file, self.args.duration, resample)
+            audio_tensor = audio_tensor.reshape(
+                1, -1).cuda() if self.use_cuda and torch.cuda.is_available() else audio_tensor.reshape(1, -1)
+            audio_tensors.append(audio_tensor)
+        return self.default_collate(audio_tensors)
+
+    def preprocess_text(self, text_queries):
+        r"""Load list of class labels and return tokenized text"""
+        tokenized_texts = []
+        for ttext in text_queries:
+            if 'gpt' in self.args.text_model:
+                ttext = ttext + ' <|endoftext|>'
+            tok = self.tokenizer.encode_plus(
+                text=ttext, add_special_tokens=True, max_length=self.args.text_len, padding='max_length', return_tensors="pt")
+            for key in self.token_keys:
+                tok[key] = tok[key].reshape(-1).cuda() if self.use_cuda and torch.cuda.is_available() else tok[key].reshape(-1)
+            tokenized_texts.append(tok)
+        return self.default_collate(tokenized_texts)
+
+    def get_text_embeddings(self, class_labels):
+        r"""Load list of class labels and return text embeddings"""
+        preprocessed_text = self.preprocess_text(class_labels)
+        return self._get_text_embeddings(preprocessed_text)
+
+    def get_audio_embeddings(self, audio_files, resample=True):
+        r"""Load list of audio files and return a audio embeddings"""
+        preprocessed_audio = self.preprocess_audio(audio_files, resample)
+        return self._get_audio_embeddings(preprocessed_audio)
+
+    def _get_text_embeddings(self, preprocessed_text):
+        r"""Load preprocessed text and return text embeddings"""
+        with torch.no_grad():
+            return self.clap.caption_encoder(preprocessed_text)
+
+    def _get_audio_embeddings(self, preprocessed_audio):
+        r"""Load preprocessed audio and return a audio embeddings"""
+        with torch.no_grad():
+            preprocessed_audio = preprocessed_audio.reshape(
+                preprocessed_audio.shape[0], preprocessed_audio.shape[2])
+            #Append [0] the audio emebdding, [1] has output class probabilities
+            return self.clap.audio_encoder(preprocessed_audio)[0]
+
+    def _generic_batch_inference(self, func, *args):
+        r"""Process audio and/or text per batch"""
+        input_tmp = args[0]
+        batch_size = args[-1]
+        # args[0] has audio_files, args[1] has class_labels
+        inputs = [args[0], args[1]] if len(args) == 3 else [args[0]]
+        args0_len = len(args[0])
+        # compute text_embeddings once for all the audio_files batches
+        if len(inputs) == 2:
+            text_embeddings = self.get_text_embeddings(args[1])
+            inputs = [args[0], args[1], text_embeddings]
+        dataset_idx = 0
+        for _ in range(math.ceil(args0_len/batch_size)):
+            next_batch_idx = dataset_idx + batch_size
+            # batch size is bigger than available audio/text items
+            if next_batch_idx >= args0_len:
+                inputs[0] = input_tmp[dataset_idx:]
+                yield func(*tuple(inputs))
+            else:
+                inputs[0] = input_tmp[dataset_idx:next_batch_idx]
+                yield func(*tuple(inputs))
+            dataset_idx = next_batch_idx
+
+    def get_audio_embeddings_per_batch(self, audio_files, batch_size):
+        r"""Load preprocessed audio and return a audio embeddings per batch"""
+        return self._generic_batch_inference(self.get_audio_embeddings, audio_files, batch_size)
+
+    def get_text_embeddings_per_batch(self, class_labels, batch_size):
+        r"""Load preprocessed text and return text embeddings per batch"""
+        return self._generic_batch_inference(self.get_text_embeddings, class_labels, batch_size)
+
+    def compute_similarity(self, audio_embeddings, text_embeddings):
+        r"""Compute similarity between text and audio embeddings"""
+        audio_embeddings = audio_embeddings/torch.norm(audio_embeddings, dim=-1, keepdim=True)
+        text_embeddings = text_embeddings/torch.norm(text_embeddings, dim=-1, keepdim=True)
+    
+        logit_scale = self.clap.logit_scale.exp()
+        similarity = logit_scale*text_embeddings @ audio_embeddings.T
+        return similarity.T
+
+    def classify_audio_files_per_batch(self, audio_files, class_labels, batch_size):
+        r"""Compute classification probabilities for each audio recording in a batch and each class label"""
+        return self._generic_batch_inference(self.classify_audio_files, audio_files, class_labels, batch_size)
+    
+    def generate_caption(self, audio_files, resample=True, beam_size: int = 5, entry_length=67, temperature=1.):
+        r"""Generate audio captions for each audio recording in a batch"""
+        captions = []
+        audio_tensors = self.preprocess_audio(audio_files, resample)
+
+        with torch.no_grad():
+            prefix = self.clapcap.clap(audio_tensors.squeeze(1))[0]
+            if self.args.normalize_prefix:
+                prefix = prefix / prefix.norm(2, -1).reshape(-1,1)
+            prefix_embed = self.clapcap.clap_project(prefix).view(-1, self.args.prefix_length, self.clapcap.gpt.transformer.wte.weight.shape[1])
+
+            for i in range(len(audio_tensors)):
+                gen_caption = self._generate_beam(embed=prefix_embed[i].unsqueeze(0),\
+                                                            beam_size=beam_size,\
+                                                            entry_length=entry_length,\
+                                                            temperature=temperature)[0]
+                captions.append(gen_caption.capitalize())
+        return captions
+    
+    def _generate_beam(self, beam_size: int = 5, prompt=None, embed=None,
+                  entry_length=67, temperature=1., stop_token: str = ' <|endoftext|>'):
+        r"""Generate captions by beam search decoding"""
+        self.clapcap.eval()
+        stop_token_index = self.tokenizer.encode(stop_token)[0]
+        tokens = None
+        scores = None
+        device = next(self.clapcap.parameters()).device
+        seq_lengths = torch.ones(beam_size, device=device)
+        is_stopped = torch.zeros(beam_size, device=device, dtype=torch.bool)
+        with torch.no_grad():
+            if embed is not None:
+                generated = embed
+            else:
+                if tokens is None:
+                    tokens = torch.tensor(self.tokenizer.encode(prompt))
+                    tokens = tokens.unsqueeze(0).to(device)
+                    generated = self.clapcap.gpt.transformer.wte(tokens)
+            for i in range(entry_length):
+                outputs = self.clapcap.gpt(inputs_embeds=generated)
+                logits = outputs.logits
+                logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
+                logits = logits.softmax(-1).log()
+                if scores is None:
+                    scores, next_tokens = logits.topk(beam_size, -1)
+                    generated = generated.expand(beam_size, *generated.shape[1:])
+                    next_tokens, scores = next_tokens.permute(1, 0), scores.squeeze(0)
+                    if tokens is None:
+                        tokens = next_tokens
+                    else:
+                        tokens = tokens.expand(beam_size, *tokens.shape[1:])
+                        tokens = torch.cat((tokens, next_tokens), dim=1)
+                else:
+                    logits[is_stopped] = -float(np.inf)
+                    logits[is_stopped, 0] = 0
+                    scores_sum = scores[:, None] + logits
+                    seq_lengths[~is_stopped] += 1
+                    scores_sum_average = scores_sum / seq_lengths[:, None]
+                    scores_sum_average, next_tokens = scores_sum_average.view(-1).topk(beam_size, -1)
+                    next_tokens_source = next_tokens // scores_sum.shape[1]
+                    seq_lengths = seq_lengths[next_tokens_source]
+                    next_tokens = next_tokens % scores_sum.shape[1]
+                    next_tokens = next_tokens.unsqueeze(1)
+                    tokens = tokens[next_tokens_source]
+                    tokens = torch.cat((tokens, next_tokens), dim=1)
+                    generated = generated[next_tokens_source]
+                    scores = scores_sum_average * seq_lengths
+                    is_stopped = is_stopped[next_tokens_source]
+                next_token_embed = self.clapcap.gpt.transformer.wte(next_tokens.squeeze()).view(generated.shape[0], 1, -1)
+                generated = torch.cat((generated, next_token_embed), dim=1)
+                is_stopped = is_stopped + next_tokens.eq(stop_token_index).squeeze()
+                if is_stopped.all():
+                    break
+        scores = scores / seq_lengths
+        output_list = tokens.cpu().numpy()
+        output_texts = [self.tokenizer.decode(output[:int(length)]) for output, length in zip(output_list, seq_lengths)]
+        order = scores.argsort(descending=True)
+        output_texts = [output_texts[i] for i in order]
+        return output_texts

msclap/__init__.py

@@ -0,0 +1 @@
+from .CLAPWrapper import CLAPWrapper as CLAP

msclap/configs/config_2023.yml

@@ -0,0 +1,26 @@
+# TEXT ENCODER CONFIG
+text_model: 'gpt2'
+text_len: 77
+transformer_embed_dim: 768
+freeze_text_encoder_weights: True
+
+# AUDIO ENCODER CONFIG
+audioenc_name: 'HTSAT'
+out_emb: 768
+sampling_rate: 44100
+duration: 7
+fmin: 50
+fmax: 8000 #14000 
+n_fft: 1024 # 1028 
+hop_size: 320
+mel_bins: 64
+window_size: 1024
+
+# PROJECTION SPACE CONFIG 
+d_proj: 1024
+temperature: 0.003
+
+# TRAINING AND EVALUATION CONFIG
+num_classes: 527
+batch_size: 1024
+demo: False

msclap/configs/config_clapcap.yml

@@ -0,0 +1,34 @@
+# TEXT ENCODER CONFIG
+text_model: 'gpt2'
+transformer_embed_dim: 768
+freeze_text_encoder_weights: True
+
+# AUDIO ENCODER CONFIG
+audioenc_name: 'HTSAT'
+out_emb: 768
+sampling_rate: 44100
+duration: 7
+fmin: 50
+fmax: 8000
+n_fft: 1024
+hop_size: 320
+mel_bins: 64
+window_size: 1024
+
+# PROJECTION SPACE CONFIG
+d_proj: 1024
+temperature: 0.003
+
+# TRAINING AND EVALUATION CONFIG
+batch_size: 128
+num_classes: 527
+
+# CLAPCAP CONFIG
+clapcap_model: 'ClapCaption'
+text_decoder: 'gpt2'
+prefix_length: 40
+prefix_length_clip: 40
+mapping_type: 'transformer'
+num_layers: 8
+normalize_prefix: True
+freeze_gpt_weights: True

msclap/models/__init__.py

@@ -0,0 +1,6 @@
+from . import clap 
+from . import audio
+from . import htsat
+from . import config
+from . import pytorch_utils
+from . import htsat

msclap/models/audio.py

@@ -2,10 +2,13 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from torchlibrosa.stft import Spectrogram, LogmelFilterBank
+from .htsat import HTSATWrapper
 
 def get_audio_encoder(name: str):
     if name == "Cnn14":
         return Cnn14
+    elif name == "HTSAT":
+        return HTSATWrapper
     else:
         raise Exception('The audio encoder name {} is incorrect or not supported'.format(name))
 

msclap/models/clap.py

@@ -42,14 +42,33 @@ class AudioEncoder(nn.Module):
 class TextEncoder(nn.Module):
     def __init__(self, d_out: int, text_model: str, transformer_embed_dim: int) -> None:
         super().__init__()
+        self.text_model = text_model
         self.base = AutoModel.from_pretrained(text_model)
+
+        if 'clip' in text_model:
+            self.clip_text_projection = self.base.text_projection
+            self.base = self.base.text_model
+            if 'base' in text_model:
+                transformer_embed_dim = 512
         
         self.projection = Projection(transformer_embed_dim, d_out)
 
     def forward(self, x):
-        out = self.base(**x)[0]
-        out = out[:, 0, :]  # get CLS token output
+        if 'clip' in self.text_model:
+            pooled_output = self.base(**x)[1] # get pooled output
+            out = self.clip_text_projection(pooled_output)  # get CLS token output
+        elif 'gpt' in self.text_model:
+            batch_size = x['input_ids'].shape[0]
+            hidden_states = self.base(**x)[0] # (batch_size=4, seq_len, 768)
+
+            sequence_lengths = torch.ne(x['input_ids'], 0).sum(-1) - 1 # tensor([13, 14, 18, 17])
+            out = hidden_states[torch.arange(batch_size, device=hidden_states.device), sequence_lengths] # [batch_size, 768] = [4, 768]
+        else:
+            out = self.base(**x)[0]
+            out = out[:, 0, :]  # get CLS token output
+        
         projected_vec = self.projection(out)
+
         return projected_vec
 
 class CLAP(nn.Module):

msclap/models/config.py

@@ -0,0 +1,128 @@
+# Ke Chen
+# knutchen@ucsd.edu
+# HTS-AT: A HIERARCHICAL TOKEN-SEMANTIC AUDIO TRANSFORMER FOR SOUND CLASSIFICATION AND DETECTION
+# The configuration for training the model
+
+exp_name = "exp_htsat_pretrain" # the saved ckpt prefix name of the model 
+workspace = "/home/kechen/Research/HTSAT" # the folder of your code
+dataset_path = "/home/Research/audioset" # the dataset path
+desed_folder = "/home/Research/DESED" # the desed file
+
+dataset_type = "audioset" # "audioset" "esc-50" "scv2"
+index_type = "full_train" # only works for audioset
+balanced_data = True # only works for audioset
+
+loss_type = "clip_bce" # 
+# AudioSet & SCV2: "clip_bce" |  ESC-50: "clip_ce" 
+
+# trained from a checkpoint, or evaluate a single model 
+resume_checkpoint = None 
+# "/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_1.ckpt"
+ 
+esc_fold = 0 # just for esc dataset, select the fold you need for evaluation and (+1) validation
+
+
+debug = False
+
+random_seed = 970131 # 19970318 970131 12412 127777 1009 34047
+batch_size = 32 * 4 # batch size per GPU x GPU number , default is 32 x 4 = 128
+learning_rate = 1e-3 # 1e-4 also workable 
+max_epoch = 100
+num_workers = 3
+
+lr_scheduler_epoch = [10,20,30]
+lr_rate = [0.02, 0.05, 0.1]
+
+# these data preparation optimizations do not bring many improvements, so deprecated
+enable_token_label = False # token label
+class_map_path = "class_hier_map.npy"
+class_filter = None 
+retrieval_index = [15382, 9202, 130, 17618, 17157, 17516, 16356, 6165, 13992, 9238, 5550, 5733, 1914, 1600, 3450, 13735, 11108, 3762, 
+    9840, 11318, 8131, 4429, 16748, 4992, 16783, 12691, 4945, 8779, 2805, 9418, 2797, 14357, 5603, 212, 3852, 12666, 1338, 10269, 2388, 8260, 4293, 14454, 7677, 11253, 5060, 14938, 8840, 4542, 2627, 16336, 8992, 15496, 11140, 446, 6126, 10691, 8624, 10127, 9068, 16710, 10155, 14358, 7567, 5695, 2354, 8057, 17635, 133, 16183, 14535, 7248, 4560, 14429, 2463, 10773, 113, 2462, 9223, 4929, 14274, 4716, 17307, 4617, 2132, 11083, 1039, 1403, 9621, 13936, 2229, 2875, 17840, 9359, 13311, 9790, 13288, 4750, 17052, 8260, 14900]
+token_label_range = [0.2,0.6]
+enable_time_shift = False # shift time
+enable_label_enhance = False # enhance hierarchical label
+enable_repeat_mode = False # repeat the spectrogram / reshape the spectrogram
+
+
+
+# for model's design
+enable_tscam = True # enbale the token-semantic layer
+
+# for signal processing
+sample_rate = 32000 # 16000 for scv2, 32000 for audioset and esc-50
+clip_samples = sample_rate * 10 # audio_set 10-sec clip
+window_size = 1024
+hop_size = 320 # 160 for scv2, 320 for audioset and esc-50
+mel_bins = 64
+fmin = 50
+fmax = 14000
+shift_max = int(clip_samples * 0.5)
+
+# for data collection
+classes_num = 527 # esc: 50 | audioset: 527 | scv2: 35
+patch_size = (25, 4) # deprecated
+crop_size = None # int(clip_samples * 0.5) deprecated
+
+# for htsat hyperparamater
+htsat_window_size = 8
+htsat_spec_size =  256
+htsat_patch_size = 4 
+htsat_stride = (4, 4)
+htsat_num_head = [4,8,16,32]
+htsat_dim = 96 
+htsat_depth = [2,2,6,2]
+
+swin_pretrain_path = None
+# "/home/Research/model_backup/pretrain/swin_tiny_c24_patch4_window8_256.pth"
+
+# Some Deprecated Optimization in the model design, check the model code for details
+htsat_attn_heatmap = False
+htsat_hier_output = False 
+htsat_use_max = False
+
+
+# for ensemble test 
+
+ensemble_checkpoints = []
+ensemble_strides = []
+
+
+# weight average folder
+wa_folder = "/home/version_0/checkpoints/"
+# weight average output filename
+wa_model_path = "HTSAT_AudioSet_Saved_x.ckpt"
+
+esm_model_pathes = [
+    "/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_1.ckpt",
+    "/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_2.ckpt",
+    "/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_3.ckpt",
+    "/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_4.ckpt",
+    "/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_5.ckpt",
+    "/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_6.ckpt"
+]
+
+# for framewise localization
+heatmap_dir = "/home/Research/heatmap_output"
+test_file = "htsat-test-ensemble"
+fl_local = False # indicate if we need to use this dataset for the framewise detection
+fl_dataset = "/home/Research/desed/desed_eval.npy"  
+fl_class_num = [
+    "Speech", "Frying", "Dishes", "Running_water",
+    "Blender", "Electric_shaver_toothbrush", "Alarm_bell_ringing",
+    "Cat", "Dog", "Vacuum_cleaner"
+]
+
+# map 527 classes into 10 classes
+fl_audioset_mapping = [
+    [0,1,2,3,4,5,6,7],
+    [366, 367, 368],
+    [364],
+    [288, 289, 290, 291, 292, 293, 294, 295, 296, 297],
+    [369],
+    [382],
+    [310, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402],
+    [81, 82, 83, 84, 85],
+    [74, 75, 76, 77, 78, 79],
+    [377]
+]

msclap/models/htsat.py

@@ -0,0 +1,942 @@
+# Ke Chen
+# knutchen@ucsd.edu
+# HTS-AT: A HIERARCHICAL TOKEN-SEMANTIC AUDIO TRANSFORMER FOR SOUND CLASSIFICATION AND DETECTION
+# Model Core
+# below codes are based and referred from https://github.com/microsoft/Swin-Transformer
+# Swin Transformer for Computer Vision: https://arxiv.org/pdf/2103.14030.pdf
+
+
+import math
+import random
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as checkpoint
+
+from torchlibrosa.stft import Spectrogram, LogmelFilterBank
+from torchlibrosa.augmentation import SpecAugmentation
+
+from itertools import repeat
+
+from .pytorch_utils import do_mixup, interpolate
+from . import config
+
+import collections.abc
+import warnings
+
+from torch.nn.init import _calculate_fan_in_and_fan_out
+
+def _ntuple(n):
+    def parse(x):
+        if isinstance(x, collections.abc.Iterable):
+            return x
+        return tuple(repeat(x, n))
+    return parse
+
+to_1tuple = _ntuple(1)
+to_2tuple = _ntuple(2)
+to_3tuple = _ntuple(3)
+to_4tuple = _ntuple(4)
+to_ntuple = _ntuple
+
+
+def drop_path(x, drop_prob: float = 0., training: bool = False):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
+    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
+    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
+    'survival rate' as the argument.
+    """
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
+    random_tensor.floor_()  # binarize
+    output = x.div(keep_prob) * random_tensor
+    return output
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
+
+class PatchEmbed(nn.Module):
+    """ 2D Image to Patch Embedding
+    """
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True, patch_stride = 16):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patch_stride = to_2tuple(patch_stride)
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patch_stride = patch_stride
+        self.grid_size = (img_size[0] // patch_stride[0], img_size[1] // patch_stride[1])
+        self.num_patches = self.grid_size[0] * self.grid_size[1]
+        self.flatten = flatten
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+        
+        padding = ((patch_size[0] - patch_stride[0]) // 2, (patch_size[1] - patch_stride[1]) // 2)
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_stride, padding=padding)
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        assert H == self.img_size[0] and W == self.img_size[1], \
+            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        x = self.proj(x)
+        if self.flatten:
+            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
+        x = self.norm(x)
+        return x
+
+class Mlp(nn.Module):
+    """ MLP as used in Vision Transformer, MLP-Mixer and related networks
+    """
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1. + math.erf(x / math.sqrt(2.))) / 2.
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+                      "The distribution of values may be incorrect.",
+                      stacklevel=2)
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        l = norm_cdf((a - mean) / std)
+        u = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [l, u], then translate to
+        # [2l-1, 2u-1].
+        tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
+    # type: (Tensor, float, float, float, float) -> Tensor
+    r"""Fills the input Tensor with values drawn from a truncated
+    normal distribution. The values are effectively drawn from the
+    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \leq \text{mean} \leq b`.
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        a: the minimum cutoff value
+        b: the maximum cutoff value
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.trunc_normal_(w)
+    """
+    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
+
+
+def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'):
+    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
+    if mode == 'fan_in':
+        denom = fan_in
+    elif mode == 'fan_out':
+        denom = fan_out
+    elif mode == 'fan_avg':
+        denom = (fan_in + fan_out) / 2
+
+    variance = scale / denom
+
+    if distribution == "truncated_normal":
+        # constant is stddev of standard normal truncated to (-2, 2)
+        trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978)
+    elif distribution == "normal":
+        tensor.normal_(std=math.sqrt(variance))
+    elif distribution == "uniform":
+        bound = math.sqrt(3 * variance)
+        tensor.uniform_(-bound, bound)
+    else:
+        raise ValueError(f"invalid distribution {distribution}")
+
+
+def lecun_normal_(tensor):
+    variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal')
+
+
+# below codes are based and referred from https://github.com/microsoft/Swin-Transformer
+# Swin Transformer for Computer Vision: https://arxiv.org/pdf/2103.14030.pdf
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class WindowAttention(nn.Module):
+    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        trunc_normal_(self.relative_position_bias_table, std=.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x, attn
+
+    def extra_repr(self):
+        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
+
+
+# We use the model based on Swintransformer Block, therefore we can use the swin-transformer pretrained model
+class SwinTransformerBlock(nn.Module):
+    r""" Swin Transformer Block.
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resulotion.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
+                 act_layer=nn.GELU, norm_layer=nn.LayerNorm, norm_before_mlp='ln'):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        self.norm_before_mlp = norm_before_mlp
+        if min(self.input_resolution) <= self.window_size:
+            # if window size is larger than input resolution, we don't partition windows
+            self.shift_size = 0
+            self.window_size = min(self.input_resolution)
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        if self.norm_before_mlp == 'ln':
+            self.norm2 = nn.LayerNorm(dim)
+        elif self.norm_before_mlp == 'bn':
+            self.norm2 = lambda x: nn.BatchNorm1d(dim)(x.transpose(1, 2)).transpose(1, 2)
+        else:
+            raise NotImplementedError
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+        if self.shift_size > 0:
+            # calculate attention mask for SW-MSA
+            H, W = self.input_resolution
+            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
+            h_slices = (slice(0, -self.window_size),
+                        slice(-self.window_size, -self.shift_size),
+                        slice(-self.shift_size, None))
+            w_slices = (slice(0, -self.window_size),
+                        slice(-self.window_size, -self.shift_size),
+                        slice(-self.shift_size, None))
+            cnt = 0
+            for h in h_slices:
+                for w in w_slices:
+                    img_mask[:, h, w, :] = cnt
+                    cnt += 1
+
+            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
+            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+        else:
+            attn_mask = None
+
+        self.register_buffer("attn_mask", attn_mask)
+
+    def forward(self, x):
+        # pdb.set_trace()
+        H, W = self.input_resolution
+        # print("H: ", H)
+        # print("W: ", W)
+        # pdb.set_trace()
+        B, L, C = x.shape
+        # assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+        else:
+            shifted_x = x
+
+        # partition windows
+        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA
+        attn_windows, attn = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            x = shifted_x
+        x = x.view(B, H * W, C)
+
+        # FFN
+        x = shortcut + self.drop_path(x)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x, attn
+
+    def extra_repr(self):
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
+               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+
+
+
+class PatchMerging(nn.Module):
+    r""" Patch Merging Layer.
+    Args:
+        input_resolution (tuple[int]): Resolution of input feature.
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.input_resolution = input_resolution
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def forward(self, x):
+        """
+        x: B, H*W, C
+        """
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+        x = x.view(B, H, W, C)
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+    def extra_repr(self):
+        return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+                 norm_before_mlp='ln'):
+
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+                                 num_heads=num_heads, window_size=window_size,
+                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
+                                 mlp_ratio=mlp_ratio,
+                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
+                                 drop=drop, attn_drop=attn_drop,
+                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                                 norm_layer=norm_layer, norm_before_mlp=norm_before_mlp)
+            for i in range(depth)])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x):
+        attns = []
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x)
+            else:
+                x, attn = blk(x)
+                if not self.training:
+                    attns.append(attn.unsqueeze(0))
+        if self.downsample is not None:
+            x = self.downsample(x)
+        if not self.training:
+            attn = torch.cat(attns, dim = 0)
+            attn = torch.mean(attn, dim = 0)
+        return x, attn
+
+    def extra_repr(self):
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+
+# The Core of HTSAT
+class HTSAT_Swin_Transformer(nn.Module):
+    r"""HTSAT based on the Swin Transformer
+    Args:
+        spec_size (int | tuple(int)): Input Spectrogram size. Default 256
+        patch_size (int | tuple(int)): Patch size. Default: 4
+        path_stride (iot | tuple(int)): Patch Stride for Frequency and Time Axis. Default: 4
+        in_chans (int): Number of input image channels. Default: 1 (mono)
+        num_classes (int): Number of classes for classification head. Default: 527
+        embed_dim (int): Patch embedding dimension. Default: 96
+        depths (tuple(int)): Depth of each HTSAT-Swin Transformer layer.
+        num_heads (tuple(int)): Number of attention heads in different layers.
+        window_size (int): Window size. Default: 8
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
+        drop_rate (float): Dropout rate. Default: 0
+        attn_drop_rate (float): Attention dropout rate. Default: 0
+        drop_path_rate (float): Stochastic depth rate. Default: 0.1
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
+        config (module): The configuration Module from config.py
+    """
+
+    def __init__(self, spec_size=256, patch_size=4, patch_stride=(4,4), 
+                in_chans=1, num_classes=527,
+                 embed_dim=96, depths=[2, 2, 6, 2], num_heads=[4, 8, 16, 32],
+                 window_size=8, mlp_ratio=4., qkv_bias=True, qk_scale=None,
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+                 norm_layer=nn.LayerNorm, 
+                 ape=False, patch_norm=True,
+                 use_checkpoint=False, norm_before_mlp='ln', config = None, **kwargs):
+        super(HTSAT_Swin_Transformer, self).__init__()
+
+        self.config = config
+        self.spec_size = spec_size 
+        self.patch_stride = patch_stride
+        self.patch_size = patch_size
+        self.window_size = window_size
+        self.embed_dim = embed_dim
+        self.depths = depths
+        self.ape = ape
+        self.in_chans = in_chans
+        self.num_classes = num_classes
+        self.num_heads = num_heads
+        self.num_layers = len(self.depths)
+        self.num_features = int(self.embed_dim * 2 ** (self.num_layers - 1))
+        
+        self.drop_rate = drop_rate
+        self.attn_drop_rate = attn_drop_rate
+        self.drop_path_rate = drop_path_rate
+
+        self.qkv_bias = qkv_bias
+        self.qk_scale = None
+
+        self.patch_norm = patch_norm
+        self.norm_layer = norm_layer if self.patch_norm else None
+        self.norm_before_mlp = norm_before_mlp
+        self.mlp_ratio = mlp_ratio
+
+        self.use_checkpoint = use_checkpoint
+
+        #  process mel-spec ; used only once
+        self.freq_ratio = self.spec_size // self.config.mel_bins
+        window = 'hann'
+        center = True
+        pad_mode = 'reflect'
+        ref = 1.0
+        amin = 1e-10
+        top_db = None
+        self.interpolate_ratio = 32     # Downsampled ratio
+        # Spectrogram extractor
+        self.spectrogram_extractor = Spectrogram(n_fft=config.window_size, hop_length=config.hop_size, 
+            win_length=config.window_size, window=window, center=center, pad_mode=pad_mode, 
+            freeze_parameters=True)
+        # Logmel feature extractor
+        self.logmel_extractor = LogmelFilterBank(sr=config.sample_rate, n_fft=config.window_size, 
+            n_mels=config.mel_bins, fmin=config.fmin, fmax=config.fmax, ref=ref, amin=amin, top_db=top_db, 
+            freeze_parameters=True)
+        # Spec augmenter
+        self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, 
+            freq_drop_width=8, freq_stripes_num=2) # 2 2
+        self.bn0 = nn.BatchNorm2d(self.config.mel_bins)
+
+
+        # split spctrogram into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            img_size=self.spec_size, patch_size=self.patch_size, in_chans=self.in_chans, 
+            embed_dim=self.embed_dim, norm_layer=self.norm_layer, patch_stride = patch_stride)
+
+        num_patches = self.patch_embed.num_patches
+        patches_resolution = self.patch_embed.grid_size
+        self.patches_resolution = patches_resolution
+
+        # absolute position embedding
+        if self.ape:
+            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, self.embed_dim))
+            trunc_normal_(self.absolute_pos_embed, std=.02)
+
+        self.pos_drop = nn.Dropout(p=self.drop_rate)
+
+        # stochastic depth
+        dpr = [x.item() for x in torch.linspace(0, self.drop_path_rate, sum(self.depths))]  # stochastic depth decay rule
+
+        # build layers
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = BasicLayer(dim=int(self.embed_dim * 2 ** i_layer),
+                input_resolution=(patches_resolution[0] // (2 ** i_layer),
+                                    patches_resolution[1] // (2 ** i_layer)),
+                depth=self.depths[i_layer],
+                num_heads=self.num_heads[i_layer],
+                window_size=self.window_size,
+                mlp_ratio=self.mlp_ratio,
+                qkv_bias=self.qkv_bias, qk_scale=self.qk_scale,
+                drop=self.drop_rate, attn_drop=self.attn_drop_rate,
+                drop_path=dpr[sum(self.depths[:i_layer]):sum(self.depths[:i_layer + 1])],
+                norm_layer=self.norm_layer,
+                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
+                use_checkpoint=use_checkpoint,
+                norm_before_mlp=self.norm_before_mlp)
+            self.layers.append(layer)
+
+        self.norm = self.norm_layer(self.num_features)
+        self.avgpool = nn.AdaptiveAvgPool1d(1)
+        self.maxpool = nn.AdaptiveMaxPool1d(1)
+
+        if self.config.enable_tscam:
+            SF = self.spec_size // (2 ** (len(self.depths) - 1)) // self.patch_stride[0] // self.freq_ratio
+            self.tscam_conv = nn.Conv2d(
+                in_channels = self.num_features,
+                out_channels = self.num_classes,
+                kernel_size = (SF,3),
+                padding = (0,1)
+            )
+            self.head = nn.Linear(num_classes, num_classes)
+        else:
+            self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'absolute_pos_embed'}
+
+    @torch.jit.ignore
+    def no_weight_decay_keywords(self):
+        return {'relative_position_bias_table'}
+
+    def forward_features(self, x):
+        frames_num = x.shape[2]        
+        x = self.patch_embed(x)
+        if self.ape:
+            x = x + self.absolute_pos_embed
+        x = self.pos_drop(x)
+        for i, layer in enumerate(self.layers):
+            x, attn = layer(x)
+
+        if self.config.enable_tscam:
+            # for x
+            x = self.norm(x)
+            B, N, C = x.shape
+            SF = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[0]
+            ST = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[1]
+            x = x.permute(0,2,1).contiguous().reshape(B, C, SF, ST)
+            B, C, F, T = x.shape
+            # group 2D CNN
+            c_freq_bin = F // self.freq_ratio
+            x = x.reshape(B, C, F // c_freq_bin, c_freq_bin, T)
+            x = x.permute(0,1,3,2,4).contiguous().reshape(B, C, c_freq_bin, -1)
+
+            # get latent_output
+            latent_output = self.avgpool(torch.flatten(x,2))
+            latent_output = torch.flatten(latent_output, 1)
+
+            # display the attention map, if needed
+            if self.config.htsat_attn_heatmap:
+                # for attn
+                attn = torch.mean(attn, dim = 1)
+                attn = torch.mean(attn, dim = 1)
+                attn = attn.reshape(B, SF, ST)
+                c_freq_bin = SF // self.freq_ratio
+                attn = attn.reshape(B, SF // c_freq_bin, c_freq_bin, ST) 
+                attn = attn.permute(0,2,1,3).contiguous().reshape(B, c_freq_bin, -1)
+                attn = attn.mean(dim = 1)
+                attn_max = torch.max(attn, dim = 1, keepdim = True)[0]
+                attn_min = torch.min(attn, dim = 1, keepdim = True)[0]
+                attn = ((attn * 0.15) + (attn_max * 0.85 - attn_min)) / (attn_max - attn_min)
+                attn = attn.unsqueeze(dim = 2)
+
+            x = self.tscam_conv(x)
+            x = torch.flatten(x, 2) # B, C, T
+
+            if self.config.htsat_attn_heatmap:
+                fpx = interpolate(torch.sigmoid(x).permute(0,2,1).contiguous() * attn, 8 * self.patch_stride[1]) 
+            else: 
+                fpx = interpolate(torch.sigmoid(x).permute(0,2,1).contiguous(), 8 * self.patch_stride[1]) 
+            
+            x = self.avgpool(x)
+            x = torch.flatten(x, 1)
+
+            if self.config.loss_type == "clip_ce":
+                output_dict = {
+                    'framewise_output': fpx, # already sigmoided
+                    'clipwise_output': x,
+                    'latent_output': latent_output
+                }
+            else:
+                output_dict = {
+                    'framewise_output': fpx, # already sigmoided
+                    'clipwise_output': torch.sigmoid(x),
+                    'latent_output': latent_output
+                }
+           
+        else:
+            x = self.norm(x)  # B N C
+            B, N, C = x.shape
+            
+            fpx = x.permute(0,2,1).contiguous().reshape(B, C, frames_num // (2 ** (len(self.depths) + 1)), frames_num // (2 ** (len(self.depths) + 1)) )
+            B, C, F, T = fpx.shape
+            c_freq_bin = F // self.freq_ratio
+            fpx = fpx.reshape(B, C, F // c_freq_bin, c_freq_bin, T)
+            fpx = fpx.permute(0,1,3,2,4).contiguous().reshape(B, C, c_freq_bin, -1)
+            fpx = torch.sum(fpx, dim = 2)
+            fpx = interpolate(fpx.permute(0,2,1).contiguous(), 8 * self.patch_stride[1]) 
+            x = self.avgpool(x.transpose(1, 2))  # B C 1
+            x = torch.flatten(x, 1)
+            if self.num_classes > 0:
+                x = self.head(x)
+                fpx = self.head(fpx)
+            output_dict = {'framewise_output': torch.sigmoid(fpx), 
+                'clipwise_output': torch.sigmoid(x)}
+        return output_dict
+
+    def crop_wav(self, x, crop_size, spe_pos = None):
+        time_steps = x.shape[2]
+        tx = torch.zeros(x.shape[0], x.shape[1], crop_size, x.shape[3]).to(x.device)
+        for i in range(len(x)):
+            if spe_pos is None:
+                crop_pos = random.randint(0, time_steps - crop_size - 1)
+            else:
+                crop_pos = spe_pos
+            tx[i][0] = x[i, 0, crop_pos:crop_pos + crop_size,:]
+        return tx
+
+    # Reshape the wavform to a img size, if you want to use the pretrained swin transformer model
+    def reshape_wav2img(self, x):
+        B, C, T, F = x.shape
+        target_T = int(self.spec_size * self.freq_ratio)
+        target_F = self.spec_size // self.freq_ratio
+        assert T <= target_T and F <= target_F, "the wav size should less than or equal to the swin input size"
+        # to avoid bicubic zero error
+        if T < target_T:
+            x = nn.functional.interpolate(x, (target_T, x.shape[3]), mode="bicubic", align_corners=True)
+        if F < target_F:
+            x = nn.functional.interpolate(x, (x.shape[2], target_F), mode="bicubic", align_corners=True)
+        x = x.permute(0,1,3,2).contiguous()
+        x = x.reshape(x.shape[0], x.shape[1], x.shape[2], self.freq_ratio, x.shape[3] // self.freq_ratio)
+        # print(x.shape)
+        x = x.permute(0,1,3,2,4).contiguous()
+        x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3], x.shape[4])
+        return x
+    
+    # Repeat the wavform to a img size, if you want to use the pretrained swin transformer model
+    def repeat_wat2img(self, x, cur_pos):
+        B, C, T, F = x.shape
+        target_T = int(self.spec_size * self.freq_ratio)
+        target_F = self.spec_size // self.freq_ratio
+        assert T <= target_T and F <= target_F, "the wav size should less than or equal to the swin input size"
+        # to avoid bicubic zero error
+        if T < target_T:
+            x = nn.functional.interpolate(x, (target_T, x.shape[3]), mode="bicubic", align_corners=True)
+        if F < target_F:
+            x = nn.functional.interpolate(x, (x.shape[2], target_F), mode="bicubic", align_corners=True)  
+        x = x.permute(0,1,3,2).contiguous() # B C F T
+        x = x[:,:,:,cur_pos:cur_pos + self.spec_size]
+        x = x.repeat(repeats = (1,1,4,1))
+        return x
+
+    def forward(self, x: torch.Tensor, mixup_lambda = None, infer_mode = False):# out_feat_keys: List[str] = None):
+        x = self.spectrogram_extractor(x)   # (batch_size, 1, time_steps, freq_bins)
+        x = self.logmel_extractor(x)    # (batch_size, 1, time_steps, mel_bins)
+        
+        
+        x = x.transpose(1, 3)
+        x = self.bn0(x)
+        x = x.transpose(1, 3)
+        if self.training:
+            x = self.spec_augmenter(x)
+        if self.training and mixup_lambda is not None:
+            x = do_mixup(x, mixup_lambda)
+        
+        if infer_mode:
+            # in infer mode. we need to handle different length audio input
+            frame_num = x.shape[2]
+            target_T = int(self.spec_size * self.freq_ratio)
+            repeat_ratio = math.floor(target_T / frame_num)
+            x = x.repeat(repeats=(1,1,repeat_ratio,1))
+            x = self.reshape_wav2img(x)
+            output_dict = self.forward_features(x)
+        elif self.config.enable_repeat_mode:
+            if self.training:
+                cur_pos = random.randint(0, (self.freq_ratio - 1) * self.spec_size - 1)
+                x = self.repeat_wat2img(x, cur_pos)
+                output_dict = self.forward_features(x)
+            else:
+                output_dicts = []
+                for cur_pos in range(0, (self.freq_ratio - 1) * self.spec_size + 1, self.spec_size):
+                    tx = x.clone()
+                    tx = self.repeat_wat2img(tx, cur_pos)
+                    output_dicts.append(self.forward_features(tx))
+                clipwise_output = torch.zeros_like(output_dicts[0]["clipwise_output"]).float().to(x.device)
+                framewise_output = torch.zeros_like(output_dicts[0]["framewise_output"]).float().to(x.device)
+                for d in output_dicts:
+                    clipwise_output += d["clipwise_output"]
+                    framewise_output += d["framewise_output"]
+                clipwise_output  = clipwise_output / len(output_dicts)
+                framewise_output = framewise_output / len(output_dicts)
+
+                output_dict = {
+                    'framewise_output': framewise_output, 
+                    'clipwise_output': clipwise_output
+                }
+        else:
+            if x.shape[2] > self.freq_ratio * self.spec_size:
+                if self.training:
+                    x = self.crop_wav(x, crop_size=self.freq_ratio * self.spec_size)
+                    x = self.reshape_wav2img(x)
+                    output_dict = self.forward_features(x)
+                else:
+                    # Change: Hard code here
+                    overlap_size = 344 #(x.shape[2] - 1) // 4
+                    output_dicts = []
+                    crop_size = 689 #(x.shape[2] - 1) // 2
+                    for cur_pos in range(0, x.shape[2] - crop_size - 1, overlap_size):
+                        tx = self.crop_wav(x, crop_size = crop_size, spe_pos = cur_pos)
+                        tx = self.reshape_wav2img(tx)
+                        output_dicts.append(self.forward_features(tx))
+                    clipwise_output = torch.zeros_like(output_dicts[0]["clipwise_output"]).float().to(x.device)
+                    framewise_output = torch.zeros_like(output_dicts[0]["framewise_output"]).float().to(x.device)
+                    latent_output = torch.zeros_like(output_dicts[0]["latent_output"]).float().to(x.device)
+                    for d in output_dicts:
+                        clipwise_output += d["clipwise_output"]
+                        framewise_output += d["framewise_output"]
+                        latent_output += d["latent_output"]
+                    clipwise_output  = clipwise_output / len(output_dicts)
+                    framewise_output = framewise_output / len(output_dicts)
+                    latent_output = latent_output / len(output_dicts)
+                    output_dict = {
+                        'framewise_output': framewise_output, 
+                        'clipwise_output': clipwise_output,
+                        'latent_output': latent_output,
+                    }
+            else: # this part is typically used, and most easy one
+                x = self.reshape_wav2img(x)
+                output_dict = self.forward_features(x)
+        # x = self.head(x)
+        return output_dict
+
+class HTSATWrapper(nn.Module):
+    def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, 
+        fmax, classes_num, out_emb):
+        super().__init__()
+
+        # print("parameters are being overidden when using HTSAT")
+        # print("HTSAT only support loading a pretrained model on AudioSet")
+        # @TODO later look at what parameters are same and can be merged
+
+        self.htsat = HTSAT_Swin_Transformer(config=config)
+
+    def forward(self, x):
+        out_dict = self.htsat(x)
+        out_dict['embedding'] = out_dict['latent_output']
+        return out_dict

msclap/models/mapper.py

@@ -0,0 +1,199 @@
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as nnf
+from enum import Enum
+from transformers import GPT2LMHeadModel
+from typing import Tuple, Optional
+
+def get_clapcap(name: str):
+    if name == "ClapCaption":
+        return ClapCaptionModel
+    else:
+        raise Exception('The ClapCap model {} is incorrect or not supported'.format(name))
+
+class MappingType(Enum):
+    MLP = 'mlp'
+    Transformer = 'transformer'
+
+class MLP(nn.Module):
+    def __init__(self, sizes: Tuple[int, ...], bias=True, act=nn.Tanh):
+        super(MLP, self).__init__()
+        layers = []
+        for i in range(len(sizes) - 1):
+            layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=bias))
+            if i < len(sizes) - 2:
+                layers.append(act())
+        self.model = nn.Sequential(*layers)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.model(x)
+
+
+class MlpTransformer(nn.Module):
+    def __init__(self, in_dim, h_dim, out_d: Optional[int] = None, act=nnf.relu, dropout=0.):
+        super().__init__()
+        out_d = out_d if out_d is not None else in_dim
+        self.fc1 = nn.Linear(in_dim, h_dim)
+        self.act = act
+        self.fc2 = nn.Linear(h_dim, out_d)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.dropout(x)
+        x = self.fc2(x)
+        x = self.dropout(x)
+        return x
+
+class MultiHeadAttention(nn.Module):
+
+    def __init__(self, dim_self, dim_ref, num_heads, bias=True, dropout=0.):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim_self // num_heads
+        self.scale = head_dim ** -0.5
+        self.to_queries = nn.Linear(dim_self, dim_self, bias=bias)
+        self.to_keys_values = nn.Linear(dim_ref, dim_self * 2, bias=bias)
+        self.project = nn.Linear(dim_self, dim_self)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x, y=None, mask=None):
+        y = y if y is not None else x
+        b, n, c = x.shape
+        _, m, d = y.shape
+        # b n h dh
+        queries = self.to_queries(x).reshape(b, n, self.num_heads, c // self.num_heads)
+        # b m 2 h dh
+        keys_values = self.to_keys_values(y).reshape(b, m, 2, self.num_heads, c // self.num_heads)
+        keys, values = keys_values[:, :, 0], keys_values[:, :, 1]
+        attention = torch.einsum('bnhd,bmhd->bnmh', queries, keys) * self.scale
+        if mask is not None:
+            if mask.dim() == 2:
+                mask = mask.unsqueeze(1)
+            attention = attention.masked_fill(mask.unsqueeze(3), float("-inf"))
+        attention = attention.softmax(dim=2)
+        out = torch.einsum('bnmh,bmhd->bnhd', attention, values).reshape(b, n, c)
+        out = self.project(out)
+        return out, attention
+
+
+class TransformerLayer(nn.Module):
+
+    def forward_with_attention(self, x, y=None, mask=None):
+        x_, attention = self.attn(self.norm1(x), y, mask)
+        x = x + x_
+        x = x + self.mlp(self.norm2(x))
+        return x, attention
+
+    def forward(self, x, y=None, mask=None):
+        x = x + self.attn(self.norm1(x), y, mask)[0]
+        x = x + self.mlp(self.norm2(x))
+        return x
+
+    def __init__(self, dim_self, dim_ref, num_heads, mlp_ratio=4., bias=False, dropout=0., act=nnf.relu,
+                 norm_layer: nn.Module = nn.LayerNorm):
+        super().__init__()
+        self.norm1 = norm_layer(dim_self)
+        self.attn = MultiHeadAttention(dim_self, dim_ref, num_heads, bias=bias, dropout=dropout)
+        self.norm2 = norm_layer(dim_self)
+        self.mlp = MlpTransformer(dim_self, int(dim_self * mlp_ratio), act=act, dropout=dropout)
+
+
+class Transformer(nn.Module):
+    def __init__(self, dim_self: int, num_heads: int, num_layers: int, dim_ref: Optional[int] = None,
+                 mlp_ratio: float = 2., act=nnf.relu, norm_layer: nn.Module = nn.LayerNorm, enc_dec: bool = False):
+        super(Transformer, self).__init__()
+        dim_ref = dim_ref if dim_ref is not None else dim_self
+        self.enc_dec = enc_dec
+        if enc_dec:
+            num_layers = num_layers * 2
+        layers = []
+        for i in range(num_layers):
+            if i % 2 == 0 and enc_dec:  # cross
+                layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer))
+            elif enc_dec:  # self
+                layers.append(TransformerLayer(dim_self, dim_self, num_heads, mlp_ratio, act=act, norm_layer=norm_layer))
+            else:  # self or cross
+                layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer))
+        self.layers = nn.ModuleList(layers)
+
+    def forward_with_attention(self, x, y=None, mask=None):
+        attentions = []
+        for layer in self.layers:
+            x, att = layer.forward_with_attention(x, y, mask)
+            attentions.append(att)
+        return x, attentions
+
+    def forward(self, x, y=None, mask=None):
+        for i, layer in enumerate(self.layers):
+            if i % 2 == 0 and self.enc_dec: # cross
+                x = layer(x, y)
+            elif self.enc_dec:  # self
+                x = layer(x, x, mask)
+            else:  # self or cross
+                x = layer(x, y, mask)
+        return x
+
+
+class TransformerMapper(nn.Module):
+    def __init__(self, dim_clip: int, dim_embedding: int, prefix_length: int, clip_length: int, num_layers: int = 8):
+        super(TransformerMapper, self).__init__()
+        self.clip_length = clip_length
+        self.transformer = Transformer(dim_embedding, 8, num_layers)
+        self.linear = nn.Linear(dim_clip, clip_length * dim_embedding)
+        self.prefix_const = nn.Parameter(torch.randn(prefix_length, dim_embedding), requires_grad=True)
+
+    def forward(self, x):
+        x = self.linear(x).view(x.shape[0], self.clip_length, -1)
+        prefix = self.prefix_const.unsqueeze(0).expand(x.shape[0], *self.prefix_const.shape)
+        prefix = torch.cat((x, prefix), dim=1)
+        out = self.transformer(prefix)[:, self.clip_length:]
+        return out
+
+class ClapCaptionModel(nn.Module):
+    def __init__(self, clap, text_decoder: str, prefix_length: int, clip_length: Optional[int] = None, prefix_size: int = 512,
+                 num_layers: int = 8, normalize_prefix: bool = True, mapping_type: str = None,\
+                 freeze_audio_encoder_weights: bool = True, freeze_gpt_weights: bool = True):
+        super(ClapCaptionModel, self).__init__()
+        self.clap = clap.audio_encoder
+        self.prefix_length = prefix_length
+        self.normalize_prefix = normalize_prefix
+        self.gpt = GPT2LMHeadModel.from_pretrained(text_decoder)
+        self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1]
+        if mapping_type == 'mlp':
+            self.clap_project = MLP((prefix_size, (self.gpt_embedding_size * prefix_length) // 2,
+                                     self.gpt_embedding_size * prefix_length))
+        else:
+            self.clap_project = TransformerMapper(prefix_size, self.gpt_embedding_size, prefix_length,
+                                                                     clip_length, num_layers)
+
+        # Freeze all CLAP parameters
+        if freeze_audio_encoder_weights:
+            for p in self.clap.parameters():
+                p.requires_grad = False
+        
+        if freeze_gpt_weights:
+            for p in self.gpt.parameters():
+                p.requires_grad = False
+
+    def get_dummy_token(self, batch_size: int, device: torch.device) -> torch.Tensor:
+        return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device)
+
+    def forward(self, audios: torch.Tensor, tokens: torch.Tensor, mask: Optional[torch.Tensor] = None,
+                labels: Optional[torch.Tensor] = None):
+        # get audio embeddings
+        prefix, _ = self.clap(audios)
+        # normalize prefix (audio embedding)
+        if self.normalize_prefix:
+            prefix = prefix / prefix.norm(2, -1).reshape(-1,1)
+
+        embedding_text = self.gpt.transformer.wte(tokens['input_ids'])
+        prefix_projections = self.clap_project(prefix).view(-1, self.prefix_length, self.gpt_embedding_size)
+        embedding_cat = torch.cat((prefix_projections, embedding_text), dim=1)
+        if labels is not None:
+            dummy_token = self.get_dummy_token(tokens['input_ids'].shape[0], tokens['input_ids'].device)
+            labels = torch.cat((dummy_token, tokens), dim=1)
+        out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask)
+        return out

msclap/models/pytorch_utils.py

@@ -0,0 +1,182 @@
+import torch
+import torch.nn as nn
+
+
+def move_data_to_device(x, device):
+    if 'float' in str(x.dtype):
+        x = torch.Tensor(x)
+    elif 'int' in str(x.dtype):
+        x = torch.LongTensor(x)
+    else:
+        return x
+
+    return x.to(device)
+
+
+def do_mixup(x, mixup_lambda):
+    """Mixup x of even indexes (0, 2, 4, ...) with x of odd indexes 
+    (1, 3, 5, ...).
+    Args:
+      x: (batch_size * 2, ...)
+      mixup_lambda: (batch_size * 2,)
+    Returns:
+      out: (batch_size, ...)
+    """
+    out = (x[0 :: 2].transpose(0, -1) * mixup_lambda[0 :: 2] + \
+        x[1 :: 2].transpose(0, -1) * mixup_lambda[1 :: 2]).transpose(0, -1)
+    return out
+    
+
+def append_to_dict(dict, key, value):
+    if key in dict.keys():
+        dict[key].append(value)
+    else:
+        dict[key] = [value]
+
+
+def interpolate(x, ratio):
+    """Interpolate data in time domain. This is used to compensate the 
+    resolution reduction in downsampling of a CNN.
+    
+    Args:
+      x: (batch_size, time_steps, classes_num)
+      ratio: int, ratio to interpolate
+    Returns:
+      upsampled: (batch_size, time_steps * ratio, classes_num)
+    """
+    (batch_size, time_steps, classes_num) = x.shape
+    upsampled = x[:, :, None, :].repeat(1, 1, ratio, 1)
+    upsampled = upsampled.reshape(batch_size, time_steps * ratio, classes_num)
+    return upsampled
+
+
+def pad_framewise_output(framewise_output, frames_num):
+    """Pad framewise_output to the same length as input frames. The pad value 
+    is the same as the value of the last frame.
+    Args:
+      framewise_output: (batch_size, frames_num, classes_num)
+      frames_num: int, number of frames to pad
+    Outputs:
+      output: (batch_size, frames_num, classes_num)
+    """
+    pad = framewise_output[:, -1 :, :].repeat(1, frames_num - framewise_output.shape[1], 1)
+    """tensor for padding"""
+
+    output = torch.cat((framewise_output, pad), dim=1)
+    """(batch_size, frames_num, classes_num)"""
+
+    return output
+
+
+def count_parameters(model):
+    return sum(p.numel() for p in model.parameters() if p.requires_grad)
+
+
+def count_flops(model, audio_length):
+    """Count flops. Code modified from others' implementation.
+    """
+    multiply_adds = True
+    list_conv2d=[]
+    def conv2d_hook(self, input, output):
+        batch_size, input_channels, input_height, input_width = input[0].size()
+        output_channels, output_height, output_width = output[0].size()
+ 
+        kernel_ops = self.kernel_size[0] * self.kernel_size[1] * (self.in_channels / self.groups) * (2 if multiply_adds else 1)
+        bias_ops = 1 if self.bias is not None else 0
+ 
+        params = output_channels * (kernel_ops + bias_ops)
+        flops = batch_size * params * output_height * output_width
+ 
+        list_conv2d.append(flops)
+
+    list_conv1d=[]
+    def conv1d_hook(self, input, output):
+        batch_size, input_channels, input_length = input[0].size()
+        output_channels, output_length = output[0].size()
+ 
+        kernel_ops = self.kernel_size[0] * (self.in_channels / self.groups) * (2 if multiply_adds else 1)
+        bias_ops = 1 if self.bias is not None else 0
+ 
+        params = output_channels * (kernel_ops + bias_ops)
+        flops = batch_size * params * output_length
+ 
+        list_conv1d.append(flops)
+ 
+    list_linear=[] 
+    def linear_hook(self, input, output):
+        batch_size = input[0].size(0) if input[0].dim() == 2 else 1
+ 
+        weight_ops = self.weight.nelement() * (2 if multiply_adds else 1)
+        bias_ops = self.bias.nelement()
+ 
+        flops = batch_size * (weight_ops + bias_ops)
+        list_linear.append(flops)
+ 
+    list_bn=[] 
+    def bn_hook(self, input, output):
+        list_bn.append(input[0].nelement() * 2)
+ 
+    list_relu=[] 
+    def relu_hook(self, input, output):
+        list_relu.append(input[0].nelement() * 2)
+ 
+    list_pooling2d=[]
+    def pooling2d_hook(self, input, output):
+        batch_size, input_channels, input_height, input_width = input[0].size()
+        output_channels, output_height, output_width = output[0].size()
+ 
+        kernel_ops = self.kernel_size * self.kernel_size
+        bias_ops = 0
+        params = output_channels * (kernel_ops + bias_ops)
+        flops = batch_size * params * output_height * output_width
+ 
+        list_pooling2d.append(flops)
+
+    list_pooling1d=[]
+    def pooling1d_hook(self, input, output):
+        batch_size, input_channels, input_length = input[0].size()
+        output_channels, output_length = output[0].size()
+ 
+        kernel_ops = self.kernel_size[0]
+        bias_ops = 0
+        
+        params = output_channels * (kernel_ops + bias_ops)
+        flops = batch_size * params * output_length
+ 
+        list_pooling2d.append(flops)
+ 
+    def foo(net):
+        childrens = list(net.children())
+        if not childrens:
+            if isinstance(net, nn.Conv2d):
+                net.register_forward_hook(conv2d_hook)
+            elif isinstance(net, nn.Conv1d):
+                net.register_forward_hook(conv1d_hook)
+            elif isinstance(net, nn.Linear):
+                net.register_forward_hook(linear_hook)
+            elif isinstance(net, nn.BatchNorm2d) or isinstance(net, nn.BatchNorm1d):
+                net.register_forward_hook(bn_hook)
+            elif isinstance(net, nn.ReLU):
+                net.register_forward_hook(relu_hook)
+            elif isinstance(net, nn.AvgPool2d) or isinstance(net, nn.MaxPool2d):
+                net.register_forward_hook(pooling2d_hook)
+            elif isinstance(net, nn.AvgPool1d) or isinstance(net, nn.MaxPool1d):
+                net.register_forward_hook(pooling1d_hook)
+            else:
+                print('Warning: flop of module {} is not counted!'.format(net))
+            return
+        for c in childrens:
+            foo(c)
+
+    # Register hook
+    foo(model)
+    
+    device = device = next(model.parameters()).device
+    input = torch.rand(1, audio_length).to(device)
+
+    out = model(input)
+ 
+    total_flops = sum(list_conv2d) + sum(list_conv1d) + sum(list_linear) + \
+        sum(list_bn) + sum(list_relu) + sum(list_pooling2d) + sum(list_pooling1d)
+    
+    return total_flops

pyproject.toml

@@ -0,0 +1,28 @@
+[tool.poetry]
+name = "msclap"
+version = "1.3.4"
+description = "CLAP (Contrastive Language-Audio Pretraining) is a model that learns acoustic concepts from natural language supervision and enables “Zero-Shot” inference. The model has been extensively evaluated in 26 audio downstream tasks achieving SoTA in several of them including classification, retrieval, and captioning."
+authors = ["Benjamin Elizalde", "Soham Deshmukh", "Huaming Wang"]
+license = "MIT"
+readme = "README.md"
+packages = [
+    { include = "msclap" },
+]
+
+[tool.poetry.dependencies]
+python = "^3.8"
+librosa = "^0.10.1"
+numpy = "^1.23.0"
+pandas = "^2.0.0"
+torch = "^2.1.0"
+torchaudio = "^2.1.0"
+torchlibrosa = "^0.1.0"
+tqdm = "^4.66.1"
+transformers = "^4.34.0"
+pyyaml = "^6.0.1"
+scikit-learn = "^1.3.1"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

requirements.txt

@@ -1,59 +0,0 @@
-appdirs==1.4.4
-audioread==2.1.9
-certifi==2020.12.5
-cffi==1.14.5
-chardet==4.0.0
-click==7.1.2
-configparser==5.0.2
-cycler==0.10.0
-decorator==5.0.7
-docker-pycreds==0.4.0
-filelock==3.0.12
-gitdb==4.0.7
-GitPython==3.1.14
-h5py==3.2.1
-idna==2.10
-joblib==1.2.0
-kiwisolver==1.3.1
-librosa==0.8.0
-llvmlite==0.36.0
-matplotlib==3.4.1
-numba==0.53.1
-numpy==1.22.0
-packaging==20.9
-pandas==1.2.4
-pathtools==0.1.2
-Pillow==9.0.1
-pooch==1.3.0
-promise==2.3
-protobuf==3.18.3
-psutil==5.8.0
-pycparser==2.20
-pyparsing==2.4.7
-python-dateutil==2.8.1
-pytz==2021.1
-PyYAML==5.4.1
-regex==2021.4.4
-requests==2.25.1
-resampy==0.2.2
-sacremoses==0.0.45
-scikit-learn==0.24.2
-scipy==1.6.3
-sentry-sdk==1.0.0
-shortuuid==1.0.1
-six==1.15.0
-smmap==4.0.0
-SoundFile==0.10.3.post1
-subprocess32==3.5.4
-threadpoolctl==2.1.0
-tokenizers==0.10.2
---find-links https://download.pytorch.org/whl/torch_stable.html
-torch==1.8.1+cu111
-torchaudio==0.8.1
-torchlibrosa==0.0.9
-torchvision==0.9.1+cu111
-tqdm==4.60.0
-transformers==4.5.1
-typing-extensions==3.10.0.0
-urllib3==1.26.5
-importlib-resources==5.10.0

src/CLAPWrapper.py

@@ -1,237 +0,0 @@
-import random
-import torchaudio
-from torch._six import string_classes
-import collections
-import re
-import torch.nn.functional as F
-import numpy as np
-from transformers import AutoTokenizer
-from models.utils import read_config_as_args
-from models.clap import CLAP
-import math
-import torchaudio.transforms as T
-import os
-import torch
-from importlib_resources import files
-
-
-class CLAPWrapper():
-    """
-    A class for interfacing CLAP model.  
-    """
-
-    def __init__(self, model_fp, use_cuda=False):
-        self.np_str_obj_array_pattern = re.compile(r'[SaUO]')
-        self.file_path = os.path.realpath(__file__)
-        self.default_collate_err_msg_format = (
-            "default_collate: batch must contain tensors, numpy arrays, numbers, "
-            "dicts or lists; found {}")
-        self.config_as_str = files('configs').joinpath('config.yml').read_text()
-        self.model_fp = model_fp
-        self.use_cuda = use_cuda
-        self.clap, self.tokenizer, self.args = self.load_clap()
-
-    def load_clap(self):
-        r"""Load CLAP model with args from config file"""
-
-        args = read_config_as_args(self.config_as_str, is_config_str=True)
-
-        if 'bert' in args.text_model:
-            self.token_keys = ['input_ids', 'token_type_ids', 'attention_mask']
-        else:
-            self.token_keys = ['input_ids', 'attention_mask']
-
-        clap = CLAP(
-            audioenc_name=args.audioenc_name,
-            sample_rate=args.sampling_rate,
-            window_size=args.window_size,
-            hop_size=args.hop_size,
-            mel_bins=args.mel_bins,
-            fmin=args.fmin,
-            fmax=args.fmax,
-            classes_num=args.num_classes,
-            out_emb=args.out_emb,
-            text_model=args.text_model,
-            transformer_embed_dim=args.transformer_embed_dim,
-            d_proj=args.d_proj
-        )
-
-        # Load pretrained weights for model
-        model_state_dict = torch.load(self.model_fp, map_location=torch.device('cpu'))['model']
-        clap.load_state_dict(model_state_dict)
-
-        clap.eval()  # set clap in eval mode
-        tokenizer = AutoTokenizer.from_pretrained(args.text_model)
-
-        if self.use_cuda and torch.cuda.is_available():
-            clap = clap.cuda()
-
-        return clap, tokenizer, args
-
-    def default_collate(self, batch):
-        r"""Puts each data field into a tensor with outer dimension batch size"""
-        elem = batch[0]
-        elem_type = type(elem)
-        if isinstance(elem, torch.Tensor):
-            out = None
-            if torch.utils.data.get_worker_info() is not None:
-                # If we're in a background process, concatenate directly into a
-                # shared memory tensor to avoid an extra copy
-                numel = sum([x.numel() for x in batch])
-                storage = elem.storage()._new_shared(numel)
-                out = elem.new(storage)
-            return torch.stack(batch, 0, out=out)
-        elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
-                and elem_type.__name__ != 'string_':
-            if elem_type.__name__ == 'ndarray' or elem_type.__name__ == 'memmap':
-                # array of string classes and object
-                if self.np_str_obj_array_pattern.search(elem.dtype.str) is not None:
-                    raise TypeError(
-                        self.default_collate_err_msg_format.format(elem.dtype))
-
-                return self.default_collate([torch.as_tensor(b) for b in batch])
-            elif elem.shape == ():  # scalars
-                return torch.as_tensor(batch)
-        elif isinstance(elem, float):
-            return torch.tensor(batch, dtype=torch.float64)
-        elif isinstance(elem, int):
-            return torch.tensor(batch)
-        elif isinstance(elem, string_classes):
-            return batch
-        elif isinstance(elem, collections.abc.Mapping):
-            return {key: self.default_collate([d[key] for d in batch]) for key in elem}
-        elif isinstance(elem, tuple) and hasattr(elem, '_fields'):  # namedtuple
-            return elem_type(*(self.default_collate(samples) for samples in zip(*batch)))
-        elif isinstance(elem, collections.abc.Sequence):
-            # check to make sure that the elements in batch have consistent size
-            it = iter(batch)
-            elem_size = len(next(it))
-            if not all(len(elem) == elem_size for elem in it):
-                raise RuntimeError(
-                    'each element in list of batch should be of equal size')
-            transposed = zip(*batch)
-            return [self.default_collate(samples) for samples in transposed]
-
-        raise TypeError(self.default_collate_err_msg_format.format(elem_type))
-
-    def load_audio_into_tensor(self, audio_path, audio_duration, resample=False):
-        r"""Loads audio file and returns raw audio."""
-        # Randomly sample a segment of audio_duration from the clip or pad to match duration
-        audio_time_series, sample_rate = torchaudio.load(audio_path)
-        resample_rate = self.args.sampling_rate
-        if resample:
-            resampler = T.Resample(sample_rate, resample_rate)
-            audio_time_series = resampler(audio_time_series)
-        audio_time_series = audio_time_series.reshape(-1)
-
-        # audio_time_series is shorter than predefined audio duration,
-        # so audio_time_series is extended
-        if audio_duration*sample_rate >= audio_time_series.shape[0]:
-            repeat_factor = int(np.ceil((audio_duration*sample_rate) /
-                                        audio_time_series.shape[0]))
-            # Repeat audio_time_series by repeat_factor to match audio_duration
-            audio_time_series = audio_time_series.repeat(repeat_factor)
-            # remove excess part of audio_time_series
-            audio_time_series = audio_time_series[0:audio_duration*sample_rate]
-        else:
-            # audio_time_series is longer than predefined audio duration,
-            # so audio_time_series is trimmed
-            start_index = random.randrange(
-                audio_time_series.shape[0] - audio_duration*sample_rate)
-            audio_time_series = audio_time_series[start_index:start_index +
-                                                  audio_duration*sample_rate]
-        return torch.FloatTensor(audio_time_series)
-
-    def preprocess_audio(self, audio_files, resample):
-        r"""Load list of audio files and return raw audio"""
-        audio_tensors = []
-        for audio_file in audio_files:
-            audio_tensor = self.load_audio_into_tensor(
-                audio_file, self.args.duration, resample)
-            audio_tensor = audio_tensor.reshape(
-                1, -1).cuda() if self.use_cuda and torch.cuda.is_available() else audio_tensor.reshape(1, -1)
-            audio_tensors.append(audio_tensor)
-        return self.default_collate(audio_tensors)
-
-    def preprocess_text(self, text_queries):
-        r"""Load list of class labels and return tokenized text"""
-        tokenized_texts = []
-        for ttext in text_queries:
-            tok = self.tokenizer.encode_plus(
-                text=ttext, add_special_tokens=True, max_length=self.args.text_len, pad_to_max_length=True, return_tensors="pt")
-            for key in self.token_keys:
-                tok[key] = tok[key].reshape(-1).cuda() if self.use_cuda and torch.cuda.is_available() else tok[key].reshape(-1)
-            tokenized_texts.append(tok)
-        return self.default_collate(tokenized_texts)
-
-    def get_text_embeddings(self, class_labels):
-        r"""Load list of class labels and return text embeddings"""
-        preprocessed_text = self.preprocess_text(class_labels)
-        text_embeddings = self._get_text_embeddings(preprocessed_text)
-        text_embeddings = text_embeddings/torch.norm(text_embeddings, dim=-1, keepdim=True)
-        return text_embeddings
-
-    def get_audio_embeddings(self, audio_files, resample):
-        r"""Load list of audio files and return a audio embeddings"""
-        preprocessed_audio = self.preprocess_audio(audio_files, resample)
-        audio_embeddings = self._get_audio_embeddings(preprocessed_audio)
-        audio_embeddings = audio_embeddings/torch.norm(audio_embeddings, dim=-1, keepdim=True)
-        return audio_embeddings
-
-    def _get_text_embeddings(self, preprocessed_text):
-        r"""Load preprocessed text and return text embeddings"""
-        with torch.no_grad():
-            text_embeddings = self.clap.caption_encoder(preprocessed_text)
-            text_embeddings = text_embeddings/torch.norm(text_embeddings, dim=-1, keepdim=True)
-            return text_embeddings
-
-    def _get_audio_embeddings(self, preprocessed_audio):
-        r"""Load preprocessed audio and return a audio embeddings"""
-        with torch.no_grad():
-            preprocessed_audio = preprocessed_audio.reshape(
-                preprocessed_audio.shape[0], preprocessed_audio.shape[2])
-            #Append [0] the audio emebdding, [1] has output class probabilities
-            audio_embeddings = self.clap.audio_encoder(preprocessed_audio)[0]
-            audio_embeddings = audio_embeddings/torch.norm(audio_embeddings, dim=-1, keepdim=True)
-            return audio_embeddings
-    
-    def compute_similarity(self, audio_embeddings, text_embeddings):
-        r"""Compute similarity between text and audio embeddings"""
-        logit_scale = self.clap.logit_scale.exp()
-        similarity = logit_scale*text_embeddings @ audio_embeddings.T
-        return similarity.T
-
-    def _generic_batch_inference(self, func, *args):
-        r"""Process audio and/or text per batch"""
-        input_tmp = args[0]
-        batch_size = args[-1]
-        # args[0] has audio_files, args[1] has class_labels
-        inputs = [args[0], args[1]] if len(args) == 3 else [args[0]]
-        args0_len = len(args[0])
-        # compute text_embeddings once for all the audio_files batches
-        if len(inputs) == 2:
-            text_embeddings = self.get_text_embeddings(args[1])
-            inputs = [args[0], args[1], text_embeddings]
-        dataset_idx = 0
-        for _ in range(math.ceil(args0_len/batch_size)):
-            next_batch_idx = dataset_idx + batch_size
-            # batch size is bigger than available audio/text items
-            if next_batch_idx >= args0_len:
-                inputs[0] = input_tmp[dataset_idx:]
-                return func(*tuple(inputs))
-            else:
-                inputs[0] = input_tmp[dataset_idx:next_batch_idx]
-                yield func(*tuple(inputs))
-            dataset_idx = next_batch_idx
-
-    def get_audio_embeddings_per_batch(self, audio_files, batch_size):
-        r"""Load preprocessed audio and return a audio embeddings per batch"""
-        return self._generic_batch_inference(self.get_audio_embeddings, audio_files, batch_size)
-
-    def get_text_embeddings_per_batch(self, class_labels, batch_size):
-        r"""Load preprocessed text and return text embeddings per batch"""
-        return self._generic_batch_inference(self.get_text_embeddings, class_labels, batch_size)
-
-    def classify_audio_files_per_batch(self, audio_files, class_labels, batch_size):
-        r"""Compute classification probabilities for each audio recording in a batch and each class label"""
-        return self._generic_batch_inference(self.classify_audio_files, audio_files, class_labels, batch_size)

src/models/__init__.py

@@ -1,3 +0,0 @@
-from . import clap 
-from . import audio
-from . import utils

src/zero_shot_predictions.py

@@ -1,52 +0,0 @@
-"""
-This is an example using CLAP for zero-shot 
-        inference using ESC50 (https://github.com/karolpiczak/ESC-50).
-"""
-
-from CLAPWrapper import CLAPWrapper
-from esc50_dataset import ESC50
-import torch.nn.functional as F
-
-# Load ESC50 dataset
-dataset = ESC50(root="data_path", download=True) # set download=True when dataset is not downloaded
-audio_file, target, one_hot_target = dataset[1000]
-audio_file = [audio_file]
-prompt = 'this is a sound of '
-y = [prompt + x for x in dataset.classes]
-
-# Load and initialize CLAP
-weights_path = "weights_path"
-
-# Setting use_cuda = True will load the model on a GPU using CUDA
-clap_model = CLAPWrapper(weights_path, use_cuda=False)
-
-# compute text embeddings from natural text 
-text_embeddings = clap_model.get_text_embeddings(y)
-
-# compute the audio embeddings from an audio file 
-audio_embeddings = clap_model.get_audio_embeddings(audio_file, resample=True)
-
-# compute the similarity between audio_embeddings and text_embeddings
-similarity = clap_model.compute_similarity(audio_embeddings, text_embeddings)
-
-similarity = F.softmax(similarity, dim=1)
-values, indices = similarity[0].topk(5)
-
-# view the results
-print("Ground Truth: {}".format(target))
-print("Top predictions:\n")
-for value, index in zip(values, indices):
-    print(f"{dataset.classes[index]:>16s}: {100 * value.item():.2f}%")
-
-"""
-The output (the exact numbers may vary):
-
-Ground Truth: coughing
-Top predictions:
-
-        coughing: 86.34%
-        sneezing: 9.30%
-drinking sipping: 1.31%
-        laughing: 1.20%
-  glass breaking: 0.81%
-"""