version bump

README.md

@@ -1,30 +1,37 @@
 # PaSST package for HEAR 2021 NeurIPS Challenge Holistic Evaluation of Audio Representations
 
-
 This is an implementation for [Efficient Training of Audio Transformers with Patchout](https://arxiv.org/abs/2110.05069) for HEAR 2021 NeurIPS Challenge
 Holistic Evaluation of Audio Representations
 
-# CUDA version 
+# CUDA version
+
 This is an implementation is tested with CUDA version 11.1, and torch installed:
+
 ```shell
 pip3 install torch==1.8.1+cu111  torchaudio==0.8.1 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
 ```
+
 but should work on newer versions of CUDA and torch.
-# Installation 
+
+# Installation
+
 Install the latest version of this repo:
+
 ```shell
-pip install -e 'git+https://github.com/kkoutini/[email protected]#egg=hear21passt' 
+pip install hear21passt
 ```
 
-The models follow the [common API](https://neuralaudio.ai/hear2021-holistic-evaluation-of-audio-representations.html#common-api) of HEAR 21 
+The models follow the [common API](https://neuralaudio.ai/hear2021-holistic-evaluation-of-audio-representations.html#common-api) of HEAR 21
 :
+
 ```shell
 hear-validator --model hear21passt.base.pt hear21passt.base
 hear-validator --model noweights.txt hear21passt.base2levelF
 hear-validator --model noweights.txt hear21passt.base2levelmel
  ```
 
 There are three modules available `hear21passt.base`,`hear21passt.base2level`, `hear21passt.base2levelmel` :
+
 ```python
 import torch
 
@@ -42,16 +49,18 @@ print(embed.shape)
 # Getting the Logits/Class Labels
 
 You can get the logits (before the sigmoid activation) for the 527 classes of audioset:
+
 ```python
 from hear21passt.base import load_model
 
 model = load_model(mode="logits").cuda()
 logits = model(wave_signal)
 ```
-The class labels indices can be found [here](https://github.com/qiuqiangkong/audioset_tagging_cnn/blob/master/metadata/class_labels_indices.csv)
 
+The class labels indices can be found [here](https://github.com/qiuqiangkong/audioset_tagging_cnn/blob/master/metadata/class_labels_indices.csv)
 
 You can also use different pre-trained models, for example, the model trained with KD `passt_s_kd_p16_128_ap486`:
+
 ```python
 from hear21passt.base import get_basic_model
 
@@ -62,7 +71,7 @@ logits = model(wave_signal)
 
 # Supporting longer clips
 
-In case of an input longer than 10 seconds, the `get_scene_embeddings` method compute the average of the embedding of a 10-second overlapping windows. 
+In case of an input longer than 10 seconds, the `get_scene_embeddings` method compute the average of the embedding of a 10-second overlapping windows.
 Depending on the application, it may be useful to use a pre-trained that can extract embeddings from 20 or 30 seconds without averaging. These variant has pre-trained time positional encoding or 20/30 seconds:
 
 ```python
@@ -79,8 +88,6 @@ logits = model(wave_signal)
 
 Each pre-trained model has a specific frequency/time positional encoding, it's necessary to select the correct input shape to be able to load the models. The important variables for loading are `input_tdim`, `fstride` and `tstride` to specify the spectrograms time frames, the patches stride over frequency, and patches stride over time, respectively.
 
-
-
 ```python
 import torch
 
@@ -105,7 +112,7 @@ model.net = get_model_passt("passt_20sec", input_tdim=2000)
 model.net = get_model_passt("passt_30sec", input_tdim=3000)
 ```
 
-If you provide the wrong spectrograms, the model may fail silently, by generating low-quality embeddings and logits. Make sure you have the correct spectrograms' config for the selected pre-trained models. 
+If you provide the wrong spectrograms, the model may fail silently, by generating low-quality embeddings and logits. Make sure you have the correct spectrograms' config for the selected pre-trained models.
 Models with higher spectrogram resolutions, need to specify the correct spectrogram config:
 
 ```python
@@ -134,5 +141,3 @@ model.mel = AugmentMelSTFT(n_mels=128, sr=32000, win_length=800, hopsize=100, n_
 
 
 ```
-
-

hear21passt/__init__.py

@@ -1,8 +1,6 @@
-
-__version__ = "0.0.25"
+__version__ = "0.0.26"
 
 
 def embeding_size(hop=50, embeding_size=1000):
     embedings = 20 * 60 * (1000 / hop)
     return embedings * embeding_size * 4 / (1024 * 1024 * 1024)  # float32 in GB
-

setup.py

@@ -18,6 +18,5 @@
     },
     packages=find_packages(exclude=("tests",)),
     python_requires=">=3.7",
-    install_requires=["timm==0.4.12",
-                      "torchaudio>=0.7.0"]
-)
+    install_requires=["timm>=0.4.12", "torchaudio>=0.7.0"],
+)