Add missing models (#7)

* update requirement to conda

* update env

* add argument to change model

* update models

* remove model details as we now have multiple models

Author: faroit

.gitignore

@@ -1,7 +1,6 @@
 #### joe made this: http://goel.io/joe
 
-env3/
-env/
+env*/
 
 #####=== Python ===#####
 
@@ -70,7 +69,8 @@ target/
 .LSOverride
 
 # Icon must end with two \r
-Icon
+Icon
+
 
 # Thumbnails
 ._*

Pipfile

@@ -2,24 +2,29 @@
 
 <img width="400" align="right" alt="screen shot 2017-11-21 at 12 35 28" src="https://user-images.githubusercontent.com/72940/33071669-be6c35b2-cebc-11e7-8822-9b998ad1ea09.png">
 
-Estimating the number of concurrent speakers from single channel mixtures is a very challenging task that is a mandatory first step to address any realistic “cocktail-party” scenario. It has various audio-based applications such as blind source separation, speaker diarisation, and audio surveillance. Building upon powerful machine learning methodology and the possibility to generate large amounts of learning data, Deep Neural Network (DNN) architectures are well suited to directly estimate speaker counts.
+_CountNet_ is a deep learning model to estimate the number of concurrent speakers from single channel mixtures is a very challenging task that is a mandatory first step to address any realistic “cocktail-party” scenario. It has various audio-based applications such as blind source separation, speaker diarisation, and audio surveillance.
 
-## Publication
+This repo provides pre-trained models.
 
-#### Accepted for ICASSP 2018
+## Publications
 
-* __Title__: Classification vs. Regression in Supervised Learning for Single Channel
+### 2019: IEEE/ACM Transactions on Audio, Speech, and Language Processing
+
+* __Title__: CountNet: Estimating the Number of Concurrent Speakers Using Supervised Learning
  Speaker Count Estimation
-* __Authors__: Fabian-Robert Stöter, Soumitro Chakrabarty, Bernd Edler, Emanuël
+* __Authors__: [Fabian-Robert Stöter](https://faroit.com), Soumitro Chakrabarty, Bernd Edler, Emanuël
  A. P. Habets
-* __Preprint__: [arXiv 1712.04555](http://arxiv.org/abs/1712.04555)
-
-## Model
+* __Preprint__: [HAL](https://hal-lirmm.ccsd.cnrs.fr/lirmm-02010805)
+* __Proceedings__: [IEEE](https://ieeexplore.ieee.org/document/8506601) (paywall)
 
-<img width="360" align="right"  alt="screen shot 2017-11-21 at 12 35 28" src="https://user-images.githubusercontent.com/72940/33072095-60d1929c-cebe-11e7-91de-1dff3fc50bde.png">
-
-In this work a recurrent neural network was trained to generate speaker count estimates for 0 to 10 speakers. The model uses three Bi-LSTM layers inspired by a model for singing voice separation by [Leglaive15](https://hal.archives-ouvertes.fr/hal-01110035).
+### 2018: ICASSP 
 
+* __Title__: Classification vs. Regression in Supervised Learning for Single Channel
+ Speaker Count Estimation
+* __Authors__: [Fabian-Robert Stöter](https://faroit.com), Soumitro Chakrabarty, Bernd Edler, Emanuël
+ A. P. Habets
+* __Preprint__: [arXiv 1712.04555](http://arxiv.org/abs/1712.04555)
+* __Proceedings__: [IEEE](https://ieeexplore.ieee.org/document/8462159) (paywall)
 
 ## Demos
 
@@ -34,22 +39,22 @@ This repository provides the [keras](https://keras.io/) model to be used from Py
 [Docker](https://www.docker.com/) makes it easy to reproduce the results and install all requirements. If you have docker installed, run the following steps to predict a count from the provided test sample.
 
 * Build the docker image: `docker build -t countnet .`
-* Predict from example: `docker run -i countnet python predict_audio.py examples/5_speakers.wav`
+* Predict from example: `docker run -i countnet python predict.py --model CRNN examples/5_speakers.wav`
 
 ### Manual Installation 
 
-Make sure you have Python 3.6, `libsndfile` and `libhdf5` installed on your system (e.g. through Anaconda). To install the requirements run
+To install the requirements using Anaconda Python, run
 
-`pip install -r requirements.txt`
+`conda env create -f env.yml`
 
-You can now run the command line script and process wav files
+You can now run the command line script and process wav files using the pre-trained model `CRNN` (best peformance).
 
-`python predict_audio.py examples/5_speakers.wav`
+`python predict.py examples/5_speakers.wav --model CRNN`
 
 ## Reproduce Paper Results using the LibriCount Dataset
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1216072.svg)](https://doi.org/10.5281/zenodo.1216072)
 
-The full test dataset is available for download on Zenodo.
+The full test dataset is available for download on [Zenodo](https://doi.org/10.5281/zenodo.1216072).
 
 ### LibriCount10 0dB Dataset
 
@@ -85,6 +90,24 @@ In the following example a speaker count of 3 speakers is the ground truth.
 ]
 ```
 
+### Running evaluation
+
+```python eval.py ~/path/to/LibriCount10-0dB --model CRNN``` outputs the _mean absolute error_ per class and averaged.
+
+### Pretrained models
+
+| Name     | Number of Parameters | MAE on test set |
+|----------|----------------------|-----------------|
+| `RNN`    | 0.31M                | 0.38            |
+| `F-CRNN` | 0.06M                | 0.36            |
+| `CRNN`   | 0.35M                | __0.27__        |
+
+
+## FAQ
+
+#### Is it possible to convert the model to run on a modern version of keras with tensorflow backend?
+
+Yes, its possible. But I was unable to get identical results when converting model. I tried this [guide](https://github.com/keras-team/keras/wiki/Converting-convolution-kernels-from-Theano-to-TensorFlow-and-vice-versa) but it still didn't help to get to the same performance compared to keras 1.2.2 and theano.
 
 ## License
 

Pipfile.lock

@@ -0,0 +1,49 @@
+name: countnet
+channels:
+  - defaults
+dependencies:
+  - ca-certificates=2020.1.1
+  - certifi=2020.4.5.2
+  - intel-openmp=2019.4
+  - libcxx=10.0.0
+  - libedit=3.1.20191231
+  - libffi=3.3
+  - mkl=2019.4
+  - mkl-service=2.3.0
+  - ncurses=6.2
+  - openssl=1.1.1g
+  - pip=20.1.1
+  - python=3.6.10
+  - readline=8.0
+  - setuptools=47.3.0
+  - six=1.15.0
+  - sqlite=3.32.2
+  - tk=8.6.10
+  - wheel=0.34.2
+  - xz=5.2.5
+  - zlib=1.2.11
+  - pip:
+    - audioread==2.1.8
+    - backports-weakref==1.0rc1
+    - bleach==1.5.0
+    - cffi==1.14.0
+    - decorator==4.4.2
+    - h5py==2.10.0
+    - html5lib==0.9999999
+    - joblib==0.15.1
+    - keras==1.2.2
+    - librosa==0.7.2
+    - llvmlite==0.32.1
+    - markdown==2.2.0
+    - numba==0.43.0
+    - numpy==1.18.5
+    - protobuf==3.12.2
+    - pycparser==2.20
+    - pyyaml==5.3.1
+    - resampy==0.2.2
+    - scikit-learn==0.22
+    - scipy==1.4.1
+    - soundfile==0.10.3.post1
+    - theano==0.9.0
+    - werkzeug==1.0.1
+    - tqdm

README.md

@@ -0,0 +1,101 @@
+import numpy as np
+import soundfile as sf
+import argparse
+import os
+import keras
+import sklearn
+import glob
+import predict
+import json
+from keras import backend as K
+
+import tqdm
+
+eps = np.finfo(np.float).eps
+
+
+def mae(y, p):
+    return np.mean([abs(a - b) for a, b in zip(p, y)])
+
+
+def mae_by_count(y, p):
+    diffs = []
+    for c in range(0, int(np.max(y)) + 1):
+        ind = np.where(y == c)
+        diff = mae(y[ind], np.round(p[ind]))
+        diffs.append(diff)
+
+    return diffs
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(
+        description='Load keras model and predict speaker count'
+    )
+    parser.add_argument(
+        'root',
+        help='root dir to evaluation data set'
+    )
+
+    parser.add_argument(
+        '--model', default='CRNN',
+        help='model name'
+    )
+
+    args = parser.parse_args()
+
+    # load model
+    model = keras.models.load_model(
+        os.path.join('models', args.model + '.h5'),
+        custom_objects={
+            'class_mae': predict.class_mae,
+            'exp': K.exp
+        }
+    )
+
+
+    # print model configuration
+    model.summary()
+
+    # load standardisation parameters
+    scaler = sklearn.preprocessing.StandardScaler()
+    with np.load(os.path.join("models", 'scaler.npz')) as data:
+        scaler.mean_ = data['arr_0']
+        scaler.scale_ = data['arr_1']
+
+    input_files = glob.glob(os.path.join(
+        args.root, 'test', '*.wav'
+    ))
+
+    y_trues = []
+    y_preds = []
+
+    for input_file in tqdm.tqdm(input_files):
+
+        metadata_file = os.path.splitext(
+            os.path.basename(input_file)
+        )[0] + ".json"
+        metadata_path = os.path.join(args.root, 'test', metadata_file)
+
+        with open(metadata_path) as data_file:
+            data = json.load(data_file)
+            # add ground truth
+            y_trues.append(len(data))
+
+        # compute audio
+        audio, rate = sf.read(input_file, always_2d=True)
+
+        # downmix to mono
+        audio = np.mean(audio, axis=1)
+
+        count = predict.count(audio, model, scaler)
+        # add prediction
+        y_preds.append(count)
+
+    y_preds = np.array(y_preds)
+    y_trues = np.array(y_trues)
+
+
+mae_k = mae_by_count(y_trues, y_preds)
+print("MAE per Count: ", {k: v for k, v in enumerate(mae_k)})
+print("Mean MAE", mae(y_trues, y_preds))