cog-ified

README.md

@@ -1,12 +1,16 @@
 # Speech Emotion Recognition
 ## Introduction
+<a href="https://replicate.ai/x4nth055/emotion-recognition-using-speech"><img src="https://img.shields.io/static/v1?label=Replicate&message=Demo and Docker Image&color=darkgreen" height=20></a>
+
+
 - This repository handles building and training Speech Emotion Recognition System.
 - The basic idea behind this tool is to build and train/test a suited machine learning ( as well as deep learning ) algorithm that could recognize and detects human emotions from speech.
 - This is useful for many industry fields such as making product recommendations, affective computing, etc.
 - Check this [tutorial](https://www.thepythoncode.com/article/building-a-speech-emotion-recognizer-using-sklearn) for more information.
 ## Requirements
 - **Python 3.6+**
 ### Python Packages
+- **tensorflow**
 - **librosa==0.6.3**
 - **numpy**
 - **pandas**
@@ -38,7 +42,7 @@ Feature extraction is the main part of the speech emotion recognition system. It
 
 In this repository, we have used the most used features that are available in [librosa](https://github.com/librosa/librosa) library including:
 - [MFCC](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum)
-- Chromagram 
+- Chromagram
 - MEL Spectrogram Frequency (mel)
 - Contrast
 - Tonnetz (tonal centroid features)
@@ -102,6 +106,7 @@ print("Prediction:", rec.predict("data/tess_ravdess/validation/Actor_25/25_01_01
 Prediction: neutral
 Prediction: sad
 ```
+You can pass any audio file, if it's not in the appropriate format (16000Hz and mono channel), then it'll be automatically converted, make sure you have `ffmpeg` installed in your system and added to *PATH*.
 ## Example 2: Using RNNs for 5 Emotions
 ```python
 from deep_emotion_recognition import DeepEmotionRecognizer
@@ -143,6 +148,45 @@ true_neutral         3.846154       8.974360          82.051285      2.564103
 true_ps              2.564103       0.000000           1.282051     83.333328        12.820514
 true_happy          20.512821       2.564103           2.564103      2.564103        71.794876
 ```
+## Example 3: Not Passing any Model and Removing the Custom Dataset
+Below code initializes `EmotionRecognizer` with 3 chosen emotions while removing Custom dataset, and setting `balance` to `False`:
+```python
+from emotion_recognition import EmotionRecognizer
+# initialize instance, this will take a bit the first time executed
+# as it'll extract the features and calls determine_best_model() automatically
+# to load the best performing model on the picked dataset
+rec = EmotionRecognizer(emotions=["angry", "neutral", "sad"], balance=False, verbose=1, custom_db=False)
+# it will be trained, so no need to train this time
+# get the accuracy on the test set
+print(rec.confusion_matrix())
+# predict angry audio sample
+prediction = rec.predict('data/validation/Actor_10/03-02-05-02-02-02-10_angry.wav')
+print(f"Prediction: {prediction}")
+```
+**Output:**
+```
+[+] Best model determined: RandomForestClassifier with 93.454% test accuracy
+
+              predicted_angry  predicted_neutral  predicted_sad
+true_angry          98.275864           1.149425       0.574713
+true_neutral         0.917431          88.073395      11.009174
+true_sad             6.250000           1.875000      91.875000
+
+Prediction: angry
+```
+You can print the number of samples on each class:
+```python
+rec.get_samples_by_class()
+```
+**Output:**
+```
+         train  test  total
+angry      910   174   1084
+neutral    650   109    759
+sad        862   160   1022
+total     2422   443   2865
+```
+In this case, the dataset is only from TESS and RAVDESS, and not balanced, you can pass `True` to `balance` on the `EmotionRecognizer` instance to balance the data.
 ## Algorithms Used
 This repository can be used to build machine learning classifiers as well as regressors for the case of 3 emotions {'sad': 0, 'neutral': 1, 'happy': 2} and the case of 5 emotions {'angry': 1, 'sad': 2, 'neutral': 3, 'ps': 4, 'happy': 5}
 ### Classifiers
@@ -207,4 +251,4 @@ plot_histograms(classifiers=True)
 **Output:**
 
 <img src="images/Figure.png">
-<p align="center">A Histogram shows different algorithms metric results on different data sizes as well as time consumed to train/predict.</p>
+<p align="center">A Histogram shows different algorithms metric results on different data sizes as well as time consumed to train/predict.</p>

cog.yaml

@@ -0,0 +1,18 @@
+build:
+  python_version: "3.6"
+  gpu: false
+  python_packages:
+    - pandas==1.1.5
+    - numpy==1.17.3
+    - wave==0.0.2
+    - sklearn==0.0
+    - librosa==0.6.3
+    - soundfile==0.9.0
+    - tqdm==4.28.1
+    - matplotlib==2.2.3
+    - pyaudio==0.2.11
+    - numba==0.48
+  system_packages:
+    - "ffmpeg"
+    - "portaudio19-dev"
+predict: "predict.py:EmoPredictor"

predict.py

@@ -0,0 +1,28 @@
+import json
+import os
+import tempfile
+from pathlib import Path
+
+import cog
+from emotion_recognition import EmotionRecognizer
+
+
+class EmoPredictor(cog.Predictor):
+    def setup(self):
+        """Load the emotion recognition model and (quickly) train it"""
+        # self.rec = EmotionRecognizer(None, emotions=["boredom", "neutral"], features=["mfcc"])
+        self.rec = EmotionRecognizer(
+            None,
+            emotions=["sad", "neutral", "happy"],
+            features=["mfcc"],
+            probability=True,
+        )
+        # evaluate all models in `grid` folder and determine the best one in terms of test accuracy
+        self.rec.determine_best_model()
+
+    @cog.input("input", type=Path, help="Speech audio file")
+    def predict(self, input):
+        """Compute emotion prediction"""
+        prediction = self.rec.predict_proba(str(input))
+
+        return prediction