Update feat.py

feat.py

@@ -1,134 +1,134 @@
-
-import librosa
-import numpy as np
-
-def features_extractor(file_name):
-    audio, sample_rate = librosa.load(file_name, res_type='kaiser_fast')
-    
-    # Extract MFCC features
-    mfccs_features = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=25)
-    mfccs_scaled_features = np.mean(mfccs_features.T, axis=0)
-    
-    # Extract Zero Crossing Rate
-    zcr = librosa.feature.zero_crossing_rate(y=audio)
-    zcr_scaled_features = np.mean(zcr.T, axis=0)
-    
-    # Extract Chroma Features
-    chroma = librosa.feature.chroma_stft(y=audio, sr=sample_rate)
-    chroma_scaled_features = np.mean(chroma.T, axis=0)
-    
-    # Extract Mel Spectrogram Features
-    mel = librosa.feature.melspectrogram(y=audio, sr=sample_rate)
-    mel_scaled_features = np.mean(mel.T, axis=0)
-    
-    # Concatenate all features into a single array
-    features = np.hstack((mfccs_scaled_features, zcr_scaled_features, chroma_scaled_features, mel_scaled_features))
-    
-    return features
-
-
-#########################################################################################################################
-import speech_recognition as sr
-
-def recognize_speech_from_file(audio_file_path):
-    # Initialize the recognizer
-    recognizer = sr.Recognizer()
-
-    # Load the audio file
-    with sr.AudioFile(audio_file_path) as source:
-        
-        audio_data = recognizer.record(source)  # Read the entire audio file
-        
-        try:
-            # Recognize speech using Google Web Speech API
-            text = recognizer.recognize_google(audio_data)
-          
-            return text
-        except sr.RequestError as e:
-            print(f"Could not request results; {e}")
-        except sr.UnknownValueError:
-            print("Could not understand the audio")
-
-def count_words(text):
-    words = text.split()
-    return len(words)
-
-def word_count(audio_path):
-    transcript = recognize_speech_from_file(audio_file_path=audio_path)
-    if transcript:
-        return [count_words(transcript),transcript]
-
-########################################################################################################################
-import speech_recognition as sr
-import wave
-
-def recognize_speech_from_file(audio_file_path):
-    recognizer = sr.Recognizer()
-    audio_file = sr.AudioFile(audio_file_path)
-    with audio_file as source:
-        audio = recognizer.record(source)
-    try:
-        transcript = recognizer.recognize_google(audio)
-        return transcript
-    except sr.UnknownValueError:
-        return None
-    except sr.RequestError as e:
-        print(f"Could not request results from Google Speech Recognition service; {e}")
-        return None
-
-def count_words(text):
-    words = text.split()
-    return len(words)
-
-def get_audio_duration(audio_file_path):
-    with wave.open(audio_file_path, 'r') as audio_file:
-        frames = audio_file.getnframes()
-        rate = audio_file.getframerate()
-        duration = frames / float(rate)
-    return duration
-
-def word_count1(audio_path):
-    transcript = recognize_speech_from_file(audio_file_path=audio_path)
-    if transcript:
-        duration = get_audio_duration(audio_path)
-        return [count_words(transcript), transcript, duration]
-    else:
-        return [0, None, 0.0]
-    
-word_count('angry_Akash.wav')
-
-# print(word_count1(r'c:\Users\hp\OneDrive\Desktop\Major Emotions\Mixed\Angry-1-3-1.wav'))
-# Example usage
-# audio_path = 'angry_Ansh.wav'
-# result = word_count(audio_path)
-# print(result)
-
-import librosa
-import numpy as np
-from pyAudioAnalysis import audioSegmentation as aS
-
-def get_speaking_rate(file_path):
-    # Load audio file
-    y, sr = librosa.load(file_path, sr=None)
-    
-    # Extract speech segments
-    segments = aS.silence_removal(y, sr, 0.020, 0.020, smooth_window=1.0, weight=0.3, plot=False)
-    
-    # Total speech duration
-    speech_duration = sum([end - start for start, end in segments])
-    
-    # Number of syllables (approximation)
-    num_syllables = len(librosa.effects.split(y, top_db=30))
-    
-    # Calculate speaking rate (syllables per second)
-    speaking_rate = num_syllables / speech_duration if speech_duration > 0 else 0
-    
-    return speaking_rate
-
-# Example usage
-# file_path = 'angry_Ansh.wav'
-# speaking_rate = get_speaking_rate(file_path)[0]
-# print(f"Speaking Rate: {speaking_rate:.2f} syllables per second")
-# print(get_speaking_rate(file_path)[1])
-# print(get_speaking_rate(file_path)[2])
-
+
+import librosa
+import numpy as np
+
+def features_extractor(file_name):
+    audio, sample_rate = librosa.load(file_name, res_type='kaiser_fast')
+    
+    # Extract MFCC features
+    mfccs_features = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=25)
+    mfccs_scaled_features = np.mean(mfccs_features.T, axis=0)
+    
+    # Extract Zero Crossing Rate
+    zcr = librosa.feature.zero_crossing_rate(y=audio)
+    zcr_scaled_features = np.mean(zcr.T, axis=0)
+    
+    # Extract Chroma Features
+    chroma = librosa.feature.chroma_stft(y=audio, sr=sample_rate)
+    chroma_scaled_features = np.mean(chroma.T, axis=0)
+    
+    # Extract Mel Spectrogram Features
+    mel = librosa.feature.melspectrogram(y=audio, sr=sample_rate)
+    mel_scaled_features = np.mean(mel.T, axis=0)
+    
+    # Concatenate all features into a single array
+    features = np.hstack((mfccs_scaled_features, zcr_scaled_features, chroma_scaled_features, mel_scaled_features))
+    
+    return features
+
+
+#########################################################################################################################
+import speech_recognition as sr
+
+def recognize_speech_from_file(audio_file_path):
+    # Initialize the recognizer
+    recognizer = sr.Recognizer()
+
+    # Load the audio file
+    with sr.AudioFile(audio_file_path) as source:
+        
+        audio_data = recognizer.record(source)  # Read the entire audio file
+        
+        try:
+            # Recognize speech using Google Web Speech API
+            text = recognizer.recognize_google(audio_data)
+          
+            return text
+        except sr.RequestError as e:
+            print(f"Could not request results; {e}")
+        except sr.UnknownValueError:
+            print("Could not understand the audio")
+
+def count_words(text):
+    words = text.split()
+    return len(words)
+
+def word_count(audio_path):
+    transcript = recognize_speech_from_file(audio_file_path=audio_path)
+    if transcript:
+        return [count_words(transcript),transcript]
+
+########################################################################################################################
+import speech_recognition as sr
+import wave
+
+def recognize_speech_from_file(audio_file_path):
+    recognizer = sr.Recognizer()
+    audio_file = sr.AudioFile(audio_file_path)
+    with audio_file as source:
+        audio = recognizer.record(source)
+    try:
+        transcript = recognizer.recognize_google(audio)
+        return transcript
+    except sr.UnknownValueError:
+        return None
+    except sr.RequestError as e:
+        print(f"Could not request results from Google Speech Recognition service; {e}")
+        return None
+
+def count_words(text):
+    words = text.split()
+    return len(words)
+
+def get_audio_duration(audio_file_path):
+    with wave.open(audio_file_path, 'r') as audio_file:
+        frames = audio_file.getnframes()
+        rate = audio_file.getframerate()
+        duration = frames / float(rate)
+    return duration
+
+def word_count1(audio_path):
+    transcript = recognize_speech_from_file(audio_file_path=audio_path)
+    if transcript:
+        duration = get_audio_duration(audio_path)
+        return [count_words(transcript), transcript, duration]
+    else:
+        return [0, None, 0.0]
+    
+# word_count('angry_Akash.wav')
+
+# print(word_count1(r'c:\Users\hp\OneDrive\Desktop\Major Emotions\Mixed\Angry-1-3-1.wav'))
+# Example usage
+# audio_path = 'angry_Ansh.wav'
+# result = word_count(audio_path)
+# print(result)
+
+import librosa
+import numpy as np
+from pyAudioAnalysis import audioSegmentation as aS
+
+def get_speaking_rate(file_path):
+    # Load audio file
+    y, sr = librosa.load(file_path, sr=None)
+    
+    # Extract speech segments
+    segments = aS.silence_removal(y, sr, 0.020, 0.020, smooth_window=1.0, weight=0.3, plot=False)
+    
+    # Total speech duration
+    speech_duration = sum([end - start for start, end in segments])
+    
+    # Number of syllables (approximation)
+    num_syllables = len(librosa.effects.split(y, top_db=30))
+    
+    # Calculate speaking rate (syllables per second)
+    speaking_rate = num_syllables / speech_duration if speech_duration > 0 else 0
+    
+    return speaking_rate
+
+# Example usage
+# file_path = 'angry_Ansh.wav'
+# speaking_rate = get_speaking_rate(file_path)[0]
+# print(f"Speaking Rate: {speaking_rate:.2f} syllables per second")
+# print(get_speaking_rate(file_path)[1])
+# print(get_speaking_rate(file_path)[2])
+