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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])
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