Q/Complete the following code in Python language (biometrics) for voice recognition and apply the code, mentioning the approved source if it exists import os import numpy as np from pyAudioAnalysis import audioBasicIO, audioFeatureExtraction, audioTrainTest from pydub import AudioSegment # Function to capture and save voice samples def capture_voice_samples(num_samples, speaker_name): os.makedirs("speakers", exist_ok=True) os.makedirs(f"speakers/{speaker_name}", exist_ok=True) for i in range(num_samples): input(f"Press Enter and start speaking for sample {i + 1}...") # Recording audio using pyAudioAnalysis audio = audioBasicIO.record_audio(4, 44100) filepath = f"speakers/{speaker_name}/sample_{i + 1}.wav" audioBasicIO.write_audio_file(filepath, audio, 44100) print(f"Sample {i + 1} saved for {speaker_name}") # Function to extract features from voice samples def extract_features(): speakers = [d for d in os.listdir("speakers") if os.path.isdir(os.path.join("speakers", d))] all_features = [] all_labels = [] for i, speaker in enumerate(speakers): features = [] labels = [] for filename in os.listdir(f"speakers/{speaker}"): if filename.endswith(".wav"): filepath = os.path.join(f"speakers/{speaker}", filename) print(f"Extracting features from {filepath}") [Fs, x] = audioBasicIO.read_audio_file(filepath) F, f_names = audioFeatureExtraction.stFeatureExtraction(x[:, 0], Fs, 0.050 * Fs, 0.025 * Fs) features.append(F.T) labels.append(i) all_features.extend(features) all_labels.extend(labels) return np.array(all_features), np.array(all_labels) # Function to perform speaker identification def identify_speaker(): features, labels = extract_features() model = audioTrainTest.gmm_train(features, labels) while True: filepath = input("Enter the path of the voice sample to identify (or 'exit' to quit): ") if filepath.lower() == "exit": break [Fs, x] = audioBasicIO.read_audio_file(filepath) F, _ = audioFeatureExtraction.stFeatureExtraction(x[:, 0], Fs, 0.050 * Fs, 0.025 * Fs) winner, _, _ = audioTrainTest.gmm_classify(model, F.T) identified_speaker = os.listdir("speakers")[winner] print(f"The identified speaker is: {identified_speaker}") # Main function def main(): num_samples = int(input("Enter the number of voice samples to capture per speaker: ")) num_speakers = int(input("Enter the number of speakers: ")) for i in range(num_speakers): speaker_name = input(f"Enter the name of speaker {i + 1}: ") capture_voice_samples(num_samples, speaker_name) # Identify speaker from a given voice sample identify_speaker() if __name__ == "__main__": main()
Q/Complete the following code in Python language (biometrics) for voice recognition and apply the code, mentioning the approved source if it exists import os import numpy as np from pyAudioAnalysis import audioBasicIO, audioFeatureExtraction, audioTrainTest from pydub import AudioSegment # Function to capture and save voice samples def capture_voice_samples(num_samples, speaker_name): os.makedirs("speakers", exist_ok=True) os.makedirs(f"speakers/{speaker_name}", exist_ok=True) for i in range(num_samples): input(f"Press Enter and start speaking for sample {i + 1}...") # Recording audio using pyAudioAnalysis audio = audioBasicIO.record_audio(4, 44100) filepath = f"speakers/{speaker_name}/sample_{i + 1}.wav" audioBasicIO.write_audio_file(filepath, audio, 44100) print(f"Sample {i + 1} saved for {speaker_name}") # Function to extract features from voice samples def extract_features(): speakers = [d for d in os.listdir("speakers") if os.path.isdir(os.path.join("speakers", d))] all_features = [] all_labels = [] for i, speaker in enumerate(speakers): features = [] labels = [] for filename in os.listdir(f"speakers/{speaker}"): if filename.endswith(".wav"): filepath = os.path.join(f"speakers/{speaker}", filename) print(f"Extracting features from {filepath}") [Fs, x] = audioBasicIO.read_audio_file(filepath) F, f_names = audioFeatureExtraction.stFeatureExtraction(x[:, 0], Fs, 0.050 * Fs, 0.025 * Fs) features.append(F.T) labels.append(i) all_features.extend(features) all_labels.extend(labels) return np.array(all_features), np.array(all_labels) # Function to perform speaker identification def identify_speaker(): features, labels = extract_features() model = audioTrainTest.gmm_train(features, labels) while True: filepath = input("Enter the path of the voice sample to identify (or 'exit' to quit): ") if filepath.lower() == "exit": break [Fs, x] = audioBasicIO.read_audio_file(filepath) F, _ = audioFeatureExtraction.stFeatureExtraction(x[:, 0], Fs, 0.050 * Fs, 0.025 * Fs) winner, _, _ = audioTrainTest.gmm_classify(model, F.T) identified_speaker = os.listdir("speakers")[winner] print(f"The identified speaker is: {identified_speaker}") # Main function def main(): num_samples = int(input("Enter the number of voice samples to capture per speaker: ")) num_speakers = int(input("Enter the number of speakers: ")) for i in range(num_speakers): speaker_name = input(f"Enter the name of speaker {i + 1}: ") capture_voice_samples(num_samples, speaker_name) # Identify speaker from a given voice sample identify_speaker() if __name__ == "__main__": main()
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
Related questions
Topic Video
Question
Q/Complete the following code in Python language (biometrics) for voice recognition and apply the code, mentioning the approved source if it exists
import os
import numpy as np
from pyAudioAnalysis import audioBasicIO, audioFeatureExtraction, audioTrainTest
from pydub import AudioSegment
# Function to capture and save voice samples
def capture_voice_samples(num_samples, speaker_name):
os.makedirs("speakers", exist_ok=True)
os.makedirs(f"speakers/{speaker_name}", exist_ok=True)
for i in range(num_samples):
input(f"Press Enter and start speaking for sample {i + 1}...")
# Recording audio using pyAudioAnalysis
audio = audioBasicIO.record_audio(4, 44100)
filepath = f"speakers/{speaker_name}/sample_{i + 1}.wav"
audioBasicIO.write_audio_file(filepath, audio, 44100)
print(f"Sample {i + 1} saved for {speaker_name}")
# Function to extract features from voice samples
def extract_features():
speakers = [d for d in os.listdir("speakers") if os.path.isdir(os.path.join("speakers", d))]
all_features = []
all_labels = []
for i, speaker in enumerate(speakers):
features = []
labels = []
for filename in os.listdir(f"speakers/{speaker}"):
if filename.endswith(".wav"):
filepath = os.path.join(f"speakers/{speaker}", filename)
print(f"Extracting features from {filepath}")
[Fs, x] = audioBasicIO.read_audio_file(filepath)
F, f_names = audioFeatureExtraction.stFeatureExtraction(x[:, 0], Fs, 0.050 * Fs, 0.025 * Fs)
features.append(F.T)
labels.append(i)
all_features.extend(features)
all_labels.extend(labels)
return np.array(all_features), np.array(all_labels)
# Function to perform speaker identification
def identify_speaker():
features, labels = extract_features()
model = audioTrainTest.gmm_train(features, labels)
while True:
filepath = input("Enter the path of the voice sample to identify (or 'exit' to quit): ")
if filepath.lower() == "exit":
break
[Fs, x] = audioBasicIO.read_audio_file(filepath)
F, _ = audioFeatureExtraction.stFeatureExtraction(x[:, 0], Fs, 0.050 * Fs, 0.025 * Fs)
winner, _, _ = audioTrainTest.gmm_classify(model, F.T)
identified_speaker = os.listdir("speakers")[winner]
print(f"The identified speaker is: {identified_speaker}")
# Main function
def main():
num_samples = int(input("Enter the number of voice samples to capture per speaker: "))
num_speakers = int(input("Enter the number of speakers: "))
for i in range(num_speakers):
speaker_name = input(f"Enter the name of speaker {i + 1}: ")
capture_voice_samples(num_samples, speaker_name)
# Identify speaker from a given voice sample
identify_speaker()
if __name__ == "__main__":
main()
import numpy as np
from pyAudioAnalysis import audioBasicIO, audioFeatureExtraction, audioTrainTest
from pydub import AudioSegment
# Function to capture and save voice samples
def capture_voice_samples(num_samples, speaker_name):
os.makedirs("speakers", exist_ok=True)
os.makedirs(f"speakers/{speaker_name}", exist_ok=True)
for i in range(num_samples):
input(f"Press Enter and start speaking for sample {i + 1}...")
# Recording audio using pyAudioAnalysis
audio = audioBasicIO.record_audio(4, 44100)
filepath = f"speakers/{speaker_name}/sample_{i + 1}.wav"
audioBasicIO.write_audio_file(filepath, audio, 44100)
print(f"Sample {i + 1} saved for {speaker_name}")
# Function to extract features from voice samples
def extract_features():
speakers = [d for d in os.listdir("speakers") if os.path.isdir(os.path.join("speakers", d))]
all_features = []
all_labels = []
for i, speaker in enumerate(speakers):
features = []
labels = []
for filename in os.listdir(f"speakers/{speaker}"):
if filename.endswith(".wav"):
filepath = os.path.join(f"speakers/{speaker}", filename)
print(f"Extracting features from {filepath}")
[Fs, x] = audioBasicIO.read_audio_file(filepath)
F, f_names = audioFeatureExtraction.stFeatureExtraction(x[:, 0], Fs, 0.050 * Fs, 0.025 * Fs)
features.append(F.T)
labels.append(i)
all_features.extend(features)
all_labels.extend(labels)
return np.array(all_features), np.array(all_labels)
# Function to perform speaker identification
def identify_speaker():
features, labels = extract_features()
model = audioTrainTest.gmm_train(features, labels)
while True:
filepath = input("Enter the path of the voice sample to identify (or 'exit' to quit): ")
if filepath.lower() == "exit":
break
[Fs, x] = audioBasicIO.read_audio_file(filepath)
F, _ = audioFeatureExtraction.stFeatureExtraction(x[:, 0], Fs, 0.050 * Fs, 0.025 * Fs)
winner, _, _ = audioTrainTest.gmm_classify(model, F.T)
identified_speaker = os.listdir("speakers")[winner]
print(f"The identified speaker is: {identified_speaker}")
# Main function
def main():
num_samples = int(input("Enter the number of voice samples to capture per speaker: "))
num_speakers = int(input("Enter the number of speakers: "))
for i in range(num_speakers):
speaker_name = input(f"Enter the name of speaker {i + 1}: ")
capture_voice_samples(num_samples, speaker_name)
# Identify speaker from a given voice sample
identify_speaker()
if __name__ == "__main__":
main()
Expert Solution
This question has been solved!
Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.
This is a popular solution!
Trending now
This is a popular solution!
Step by step
Solved in 4 steps with 2 images
Knowledge Booster
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, computer-science and related others by exploring similar questions and additional content below.Recommended textbooks for you
Database System Concepts
Computer Science
ISBN:
9780078022159
Author:
Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:
McGraw-Hill Education
Starting Out with Python (4th Edition)
Computer Science
ISBN:
9780134444321
Author:
Tony Gaddis
Publisher:
PEARSON
Digital Fundamentals (11th Edition)
Computer Science
ISBN:
9780132737968
Author:
Thomas L. Floyd
Publisher:
PEARSON
Database System Concepts
Computer Science
ISBN:
9780078022159
Author:
Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:
McGraw-Hill Education
Starting Out with Python (4th Edition)
Computer Science
ISBN:
9780134444321
Author:
Tony Gaddis
Publisher:
PEARSON
Digital Fundamentals (11th Edition)
Computer Science
ISBN:
9780132737968
Author:
Thomas L. Floyd
Publisher:
PEARSON
C How to Program (8th Edition)
Computer Science
ISBN:
9780133976892
Author:
Paul J. Deitel, Harvey Deitel
Publisher:
PEARSON
Database Systems: Design, Implementation, & Manag…
Computer Science
ISBN:
9781337627900
Author:
Carlos Coronel, Steven Morris
Publisher:
Cengage Learning
Programmable Logic Controllers
Computer Science
ISBN:
9780073373843
Author:
Frank D. Petruzella
Publisher:
McGraw-Hill Education