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18fa92b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | import numpy as np
import librosa
import librosa.display
import torch
import torchaudio
import torchaudio.transforms as T
from pydub import AudioSegment
from transformers import pipeline
import scipy.signal
from speechbrain.pretrained import EncoderClassifier
# Load Audio File
def load_audio(file_path, target_sr=16000):
y, sr = librosa.load(file_path, sr=target_sr)
return y, sr
# Noise Reduction using Spectral Subtraction
def reduce_noise(y, sr):
reduced_noise = scipy.signal.medfilt(y, kernel_size=3)
return reduced_noise
# Convert Speech to Text (ASR)
def speech_to_text(file_path):
asr_model = pipeline("automatic-speech-recognition", model="openai/whisper-small")
transcript = asr_model(file_path)
return transcript["text"]
# Emotion Detection from Voice
def detect_emotion(file_path):
classifier = EncoderClassifier.from_hparams(source="speechbrain/emotion-recognition-wav2vec2",
savedir="pretrained_models/emotion-recognition")
signal, sr = torchaudio.load(file_path)
emotion = classifier.classify_batch(signal)
return emotion[3] # Returns predicted emotion
# Sound Classification (e.g., Speech, Music, Noise)
def classify_sound(file_path):
classifier = EncoderClassifier.from_hparams(source="speechbrain/sound-classification",
savedir="pretrained_models/sound-classification")
signal, sr = torchaudio.load(file_path)
category = classifier.classify_batch(signal)
return category[3] # Returns predicted sound category
# Save Processed Audio
def save_audio(y, sr, output_path):
librosa.output.write_wav(output_path, y, sr)
# Audio Feature Extraction for AI Model Input
def extract_features(file_path):
y, sr = librosa.load(file_path, sr=16000)
mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
chroma = librosa.feature.chroma_stft(y=y, sr=sr)
mel_spec = librosa.feature.melspectrogram(y=y, sr=sr)
return {"mfccs": mfccs, "chroma": chroma, "mel_spec": mel_spec} |