pragatibaheti

from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from langchain.chat_models import ChatOpenAI
import gradio as gr
import sys
import os

os.environ["OPENAI_API_KEY"] = '#unique_id'

def construct_index(directory_path):
    max_input_size = 4096

pragatibaheti

import sounddevice as sd
import soundfile as sf

samplerate = 16000  
duration = 1 # seconds
filename = 'mytest.wav'
print("start recording")
mydata = sd.rec(int(samplerate * duration), samplerate=samplerate,
    channels=1, blocking=True)
print("end")

pragatibaheti

duration_of_recordings=[]
for label in labels:
    waves = [f for f in os.listdir(audio_path + '/'+ label) if f.endswith('.wav')]
    for wav in waves:
        sample_rate, samples = wavfile.read(audio_path + '/' + label + '/' + wav)
        duration_of_recordings.append(float(len(samples)/sample_rate))
    
plt.hist(np.array(duration_of_recordings))

pragatibaheti

audio_path = '../dataset/audio/'
samples, sample_rate = librosa.load(audio_path+'hello/001.wav', sr = 16000)
fig = plt.figure(figsize=(14, 8))
ax1 = fig.add_subplot(211)
ax1.set_title('Raw wave of ' + '../dataset/audio/1_001.wav')
ax1.set_xlabel('Time')
ax1.set_ylabel('Amplitude')
ax1.plot(np.linspace(0, sample_rate/len(samples), sample_rate), samples)

pragatibaheti

import os
#for audio processing
import librosa
from scipy.io import wavfile 
import IPython.display as ipd
#for visualization via plots
import matplotlib.pyplot as plt
#matrix related functions
import numpy as np 

pragatibaheti

import os
#for audio processing
import librosa
from scipy.io import wavfile 
import IPython.display as ipd
#for visualization via plots
import matplotlib.pyplot as plt
#matrix related functions
import numpy as np 

pragatibaheti

y, sr = librosa.load(librosa.util.example_audio_file())
# Approximate maximum frequencies with roll_percent=0.85 (default)
rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)

# Approximate minimum frequencies with roll_percent=0.1
rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr, roll_percent=0.1)

pragatibaheti

x, sr = librosa.load('audio.wav')
ipd.Audio(x, rate=sr)

hop_length = 512
# returns normalized energy for each chroma bin at each frame.
chromagram = librosa.feature.chroma_stft(x, sr=sr, hop_length=hop_length)
plt.figure(figsize=(15, 5))
librosa.display.specshow(chromagram, x_axis='time', y_axis='chroma', hop_length=hop_length, cmap='coolwarm')

pragatibaheti

import matplotlib.pyplot as plt
y, sr = librosa.load(audio_file.wav, offset=30, duration=10)
y_filt = librosa.effects.preemphasis(y)
# and plot the results for comparison
S_orig = librosa.amplitude_to_db(np.abs(librosa.stft(y)), ref=np.max)
S_preemph = librosa.amplitude_to_db(np.abs(librosa.stft(y_filt)), ref=np.max)

librosa.display.specshow(S_orig, y_axis='log', x_axis='time')
plt.title('Original signal')
librosa.display.specshow(S_preemph, y_axis='log', x_axis='time')

pragatibaheti

#min = minimum value for each row of the vector signal
#max = maximum value for each row of the vector signal
def normalize(x, axis=0):
    return sklearn.preprocessing.minmax_scale(x, axis=axis)

#Plotting the Spectral Centroid along the waveform
librosa.display.waveplot(x, sr=sr, alpha=0.4)
plt.plot(t, normalize(spectral_centroids), color='r')