import librosa
import os
from matplotlib.patches import ConnectionPatch
import matplotlib.pyplot as plt
import numpy as np
import scipy.spatial.distance as dist
import pyaudio
import wave
def dp(distmat):
    N,M = distmat.shape
    # Initialisons the cost matrix
    costmat =np.zeros((N+1,M+1))
    for i in range (1,N+1):
        costmat[i,0]=np.inf
    for i in range (1,M+1):
        costmat[0,i]=np.inf

    for i in range (N):
        for j in range (M):
            #on calcule le cout minimal pour chaque chemin.pour atteindre the costmat[i][j] il y a trois chemins possibles on choisit celui de cout minimal
            penalty = [
              costmat[i,j],     # cas T==0
              costmat[i,j+1] ,  # cas T==1
              costmat[i+1,j]]   # cas T==2
            ipenalty = np.argmin(penalty)
            costmat[i+1,j+1] = distmat[i,j] + penalty[ipenalty]

    #enlever les valeurs de l infini
    costmat = costmat[1: , 1:]
    return (costmat, costmat[-1, -1]/(N+M))
def calculate_mfcc(audio, sr):
    # Define parameters for MFCC calculation
    n_mfcc = 13
    n_fft = 2048
    hop_length = 512
    fmin = 0
    fmax = sr/2

    # Calculate MFCCs
    mfccs = librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=n_mfcc, n_fft=n_fft, hop_length=hop_length, fmin=fmin, fmax=fmax)
    return mfccs.T
def calculate_dtw_cost(mfccs_query , mfccs_train):
    distmat = dist.cdist(mfccs_query, mfccs_train,"cosine")
    costmat,mincost = dp(distmat)
    return mincost
def recognize_speech(audio_query, audio_train_list, sr):#sr frequence d echantillonnage
    # Calculate MFCCs for query audio
    mfccs_query = calculate_mfcc(audio_query, sr)

    # Calculate DTW cost for each audio in training data
    dtw_costs = []
    for audio_train in audio_train_list:
        mfccs_train = calculate_mfcc(audio_train, sr)
        mincost = calculate_dtw_cost(mfccs_query, mfccs_train)
        dtw_costs.append(mincost)

    # Find index of word with lowest DTW cost
    index = np.argmin(dtw_costs)

    # Return recognized word
    return index

# Example usage
def get_recognized_word(recognized_word_index):

    # Define a dictionary to map recognized word indices to actual words
    word_map = {
    "un" : [0,1,2,3,4,5,6],
    "deux" : [7, 8, 9, 10, 11, 12, 13],
    "trois" : [14, 15, 16, 17, 18, 19],
    "quatre" : [20,21, 22, 23, 24, 25, 26],
    "cinq" : [27 ,28, 29, 30, 31, 32],
    "six" : [33 ,34, 35, 36, 37, 38],
    "sept" : [39 , 40, 41, 42, 43, 44],
    "huit" : [45,46, 47, 48, 49, 50, 51],
    "neuf" : [52,53, 54, 55, 56, 57, 58],
    "dix" : [59,60, 61, 62, 63, 64, 65],
    "bien" : [66 ,67, 68, 69, 70, 71, 72],
    "super" : [127,128,129,130, 131, 132, 133],
    "génial" : [87,88, 89, 90, 91, 92, 93],
    "sympa" : [134,135,136,137, 138, 139, 140],
    "propre" : [122, 123, 124, 125, 126],
    "nul" : [115 ,116, 117, 118, 119, 120, 121],
    "ennuyant" : [80 ,81, 82, 83, 84, 85, 86],
    "j'ai beaucoup aimé" : [94 ,95, 96, 97, 98, 99, 100],
    "j'ai trouvé ça génial" : [101 ,102, 103, 104, 105, 106, 107],
    "je n'ai pas aimé" : [108 ,109, 110, 111, 112, 113, 114],
    "c'était drole" : [73,74, 75, 76, 77, 78, 79],
    }
    for word, indices in word_map.items():
        if recognized_word_index in indices:
            return word
    return "Word not recognized"
def record_audio(filename, duration, sr):
    chunk = 1024
    sample_format = pyaudio.paInt16
    channels = 1
    record_seconds = duration
    filename = f"{filename}.wav"

    p = pyaudio.PyAudio()

    stream = p.open(format=sample_format,
                    channels=channels,
                    rate=sr,
                    frames_per_buffer=chunk,
                    input=True)

    frames = []

    print(f"Enregistrement en cours...")

    for i in range(0, int(sr / chunk * record_seconds)):
        data = stream.read(chunk)
        frames.append(data)

    stream.stop_stream()
    stream.close()

    p.terminate()


    print("Enregistrement terminé")

    wf = wave.open(filename, "wb")
    wf.setnchannels(channels)
    wf.setsampwidth(p.get_sample_size(sample_format))
    wf.setframerate(sr)
    wf.writeframes(b"".join(frames))
    wf.close()

    print(f"Fichier enregistré sous {filename}")
def coupe_silence(signal):
    t = 0
    if signal[t] == 0 :
        p = 0
        while signal[t+p] == 0 :
            if p == 88 :
                signal = signal[:t] + signal[t+p:]
                coupe_silence(signal)
            else :
                p = p+1
"""
sr = 44100  # fréquence d'échantillonnage
duration = 2.5  # durée d'enregistrement en secondes
filename = "audio_query"  # nom du fichier à enregistrer

record_audio(filename, duration, sr)
audio_query, sr = librosa.load('C:\\Users\\HP\\audio_query.wav', sr=sr)

audio_train_list = [librosa.load('C:\\Users\\HP\\Documents\\cool.wav', sr=sr)[0], librosa.load('C:\\Users\\HP\\Documents\\formidable.wav', sr=sr)[0], librosa.load('C:\\Users\\HP\\Documents\\cest mauvais.wav', sr=sr)[0] , librosa.load('C:\\Users\\HP\\Documents\\un.wav', sr=sr)[0], librosa.load('C:\\Users\\HP\\Documents\\parfait.wav', sr=sr)[0]]
recognized_word_index = recognize_speech(audio_query, audio_train_list, sr)
print(f'Recognized word: {recognized_word_index}')
"""
sr = 44100  # fréquence d'échantillonnage
duration = 6  # durée d'enregistrement en secondes
filename = "audio_query"  # nom du fichier à enregistrer

record_audio(filename, duration, sr)
audio_query, sr = librosa.load('C:\\Users\\HP\\audio_query.wav', sr=sr)
coupe_silence(audio_query)
audio_train_list = []
for file in os.listdir('C:\\Users\\HP\\Documents\\Base de données') :
    audio_train_list.append(librosa.load('C:\\Users\\HP\\Documents\\Base de données\\' + file, sr=sr)[0])
recognized_word_index = recognize_speech(audio_query, audio_train_list, sr)
recognized_word = get_recognized_word(recognized_word_index)
print(f'Recognized word: {recognized_word}')