reconnaissance

code-speech-To-Text.py

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+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}')