gradio inference

app.py

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+import gradio as gr
+import librosa
+import pickle
+import numpy as np
+import spafe
+from spafe.frequencies import dominant_frequencies
+from spafe.features.mfcc import mfcc, imfcc
+from spafe.features.bfcc import bfcc
+from spafe.features.cqcc import cqcc
+from spafe.features.gfcc import erb_spectrogram
+from spafe.features.lfcc import linear_spectrogram
+from spafe.features.msrcc import msrcc
+from spafe.features.ngcc import ngcc
+from spafe.utils.preprocessing import SlidingWindow
+from sklearn.metrics.pairwise import cosine_similarity
+def dominant_freq_density(min_dom_freq,max_dom_freq,signal,sr):
+    dom_f = dominant_frequencies.get_dominant_frequencies(signal,sr,nfft=512,butter_filter=True)
+    dom_f = dom_f[(dom_f>min_dom_freq) & (dom_f<max_dom_freq)]
+    h,e = np.histogram(dom_f,bins = range(min_dom_freq,max_dom_freq,100),density=True)
+    return h
+
+def dominant_freq(x):
+    return dominant_freq_density(100,1000,x['y'],x['sr'])
+def apply_mfcc(x):
+    return np.mean(np.nan_to_num(mfcc(x['y'],fs=x['sr'],pre_emph=1,pre_emph_coeff=0.97,window=SlidingWindow(0.03, 0.015, "hamming"),nfilts=128,nfft=512,low_freq=50,high_freq=4000,normalize="mvn"),posinf=0,neginf=0),axis=0)
+
+def apply_bfcc(x):
+    return np.mean(np.nan_to_num(bfcc(x['y'],fs=x['sr'],pre_emph=1,pre_emph_coeff=0.97,window=SlidingWindow(0.03, 0.015, "hamming"),nfilts=128,nfft=512,low_freq=50,high_freq=4000,normalize="mvn"),posinf=0,neginf=0),axis=0)
+def apply_cqcc(x):
+    return np.mean(np.nan_to_num(cqcc(x['y'],fs=x['sr'],pre_emph=True,pre_emph_coeff=0.97,window=SlidingWindow(0.03, 0.015, "hamming"),nfft=512,low_freq=0,high_freq=None,number_of_octaves=7,number_of_bins_per_octave=24,spectral_threshold=0.005,f0=120,q_rate=1.0),posinf=0,neginf=0),axis=0)
+
+    
+def apply_gfcc(x):
+    return np.mean(np.nan_to_num(erb_spectrogram(x['y'],fs=x['sr'],pre_emph=True,pre_emph_coeff=0.97,window=SlidingWindow(0.03, 0.015, "hamming"),nfilts=24,nfft=512,low_freq=0,high_freq=None,scale='constant',fbanks=None,conversion_approach='Glasberg')[0],posinf=0,neginf=0),axis=0)
+
+def apply_lfcc(x):
+    return np.mean(np.nan_to_num(linear_spectrogram(x['y'],fs=x['sr'],pre_emph=True,pre_emph_coeff=0.97,window=SlidingWindow(0.03, 0.015, "hamming"),nfilts=24,nfft=512,low_freq=0,high_freq=None,scale='constant',fbanks=None)[0],posinf=0,neginf=0),axis=0)
+
+def apply_msrcc(x):
+    return np.mean(np.nan_to_num(msrcc(x['y'],fs=x['sr'],num_ceps=13,pre_emph=True,pre_emph_coeff=0.97,window=SlidingWindow(0.03, 0.015, "hamming"),nfilts=24,nfft=512,low_freq=0,high_freq=None,scale='ascendant',gamma=-0.14285714285714285,dct_type=2,use_energy=False,lifter=None,normalize=None,fbanks=None,conversion_approach='Oshaghnessy'),posinf=0,neginf=0),axis=0)
+
+def apply_ngcc(x):
+    return np.mean(np.nan_to_num(ngcc(x['y'],fs=x['sr'],num_ceps=13,pre_emph=True,pre_emph_coeff=0.97,window=SlidingWindow(0.03, 0.015, "hamming"),nfilts=24,nfft=512,low_freq=0,high_freq=None,scale='constant',dct_type=2,use_energy=False,lifter=None,normalize=None,fbanks=None,conversion_approach='Glasberg'),posinf=0,neginf=0),axis=0)
+
+def load_model(checkpoint):
+    model = pickle.load(open(checkpoint, 'rb'))
+    return model
+
+def extract_features(audio):
+    y, sr = librosa.load(audio)
+    features = []
+    dom_freq = dominant_freq({'y':y, 'sr':sr})
+    features.append(dom_freq)
+    mfcc = apply_mfcc({'y':y, 'sr':sr})
+    features.append(mfcc)
+    bfcc = apply_bfcc({'y':y, 'sr':sr})
+    features.append(bfcc)
+    cqcc = apply_cqcc({'y':y, 'sr':sr})
+    features.append(cqcc)
+    gfcc = apply_gfcc({'y':y, 'sr':sr})
+    features.append(gfcc)   
+    lfcc = apply_lfcc({'y':y, 'sr':sr})
+    features.append(lfcc)
+    msrcc = apply_msrcc({'y':y, 'sr':sr})
+    features.append(msrcc)
+    ngcc = apply_ngcc({'y':y, 'sr':sr})
+    features.append(ngcc)
+    features = np.concatenate(features).flatten()
+
+    return features
+
+def inference_Verification(audio_1, audio_2):
+    model = load_model('lda.pkl')
+    
+    features1 = extract_features(audio_1)
+    features2 = extract_features(audio_2)
+
+
+
+    embed1 = model.transform([features1])
+    embed2 = model.transform([features2])
+    return cosine_similarity(embed1, embed2).flatten()[0].round(4) 
+
+audio_1 = gr.Audio(sources="upload", type="filepath", label="Audio 1")
+audio_2 = gr.Audio(sources="upload", type="filepath", label="Audio 2")
+text_output = gr.Textbox(label="Similarity Score")
+gr.Interface(
+    fn=inference_Verification,
+    inputs=[audio_1, audio_2],
+    outputs=text_output,
+    title="Speaker Verification",
+    description="Speaker Verification on Multilingual dataset.",
+).launch()

lda.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d7f8ec4727c15b390439f9a81a6a30098ba680af872a61d81b7a7f51e1822b3a
+size 59305

requirements.txt

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+numpy
+spafe
+librosa
+scikit-learn