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import torch
import gradio as gr
from torchaudio.sox_effects import apply_effects_file
from transformers import AutoFeatureExtractor, AutoModelForAudioXVector
device = "cuda" if toch.cuda.is_available() else "cpu"
EFFECTS = [
['remix', '-'], # pour fusionner tous les canaux
["channels", "1"], #channel-->mono
["rate", "16000"], # rééchantillonner à 16000 Hz
["gain", "-1.0"], #Atténuation -1 dB
["silence", "1", "0.1", "0.1%", "-1", "0.1", "0.1%"],
# ['pad', '0', '1.5'], # pour ajouter 1,5 seconde à la fin
['trim', '0', '10'], # obtenir les 10 premières secondes
]
model_name = "microsoft/unispeech-sat-base-plus-sv"
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
model = AutoModelForAudioXVector.from_pretrained(model_name).to(device)
#Réglage de la valeur seuil
SEUIL = 0,85
cosine_similarity = torch.nn.CosineSimilarity(dim=-1)
def similarity_fn(path1, path2):
if not (path1 and path2):
return 'ERROR: Please record audio for *both* speakers!'
#Applying the effects to both the audio input files
wav1, _ = apply_effects_file(path1, EFFECTS)
wav2, _ = apply_effects_file(path2,EFFECTS)
#Extracting features
input1 = feature_extractor(wav1.squeeze(0), return_tensors="pt", sampling_rate=16000).input_values.to(device)
input2 = feature_extractor(wav2.squeeze(0), return_tensors="pt", sampling_rate=16000).input_values.to(device)
with torch.no_grad():
emb1 = model(input1).embeddings
emb2 = model(input2).embeddings
emb1 = torch.nn.functional.normalize(emb1, dim=-1).to(device)
emb2 = torch.nn.functional.normalize(emb2, dim=-1).to(device)
similarity = cosine_similarity(emb1, emb2).numpy()[0]
if similarity>= THRESHOLD:
return f"Similarity score is {similarity :.0%}. Audio belongs to the same person "
elif similarity< THRESHOLD:
return f"Similarity score is {similarity:.0%}. Audio doesn't belong to the same person.Authentication failed!"
inputs = [
gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Speaker #1"),
gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Speaker #2"),
]
outputs = gr.outputs.Textbox(label="Output Text")
description = (
"This app evaluates whether the given audio speech inputs belong to the same individual based on Cosine Similarity score. "
)
interface = gr.Interface(
fn=similarity_fn,
inputs=inputs,
outputs=outputs,
title="Voice Authentication with UniSpeech-SAT + X-Vectors",
description=description,
layout="horizontal",
theme="grass",
allow_flagging=False,
live=False,
examples=[
["cate_blanch.mp3", "cate_blanch_2.mp3"],
["cate_blanch.mp3", "denzel_washington.mp3"]
]
)
interface.launch(enable_queue=True) |