(You must get at least 80% to be considered the same person)
"""
)
THRESHOLD = 0.80
model_name = "microsoft/wavlm-base-plus-sv"
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
model = AutoModelForAudioXVector.from_pretrained(model_name).to(device)
cosine_sim = torch.nn.CosineSimilarity(dim=-1)
def preprocess_audio(file_path, target_sr=16000):
wav, sr = torchaudio.load(file_path)
if sr != target_sr:
wav = Resample(orig_freq=sr, new_freq=target_sr)(wav)
return wav
def similarity_fn(path1, path2):
if not (path1 and path2):
return '
ERROR: Please record audio for *both* speakers!'
wav1 = preprocess_audio(path1)
wav2 = preprocess_audio(path2)
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).cpu()
emb2 = torch.nn.functional.normalize(emb2, dim=-1).cpu()
similarity = cosine_sim(emb1, emb2).numpy()[0]
if similarity >= THRESHOLD:
output = OUTPUT_OK.format(similarity * 100)
else:
output = OUTPUT_FAIL.format(similarity * 100)
return output
with gr.Blocks() as demo:
gr.Markdown("# Voice Authentication with WavLM + X-Vectors")
gr.Markdown(
"This demo compares two speech samples to determine if they are from the same speaker. "
"Try it with your own voice!"
)
with gr.Row():
input1 = gr.Audio(sources=["microphone", "upload"], type="filepath", label="Speaker #1")
input2 = gr.Audio(sources=["microphone", "upload"], type="filepath", label="Speaker #2")
output = gr.HTML(label="Result")
btn = gr.Button("Compare Speakers")
btn.click(similarity_fn, inputs=[input1, input2], outputs=output)
gr.Examples(
examples=[
["samples/denzel_washington.mp3", "samples/denzel_washington.mp3"],
["samples/heath_ledger_2.mp3", "samples/heath_ledger_3.mp3"],
["samples/heath_ledger_3.mp3", "samples/denzel_washington.mp3"],
["samples/denzel_washington.mp3", "samples/heath_ledger_2.mp3"],
],
inputs=[input1, input2],
)
gr.Markdown(
"
"
"🎙️ Learn more about WavLM | "
"📚 WavLM paper | "
"📚 X-Vector paper"
"
"
)
demo.launch()