(You must get 89% or more to be considered the same person)
"""
EFFECTS = [
["channels", "1"],
["rate", "16000"],
["gain", "-3.0"],
["silence", "1", "0.1", "0.1%", "-1", "0.1", "0.1%"],
]
THRESHOLD = 0.89
model_name = "microsoft/unispeech-sat-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 similarity_fn(mic_path1, file_path1, mic_path2, file_path2):
if not ((mic_path1 or file_path1) and (mic_path2 or file_path2)):
return '
ERROR: Please record or upload audio for *both* speakers!'
wav1, _ = apply_effects_file(mic_path1 if mic_path1 else file_path1, EFFECTS)
wav2, _ = apply_effects_file(mic_path2 if mic_path2 else file_path2, EFFECTS)
print(wav1.shape, wav2.shape)
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
inputs = [
gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Speaker #1"),
gr.inputs.Audio(source="upload", type="filepath", optional=True, label="or"),
gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Speaker #2"),
gr.inputs.Audio(source="upload", type="filepath", optional=True, label="or"),
]
output = gr.outputs.HTML(label="")
description = (
"Speaker Verification demo based on "
"UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware Pre-Training"
)
article = (
"
"
"🎙️ Learn more about UniSpeech-SAT | "
"📚 UniSpeech-SAT paper | "
"📚 X-Vector paper"
"
"
)
interface = gr.Interface(
fn=similarity_fn,
inputs=inputs,
outputs=output,
title="Speaker Verification with UniSpeech-SAT + X-Vectors",
description=description,
article=article,
layout="horizontal",
theme="huggingface",
allow_flagging=False,
live=False,
)
interface.launch(enable_queue=True)