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import gradio as gr
import torch
from torchaudio.sox_effects import apply_effects_file
from transformers import AutoFeatureExtractor, AutoModelForAudioXVector
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
OUTPUT_OK = (
"""
<div class="container">
<div class="row"><h1 style="text-align: center">The speakers are</h1></div>
<div class="row"><h1 class="display-1 text-success" style="text-align: center">{:.1f}%</h1></div>
<div class="row"><h1 style="text-align: center">similar</h1></div>
<div class="row"><h1 class="text-success" style="text-align: center">Welcome, human!</h1></div>
<div class="row"><small style="text-align: center">(You must get at least 85% to be considered the same person)</small><div class="row">
</div>
"""
)
OUTPUT_FAIL = (
"""
<div class="container">
<div class="row"><h1 style="text-align: center">The speakers are</h1></div>
<div class="row"><h1 class="display-1 text-danger" style="text-align: center">{:.1f}%</h1></div>
<div class="row"><h1 style="text-align: center">similar</h1></div>
<div class="row"><h1 class="text-danger" style="text-align: center">You shall not pass!</h1></div>
<div class="row"><small style="text-align: center">(You must get at least 85% to be considered the same person)</small><div class="row">
</div>
"""
)
EFFECTS = [
["remix", "-"],
["channels", "1"],
["rate", "16000"],
["gain", "-1.0"],
["silence", "1", "0.1", "0.1%", "-1", "0.1", "0.1%"],
["trim", "0", "10"],
]
THRESHOLD = 0.85
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(path1, path2):
if not (path1 and path2):
return '<b style="color:red">ERROR: Please record audio for *both* speakers!</b>'
wav1, _ = apply_effects_file(path1, EFFECTS)
wav2, _ = apply_effects_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="microphone", type="filepath", optional=True, label="Speaker #2"),
]
output = gr.outputs.HTML(label="")
description = (
"This demo from Microsoft will compare two speech samples and determine if they are from the same speaker. "
"Try it with your own voice!"
)
article = (
"<p style='text-align: center'>"
"<a href='https://huggingface.co/microsoft/unispeech-sat-large-sv' target='_blank'>🎙️ Learn more about UniSpeech-SAT</a> | "
"<a href='https://arxiv.org/abs/2110.05752' target='_blank'>📚 UniSpeech-SAT paper</a> | "
"<a href='https://www.danielpovey.com/files/2018_icassp_xvectors.pdf' target='_blank'>📚 X-Vector paper</a>"
"</p>"
)
examples = [
["samples/cate_blanch.mp3", "samples/cate_blanch_2.mp3"],
["samples/cate_blanch.mp3", "samples/heath_ledger.mp3"],
]
interface = gr.Interface(
fn=similarity_fn,
inputs=inputs,
outputs=output,
layout="horizontal",
theme="huggingface",
allow_flagging=False,
live=False,
examples=examples,
cache_examples=False
)
interface.launch()
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