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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") | |
STYLE = """ | |
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css" integrity="sha256-YvdLHPgkqJ8DVUxjjnGVlMMJtNimJ6dYkowFFvp4kKs=" crossorigin="anonymous"> | |
""" | |
OUTPUT_OK = ( | |
STYLE | |
+ """ | |
<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 = ( | |
STYLE | |
+ """ | |
<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'>ποΈ Learn more about UniSpeech-SAT</a> | " | |
"<a href='https://arxiv.org/abs/2110.05752' target='_blank'>π UniSpeech-SAT paper</a> | " | |
"<a href='https://www.danielpovey.com/files/2018_icassp_xvectors.pdf' target='_blank'>π X-Vector paper</a>" | |
"</p>" | |
) | |
examples = [ | |
["samples/cate_blanch.mp3", "samples/cate_blanch_2.mp3"], | |
["samples/cate_blanch.mp3", "samples/kirsten_dunst.wav"], | |
] | |
interface = gr.Interface( | |
fn=similarity_fn, | |
inputs=inputs, | |
outputs=output, | |
description=description, | |
layout="horizontal", | |
theme="huggingface", | |
allow_flagging=False, | |
live=False, | |
examples=examples, | |
) | |
interface.launch(enable_queue=True) | |