Spaces:
Runtime error
Runtime error
File size: 4,029 Bytes
420400a 0a57289 420400a 0a57289 477d5db 7275eb6 670efcf 7275eb6 0a57289 477d5db 420400a 7275eb6 420400a 7275eb6 670efcf 420400a 0a57289 420400a 0a57289 420400a 670efcf 420400a 0a57289 420400a 670efcf 7275eb6 420400a 670efcf 420400a 670efcf 7275eb6 420400a 7275eb6 420400a 7275eb6 420400a 09da2a2 670efcf 420400a 670efcf 7275eb6 420400a 0a57289 420400a 0a57289 420400a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 |
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'>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)
|