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* more typing * add changeset * tweaks * more changes * more fixes * more changes * more fixes * more fixes * delete * add changeset * notebooks * restore * restore * format * add changeset * more typing fixes * fixes * change * fixes * fix * format * more fixes * fixes * fixes for python3.9 * demo * fix * fixes * fix typo * type * formatting * add changeset --------- Co-authored-by: gradio-pr-bot <gradio-pr-bot@users.noreply.github.com> Co-authored-by: aliabd <ali.si3luwa@gmail.com>
106 lines
3.8 KiB
Python
106 lines
3.8 KiB
Python
import gradio as gr
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import torch
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from torchaudio.sox_effects import apply_effects_file
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from transformers import AutoFeatureExtractor, AutoModelForAudioXVector
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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OUTPUT_OK = (
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"""
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<div class="container">
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<div class="row"><h1 style="text-align: center">The speakers are</h1></div>
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<div class="row"><h1 class="display-1 text-success" style="text-align: center">{:.1f}%</h1></div>
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<div class="row"><h1 style="text-align: center">similar</h1></div>
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<div class="row"><h1 class="text-success" style="text-align: center">Welcome, human!</h1></div>
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<div class="row"><small style="text-align: center">(You must get at least 85% to be considered the same person)</small><div class="row">
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</div>
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"""
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)
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OUTPUT_FAIL = (
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"""
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<div class="container">
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<div class="row"><h1 style="text-align: center">The speakers are</h1></div>
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<div class="row"><h1 class="display-1 text-danger" style="text-align: center">{:.1f}%</h1></div>
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<div class="row"><h1 style="text-align: center">similar</h1></div>
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<div class="row"><h1 class="text-danger" style="text-align: center">You shall not pass!</h1></div>
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<div class="row"><small style="text-align: center">(You must get at least 85% to be considered the same person)</small><div class="row">
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</div>
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"""
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)
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EFFECTS = [
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["remix", "-"],
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["channels", "1"],
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["rate", "16000"],
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["gain", "-1.0"],
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["silence", "1", "0.1", "0.1%", "-1", "0.1", "0.1%"],
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["trim", "0", "10"],
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]
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THRESHOLD = 0.85
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model_name = "microsoft/unispeech-sat-base-plus-sv"
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feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
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model = AutoModelForAudioXVector.from_pretrained(model_name).to(device)
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cosine_sim = torch.nn.CosineSimilarity(dim=-1)
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def similarity_fn(path1, path2):
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if not (path1 and path2):
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return '<b style="color:red">ERROR: Please record audio for *both* speakers!</b>'
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wav1, _ = apply_effects_file(path1, EFFECTS)
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wav2, _ = apply_effects_file(path2, EFFECTS)
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print(wav1.shape, wav2.shape)
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input1 = feature_extractor(wav1.squeeze(0), return_tensors="pt", sampling_rate=16000).input_values.to(device)
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input2 = feature_extractor(wav2.squeeze(0), return_tensors="pt", sampling_rate=16000).input_values.to(device)
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with torch.no_grad():
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emb1 = model(input1).embeddings
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emb2 = model(input2).embeddings
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emb1 = torch.nn.functional.normalize(emb1, dim=-1).cpu()
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emb2 = torch.nn.functional.normalize(emb2, dim=-1).cpu()
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similarity = cosine_sim(emb1, emb2).numpy()[0]
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if similarity >= THRESHOLD:
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output = OUTPUT_OK.format(similarity * 100)
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else:
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output = OUTPUT_FAIL.format(similarity * 100)
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return output
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inputs = [
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gr.Audio(sources=["microphone"], type="filepath", label="Speaker #1"),
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gr.Audio(sources=["microphone"], type="filepath", label="Speaker #2"),
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]
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output = gr.HTML(label="")
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description = (
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"This demo from Microsoft will compare two speech samples and determine if they are from the same speaker. "
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"Try it with your own voice!"
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)
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article = (
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"<p style='text-align: center'>"
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"<a href='https://huggingface.co/microsoft/unispeech-sat-large-sv' target='_blank'>🎙️ Learn more about UniSpeech-SAT</a> | "
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"<a href='https://arxiv.org/abs/2110.05752' target='_blank'>📚 UniSpeech-SAT paper</a> | "
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"<a href='https://www.danielpovey.com/files/2018_icassp_xvectors.pdf' target='_blank'>📚 X-Vector paper</a>"
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"</p>"
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)
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examples = [
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["samples/cate_blanch.mp3", "samples/cate_blanch_2.mp3"],
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["samples/cate_blanch.mp3", "samples/heath_ledger.mp3"],
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]
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interface = gr.Interface(
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fn=similarity_fn,
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inputs=inputs,
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outputs=output,
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layout="horizontal",
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allow_flagging="never",
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live=False,
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examples=examples,
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cache_examples=False
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)
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interface.launch()
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