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597337dcb8
* added playground with 12 demos * change name to recipes, restyle navbar * add explanatory text to page * fix demo mapping * categorize demos, clean up design * styling * cateogry naming and emojis * refactor and add text demos * add view code button * remove opening slash in embed * styling * add image demos * adding plot demos * remove see code button * removed submodules * changes * add audio models * remove fun section * remove tests in image semgentation demo repo * requested changes * add outbreak_forecast * fix broken demos * remove images and models, add new demos * remove readmes, change to run.py, add description as comment * move to /demos folder, clean up dict * add upload_to_spaces script * fix script, clean repos, and add to docker file * fix python versioning issue * env variable * fix * env fixes * spaces instead of tabs * revert to original networking.py * fix rate limiting in asr and autocomplete * change name to demos * clean up navbar * move url and description, remove code comments * add tabs to demos * remove margins and footer from embedded demo * font consistency Co-authored-by: Abubakar Abid <abubakar@huggingface.co>
107 lines
3.9 KiB
Python
107 lines
3.9 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.inputs.Audio(source="microphone", type="filepath", optional=True, label="Speaker #1"),
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gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Speaker #2"),
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]
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output = gr.outputs.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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theme="huggingface",
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allow_flagging=False,
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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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