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| import gradio as gr | |
| import torch | |
| from torchaudio.sox_effects import apply_effects_file | |
| from transformers import AutoFeatureExtractor, AutoModelForAudioXVector | |
| import os | |
| 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! If you find an incorrect prediction, you can click FLAG to save the recordings to a public dataset: " | |
| "https://huggingface.co/datasets/abidlabs/voice-verification-adversarial-dataset, " | |
| "consisting of samples on which the model makes mistakes, which may further improve research in this field. Disclaimer: this will " | |
| "save the recordings to a PUBLIC dataset so please be careful about what you FLAG." | |
| ) | |
| 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/kirsten_dunst.wav"], | |
| ] | |
| HF_TOKEN = os.getenv('HF_TOKEN') | |
| hf_saver = gr.HuggingFaceDatasetSaver(HF_TOKEN, "voice-verification-adversarial-dataset") | |
| interface = gr.Interface( | |
| fn=similarity_fn, | |
| inputs=inputs, | |
| outputs=output, | |
| description=description, | |
| title="Break this voice verification model!", | |
| layout="horizontal", | |
| theme="huggingface", | |
| live=False, | |
| examples=examples, | |
| article="[Link to dataset](https://huggingface.co/datasets/abidlabs/voice-verification-adversarial-dataset)", | |
| ) | |
| interface.launch(enable_queue=True) | |