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| import torch | |
| import gradio as gr | |
| 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 = """ | |
| <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css" integrity="sha256-YvdLHPgkqJ8DVUxjjnGVlMMJtNimJ6dYkowFFvp4kKs=" crossorigin="anonymous"> | |
| <div class="container"> | |
| <div class="row"><h1 style="text-align: center">The speakers are</h1></div> | |
| <div class="row"><h1 class="display-1" style="text-align: center">{:.1f}%</h1></div> | |
| <div class="row"><h1 style="text-align: center">similar</h1></div> | |
| </div> | |
| """ | |
| EFFECTS = [ | |
| ["channels", "1"], | |
| ["rate", "16000"], | |
| ["gain", "-3.0"], | |
| ["silence", "1", "0.1", "0.1%", "-1", "0.1", "0.1%"], | |
| ] | |
| model_name = "anton-l/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(mic_path1, file_path1, mic_path2, file_path2): | |
| if not ((mic_path1 or file_path1) and (mic_path2 or file_path2)): | |
| return '<b style="color:red">ERROR: Please record or upload audio for *both* speakers!</b>' | |
| wav1, _ = apply_effects_file(mic_path1 if mic_path1 else file_path1, EFFECTS) | |
| wav2, _ = apply_effects_file(mic_path2 if mic_path2 else file_path2, EFFECTS) | |
| input1 = feature_extractor(wav1.squeeze(0), return_tensors="pt").input_values.to(device) | |
| input2 = feature_extractor(wav2.squeeze(0), return_tensors="pt").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] | |
| return OUTPUT.format(similarity * 100) | |
| inputs = [ | |
| gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Speaker #1"), | |
| gr.inputs.Audio(source="upload", type="filepath", optional=True, label="or"), | |
| gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Speaker #2"), | |
| gr.inputs.Audio(source="upload", type="filepath", optional=True, label="or"), | |
| ] | |
| output = gr.outputs.HTML(label="") | |
| description = ( | |
| "Speaker Verification demo based on " | |
| "UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware Pre-Training" | |
| ) | |
| article = ( | |
| "<p style='text-align: center'>" | |
| "<a href='https://huggingface.co/microsoft/unispeech-sat-large' target='_blank'>🎙️ Learn more about UniSpeech-SAT</a> | " | |
| "<a href='https://arxiv.org/abs/2110.05752' target='_blank'>📚 Article on ArXiv</a>" | |
| "</p>" | |
| ) | |
| interface = gr.Interface( | |
| fn=similarity_fn, | |
| inputs=inputs, | |
| outputs=output, | |
| title="Speaker Verification with UniSpeech-SAT", | |
| description=description, | |
| article=article, | |
| layout="horizontal", | |
| theme="huggingface", | |
| allow_flagging=False, | |
| live=False, | |
| ) | |
| interface.launch(enable_queue=True) | |