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| import os | |
| import gradio as gr | |
| import torch | |
| import pydub | |
| import torchaudio | |
| from torchaudio.sox_effects import apply_effects_tensor | |
| import numpy as np | |
| from transformers import AutoFeatureExtractor, AutoModelForAudioXVector | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| def load_audio(file_name): | |
| audio = pydub.AudioSegment.from_file(file_name) | |
| arr = np.array(audio.get_array_of_samples(), dtype=np.float32) | |
| arr = arr / (1 << (8 * audio.sample_width - 1)) | |
| return arr.astype(np.float32), audio.frame_rate | |
| 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/wavlm-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, sr1 = load_audio(path1) | |
| print(wav1, wav1.shape, wav1.dtype) | |
| wav1, _ = apply_effects_tensor(torch.tensor(wav1).unsqueeze(0), sr1, EFFECTS) | |
| wav2, sr2 = load_audio(path2) | |
| wav2, _ = apply_effects_tensor(torch.tensor(wav2).unsqueeze(0), sr2, 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 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/wavlm-base-plus-sv' target='_blank'>🎙️ Learn more about WavLM</a> | " | |
| "<a href='https://arxiv.org/abs/2110.13900' target='_blank'>📚 WavLM 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/cate_blanch_3.mp3"], | |
| ["samples/cate_blanch_2.mp3", "samples/cate_blanch_3.mp3"], | |
| ["samples/heath_ledger.mp3", "samples/heath_ledger_3.mp3"], | |
| ["samples/russel_crowe.mp3", "samples/russel_crowe_2.mp3"], | |
| ["samples/cate_blanch.mp3", "samples/kirsten_dunst.wav"], | |
| ["samples/russel_crowe.mp3", "samples/kirsten_dunst.wav"], | |
| ["samples/russel_crowe_2.mp3", "samples/kirsten_dunst.wav"], | |
| ["samples/heath_ledger.mp3", "samples/denzel_washington.mp3"], | |
| ["samples/leonardo_dicaprio.mp3", "samples/russel_crowe.mp3"], | |
| ["samples/leonardo_dicaprio.mp3", "samples/russel_crowe_2.mp3"], | |
| ["samples/naomi_watts.mp3", "samples/denzel_washington.mp3"], | |
| ["samples/naomi_watts.mp3", "samples/leonardo_dicaprio.mp3"], | |
| ["samples/naomi_watts.mp3", "samples/cate_blanch_2.mp3"], | |
| ["samples/naomi_watts.mp3", "samples/kirsten_dunst.wav"], | |
| ] | |
| interface = gr.Interface( | |
| fn=similarity_fn, | |
| inputs=inputs, | |
| outputs=output, | |
| title="Voice Authentication with WavLM + X-Vectors", | |
| description=description, | |
| article=article, | |
| layout="horizontal", | |
| theme="huggingface", | |
| allow_flagging=False, | |
| live=False, | |
| examples=examples, | |
| ) | |
| interface.launch(enable_queue=True) | |