| import gradio as gr | |
| import torch | |
| from nemo.collections.asr.models import EncDecSpeakerLabelModel | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| STYLE = """ | |
| <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/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 provided samples are</h1></div> | |
| <div class="row"><h1 class="text-success" style="text-align: center">Same Speakers!!!</h1></div> | |
| </div> | |
| """ | |
| ) | |
| OUTPUT_FAIL = ( | |
| STYLE | |
| + """ | |
| <div class="container"> | |
| <div class="row"><h1 style="text-align: center">The provided samples are from </h1></div> | |
| <div class="row"><h1 class="text-danger" style="text-align: center">Different Speakers!!!</h1></div> | |
| </div> | |
| """ | |
| ) | |
| THRESHOLD = 0.80 | |
| model_name = "nvidia/speakerverification_en_titanet_large" | |
| model = EncDecSpeakerLabelModel.from_pretrained(model_name).to(device) | |
| def compare_samples(path1, path2): | |
| if not (path1 and path2): | |
| return '<b style="color:red">ERROR: Please record audio for *both* speakers!</b>' | |
| output = model.verify_speakers(path1,path2,THRESHOLD) | |
| return OUTPUT_OK if output else OUTPUT_FAIL | |
| 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 demonstration will analyze two recordings of speech and ascertain whether they have been spoken by the same individual.\n" | |
| "You can attempt this exercise using your own voice." | |
| ) | |
| article = ( | |
| "<p style='text-align: center'>" | |
| "<a href='https://huggingface.co/nvidia/speakerverification_en_titanet_large' target='_blank'>🎙️ Learn more about TitaNet model</a> | " | |
| "<a href='https://arxiv.org/pdf/2110.04410.pdf' target='_blank'>📚 TitaNet paper</a> | " | |
| "<a href='https://github.com/NVIDIA/NeMo' target='_blank'>🧑💻 Repository</a>" | |
| "</p>" | |
| ) | |
| examples = [ | |
| ["data/id10270_5r0dWxy17C8-00001.wav", "data/id10270_5r0dWxy17C8-00002.wav"], | |
| ["data/id10271_1gtz-CUIygI-00001.wav", "data/id10271_1gtz-CUIygI-00002.wav"], | |
| ["data/id10270_5r0dWxy17C8-00001.wav", "data/id10271_1gtz-CUIygI-00001.wav"], | |
| ["data/id10270_5r0dWxy17C8-00002.wav", "data/id10271_1gtz-CUIygI-00002.wav"], | |
| ] | |
| interface = gr.Interface( | |
| fn=compare_samples, | |
| inputs=inputs, | |
| outputs=output, | |
| title="Speaker Verification with TitaNet Embeddings", | |
| description=description, | |
| article=article, | |
| layout="horizontal", | |
| theme="huggingface", | |
| allow_flagging=False, | |
| live=False, | |
| examples=examples, | |
| ) | |
| interface.launch(enable_queue=True) |