First iteration

README.md

@@ -1,8 +1,8 @@
 ---
 title: Unispeech Speaker Verification
 emoji: 💻
-colorFrom: yellow
-colorTo: green
+colorFrom: indigo
+colorTo: blue
 sdk: gradio
 app_file: app.py
 pinned: false

app.py

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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/[email protected]/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)

requirements.txt

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+git+https://github.com/huggingface/transformers
+torchaudio