Super-squash branch 'main' using huggingface_hub

.gitattributes

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README.md

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+---
+title: Did StyleTTS 2 Generate It?
+emoji: 🤔
+colorFrom: yellow
+colorTo: yellow
+sdk: gradio
+sdk_version: 5.5.0
+app_file: app.py
+pinned: true
+short_description: Did StyleTTS 2 generate that audio?!?
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from transformers import pipeline
+import torch
+import os
+
+pipe = pipeline('audio-classification', model='mrfakename/styletts2-detector', device='cuda' if torch.cuda.is_available() else 'cpu')
+pipe_turbo = pipeline('audio-classification', model='mrfakename/styletts2-detector-turbo', device='cuda' if torch.cuda.is_available() else 'cpu', token=os.getenv('HF_TOKEN'))
+
+ABOUT = """
+# 🤔 Did StyleTTS 2 Generate It?
+
+[Model](https://huggingface.co/mrfakename/styletts2-detector)
+
+An audio classification model based on Whisper to detect StyleTTS 2 audio. Please share incorrect results in the Community tab!
+
+**NOTE: Not affiliated with the author(s) of StyleTTS 2 in any way.**
+"""
+
+DISCLAIMER = """
+## Disclaimer
+
+The author(s) of this model cannot guarantee complete accuracy. False positives or negatives may occur.
+
+Usage of this model should not replace other precautions, such as invisible watermarking or audio watermarking.
+
+This model has been trained on outputs from the StyleTTS 2 base model, not fine-tunes. The model may not identify fine-tunes properly.
+
+The author(s) of this model disclaim all liability related to or in connection with the usage of this model.
+"""
+
+def classify(audio, model):
+    if model == "turbo":
+        result = pipe_turbo(audio)
+    else:
+        result = pipe(audio)
+    res = {}
+    for r in result:
+        res[r['label']] = r['score']
+    return res
+
+with gr.Blocks() as demo:
+    gr.Markdown(ABOUT)
+    aud = gr.Audio(label="Upload audio...", interactive=True, type="filepath")
+    model = gr.Radio(["default", "turbo"], label="Model", info="Which model do you want to use?")
+    btn = gr.Button("Classify", variant="primary")
+    res = gr.Label(label="Results...")
+    btn.click(classify, inputs=aud, outputs=res)
+    gr.Markdown(DISCLAIMER)
+
+demo.queue(default_concurrency_limit=20, max_size=20, api_open=False).launch(show_api=False)

requirements.txt

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+transformers[torch]
+librosa
+soundfile