bug fix

app_backup.py

@@ -1,129 +0,0 @@
-import torch
-import gradio as gr
-import models as MOD
-import process_data as PD
-from transformers import pipeline
-from manipulate_model.utils import get_config_and_model, infere
-
-model_master = {
-    "SSL-AASIST (Trained on ASV-Spoof5)": {"eer_threshold": 3.3330237865448,
-                                           "data_process_func": "process_ssl_assist_input",
-                                           "note": "This model is trained only on ASVSpoof 2024 training data.",
-                                           "model_class": "Model",
-                                           "model_checkpoint": "ssl_aasist_epoch_7.pth"},
-    "AASIST": {"eer_threshold": 1.8018419742584229,
-               "data_process_func": "process_assist_input",
-               "note": "This model is trained on ASVSpoof 2024 training data.",
-               "model_class":"AASIST_Model",
-               "model_checkpoint": "orig_aasist_epoch_1.pth"}
-}
-
-model = MOD.Model(None, "cpu")
-model.load_state_dict(torch.load("ssl_aasist_epoch_7.pth", map_location="cpu"))
-model.eval()
-loaded_model = "SSL-AASIST (Trained on ASV-Spoof5)"
-
-manpulate_config, manipulate_model = get_config_and_model()
-
-def process(file, type):
-    global model
-    global loaded_model
-    inp = getattr(PD, model_master[type]["data_process_func"])(file)
-    if not loaded_model == type:
-        model = getattr(MOD, model_master[type]["model_class"])(None, "cpu")
-        model.load_state_dict(torch.load(model_master[type]["model_checkpoint"], map_location="cpu"))
-        model.eval()
-        loaded_model = type
-
-    op = model(inp).detach().squeeze()[1].item()
-
-    response_text = "Decision score: {} \nDecision threshold: {} \nNotes: 1. Any score below threshold is indicative of fake. \n2. {} ".format(
-        str(op), str(model_master[type]["eer_threshold"]), model_master[type]["note"])
-    return response_text
-
-
-demo = gr.Blocks()
-file_proc = gr.Interface(
-    fn=process,
-    inputs=[
-        gr.Audio(sources=["upload"], label="Audio file", type="filepath"),
-        gr.Radio(["SSL-AASIST (Trained on ASV-Spoof5)", "AASIST"], label="Select Model", type="value"),
-    ],
-    outputs="text",
-    title="Find the Fake: Analyze 'Real' or 'Fake'.",
-    description=(
-        "Analyze fake or real with a click of a button. Upload a .wav or .flac file."
-    ),
-    examples=[
-        ["./bonafide.flac", "SSL-AASIST (Trained on ASV-Spoof5)"],
-        ["./fake.flac", "SSL-AASIST (Trained on ASV-Spoof5)"],
-        ["./bonafide.flac", "AASIST"],
-        ["./fake.flac", "AASIST"],
-    ],
-    cache_examples=True,
-    allow_flagging="never",
-)
-#####################################################################################
-# For ASR interface
-pipe = pipeline(
-    task="automatic-speech-recognition",
-    model="openai/whisper-large-v3",
-    chunk_length_s=30,
-    device="cpu",
-)
-
-def transcribe(inputs):
-    if inputs is None:
-        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
-
-    op = pipe(inputs, batch_size=8, generate_kwargs={"task": "transcribe"}, return_timestamps=False, return_language=True)
-    lang = op["chunks"][0]["language"]
-    text = op["text"]
-
-    return  lang, text
-
-transcribe_proc = gr.Interface(
-    fn = transcribe,
-    inputs = [
-        gr.Audio(type="filepath", label="Speech file (<30s)", max_length=30, sources=["microphone", "upload"], show_download_button=True)
-    ],
-    outputs=[
-        gr.Text(label="Predicted Language", info="Language identification is performed automatically."),
-        gr.Text(label="Predicted transcription", info="Best hypothesis."),
-    ],
-    title="Transcribe Anything.",
-    description=(
-        "Automatactic language identification and transcription service by Whisper Large V3. Upload a .wav or .flac file."
-    ),
-    allow_flagging="never"
-)
-
-#############################################################################################
-#For manipulation detection interface
-
-def detect_manipulation(inputs):
-    global manipulate_model
-    global manpulate_config
-    out = infere(manipulate_model, inputs, manpulate_config)
-    out = out.tolist()
-    return str(out)
-
-manipulate_proc = gr.Interface(
-    fn = detect_manipulation,
-    inputs=[
-    gr.Audio(type="filepath", label="Speech file (<30s)", max_length=30, sources=["microphone", "upload"], show_download_button=True)
-    ],
-    outputs=[
-        gr.Text(label="Predicted manipulations", info="Manipulation detection is performed automatically."),
-    ],
-    title="Find the manipulated segments",
-    description=(
-        "Automatactic manipulation detection service. Upload a audio file."
-    ),
-    allow_flagging="never"
-)
-
-with demo:
-    gr.TabbedInterface([file_proc, transcribe_proc, manipulate_proc], ["Analyze Audio File", "Transcribe Audio File", "Manipulation Detection"])
-demo.queue(max_size=10)
-demo.launch(share=True)

models.py

@@ -6,12 +6,13 @@ import torch.nn as nn
 from torch import Tensor
 from typing import Union
 import torch.nn.functional as F
+from huggingface_hub import hf_hub_download
 
 class SSLModel(nn.Module):
     def __init__(self, device):
         super(SSLModel, self).__init__()
 
-        cp_path = 'xlsr2_300m.pt'  # Change the pre-trained XLSR model path.
+        cp_path = hf_hub_download("arnabdas8901/aasist-trained-asvspoof2024", filename='xlsr2_300m.pt') #'xlsr2_300m.pt'  # Change the pre-trained XLSR model path.
         model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([cp_path])
         self.model = model[0]
         self.device = device