File size: 1,618 Bytes
703fe5e 86c2562 703fe5e 86c2562 703fe5e 86c2562 703fe5e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | import gradio as gr
import json
from transformers import pipeline
import warnings
from transformers import ASTForAudioClassification, ASTFeatureExtractor
warnings.filterwarnings("ignore")
model = ASTForAudioClassification.from_pretrained("ATDI-Group/AST_finetuned_SIGIDwiki", subfolder='AST_finetuned_SIGIDwiki')
feature_extractor = ASTFeatureExtractor.from_pretrained("ATDI-Group/AST_finetuned_SIGIDwiki", subfolder='AST_finetuned_SIGIDwiki')
audio_classifier = pipeline("audio-classification", model=model, feature_extractor=feature_extractor)
def analyze_signal(audio_path):
if audio_path is None:
return {"Error": "No audio file provided. Please upload a file."}
print(f"Analysing audio file: {audio_path}...")
try:
acoustic_results = audio_classifier(audio_path, top_k=3)
detected_sounds = {item['label']: round(item['score'], 2) for item in acoustic_results}
holistic_understanding = {
"Detected Sounds": detected_sounds,
}
return holistic_understanding
except Exception as e:
return {"Error": f"Could not process the audio file. Details: {str(e)}"}
iface = gr.Interface(
fn=analyze_signal,
inputs=gr.Audio(type="filepath", label="Upload your Audio File (.wav, .mp3)"),
outputs=gr.JSON(label="Analysis Result"),
title="Audio Spectrogram Transformer finetuned on SIGID wiki for Radio Signal Classification ",
description="""
https://huggingface.co/ATDI-Group/AST_finetuned_SIGIDwiki
""",
examples=[["S5065-MSK.wav"]]
)
# This will create the web UI
iface.launch(debug=True)
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