Create app.py

app.py

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+import torchaudio
+from speechbrain.pretrained import EncoderClassifier
+import gradio as gr
+
+classifier = EncoderClassifier.from_hparams(source="speechbrain/lang-id-commonlanguage_ecapa", savedir="pretrained_models/lang-id-commonlanguage_ecapa")
+def speechbrain(aud):
+  out_prob, score, index, text_lab = classifier.classify_file(aud.name)
+  return text_lab
+  
+inputs = gr.inputs.Audio(label="Input Audio", type="file")
+outputs = "text"
+title = "Speechbrain Audio Classification"
+description = "Gradio demo for Audio Classification with SpeechBrain. To use it, simply upload your audio, or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2005.07143' target='_blank'>ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification</a> | <a href='https://github.com/speechbrain/speechbrain' '_blank'>Github Repo</a></p>"
+examples = [
+    ['samples_audio_samples_test_mixture.wav']
+]
+gr.Interface(speechbrain, inputs, outputs, title=title, description=description, article=article, examples=examples).launch()