Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import librosa
+
+# Load the model and processor
+model_name = "lnxdx/Wav2Vec2-Large-XLSR-Persian-ShEMO"
+processor = Wav2Vec2Processor.from_pretrained(model_name)
+model = Wav2Vec2ForCTC.from_pretrained(model_name)
+
+# Function to transcribe audio
+def transcribe(audio):
+    # Load the audio
+    audio, _ = librosa.load(audio, sr=16000)
+    
+    # Preprocess the audio
+    input_values = processor(audio, return_tensors="pt", sampling_rate=16000).input_values
+    
+    # Perform inference
+    with torch.no_grad():
+        logits = model(input_values).logits
+    
+    # Decode the logits to text
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.decode(predicted_ids[0])
+    
+    return transcription
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=transcribe, 
+    inputs=gr.inputs.Audio(source="microphone", type="filepath"), 
+    outputs="text",
+    title="Persian Speech Recognition",
+    description="Transcribe Persian speech to text using Wav2Vec2-Large-XLSR-Persian-ShEMO model."
+)
+
+# Launch the interface
+iface.launch()