Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import HubertForCTC, Wav2Vec2Processor
+import librosa
+
+# Load the model and processor from Hugging Face Hub
+model_name = "Ansu/mHubert_basque_ASR"  # Change this to your model
+processor = Wav2Vec2Processor.from_pretrained(model_name)
+model = HubertForCTC.from_pretrained(model_name)
+
+# Function to transcribe audio
+def transcribe(audio):
+    # Load audio file
+    audio, _ = librosa.load(audio, sr=16000)
+    
+    # Process input
+    inputs = processor(audio, sampling_rate=16000, return_tensors="pt", padding=True)
+    
+    # Get model predictions
+    with torch.no_grad():
+        logits = model(inputs.input_values).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+    
+    # Decode predictions
+    transcription = processor.batch_decode(predicted_ids)[0]
+    
+    return transcription
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.Audio(source="microphone", type="filepath", label="🎤 Record or Upload Audio")  # Microphone & Upload
+    ],
+    outputs="text",
+    title="HuBERT ASR Demo",
+    description="🎙️ Speak into the microphone or upload an audio file to get a transcription.",
+    live=True,  # Enables real-time recording
+)
+
+iface.launch()