Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+import soundfile as sf
+import numpy as np
+from scipy import signal
+
+# Ensure the model runs on GPU if available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Running on device: {device}")
+
+# Load the model and processor
+print("Loading Whisper model for Macedonian transcription...")
+processor = WhisperProcessor.from_pretrained("openai/whisper-large-v3")
+model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3").to(device)
+print("✓ Model loaded successfully!")
+
+def process_audio(audio_path):
+    try:
+        # Load and resample to 16kHz using scipy
+        waveform, sr = sf.read(audio_path)
+        if len(waveform.shape) > 1:  # Convert stereo to mono
+            waveform = waveform.mean(axis=1)
+        if sr != 16000:  # Resample if necessary
+            num_samples = int(len(waveform) * 16000 / sr)
+            waveform = signal.resample(waveform, num_samples)
+        
+        # Process the audio
+        inputs = processor(waveform, sampling_rate=16000, return_tensors="pt").to(device)
+        print("Transcribing...")
+        predicted_ids = model.generate(**inputs, language="mk")
+        transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+        return transcription
+    except Exception as e:
+        return f"Error during transcription: {str(e)}"
+
+# Gradio interface
+demo = gr.Interface(
+    fn=process_audio,
+    inputs=gr.Audio(sources=["microphone", "upload"], type="filepath"),
+    outputs="text",
+    title="Македонско препознавање на говор / Macedonian Speech Recognition",
+    description="Качете аудио или користете микрофон за транскрипција на македонски говор / Upload audio or use microphone to transcribe Macedonian speech"
+)
+
+if __name__ == "__main__":
+    demo.launch(share=True)