Add Gradio demo

app.py

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+import gradio as gr
+import torch
+import torchaudio
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+
+MODEL_ID = "codeninja3d/whisper-small-ls100"
+
+processor = WhisperProcessor.from_pretrained(MODEL_ID)
+model = WhisperForConditionalGeneration.from_pretrained(MODEL_ID)
+model = model.to("cuda" if torch.cuda.is_available() else "cpu")
+
+def transcribe(audio):
+    speech, sr = torchaudio.load(audio)
+    inputs = processor(speech.squeeze(), sampling_rate=sr, return_tensors="pt").to(model.device)
+    pred_ids = model.generate(**inputs)
+    text = processor.batch_decode(pred_ids, skip_special_tokens=True)[0]
+    return text
+
+demo = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(sources=["upload", "microphone"], type="filepath"),
+    outputs="text",
+    title="Whisper Small LS100 - Fine-tuned Model",
+    description="Upload or record an English audio sample (16 kHz recommended) to get a transcription."
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+torch
+torchaudio
+transformers>=4.45
+gradio>=4.0