Create app.py

app.py

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+from transformers import pipeline
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+from transformers import WhisperTokenizer
+from transformers import WhisperFeatureExtractor
+import gradio as gr
+
+tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", language="Spanish", task="transcribe")
+model = WhisperForConditionalGeneration.from_pretrained("mirari/whisper-small-es")
+feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small")
+
+pipe = pipeline(task="automatic-speech-recognition",model=model, tokenizer=tokenizer,feature_extractor=feature_extractor)  
+
+
+
+def transcribe(audio):
+    text = pipe(audio)["text"]
+    return text
+
+iface = gr.Interface(
+    fn=transcribe, 
+    inputs=gr.Audio(source="microphone", type="filepath"), 
+    outputs="text",
+    title="Whisper Small Hindi",
+    description="Realtime demo for Spanish speech recognition using a fine-tuned Whisper small model.",
+)
+
+iface.launch()