Update app.py

app.py

@@ -1,3 +1,31 @@
+from transformers import WhisperProcessor, WhisperFeatureExtractor, WhisperForConditionalGeneration
 import gradio as gr
+import torchaudio
 
-gr.load("models/amithm3/whisper-medium").launch()
+mdl = "models/amithm3/whisper-medium"
+processor = WhisperProcessor.from_pretrained(mdl, task="transcribe")
+feature_extractor = WhisperFeatureExtractor.from_pretrained(mdl, task="transcribe")
+model = WhisperForConditionalGeneration.from_pretrained(mdl)
+sampling_rate = 16000
+
+
+def transcribe(audio, language):
+    audio, orig_freq = torchaudio.load(audio)
+    audio = torchaudio.functional.resample(audio, orig_freq=orig_freq, new_freq=sampling_rate)
+    audio = audio.squeeze().numpy()
+    input_features = processor(audio, sampling_rate=sampling_rate, return_tensors="pt").input_features
+    model.generation_config.language = language
+    predicted_ids = model.generate(input_features)
+    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+    return transcription
+
+
+iface = gr.Interface(
+    fn=transcribe,
+    inputs=[gr.Audio(type="filepath"), gr.Dropdown(["kannada", "english", None], label="Language", value="kannada")],
+    outputs="text",
+    title="Whisper Medium Indic",
+    description="Realtime demo for Indic speech recognition using a fine-tuned Whisper Medium model.",
+)
+
+iface.launch()