Update app.py

app.py

@@ -0,0 +1,62 @@
+import gradio as gr
+from transformers import pipeline
+
+# ==============================
+# CONFIG
+# ==============================
+MODEL_PATH = "Sabbir772/BNWCH"
+LANGUAGE = "bn"  # Bengali
+TASK = "transcribe"
+
+# ==============================
+# LOAD MODEL
+# ==============================
+print(f"🔍 Loading model from {MODEL_PATH} ...")
+pipe = pipeline(
+    task="automatic-speech-recognition",
+    model=MODEL_PATH,
+    tokenizer=MODEL_PATH,
+    chunk_length_s=30,  # You can tune this for performance
+    device=-1  # Use CPU on Spaces unless GPU is enabled
+)
+pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(
+    language=LANGUAGE, task=TASK
+)
+print("✅ Model loaded successfully!\n")
+
+# ==============================
+# DEFINE INFERENCE FUNCTION
+# ==============================
+def transcribe(audio):
+    """Takes an audio file (tuple from Gradio) and returns transcription."""
+    if audio is None:
+        return "No audio provided."
+    # Gradio passes (sample_rate, data)
+    sr, data = audio
+    result = pipe(data)["text"]
+    return result.strip()
+
+# ==============================
+# DEFINE GRADIO INTERFACE
+# ==============================
+title = "Bangla Whisper ASR (Chittagong Dialect)"
+description = (
+    "🎙️ Upload or record audio to transcribe Bangla (Chittagong dialect) speech "
+    "using fine-tuned Whisper model. <br><br>"
+    "Model: **Sabbir772/BNWCH**"
+)
+
+demo = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(sources=["microphone", "upload"], type="numpy", label="🎧 Input Audio"),
+    outputs=gr.Textbox(label="📝 Transcription", placeholder="Model output will appear here..."),
+    title=title,
+    description=description,
+    allow_flagging="never",
+)
+
+# ==============================
+# LAUNCH APP
+# ==============================
+if __name__ == "__main__":
+    demo.launch()