Update the log

README.md

@@ -1,14 +1,3 @@
----
-license: mit
-tags:
-- audio
-- automatic-speech-recognition
-- endpoints-template
-library_name: generic
-inference: false
-duplicated_from: philschmid/openai-whisper-endpoint
----
-
 # OpenAI [Whisper](https://github.com/openai/whisper) Inference Endpoint example
 
 > Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.

create_handler.ipynb

@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -150,8 +150,10 @@
     "        # run inference pipeline\n",
     "        result = self.model.transcribe(audio_nparray)\n",
     "\n",
+    "        print(\"Hi this is a custom log!\")\n",
+    "\n",
     "        # postprocess the prediction\n",
-    "        return {\"text\": result[\"text\"]}"
+    "        return { \"text\": result[\"text\"] }"
    ]
   },
   {

handler.py

@@ -28,6 +28,8 @@ class EndpointHandler():
         
         # run inference pipeline
         result = self.model.transcribe(audio_nparray)
+        
+        print("Hi this is a custom log!")
 
         # postprocess the prediction
-        return {"text": result["text"]}
+        return { "text": result["text"] }