Update app.py

app.py

@@ -5,18 +5,28 @@
 
 import gradio as gr
 import os
+import transformers
+from transformers import pipeline
+import time
 
-# save your HF API token from https:/hf.co/settings/tokens as an env variable to avoid rate limiting
-auth_token = os.getenv("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")
+p = pipeline('automatic-speech-recognition', model='rohitp1/kkkh_whisper_small_distillation_att_loss_libri360_epochs_100_batch_4_concat_dataset')
 
-# automatically load the interface from a HF model 
-# you can remove the api_key parameter if you don't care about rate limiting. 
-demo = gr.load(
-    "huggingface/rohitp1/kkkh_whisper_small_distillation_att_loss_libri360_epochs_100_batch_4_concat_dataset",
-    title="Speech-to-text-LibriSpeech-Noise-Robust",
-    inputs="mic",
-    description="Let me try to guess what you're saying!",
-    api_key=auth_token
-)
+def transcribe(audio, state=""):
+    time.sleep(3)
+    text = p(audio)["text"]
+    state += text + " "
+    return state, state
 
-demo.launch()
+
+
+gr.Interface(
+    fn=transcribe, 
+    inputs=[
+        gr.inputs.Audio(source="microphone", type="filepath"),
+        'state'
+    ],
+    outputs=[
+        "textbox",
+        "state"
+    ],
+    live=False).launch()