Update app.py

app.py

@@ -1,27 +1,8 @@
-# Use a pipeline as a high-level helper
 from transformers import pipeline
-import gradio as gr
-import os
 
-# Load model directly
-#from transformers import AutoProcessor, AutoModelForCTC
-#processor = AutoProcessor.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-german")
-#model = AutoModelForCTC.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-german")
+with open("sample.flac", "rb") as f:
+  data = f.read()
 
-
-pipe = pipeline("automatic-speech-recognition", model="jonatasgrosman/wav2vec2-large-xlsr-53-german")
-
-# save your HF API token from https:/hf.co/settings/tokens as an env variable to avoid rate limiting
-auth_token = os.getenv("auth_token")
-
-# 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/facebook/wav2vec2-base-960h",
-    title="Speech-to-text",
-    inputs="mic",
-    description="Let me try to guess what you're saying!",
-    hf_token=auth_token
-)
-
-demo.launch()
+pipe = pipeline("automatic-speech-recognition", "openai/whisper-large-v2")
+pipe("sample.flac")
+# {'text': "GOING ALONG SLUSHY COUNTRY ROADS AND SPEAKING TO DAMP AUDIENCES IN DRAUGHTY SCHOOL ROOMS DAY AFTER DAY FOR A FORTNIGHT HE'LL HAVE TO PUT IN AN APPEARANCE AT SOME PLACE OF WORSHIP ON SUNDAY MORNING AND HE CAN COME TO US IMMEDIATELY AFTERWARDS"}