Update app.py

app.py

@@ -9,51 +9,38 @@ import os
 import uuid
 
 SAMPLE_RATE = 16000
-
 model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("nvidia/stt_en_conformer_transducer_xlarge")
 model.change_decoding_strategy(None)
 model.eval()
 
-
 def process_audio_file(file):
     data, sr = librosa.load(file)
-
     if sr != SAMPLE_RATE:
         data = librosa.resample(data, orig_sr=sr, target_sr=SAMPLE_RATE)
-
     # monochannel
     data = librosa.to_mono(data)
     return data
 
-
 def transcribe(audio, state=""):
     # Grant additional context
     # time.sleep(1)
-
     if state is None:
         state = ""
-
     audio_data = process_audio_file(audio)
-
     with tempfile.TemporaryDirectory() as tmpdir:
         # Filepath transcribe
         audio_path = os.path.join(tmpdir, f'audio_{uuid.uuid4()}.wav')
         soundfile.write(audio_path, audio_data, SAMPLE_RATE)
         transcriptions = model.transcribe([audio_path])
-        
-        # Direct transcribe
+       # Direct transcribe
         # transcriptions = model.transcribe([audio])
-
         # if transcriptions form a tuple (from RNNT), extract just "best" hypothesis
         if type(transcriptions) == tuple and len(transcriptions) == 2:
             transcriptions = transcriptions[0]
-
         transcriptions = transcriptions[0]
-
     state = state + transcriptions + " "
     return state, state
 
-
 iface = gr.Interface(
     fn=transcribe,
     inputs=[