Update app.py

app.py

@@ -2,31 +2,9 @@ import gradio as gr
 
 #Get models
 #ASR model for input speech
-import torch
-from transformers import Wav2Vec2Processor, HubertForCTC
-from datasets import load_dataset
-import soundfile as sf
-
-processor = Wav2Vec2Processor.from_pretrained("facebook/hubert-large-ls960-ft")
-model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft")
-
-def map_to_array(batch):
-    speech, _ = sf.read(batch["file"])
-    batch["speech"] = speech
-    return batch
-    
-ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
-ds = ds.map(map_to_array)
-
-input_values = processor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1
-logits = model(input_values).logits
-predicted_ids = torch.argmax(logits, dim=-1)
-transcription = processor.decode(predicted_ids[0])
-
-#speech2text = gr.Interface.load("huggingface/facebook/hubert-large-ls960-ft",
- #                               inputs=gr.inputs.Audio(label="Record Audio File", type="file", source = "microphone"))
-speech2text = gr.Interface.(transcription,
-                            inputs=gr.inputs.Audio(label="Record Audio File", type="file", source = "microphone"))                                        
+speech2text = gr.Interface.load("huggingface/facebook/hubert-large-ls960-ft",
+                                inputs=gr.inputs.Audio(label="Record Audio File", type="file", source = "microphone"))
+                                     
 #translates english to spanish text                      
 translator = gr.Interface.load("huggingface/Helsinki-NLP/opus-mt-en-es",
                                 input=transcription