Update app.py

app.py

@@ -1,6 +1,6 @@
 import soundfile as sf
 import torch
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor,Wav2Vec2ProcessorWithLM
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 import gradio as gr
 import sox
 import subprocess
@@ -24,32 +24,21 @@ def resampler(input_file_path, output_file_path):
     )
     subprocess.call(command, shell=True)
 
-
-def parse_transcription_with_lm(logits):
-    result = processor_with_LM.batch_decode(logits.cpu().numpy())
-    text = result.text
-    transcription = text[0].replace('<s>','')
-    return transcription
-
 def parse_transcription(logits):
     predicted_ids = torch.argmax(logits, dim=-1)
     transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
     return transcription
 
-def parse(wav_file, applyLM):
+def parse(wav_file):
     input_values = read_file_and_process(wav_file)
     with torch.no_grad():
         logits = model(**input_values).logits
    
-    if applyLM:
-        return parse_transcription_with_lm(logits)
-    else:
-        return parse_transcription(logits)
+    return parse_transcription_with_lm(logits)
 
 access_token = os.getenv("ACCESS_TOKEN")
 model_id = "Anujgr8/wav2vec2-indic-hindi-codeswitch-anuj"
 processor = Wav2Vec2Processor.from_pretrained(model_id,token=access_token)
-processor_with_LM = Wav2Vec2ProcessorWithLM.from_pretrained(model_id,token=access_token)
 model = Wav2Vec2ForCTC.from_pretrained(model_id,token=access_token)
 
     
@@ -58,9 +47,8 @@ txtbox = gr.Textbox(
             label="Output from model will appear here:",
             lines=5
         )
-chkbox = gr.Checkbox(label="Apply LM", value=False)
 
 
-gr.Interface(parse, inputs = [input_, chkbox],  outputs=txtbox,
+gr.Interface(parse, inputs = [input_],  outputs=txtbox,
              streaming=True, interactive=True,
              analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False);