adding pyctcdecode code

app.py

@@ -1,58 +1,54 @@
-#Importing all the necessary packages
 import nltk
 import librosa
 import torch
 import gradio as gr
-from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForCTC
+from pyctcdecode import build_ctcdecoder
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
 
 nltk.download("punkt")
 
 #Loading the model and the tokenizer
 model_name = "facebook/wav2vec2-base-960h"
-tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name)
+processor = Wav2Vec2Processor.from_pretrained(model_name)
 model = Wav2Vec2ForCTC.from_pretrained(model_name)
 
-def load_data(input_file):
-  
-  """ Function for resampling to ensure that the speech input is sampled at 16KHz.
-  """
+def load_data(input_file):  
   #read the file
   speech, sample_rate = librosa.load(input_file)
   #make it 1-D
   if len(speech.shape) > 1: 
       speech = speech[:,0] + speech[:,1]
-  #Resampling at 16KHz since wav2vec2-base-960h is pretrained and fine-tuned on speech audio sampled at 16 KHz.
+  #resampling to 16KHz
   if sample_rate !=16000:
     speech = librosa.resample(speech, sample_rate,16000)
   return speech
   
   
-def correct_casing(input_sentence):
-  """ This function is for correcting the casing of the letters in the sentence
-  """
+def fix_transcription_casing(input_sentence):
   sentences = nltk.sent_tokenize(input_sentence)
   return (' '.join([s.replace(s[0],s[0].capitalize(),1) for s in sentences]))
   
-def asr_transcript(input_file):
-  """This function generates transcripts for the provided audio input
-  """
+def predict_and_decode(input_file):
   speech = load_data(input_file)
-  
-  #Tokenize
-  input_values = tokenizer(speech, return_tensors="pt").input_values
-  #Take logits
+  #tokenize
+  input_values = processor(speech, return_tensors="pt", sampling_rate=16000).input_values
   logits = model(input_values).logits
-  #Take argmax
-  predicted_ids = torch.argmax(logits, dim=-1)
-  #Get the words from predicted word ids
-  transcription = tokenizer.decode(predicted_ids[0])
+  vocab_list = list(processor.tokenizer.get_vocab().keys())
+#   #Take argmax
+#   predicted_ids = torch.argmax(logits, dim=-1)
+#   #Get the words from predicted word ids
+#   transcription = tokenizer.decode(predicted_ids[0])
+  decoder = build_ctcdecoder(vocab_list)
+  pred = decoder.decode(logits)
+
   #Output is all upper case
-  transcription = correct_casing(transcription.lower())
-  return transcription
+  transcribed_text = fix_transcription_casing(pred.lower())
+  return transcribed_text
   
-gr.Interface(asr_transcript,
+gr.Interface(predict_and_decode,
              inputs = gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Speaker"),
              outputs = gr.outputs.Textbox(label="Output Text"),
-             title="ASR using Wav2Vec 2.0",
+             title="ASR using Wav2Vec 2.0 & pyctcdecode",
              description = "Wav2Vec2 in-action",
-             examples = [["test.wav"]], theme="grass").launch()
+             layout = "horizontal",
+             examples = [["test.wav"]], theme="huggingface").launch()