Update transcription.py

transcription.py

@@ -2,15 +2,24 @@ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 import torch
 import whisper
 
-
-
+# Initialize tokenizer and model for spell checking
 tokenizer = AutoTokenizer.from_pretrained("Bhuvana/t5-base-spellchecker")
-
 model = AutoModelForSeq2SeqLM.from_pretrained("Bhuvana/t5-base-spellchecker")
 
-
+# Function to correct spelling errors in a given input text
 def correct(inputs):
-    input_ids = tokenizer.encode(inputs,return_tensors='pt')
+    '''Corrects spelling errors in the input text using the spell checker model.
+    
+    Args:
+        inputs (str): The input text to be spell-checked.
+    
+    Returns:
+        str: The corrected version of the input text.
+    '''
+    # Encode the input text using the tokenizer
+    input_ids = tokenizer.encode(inputs, return_tensors='pt')
+    
+    # Generate corrected output using the spell checker model
     sample_output = model.generate(
         input_ids,
         do_sample=True,
@@ -18,11 +27,24 @@ def correct(inputs):
         top_p=0.99,
         num_return_sequences=1
     )
+    
+    # Decode the corrected output and remove special tokens
     res = tokenizer.decode(sample_output[0], skip_special_tokens=True)
     return res
 
+# Load the whisper model for audio transcription
 whisper_model = whisper.load_model("base")
+
+# Function to transcribe audio file
 def transcribe(audio_file):
+    '''Transcribes the content of an audio file.
+    
+    Args:
+        audio_file (str): The path to the audio file.
+    
+    Returns:
+        str: The transcribed text from the audio file, with spelling errors corrected.
+    '''
     # Load audio and pad/trim it to fit 30 seconds
     audio = whisper.load_audio(audio_file)
     audio = whisper.pad_or_trim(audio)
@@ -33,21 +55,16 @@ def transcribe(audio_file):
     # Make log-Mel spectrogram and move to the same device as the model
     mel = whisper.log_mel_spectrogram(mel).to(model.device)
 
-    # Detect the spoken language
+    # Detect the spoken language using the whisper model
     _, probs = whisper_model.detect_language(mel)
 
-    # Decode the audio
+    # Decode the audio using the whisper model
     options = whisper.DecodingOptions(fp16=False)
     result = whisper.decode(whisper_model, mel, options)
     result_text = result.text
     
-    print('result_text:'+result_text)
+    # Print the transcribed text
+    print('result_text:' + result_text)
 
+    # Correct any spelling errors in the transcribed text
     return correct(result_text)
-
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