Upload app.py

app.py

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+import nltk
+import librosa
+import torch
+import gradio as gr
+from transformers import WhisperProcessor, WhisperForConditionalGeneration, WhisperTokenizer
+nltk.download("punkt")
+
+
+
+model_name = "Shubham09/whisper31filescheck"
+processor = WhisperProcessor.from_pretrained(model_name)
+tokenizer = WhisperTokenizer.from_pretrained(model_name)
+model = WhisperForConditionalGeneration.from_pretrained(model_name)
+
+def load_data(input_file):
+
+  #reading the file
+  speech, sample_rate = librosa.load(input_file)
+  #make it 1-D
+  if len(speech.shape) > 1: 
+      speech = speech[:,0] + speech[:,1]
+  #Resampling the audio at 16KHz
+  if sample_rate !=16000:
+    speech = librosa.resample(speech, sample_rate,16000)
+  return speech
+
+def correct_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):
+
+  speech = load_data(input_file)
+  #Tokenize
+  input_values = tokenizer(speech, return_tensors="pt").input_values
+  #Take logits
+  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])
+  #Correcting the letter casing
+  transcription = correct_casing(transcription.lower())
+  return transcription
+
+gr.Interface(asr_transcript,
+             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",
+             description = "This application displays transcribed text for given audio input",
+             examples = [["Test_File1.wav"], ["Test_File2.wav"], ["Test_File3.wav"]], theme="grass").launch()  
+
+