Adding Wav2Vec2 code

app.py

@@ -1,7 +1,58 @@
+#Importing all the necessary packages
+import nltk
+import librosa
+import torch
 import gradio as gr
+from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForCTC
 
-def greet(name):
-    return "Hello " + name + "!!"
+nltk.download("punkt")
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+#Loading the model and the tokenizer
+model_name = "facebook/wav2vec2-base-960h"
+tokenizer = Wav2Vec2Tokenizer.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.
+  """
+  #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.
+  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
+  """
+  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
+  """
+  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])
+  #Output is all upper case
+  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 = "Wav2Vec2 in-action",
+             examples = [["test.wav"]], theme="grass").launch()