Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""Untitled29.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Lv3LjRH9bHwMhKsWvFcELMzKqmXd9UIb
+"""
+
+!pip install -q transformers
+!pip install -q gradio
+
+import nltk
+import librosa
+import torch
+import soundfile as sf
+import gradio as gr
+from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForCTC
+nltk.download("punkt")
+
+input_file = "/content/drive/MyDrive/AAAAUDIO/My Audio.wav"
+
+tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
+model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
+
+def load_data(input_file):
+  
+  """ Function for resampling to ensure that the speech input is sampled at 16KHz.
+  """
+  #read the file
+  speech, sample_rate = sf.read(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 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])
+
+  #Output is all upper case
+  transcription = correct_casing(transcription.lower())
+
+  return transcription
+
+gr.Interface(asr_transcript,
+             inputs = gr.inputs.Audio(label = "Input Audio", type= "file"),
+             outputs = gr.outputs.Textbox(label="Output Text"),
+             title="Real-time ASR using Wav2Vec 2.0",
+             description = "asdfghnjmk",
+             examples = [["/content/drive/MyDrive/AAAAUDIO/My Audio.wav"]]).launch()
+