Create app.py

app.py

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+import librosa
+import torch
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
+import gradio as gr
+from transformers import pipeline
+import IPython.display as display
+def speech_text(audio_file):
+  tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
+  model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
+  speech, rate = librosa.load(audio_file,sr=16000)
+  display.Audio(audio_file, autoplay=True)
+  print(rate)
+  input_values = tokenizer(speech, return_tensors ='pt').input_values
+  #Store logits (non-normalized predictions)
+  logits = model(input_values).logits
+  #Store predicted id's
+  predicted_ids = torch.argmax(logits, dim =-1)
+  transcriptions = tokenizer.decode(predicted_ids[0])
+  return transcriptions
+iface = gr.Interface(speech_text,inputs="audio",outputs="text",title='Sakil Transcription',description="Transcription")
+iface.launch(inline=False)
+