Create app.py

app.py

@@ -0,0 +1,41 @@
+import os
+os.system("pip install git+https://github.com/openai/whisper.git")
+import gradio as gr
+import whisper
+
+def transcribe_audio(audio):
+  # Load the audio and trim/pad it to fit for 30 seconds
+  audio = whisper.load_audio(audio)
+  audio = whisper.pad_or_trim(audio)
+
+  # Make mel log spectrogram
+  mel = whisper.log_mel_spectrogram(audio).to(model.device)
+
+  # Detect the spoken language
+  _, probs = model.detect_language(mel)
+
+  # Decode the audio
+  options = whisper.DecodingOptions()
+  result = whisper.decode(model, mel, options)
+
+  return result.text
+
+title = "Automatic Speech Recognition"
+description = "Speech to Text Conversion using whisper"
+
+# Input from user
+in_prompt = gradio.components.Audio(source="microphone", type="filepath")
+
+# Output response
+out_response = gradio.components.Textbox(label='Text')
+
+# Gradio interface to generate UI link
+iface = gradio.Interface(fn=transcribe_audio,
+                         inputs = in_prompt,
+                         outputs = out_response,
+                         title=title,
+                         description=description,
+                         live=True
+                         )
+
+iface.launch(debug = True)