Create app.py

app.py

@@ -0,0 +1,43 @@
+import gradio as gr
+from transformers import pipeline
+from pydub import AudioSegment
+import wordtodigits
+
+model = pipeline("automatic-speech-recognition",
+                 "facebook/wav2vec2-base-960h")
+                 
+model2 = gr.Interface.load("huggingface/facebook/fastspeech2-en-ljspeech")
+
+
+def asr(speech):
+  transcript = model(speech)['text']
+  strings = transcript.split()
+  text = ""
+  equation = ""
+  symbols = {"plus":"+","minus":"-","times":"*","divide":"/"}
+  for i in range(len(strings)):
+      if strings[i].lower() in symbols:
+          text = wordtodigits.convert(text)
+          equation += text + symbols[strings[i].lower()]
+          text=""
+          continue
+      text += strings[i].lower() + " "
+      if i == len(strings)-1:
+          text = wordtodigits.convert(text)
+          equation += text
+  
+  ans = round(eval(equation),2)
+  
+  return transcript, equation, ans, model2(str(ans))
+
+gr.Interface(fn=asr,
+             #inputs = gr.inputs.Audio(source="microphone", type="filepath", optional=False, label="Please record your voice"),
+             inputs = gr.inputs.Audio(source="upload", type="filepath", label="Upload your audio file here"),
+			 outputs = [gr.outputs.Textbox(type="str", label="Text Translation"),
+						gr.outputs.Textbox(type="str", label="Equation"),
+						gr.outputs.Textbox(type="str", label="Answer"),
+						gr.outputs.Audio(type="file", label="Speech Answer")],
+             title = "Speech Equation Solver",
+             description = "This app will translate your speech into text, morse code, and audio using wav2vec2-base-960h",
+			 article = "Model: <a href=\"https://huggingface.co/facebook/wav2vec2-base-960h\">Wav2Vec2-Base-960h</a>"
+             ).launch()