Setting the App

app.py

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+# Import Gradio Library
+import gradio as gr
+# Getting Pipelines
+from transformers import pipeline
+
+# Setting the pipeline model for Speech Recognition
+trans = pipeline("automatic-speech-recognition", model = "facebook/wav2vec2-large-xlsr-53-spanish")
+# Pipeline's Classifier for Text Classification
+classifier = pipeline("text-classification", model = "pysentimiento/robertuito-sentiment-analysis")
+
+
+# Function's definition
+
+def audio_to_text(audio):
+  text = trans(audio)["text"]
+  return text
+
+def text_to_sentiment(text):
+  return classifier(text)[0]["label"]
+
+
+# Setting Block
+demo = gr.Blocks()
+
+with demo:
+  # Documnetation
+  gr.Markdown("Spanish Sentiment-Demo")
+  # Receiving Audio
+  audio = gr.Audio(source="microphone", type="filepath")
+  # Text Box
+  text = gr.Textbox()
+  # Button's Set-up Box
+  b1 = gr.Button("Please, transcribe..!: ")
+  # Procedure
+  b1.click(audio_to_text, inputs=audio, outputs=text)
+  
+  # Labels
+  label = gr.Label()
+  # Sentiment classifier
+  b2 = gr.Button("Please! Classiffy the sentiment: ")
+  # Invoke text to sentiment as text and return a label
+  b2.click(text_to_sentiment, inputs=text, outputs=label)
+
+demo.launch()