Add application file

app.py

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+
+from transformers import pipeline
+
+import gradio as gr
+
+asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
+classifier = pipeline("text-classification")
+
+def speech_to_text(mic=None, file=None):
+    if mic is not None:
+        audio = mic
+    elif file is not None:
+        audio = file
+    else:
+        return "You must either provide a mic recording or a file"
+    text = asr(audio)["text"]
+    return text
+
+
+def text_to_sentiment(text):
+    return classifier(text)[0]["label"]
+
+title = "Speech-Text-Sentiment"
+description = """
+Task: Speech to Text to Sentiment\n
+Model: \n
+speech to text (Wav2Vec2ForCTC)\n
+text to sentiment (DistilBertForSequenceClassification)\n
+"""
+theme="freddyaboulton/dracula_revamped"
+
+demo = gr.Blocks(
+    title=title,
+    description=description,
+    theme=theme
+)
+
+with demo:
+    audio_file = [
+        gr.Audio(source="microphone",
+                 type="filepath",
+                 optional=True),
+        gr.Audio(source="upload",
+                 type="filepath",
+                 optional=True),
+    ]
+    text = gr.Textbox()
+    label = gr.Label()
+
+    b1 = gr.Button("Recognize Speech")
+    b2 = gr.Button("Classify Sentiment")
+
+    b1.click(speech_to_text, inputs=audio_file, outputs=text)
+    b2.click(text_to_sentiment, inputs=text, outputs=label)
+
+demo.launch()

requirements.txt

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+gradio==3.36.1
+transformers==4.30.2
+torch==2.0.1