Create app.py

app.py

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+from transformers import pipeline
+import gradio as gr
+
+model = pipeline(model="openai/whisper-base")
+en_jp_translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-jap")
+
+# "automatic-speech-recognition"
+# transcriber = pipeline(model="openai/whisper-base")
+# transcriber("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac")
+
+
+def transcribe_audio(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"
+    transcription = model(audio)["text"]
+    return transcription
+
+def translate_text(transcription):
+    return en_jp_translator(transcription)[0]["translation_text"]
+
+def combined_function(b):
+    transcribe_audio(inputs=audio_file, outputs=text)
+    translate_text(inputs=text, outputs=translate)
+
+demo = gr.Blocks()
+
+with demo:
+    audio_file = gr.Audio(type="filepath")
+    text = gr.Textbox()
+    translate = gr.Textbox()
+
+#    b1 = gr.Button("Recognize Speech & Translate")
+    b1 = gr.Button("Recognize Speech")
+    b2 = gr.Button("Translate")
+
+#    b1.click(combined_function)
+    b1.click(transcribe_audio, inputs=audio_file, outputs=text)
+    b2.click(translate_text, inputs=text, outputs= translate)
+
+demo.launch()