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{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: stt_or_tts"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "tts_examples = [\n", " \"I love learning machine learning\",\n", " \"How do you do?\",\n", "]\n", "\n", "tts_demo = gr.load(\n", " \"huggingface/facebook/fastspeech2-en-ljspeech\",\n", " title=None,\n", " examples=tts_examples,\n", " description=\"Give me something to say!\",\n", ")\n", "\n", "stt_demo = gr.load(\n", " \"huggingface/facebook/wav2vec2-base-960h\",\n", " title=None,\n", " inputs=gr.Microphone(type=\"filepath\"),\n", " description=\"Let me try to guess what you're saying!\",\n", ")\n", "\n", "demo = gr.TabbedInterface([tts_demo, stt_demo], [\"Text-to-speech\", \"Speech-to-text\"])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} |