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README.md

@@ -5,7 +5,7 @@ emoji: 🔥
 colorFrom: indigo
 colorTo: indigo
 sdk: gradio
-sdk_version: 5.49.1
+sdk_version: 6.0.0
 app_file: run.py
 pinned: false
 hf_oauth: true

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: spectogram"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio scipy numpy matplotlib"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy import signal\n", "\n", "import gradio as gr\n", "\n", "def spectrogram(audio):\n", "    sr, data = audio\n", "    if len(data.shape) == 2:\n", "        data = np.mean(data, axis=0)\n", "    frequencies, times, spectrogram_data = signal.spectrogram(\n", "        data, sr, window=\"hamming\"\n", "    )\n", "    plt.pcolormesh(times, frequencies, np.log10(spectrogram_data))\n", "    return plt\n", "\n", "demo = gr.Interface(spectrogram, \"audio\", \"plot\")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: spectogram"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio scipy numpy matplotlib"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy import signal\n", "\n", "import gradio as gr\n", "\n", "def spectrogram(audio):\n", "    sr, data = audio\n", "    if len(data.shape) == 2:\n", "        data = np.mean(data, axis=0)\n", "    frequencies, times, spectrogram_data = signal.spectrogram(\n", "        data, sr, window=\"hamming\"\n", "    )\n", "    plt.pcolormesh(times, frequencies, np.log10(spectrogram_data))\n", "    return plt\n", "\n", "demo = gr.Interface(spectrogram, \"audio\", \"plot\", api_name=\"predict\")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -14,7 +14,7 @@ def spectrogram(audio):
     plt.pcolormesh(times, frequencies, np.log10(spectrogram_data))
     return plt
 
-demo = gr.Interface(spectrogram, "audio", "plot")
+demo = gr.Interface(spectrogram, "audio", "plot", api_name="predict")
 
 if __name__ == "__main__":
     demo.launch()