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requirements.txt

@@ -1,5 +1,5 @@
-gradio-client @ git+https://github.com/gradio-app/gradio@de997e67c9a7feb9e2eccebf92969366dbd67eba#subdirectory=client/python
-https://gradio-builds.s3.amazonaws.com/de997e67c9a7feb9e2eccebf92969366dbd67eba/gradio-4.39.0-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@9b42ba8f1006c05d60a62450d3036ce0d6784f86#subdirectory=client/python
+https://gradio-builds.s3.amazonaws.com/9b42ba8f1006c05d60a62450d3036ce0d6784f86/gradio-4.39.0-py3-none-any.whl
 scipy
 numpy
 matplotlib

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", "\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", "\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\")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -4,7 +4,6 @@ from scipy import signal
 
 import gradio as gr
 
-
 def spectrogram(audio):
     sr, data = audio
     if len(data.shape) == 2:
@@ -15,7 +14,6 @@ def spectrogram(audio):
     plt.pcolormesh(times, frequencies, np.log10(spectrogram_data))
     return plt
 
-
 demo = gr.Interface(spectrogram, "audio", "plot")
 
 if __name__ == "__main__":