init2

.gitattributes

@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+<<<<<<< HEAD
+=======
+*.wav filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+>>>>>>> 98229b1 (init)

.gitignore

@@ -0,0 +1,3 @@
+.DS_Store
+.ipynb_checkpoints/
+.idea/

app.py

@@ -0,0 +1,99 @@
+import gradio as gr
+from test import predict_uri
+import sys
+import warnings
+from fastapi import FastAPI
+
+# ignore UserWarning
+warnings.simplefilter("ignore", UserWarning)
+
+examples = [
+    ['res/miaow_16k.wav'],
+    ['res/snore/pro_snore 6bee45643b45af9b_a7a3bbe6ba79af5b25b19ad10a8d9421d0d5679b.wav'],
+    ['res/snore/Snoring vs Sleep Apnea  - What the difference sounds like.mp4']
+]
+title = "yamnet test"
+description = "An audio event classifier trained on the AudioSet dataset to predict audio events from the AudioSet ontology."
+
+# # https://github.com/gradio-app/gradio/issues/2362
+# class Logger:
+#     def __init__(self, filename):
+#         self.terminal = sys.stdout
+#         self.log = open(filename, "w")
+#
+#     def write(self, message):
+#         self.terminal.write(message)
+#         self.log.write(message)
+#
+#     def flush(self):
+#         self.terminal.flush()
+#         self.log.flush()
+#
+#     def isatty(self):
+#         return False
+#
+#
+# sys.stdout = Logger("output.log")
+#
+#
+# def test(x):
+#     print("This is a test")
+#     print(f"Your function is running with input {x}...")
+#     return x
+#
+#
+# def read_logs():
+#     sys.stdout.flush()
+#     with open("output.log", "r") as f:
+#         return f.read()
+#
+#
+# with gr.Interface(predict_uri, inputs=gr.inputs.Audio(type="filepath"), outputs=["text", 'plot']) as demo:
+#     examples = examples,
+#     title = title,
+#     description = description,
+#     allow_flagging = 'never'
+#
+#     logs = gr.Textbox()
+#     demo.load(read_logs, None, logs, every=1)
+#
+# demo.launch(enable_queue=True, show_error=True)
+
+
+# with gr.Blocks() as demo:
+#     with gr.Row():
+#         inputs = gr.inputs.Audio(type="filepath")
+#         outputs = ["text", 'plot']
+#     btn = gr.Button("Run")
+#     btn.click(predict_uri, inputs, outputs)
+#
+#     logs = gr.Textbox()
+#     demo.load(read_logs, None, logs, every=1)
+#
+# demo.queue().launch()
+
+
+demo = gr.Interface(
+    predict_uri,
+    inputs=gr.inputs.Audio(type="filepath"),
+    outputs=['image', 'image', 'image', 'text', 'text', 'text', 'text'],
+    examples=examples,
+    title=title,
+    description=description,
+    allow_flagging='never'
+)
+demo.launch(enable_queue=True, show_error=True, share=False)
+
+# # FastAPI
+# CUSTOM_PATH = "/gradio"
+#
+# app = FastAPI()
+#
+#
+# @app.get("/")
+# def read_main():
+#     return {"message": "This is your main app"}
+#
+#
+# io = gr.Interface(lambda x: "Hello, " + x + "!", "textbox", "textbox")
+# app = gr.mount_gradio_app(app, io, path=CUSTOM_PATH)

python/tensorflow_audio_task.ipynb

@@ -0,0 +1,201 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LYKNvTdSQzAo"
+   },
+   "source": [
+    "https://www.tensorflow.org/lite/inference_with_metadata/task_library/audio_classifier?hl=zh-cn&authuser=1\n",
+    "\n",
+    "用 Python 运行推断\n",
+    "\n",
+    "第 1 步：安装 pip 软件包\n",
+    "\n",
+    "\n",
+    "pip install tflite-support\n",
+    "\n",
+    "注：Task Library 的 Audio API依靠 PortAudio 来录制来自设备麦克风的音频。如果您打算使用 Task Library 的 AudioRecord 进行音频录制，则需要在您的系统上安装 PortAudio。\n",
+    "\n",
+    "Linux：运行 sudo apt-get update && apt-get install libportaudio2\n",
+    "Mac 和 Windows：安装 tflite-support pip 软件包时会自动安装 PortAudio。\n",
+    "第 2 步：使用模型\n",
+    "\n",
+    "\n",
+    "\n",
+    "请参阅源代码，了解有关配置 AudioClassifier 的更多选项。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0hQPjUA6RLgz",
+    "outputId": "e2080672-4938-4cac-a361-71d047fdc17c"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking in indexes: https://mirrors.aliyun.com/pypi/simple\n",
+      "Collecting tflite-support\n",
+      "  Downloading https://mirrors.aliyun.com/pypi/packages/3e/84/0a17a606f6d1560ba8f45186730d29cd917c2a55260fd6f4a13c121eee31/tflite_support-0.4.3-cp38-cp38-macosx_10_11_x86_64.whl (53.0 MB)\n",
+      "\u001b[K     |████████████████████████████████| 53.0 MB 284 kB/s eta 0:00:01     |██████████████                  | 23.3 MB 285 kB/s eta 0:01:45     |███████████████████████▍        | 38.6 MB 274 kB/s eta 0:00:53\n",
+      "\u001b[?25hCollecting sounddevice>=0.4.4\n",
+      "  Downloading https://mirrors.aliyun.com/pypi/packages/24/5a/c0b9066fcaf783054b3f35254938dcba2d8cf02576ebdc56b6b4e85661f2/sounddevice-0.4.6-py3-none-macosx_10_6_x86_64.macosx_10_6_universal2.whl (107 kB)\n",
+      "\u001b[K     |████████████████████████████████| 107 kB 676 kB/s eta 0:00:01\n",
+      "\u001b[?25hCollecting numpy>=1.20.0\n",
+      "  Downloading https://mirrors.aliyun.com/pypi/packages/11/10/943cfb579f1a02909ff96464c69893b1d25be3731b5d3652c2e0cf1281ea/numpy-1.24.4-cp38-cp38-macosx_10_9_x86_64.whl (19.8 MB)\n",
+      "\u001b[K     |████████████████████████████████| 19.8 MB 278 kB/s eta 0:00:01\n",
+      "\u001b[?25hCollecting flatbuffers>=2.0\n",
+      "  Downloading https://mirrors.aliyun.com/pypi/packages/6f/12/d5c79ee252793ffe845d58a913197bfa02ae9a0b5c9bc3dc4b58d477b9e7/flatbuffers-23.5.26-py2.py3-none-any.whl (26 kB)\n",
+      "Requirement already satisfied: absl-py>=0.7.0 in /Users/luis/miniconda3/lib/python3.8/site-packages (from tflite-support) (0.13.0)\n",
+      "Collecting pybind11>=2.6.0\n",
+      "  Downloading https://mirrors.aliyun.com/pypi/packages/06/55/9f73c32dda93fa4f539fafa268f9504e83c489f460c380371d94296126cd/pybind11-2.11.1-py3-none-any.whl (227 kB)\n",
+      "\u001b[K     |████████████████████████████████| 227 kB 46.0 MB/s eta 0:00:01\n",
+      "\u001b[?25hCollecting protobuf<4,>=3.18.0\n",
+      "  Downloading https://mirrors.aliyun.com/pypi/packages/9f/1a/6848ed1669a6c70bf947d25d64ce6dcc65ccec06e917072df516944fa17e/protobuf-3.20.3-cp38-cp38-macosx_10_9_x86_64.whl (982 kB)\n",
+      "\u001b[K     |████████████████████████████████| 982 kB 260 kB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: CFFI>=1.0 in /Users/luis/miniconda3/lib/python3.8/site-packages (from sounddevice>=0.4.4->tflite-support) (1.14.3)\n",
+      "Requirement already satisfied: six in /Users/luis/miniconda3/lib/python3.8/site-packages (from absl-py>=0.7.0->tflite-support) (1.15.0)\n",
+      "Requirement already satisfied: pycparser in /Users/luis/miniconda3/lib/python3.8/site-packages (from CFFI>=1.0->sounddevice>=0.4.4->tflite-support) (2.20)\n",
+      "Installing collected packages: sounddevice, numpy, flatbuffers, pybind11, protobuf, tflite-support\n",
+      "  Attempting uninstall: numpy\n",
+      "    Found existing installation: numpy 1.19.5\n",
+      "    Uninstalling numpy-1.19.5:\n",
+      "      Successfully uninstalled numpy-1.19.5\n",
+      "  Attempting uninstall: flatbuffers\n",
+      "    Found existing installation: flatbuffers 1.12\n",
+      "    Uninstalling flatbuffers-1.12:\n",
+      "      Successfully uninstalled flatbuffers-1.12\n",
+      "  Attempting uninstall: protobuf\n",
+      "    Found existing installation: protobuf 3.17.3\n",
+      "    Uninstalling protobuf-3.17.3:\n",
+      "      Successfully uninstalled protobuf-3.17.3\n",
+      "\u001b[31mERROR: After October 2020 you may experience errors when installing or updating packages. This is because pip will change the way that it resolves dependency conflicts.\n",
+      "\n",
+      "We recommend you use --use-feature=2020-resolver to test your packages with the new resolver before it becomes the default.\n",
+      "\n",
+      "tensorflow 2.5.0 requires flatbuffers~=1.12.0, but you'll have flatbuffers 23.5.26 which is incompatible.\n",
+      "tensorflow 2.5.0 requires numpy~=1.19.2, but you'll have numpy 1.24.4 which is incompatible.\n",
+      "pytorch-lightning 2.0.6 requires tqdm>=4.57.0, but you'll have tqdm 4.51.0 which is incompatible.\n",
+      "pytorch-lightning 2.0.6 requires typing-extensions>=4.0.0, but you'll have typing-extensions 3.7.4.3 which is incompatible.\n",
+      "numba 0.56.0 requires numpy<1.23,>=1.18, but you'll have numpy 1.24.4 which is incompatible.\n",
+      "lightning 2.0.6 requires tqdm<6.0,>=4.57.0, but you'll have tqdm 4.51.0 which is incompatible.\n",
+      "lightning 2.0.6 requires typing-extensions<6.0,>=4.0.0, but you'll have typing-extensions 3.7.4.3 which is incompatible.\u001b[0m\n",
+      "Successfully installed flatbuffers-23.5.26 numpy-1.24.4 protobuf-3.20.3 pybind11-2.11.1 sounddevice-0.4.6 tflite-support-0.4.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install -i https://mirrors.aliyun.com/pypi/simple tflite-support"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 245.0
+    },
+    "id": "Kl6laUiQRBeT",
+    "outputId": "68130993-d7ad-46cf-a189-c67e1843b62d"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/luis/miniconda3/lib/python3.8/site-packages/tensorflow/python/framework/dtypes.py:511: FutureWarning: In the future `np.object` will be defined as the corresponding NumPy scalar.\n",
+      "  np.object,\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "module 'numpy' has no attribute 'object'.\n`np.object` was a deprecated alias for the builtin `object`. To avoid this error in existing code, use `object` by itself. Doing this will not modify any behavior and is safe. \nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/8c/clfh0x3s6wg77ms0cdgcbt1w0000gn/T/ipykernel_64159/35834893.py\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Imports\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtflite_support\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0maudio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtflite_support\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtflite_support\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprocessor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tflite_support/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow_lite_support\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmetadata_schema_py_generated\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow_lite_support\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mschema_py_generated\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow_lite_support\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtflite_support\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmetadata_writers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow_lite_support/metadata/python/metadata.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     35\u001b[0m   \u001b[0;31m# support more than local file systems.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m   \u001b[0;31m# In pip requirements, we doesn't necessarily need tensorflow as a dep.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m   \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf\u001b[0m  \u001b[0;31m# pylint: disable=g-import-not-at-top\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     38\u001b[0m   \u001b[0m_open_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGFile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m   \u001b[0m_exists_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msys\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_sys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtools\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmodule_util\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_module_util\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlazy_loader\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLazyLoader\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_LazyLoader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow/python/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;31m# Bring in subpackages.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdistribute\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow/python/data/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;31m# pylint: disable=unused-import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mexperimental\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset_ops\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAUTOTUNE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset_ops\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow/python/data/experimental/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;31m# pylint: disable=unused-import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mservice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    100\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatching\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdense_to_ragged_batch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatching\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdense_to_sparse_batch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow/python/data/experimental/service/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    138\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprint_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_service_ops\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdistribute\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_service_ops\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfrom_dataset_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_service_ops\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mregister_dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow/python/data/experimental/ops/data_service_ops.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtf2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcompression_ops\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistribute_options\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAutoShardPolicy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistribute_options\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mExternalStatePolicy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow/python/data/experimental/ops/compression_ops.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprint_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutil\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstructure\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgen_experimental_dataset_ops\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mged_ops\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow/python/data/util/structure.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwrapt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutil\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcomposite_tensor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow/python/data/util/nest.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msix\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_six\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msparse_tensor\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_sparse_tensor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutil\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_pywrap_utils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutil\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow/python/framework/sparse_tensor.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtf2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcomposite_tensor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconstant_op\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtypes_pb2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meager\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meager\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mexecute\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mop_callbacks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpywrap_tfe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meager\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensor_shape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow/python/framework/dtypes.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    509\u001b[0m     \u001b[0;31m# strings.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m     \u001b[0mtypes_pb2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDT_STRING\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 511\u001b[0;31m         \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    512\u001b[0m     \u001b[0mtypes_pb2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDT_COMPLEX64\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\u001b[0m         \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomplex64\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/numpy/__init__.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(attr)\u001b[0m\n\u001b[1;32m    303\u001b[0m         \u001b[0;31m# the full `numpy.testing` namespace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mattr\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'testing'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 305\u001b[0;31m             \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtesting\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtesting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    306\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mtesting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mattr\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Tester'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'object'.\n`np.object` was a deprecated alias for the builtin `object`. To avoid this error in existing code, use `object` by itself. Doing this will not modify any behavior and is safe. \nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations"
+     ],
+     "output_type": "error"
+    }
+   ],
+   "source": [
+    "# Imports\n",
+    "from tflite_support.task import audio\n",
+    "from tflite_support.task import core\n",
+    "from tflite_support.task import processor\n",
+    "\n",
+    "model_path = 'model_v8.1.tflite'\n",
+    "audio_path = 'res/snore/pro_snore 1f6d0dcf8499f3bc _7b23e49bc4f8d31fac175ac362ce34e76550914d.wav'\n",
+    "\n",
+    "# Initialization\n",
+    "base_options = core.BaseOptions(file_name=model_path)\n",
+    "classification_options = processor.ClassificationOptions(max_results=2)\n",
+    "options = audio.AudioClassifierOptions(base_options=base_options, classification_options=classification_options)\n",
+    "classifier = audio.AudioClassifier.create_from_options(options)\n",
+    "\n",
+    "# Alternatively, you can create an audio classifier in the following manner:\n",
+    "# classifier = audio.AudioClassifier.create_from_file(model_path)\n",
+    "\n",
+    "# Run inference\n",
+    "audio_file = audio.TensorAudio.create_from_wav_file(audio_path, classifier.required_input_buffer_size)\n",
+    "audio_result = classifier.classify(audio_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

python/util/apnea_util.py

@@ -0,0 +1,20 @@
+from python.util.time_util import int_to_min_sec
+
+
+def calc_apnea(idx, top_n, score, name):
+    result = ''
+
+    # print(' calc_apnea, idx, top_n, score, name ', int_to_min_sec(idx), top_n, '%.2f' % score, name)
+    which_sec = idx
+    start_sec = -1
+
+    if name == 'Snoring':
+        result = (' idx, top_n, score, name: ' + int_to_min_sec(idx) + ', ' + str(top_n) + ', ' + (
+                '%.2f' % score) + ', ' + name)
+
+    # if name == 'Snoring':
+    #     if start_sec == 0: start_sec = which_sec
+    # else:
+    #     if snore_sec == 60: start_sec = 0
+
+    return result

python/util/audio_util.py

@@ -0,0 +1,24 @@
+import sys
+# print('argv[1]:' + str(sys.argv[1]))
+import os
+from pydub import AudioSegment
+
+
+def audio_to_wav(wav_uri, out_sample_rate=16000):
+    src = wav_uri
+    dst = wav_uri + ".wav"
+    if os.path.exists(dst): os.remove(dst)
+
+    # convert wav to mp3
+    sound = AudioSegment.from_file(src)
+    sound = sound.set_frame_rate(out_sample_rate)
+    sound.export(dst, format="wav")
+
+    print(' audio_to_wav: ', str(dst))
+    return dst
+
+# sys.argv
+# if len(sys.argv) > 2:
+#     res = audio_to_wav(sys.argv[1], int(sys.argv[2]))
+# else:
+#     print(' usage: python audio_util.py /path/to/audio_file [out_sample_rate] ')

python/util/plt_util.py

@@ -0,0 +1,117 @@
+import librosa.display
+import matplotlib
+import matplotlib.pyplot as plt
+import matplotlib.style as ms
+from PIL import Image
+
+import matplotlib.ticker as ticker
+from librosa.feature import melspectrogram
+from python.util.time_util import int_to_min_sec
+
+import librosa
+import librosa.display
+import numpy as np
+from io import BytesIO
+
+# https://stackoverflow.com/questions/69924881/userwarning-starting-a-matplotlib-gui-outside-of-the-main-thread-will-likely-fa
+matplotlib.use('agg')
+ms.use('seaborn-muted')
+
+
+def update_ticks(x, pos):
+    which_second = (x / 16000)
+    return int_to_min_sec(which_second)
+
+
+def plt_line(y_points, sample_rate=16000):
+    # plt line
+    fig, ax = plt.subplots()
+    ax.xaxis.set_major_formatter(ticker.FuncFormatter(update_ticks))
+    plt.plot(y_points)
+
+    # plot to image
+    buffer = BytesIO()
+    plt.savefig(buffer, format='png')
+    image = Image.open(buffer)
+
+    return image
+
+
+# plt mfcc, https://www.cnblogs.com/LXP-Never/p/10918590.html
+def plt_mfcc(single_channel, sample_rate):
+    mel_spec = melspectrogram(y=single_channel, sr=sample_rate, n_fft=1024, hop_length=512, n_mels=128)
+    log_mel_spec = librosa.power_to_db(mel_spec)
+    plt.figure()
+    librosa.display.specshow(log_mel_spec, sr=sample_rate, x_axis='time', y_axis='mel')
+    plt.colorbar(format='%+2.0f dB')  # 右边的色度条
+    plt.title('mfcc waveform')
+
+    # plot to image
+    buffer = BytesIO()
+    plt.savefig(buffer, format='png')
+    image = Image.open(buffer)
+
+    return image
+
+
+# https://gist.github.com/stevemclaugh/80f192130852353ad53e6d8b6b275983
+def plt_mfcc2(wav_pathname, sample_rate):
+    y, sr = librosa.load(wav_pathname)
+    # Let's make and display a mel-scaled power (energy-squared) spectrogram
+    S = librosa.feature.melspectrogram(y=y, sr=sample_rate, n_mels=128)
+
+    # Convert to log scale (dB). We'll use the peak power as reference.
+    log_S = librosa.amplitude_to_db(S)
+
+    # Make a new figure
+    plt.figure(figsize=(12, 4))
+
+    # Display the spectrogram on a mel scale
+    # sample rate and hop length parameters are used to render the time axis
+    librosa.display.specshow(log_S, sr=sample_rate, x_axis='time', y_axis='mel')
+
+    # Put a descriptive title on the plot
+    plt.title('mel power spectrogram')
+
+    # draw a color bar
+    plt.colorbar(format='%+02.0f dB')
+
+    # Make the figure layout compact
+    plt.tight_layout()
+
+    S_rot = np.rot90(S, 3)
+
+    # Next, we'll extract the first 13 Mel-frequency cepstral coefficients (MFCCs)
+    mfcc = librosa.feature.mfcc(S=log_S, n_mfcc=13)
+
+    # Padding first and second deltas
+    delta_mfcc = librosa.feature.delta(mfcc)
+    delta2_mfcc = librosa.feature.delta(mfcc, order=2)
+
+    # We'll show each in its own subplot
+    # plt.figure(figsize=(12, 6))
+    plt.figure()
+
+    # plt.subplot(3, 1, 1)
+    # librosa.display.specshow(mfcc)
+    # plt.ylabel('MFCC')
+    # plt.colorbar()
+
+    # plt.subplot(1, 1, 1)
+    # librosa.display.specshow(delta_mfcc)
+    # plt.ylabel('MFCC-$\Delta$')
+    # plt.colorbar()
+
+    plt.subplot()
+    librosa.display.specshow(delta2_mfcc, sr=sample_rate, x_axis='time')
+    plt.ylabel('MFCC-$\Delta^2$')
+    plt.colorbar()
+
+    plt.tight_layout()
+
+    # plot to image
+    buffer = BytesIO()
+    plt.savefig(buffer, format='png')
+    image = Image.open(buffer)
+
+    return image

python/util/str_util.py

@@ -0,0 +1,6 @@
+def truncate_str(the_str, the_max):
+    return the_str[:the_max] if len(the_str) > the_max else the_str
+
+
+def format_float(f):
+    return '%.2f' % f

python/util/tensorflow_util.py

@@ -0,0 +1,28 @@
+import tensorflow as tf
+import numpy as np
+
+
+def predict(model_path, waveform):
+    # Download the model to yamnet.tflite
+    interpreter = tf.lite.Interpreter(model_path)
+
+    input_details = interpreter.get_input_details()
+    waveform_input_index = input_details[0]['index']
+    output_details = interpreter.get_output_details()
+    scores_output_index = output_details[0]['index']
+    # embeddings_output_index = output_details[1]['index']
+    # spectrogram_output_index = output_details[2]['index']
+
+    # Input: 0.975 seconds of silence as mono 16 kHz waveform samples.
+    # waveform = np.zeros(int(round(0.975 * 16000)), dtype=np.float32)
+    waveform2 = waveform[:156000]
+    print(waveform2.shape)  # Should print (15600,)
+
+    interpreter.resize_tensor_input(waveform_input_index, [waveform.size], strict=True)
+    interpreter.allocate_tensors()
+    interpreter.set_tensor(waveform_input_index, waveform)
+    interpreter.invoke()
+    scores = interpreter.get_tensor(scores_output_index)
+    # print(' scores, embeddings, spectrogram: ', scores.shape, embeddings.shape, spectrogram.shape)  # (N, 521) (N, 1024) (M, 64)
+
+    return scores

python/util/time_util.py

@@ -0,0 +1,5 @@
+import time
+
+
+def int_to_min_sec(i):
+    return time.strftime('%M:%S', time.gmtime(i))

requirements.txt

@@ -0,0 +1,10 @@
+PIL
+librosa
+soundfile
+matplotlib
+gradio
+tensorflow
+tensorflow_hub
+numpy
+scipy
+pydub

res/lite-model_yamnet_classification_tflite_1.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:10c95ea3eb9a7bb4cb8bddf6feb023250381008177ac162ce169694d05c317de
+size 4126810

res/yamnet_class_map.csv

@@ -0,0 +1,522 @@
+index,mid,display_name
+0,/m/09x0r,Speech
+1,/m/0ytgt,"Child speech, kid speaking"
+2,/m/01h8n0,Conversation
+3,/m/02qldy,"Narration, monologue"
+4,/m/0261r1,Babbling
+5,/m/0brhx,Speech synthesizer
+6,/m/07p6fty,Shout
+7,/m/07q4ntr,Bellow
+8,/m/07rwj3x,Whoop
+9,/m/07sr1lc,Yell
+10,/t/dd00135,Children shouting
+11,/m/03qc9zr,Screaming
+12,/m/02rtxlg,Whispering
+13,/m/01j3sz,Laughter
+14,/t/dd00001,Baby laughter
+15,/m/07r660_,Giggle
+16,/m/07s04w4,Snicker
+17,/m/07sq110,Belly laugh
+18,/m/07rgt08,"Chuckle, chortle"
+19,/m/0463cq4,"Crying, sobbing"
+20,/t/dd00002,"Baby cry, infant cry"
+21,/m/07qz6j3,Whimper
+22,/m/07qw_06,"Wail, moan"
+23,/m/07plz5l,Sigh
+24,/m/015lz1,Singing
+25,/m/0l14jd,Choir
+26,/m/01swy6,Yodeling
+27,/m/02bk07,Chant
+28,/m/01c194,Mantra
+29,/t/dd00005,Child singing
+30,/t/dd00006,Synthetic singing
+31,/m/06bxc,Rapping
+32,/m/02fxyj,Humming
+33,/m/07s2xch,Groan
+34,/m/07r4k75,Grunt
+35,/m/01w250,Whistling
+36,/m/0lyf6,Breathing
+37,/m/07mzm6,Wheeze
+38,/m/01d3sd,Snoring
+39,/m/07s0dtb,Gasp
+40,/m/07pyy8b,Pant
+41,/m/07q0yl5,Snort
+42,/m/01b_21,Cough
+43,/m/0dl9sf8,Throat clearing
+44,/m/01hsr_,Sneeze
+45,/m/07ppn3j,Sniff
+46,/m/06h7j,Run
+47,/m/07qv_x_,Shuffle
+48,/m/07pbtc8,"Walk, footsteps"
+49,/m/03cczk,"Chewing, mastication"
+50,/m/07pdhp0,Biting
+51,/m/0939n_,Gargling
+52,/m/01g90h,Stomach rumble
+53,/m/03q5_w,"Burping, eructation"
+54,/m/02p3nc,Hiccup
+55,/m/02_nn,Fart
+56,/m/0k65p,Hands
+57,/m/025_jnm,Finger snapping
+58,/m/0l15bq,Clapping
+59,/m/01jg02,"Heart sounds, heartbeat"
+60,/m/01jg1z,Heart murmur
+61,/m/053hz1,Cheering
+62,/m/028ght,Applause
+63,/m/07rkbfh,Chatter
+64,/m/03qtwd,Crowd
+65,/m/07qfr4h,"Hubbub, speech noise, speech babble"
+66,/t/dd00013,Children playing
+67,/m/0jbk,Animal
+68,/m/068hy,"Domestic animals, pets"
+69,/m/0bt9lr,Dog
+70,/m/05tny_,Bark
+71,/m/07r_k2n,Yip
+72,/m/07qf0zm,Howl
+73,/m/07rc7d9,Bow-wow
+74,/m/0ghcn6,Growling
+75,/t/dd00136,Whimper (dog)
+76,/m/01yrx,Cat
+77,/m/02yds9,Purr
+78,/m/07qrkrw,Meow
+79,/m/07rjwbb,Hiss
+80,/m/07r81j2,Caterwaul
+81,/m/0ch8v,"Livestock, farm animals, working animals"
+82,/m/03k3r,Horse
+83,/m/07rv9rh,Clip-clop
+84,/m/07q5rw0,"Neigh, whinny"
+85,/m/01xq0k1,"Cattle, bovinae"
+86,/m/07rpkh9,Moo
+87,/m/0239kh,Cowbell
+88,/m/068zj,Pig
+89,/t/dd00018,Oink
+90,/m/03fwl,Goat
+91,/m/07q0h5t,Bleat
+92,/m/07bgp,Sheep
+93,/m/025rv6n,Fowl
+94,/m/09b5t,"Chicken, rooster"
+95,/m/07st89h,Cluck
+96,/m/07qn5dc,"Crowing, cock-a-doodle-doo"
+97,/m/01rd7k,Turkey
+98,/m/07svc2k,Gobble
+99,/m/09ddx,Duck
+100,/m/07qdb04,Quack
+101,/m/0dbvp,Goose
+102,/m/07qwf61,Honk
+103,/m/01280g,Wild animals
+104,/m/0cdnk,"Roaring cats (lions, tigers)"
+105,/m/04cvmfc,Roar
+106,/m/015p6,Bird
+107,/m/020bb7,"Bird vocalization, bird call, bird song"
+108,/m/07pggtn,"Chirp, tweet"
+109,/m/07sx8x_,Squawk
+110,/m/0h0rv,"Pigeon, dove"
+111,/m/07r_25d,Coo
+112,/m/04s8yn,Crow
+113,/m/07r5c2p,Caw
+114,/m/09d5_,Owl
+115,/m/07r_80w,Hoot
+116,/m/05_wcq,"Bird flight, flapping wings"
+117,/m/01z5f,"Canidae, dogs, wolves"
+118,/m/06hps,"Rodents, rats, mice"
+119,/m/04rmv,Mouse
+120,/m/07r4gkf,Patter
+121,/m/03vt0,Insect
+122,/m/09xqv,Cricket
+123,/m/09f96,Mosquito
+124,/m/0h2mp,"Fly, housefly"
+125,/m/07pjwq1,Buzz
+126,/m/01h3n,"Bee, wasp, etc."
+127,/m/09ld4,Frog
+128,/m/07st88b,Croak
+129,/m/078jl,Snake
+130,/m/07qn4z3,Rattle
+131,/m/032n05,Whale vocalization
+132,/m/04rlf,Music
+133,/m/04szw,Musical instrument
+134,/m/0fx80y,Plucked string instrument
+135,/m/0342h,Guitar
+136,/m/02sgy,Electric guitar
+137,/m/018vs,Bass guitar
+138,/m/042v_gx,Acoustic guitar
+139,/m/06w87,"Steel guitar, slide guitar"
+140,/m/01glhc,Tapping (guitar technique)
+141,/m/07s0s5r,Strum
+142,/m/018j2,Banjo
+143,/m/0jtg0,Sitar
+144,/m/04rzd,Mandolin
+145,/m/01bns_,Zither
+146,/m/07xzm,Ukulele
+147,/m/05148p4,Keyboard (musical)
+148,/m/05r5c,Piano
+149,/m/01s0ps,Electric piano
+150,/m/013y1f,Organ
+151,/m/03xq_f,Electronic organ
+152,/m/03gvt,Hammond organ
+153,/m/0l14qv,Synthesizer
+154,/m/01v1d8,Sampler
+155,/m/03q5t,Harpsichord
+156,/m/0l14md,Percussion
+157,/m/02hnl,Drum kit
+158,/m/0cfdd,Drum machine
+159,/m/026t6,Drum
+160,/m/06rvn,Snare drum
+161,/m/03t3fj,Rimshot
+162,/m/02k_mr,Drum roll
+163,/m/0bm02,Bass drum
+164,/m/011k_j,Timpani
+165,/m/01p970,Tabla
+166,/m/01qbl,Cymbal
+167,/m/03qtq,Hi-hat
+168,/m/01sm1g,Wood block
+169,/m/07brj,Tambourine
+170,/m/05r5wn,Rattle (instrument)
+171,/m/0xzly,Maraca
+172,/m/0mbct,Gong
+173,/m/016622,Tubular bells
+174,/m/0j45pbj,Mallet percussion
+175,/m/0dwsp,"Marimba, xylophone"
+176,/m/0dwtp,Glockenspiel
+177,/m/0dwt5,Vibraphone
+178,/m/0l156b,Steelpan
+179,/m/05pd6,Orchestra
+180,/m/01kcd,Brass instrument
+181,/m/0319l,French horn
+182,/m/07gql,Trumpet
+183,/m/07c6l,Trombone
+184,/m/0l14_3,Bowed string instrument
+185,/m/02qmj0d,String section
+186,/m/07y_7,"Violin, fiddle"
+187,/m/0d8_n,Pizzicato
+188,/m/01xqw,Cello
+189,/m/02fsn,Double bass
+190,/m/085jw,"Wind instrument, woodwind instrument"
+191,/m/0l14j_,Flute
+192,/m/06ncr,Saxophone
+193,/m/01wy6,Clarinet
+194,/m/03m5k,Harp
+195,/m/0395lw,Bell
+196,/m/03w41f,Church bell
+197,/m/027m70_,Jingle bell
+198,/m/0gy1t2s,Bicycle bell
+199,/m/07n_g,Tuning fork
+200,/m/0f8s22,Chime
+201,/m/026fgl,Wind chime
+202,/m/0150b9,Change ringing (campanology)
+203,/m/03qjg,Harmonica
+204,/m/0mkg,Accordion
+205,/m/0192l,Bagpipes
+206,/m/02bxd,Didgeridoo
+207,/m/0l14l2,Shofar
+208,/m/07kc_,Theremin
+209,/m/0l14t7,Singing bowl
+210,/m/01hgjl,Scratching (performance technique)
+211,/m/064t9,Pop music
+212,/m/0glt670,Hip hop music
+213,/m/02cz_7,Beatboxing
+214,/m/06by7,Rock music
+215,/m/03lty,Heavy metal
+216,/m/05r6t,Punk rock
+217,/m/0dls3,Grunge
+218,/m/0dl5d,Progressive rock
+219,/m/07sbbz2,Rock and roll
+220,/m/05w3f,Psychedelic rock
+221,/m/06j6l,Rhythm and blues
+222,/m/0gywn,Soul music
+223,/m/06cqb,Reggae
+224,/m/01lyv,Country
+225,/m/015y_n,Swing music
+226,/m/0gg8l,Bluegrass
+227,/m/02x8m,Funk
+228,/m/02w4v,Folk music
+229,/m/06j64v,Middle Eastern music
+230,/m/03_d0,Jazz
+231,/m/026z9,Disco
+232,/m/0ggq0m,Classical music
+233,/m/05lls,Opera
+234,/m/02lkt,Electronic music
+235,/m/03mb9,House music
+236,/m/07gxw,Techno
+237,/m/07s72n,Dubstep
+238,/m/0283d,Drum and bass
+239,/m/0m0jc,Electronica
+240,/m/08cyft,Electronic dance music
+241,/m/0fd3y,Ambient music
+242,/m/07lnk,Trance music
+243,/m/0g293,Music of Latin America
+244,/m/0ln16,Salsa music
+245,/m/0326g,Flamenco
+246,/m/0155w,Blues
+247,/m/05fw6t,Music for children
+248,/m/02v2lh,New-age music
+249,/m/0y4f8,Vocal music
+250,/m/0z9c,A capella
+251,/m/0164x2,Music of Africa
+252,/m/0145m,Afrobeat
+253,/m/02mscn,Christian music
+254,/m/016cjb,Gospel music
+255,/m/028sqc,Music of Asia
+256,/m/015vgc,Carnatic music
+257,/m/0dq0md,Music of Bollywood
+258,/m/06rqw,Ska
+259,/m/02p0sh1,Traditional music
+260,/m/05rwpb,Independent music
+261,/m/074ft,Song
+262,/m/025td0t,Background music
+263,/m/02cjck,Theme music
+264,/m/03r5q_,Jingle (music)
+265,/m/0l14gg,Soundtrack music
+266,/m/07pkxdp,Lullaby
+267,/m/01z7dr,Video game music
+268,/m/0140xf,Christmas music
+269,/m/0ggx5q,Dance music
+270,/m/04wptg,Wedding music
+271,/t/dd00031,Happy music
+272,/t/dd00033,Sad music
+273,/t/dd00034,Tender music
+274,/t/dd00035,Exciting music
+275,/t/dd00036,Angry music
+276,/t/dd00037,Scary music
+277,/m/03m9d0z,Wind
+278,/m/09t49,Rustling leaves
+279,/t/dd00092,Wind noise (microphone)
+280,/m/0jb2l,Thunderstorm
+281,/m/0ngt1,Thunder
+282,/m/0838f,Water
+283,/m/06mb1,Rain
+284,/m/07r10fb,Raindrop
+285,/t/dd00038,Rain on surface
+286,/m/0j6m2,Stream
+287,/m/0j2kx,Waterfall
+288,/m/05kq4,Ocean
+289,/m/034srq,"Waves, surf"
+290,/m/06wzb,Steam
+291,/m/07swgks,Gurgling
+292,/m/02_41,Fire
+293,/m/07pzfmf,Crackle
+294,/m/07yv9,Vehicle
+295,/m/019jd,"Boat, Water vehicle"
+296,/m/0hsrw,"Sailboat, sailing ship"
+297,/m/056ks2,"Rowboat, canoe, kayak"
+298,/m/02rlv9,"Motorboat, speedboat"
+299,/m/06q74,Ship
+300,/m/012f08,Motor vehicle (road)
+301,/m/0k4j,Car
+302,/m/0912c9,"Vehicle horn, car horn, honking"
+303,/m/07qv_d5,Toot
+304,/m/02mfyn,Car alarm
+305,/m/04gxbd,"Power windows, electric windows"
+306,/m/07rknqz,Skidding
+307,/m/0h9mv,Tire squeal
+308,/t/dd00134,Car passing by
+309,/m/0ltv,"Race car, auto racing"
+310,/m/07r04,Truck
+311,/m/0gvgw0,Air brake
+312,/m/05x_td,"Air horn, truck horn"
+313,/m/02rhddq,Reversing beeps
+314,/m/03cl9h,"Ice cream truck, ice cream van"
+315,/m/01bjv,Bus
+316,/m/03j1ly,Emergency vehicle
+317,/m/04qvtq,Police car (siren)
+318,/m/012n7d,Ambulance (siren)
+319,/m/012ndj,"Fire engine, fire truck (siren)"
+320,/m/04_sv,Motorcycle
+321,/m/0btp2,"Traffic noise, roadway noise"
+322,/m/06d_3,Rail transport
+323,/m/07jdr,Train
+324,/m/04zmvq,Train whistle
+325,/m/0284vy3,Train horn
+326,/m/01g50p,"Railroad car, train wagon"
+327,/t/dd00048,Train wheels squealing
+328,/m/0195fx,"Subway, metro, underground"
+329,/m/0k5j,Aircraft
+330,/m/014yck,Aircraft engine
+331,/m/04229,Jet engine
+332,/m/02l6bg,"Propeller, airscrew"
+333,/m/09ct_,Helicopter
+334,/m/0cmf2,"Fixed-wing aircraft, airplane"
+335,/m/0199g,Bicycle
+336,/m/06_fw,Skateboard
+337,/m/02mk9,Engine
+338,/t/dd00065,Light engine (high frequency)
+339,/m/08j51y,"Dental drill, dentist's drill"
+340,/m/01yg9g,Lawn mower
+341,/m/01j4z9,Chainsaw
+342,/t/dd00066,Medium engine (mid frequency)
+343,/t/dd00067,Heavy engine (low frequency)
+344,/m/01h82_,Engine knocking
+345,/t/dd00130,Engine starting
+346,/m/07pb8fc,Idling
+347,/m/07q2z82,"Accelerating, revving, vroom"
+348,/m/02dgv,Door
+349,/m/03wwcy,Doorbell
+350,/m/07r67yg,Ding-dong
+351,/m/02y_763,Sliding door
+352,/m/07rjzl8,Slam
+353,/m/07r4wb8,Knock
+354,/m/07qcpgn,Tap
+355,/m/07q6cd_,Squeak
+356,/m/0642b4,Cupboard open or close
+357,/m/0fqfqc,Drawer open or close
+358,/m/04brg2,"Dishes, pots, and pans"
+359,/m/023pjk,"Cutlery, silverware"
+360,/m/07pn_8q,Chopping (food)
+361,/m/0dxrf,Frying (food)
+362,/m/0fx9l,Microwave oven
+363,/m/02pjr4,Blender
+364,/m/02jz0l,"Water tap, faucet"
+365,/m/0130jx,Sink (filling or washing)
+366,/m/03dnzn,Bathtub (filling or washing)
+367,/m/03wvsk,Hair dryer
+368,/m/01jt3m,Toilet flush
+369,/m/012xff,Toothbrush
+370,/m/04fgwm,Electric toothbrush
+371,/m/0d31p,Vacuum cleaner
+372,/m/01s0vc,Zipper (clothing)
+373,/m/03v3yw,Keys jangling
+374,/m/0242l,Coin (dropping)
+375,/m/01lsmm,Scissors
+376,/m/02g901,"Electric shaver, electric razor"
+377,/m/05rj2,Shuffling cards
+378,/m/0316dw,Typing
+379,/m/0c2wf,Typewriter
+380,/m/01m2v,Computer keyboard
+381,/m/081rb,Writing
+382,/m/07pp_mv,Alarm
+383,/m/07cx4,Telephone
+384,/m/07pp8cl,Telephone bell ringing
+385,/m/01hnzm,Ringtone
+386,/m/02c8p,"Telephone dialing, DTMF"
+387,/m/015jpf,Dial tone
+388,/m/01z47d,Busy signal
+389,/m/046dlr,Alarm clock
+390,/m/03kmc9,Siren
+391,/m/0dgbq,Civil defense siren
+392,/m/030rvx,Buzzer
+393,/m/01y3hg,"Smoke detector, smoke alarm"
+394,/m/0c3f7m,Fire alarm
+395,/m/04fq5q,Foghorn
+396,/m/0l156k,Whistle
+397,/m/06hck5,Steam whistle
+398,/t/dd00077,Mechanisms
+399,/m/02bm9n,"Ratchet, pawl"
+400,/m/01x3z,Clock
+401,/m/07qjznt,Tick
+402,/m/07qjznl,Tick-tock
+403,/m/0l7xg,Gears
+404,/m/05zc1,Pulleys
+405,/m/0llzx,Sewing machine
+406,/m/02x984l,Mechanical fan
+407,/m/025wky1,Air conditioning
+408,/m/024dl,Cash register
+409,/m/01m4t,Printer
+410,/m/0dv5r,Camera
+411,/m/07bjf,Single-lens reflex camera
+412,/m/07k1x,Tools
+413,/m/03l9g,Hammer
+414,/m/03p19w,Jackhammer
+415,/m/01b82r,Sawing
+416,/m/02p01q,Filing (rasp)
+417,/m/023vsd,Sanding
+418,/m/0_ksk,Power tool
+419,/m/01d380,Drill
+420,/m/014zdl,Explosion
+421,/m/032s66,"Gunshot, gunfire"
+422,/m/04zjc,Machine gun
+423,/m/02z32qm,Fusillade
+424,/m/0_1c,Artillery fire
+425,/m/073cg4,Cap gun
+426,/m/0g6b5,Fireworks
+427,/g/122z_qxw,Firecracker
+428,/m/07qsvvw,"Burst, pop"
+429,/m/07pxg6y,Eruption
+430,/m/07qqyl4,Boom
+431,/m/083vt,Wood
+432,/m/07pczhz,Chop
+433,/m/07pl1bw,Splinter
+434,/m/07qs1cx,Crack
+435,/m/039jq,Glass
+436,/m/07q7njn,"Chink, clink"
+437,/m/07rn7sz,Shatter
+438,/m/04k94,Liquid
+439,/m/07rrlb6,"Splash, splatter"
+440,/m/07p6mqd,Slosh
+441,/m/07qlwh6,Squish
+442,/m/07r5v4s,Drip
+443,/m/07prgkl,Pour
+444,/m/07pqc89,"Trickle, dribble"
+445,/t/dd00088,Gush
+446,/m/07p7b8y,Fill (with liquid)
+447,/m/07qlf79,Spray
+448,/m/07ptzwd,Pump (liquid)
+449,/m/07ptfmf,Stir
+450,/m/0dv3j,Boiling
+451,/m/0790c,Sonar
+452,/m/0dl83,Arrow
+453,/m/07rqsjt,"Whoosh, swoosh, swish"
+454,/m/07qnq_y,"Thump, thud"
+455,/m/07rrh0c,Thunk
+456,/m/0b_fwt,Electronic tuner
+457,/m/02rr_,Effects unit
+458,/m/07m2kt,Chorus effect
+459,/m/018w8,Basketball bounce
+460,/m/07pws3f,Bang
+461,/m/07ryjzk,"Slap, smack"
+462,/m/07rdhzs,"Whack, thwack"
+463,/m/07pjjrj,"Smash, crash"
+464,/m/07pc8lb,Breaking
+465,/m/07pqn27,Bouncing
+466,/m/07rbp7_,Whip
+467,/m/07pyf11,Flap
+468,/m/07qb_dv,Scratch
+469,/m/07qv4k0,Scrape
+470,/m/07pdjhy,Rub
+471,/m/07s8j8t,Roll
+472,/m/07plct2,Crushing
+473,/t/dd00112,"Crumpling, crinkling"
+474,/m/07qcx4z,Tearing
+475,/m/02fs_r,"Beep, bleep"
+476,/m/07qwdck,Ping
+477,/m/07phxs1,Ding
+478,/m/07rv4dm,Clang
+479,/m/07s02z0,Squeal
+480,/m/07qh7jl,Creak
+481,/m/07qwyj0,Rustle
+482,/m/07s34ls,Whir
+483,/m/07qmpdm,Clatter
+484,/m/07p9k1k,Sizzle
+485,/m/07qc9xj,Clicking
+486,/m/07rwm0c,Clickety-clack
+487,/m/07phhsh,Rumble
+488,/m/07qyrcz,Plop
+489,/m/07qfgpx,"Jingle, tinkle"
+490,/m/07rcgpl,Hum
+491,/m/07p78v5,Zing
+492,/t/dd00121,Boing
+493,/m/07s12q4,Crunch
+494,/m/028v0c,Silence
+495,/m/01v_m0,Sine wave
+496,/m/0b9m1,Harmonic
+497,/m/0hdsk,Chirp tone
+498,/m/0c1dj,Sound effect
+499,/m/07pt_g0,Pulse
+500,/t/dd00125,"Inside, small room"
+501,/t/dd00126,"Inside, large room or hall"
+502,/t/dd00127,"Inside, public space"
+503,/t/dd00128,"Outside, urban or manmade"
+504,/t/dd00129,"Outside, rural or natural"
+505,/m/01b9nn,Reverberation
+506,/m/01jnbd,Echo
+507,/m/096m7z,Noise
+508,/m/06_y0by,Environmental noise
+509,/m/07rgkc5,Static
+510,/m/06xkwv,Mains hum
+511,/m/0g12c5,Distortion
+512,/m/08p9q4,Sidetone
+513,/m/07szfh9,Cacophony
+514,/m/0chx_,White noise
+515,/m/0cj0r,Pink noise
+516,/m/07p_0gm,Throbbing
+517,/m/01jwx6,Vibration
+518,/m/07c52,Television
+519,/m/06bz3,Radio
+520,/m/07hvw1,Field recording

test.py

@@ -0,0 +1,162 @@
+# Imports
+import csv
+import sys
+
+import numpy as np
+import soundfile
+import tensorflow as tf
+
+from python.util.audio_util import audio_to_wav
+from python.util.plt_util import plt_line, plt_mfcc, plt_mfcc2
+from python.util.time_util import int_to_min_sec
+from python.util.str_util import format_float, truncate_str
+from python.util.tensorflow_util import predict
+
+# Constants
+# MODEL_PATH = 'res/lite-model_yamnet_tflite_1.tflite'
+MODEL_PATH = 'res/lite-model_yamnet_classification_tflite_1.tflite'
+OUT_SAMPLE_RATE = 16000
+OUT_PCM = 'PCM_16'
+CLASS_MAP_FILE = 'res/yamnet_class_map.csv'
+DEBUG = True
+SNORING_TOP_N = 7
+
+
+# Methods
+def to_ndarray(data):
+    return np.array(data)
+
+
+def data_to_single_channel(data):
+    result = data
+
+    try:
+        result = data[:, 0]
+    except IndexError:
+        print("An exception occurred")
+
+    return result
+
+
+def read_single_channel(audio_path):
+    data, sample_rate = soundfile.read(audio_path)
+    print(' sample_rate, audio_path: ', str(sample_rate), str(audio_path))
+    # print(' sample_rate, len, type, shape, shape[1]: ', str(sample_rate), len(data), str(type(data)), str(data.shape), str(data.shape[1]))
+
+    single_channel = data_to_single_channel(data)
+    single_channel_seconds = len(single_channel) / OUT_SAMPLE_RATE
+    # print(' single_channel, shape: ', str(single_channel), str(single_channel.shape))
+    # print(' len, seconds: ', str(len(single_channel)), str(single_channel_seconds))
+
+    return single_channel, sample_rate
+
+
+def class_names_from_csv(class_map_csv):
+    """Read the class name definition file and return a list of strings."""
+    if tf.is_tensor(class_map_csv):
+        class_map_csv = class_map_csv.numpy()
+    with open(class_map_csv) as csv_file:
+        reader = csv.reader(csv_file)
+        next(reader)  # Skip header
+        return np.array([display_name for (_, _, display_name) in reader])
+
+
+def scores_to_index(scores, order):
+    means = scores.mean(axis=0)
+    return np.argsort(means, axis=0)[order]
+
+
+def predict_waveform(idx, waveform):
+    # Download the YAMNet class map (see main YAMNet model docs) to yamnet_class_map.csv
+    # See YAMNet TF2 usage sample for class_names_from_csv() definition.
+    scores = predict(MODEL_PATH, waveform)
+    class_names = class_names_from_csv(CLASS_MAP_FILE)
+
+    top_n = SNORING_TOP_N
+    top_n_res = ''
+    snoring_score = 0.0
+    for n in range(1, top_n):
+        index = scores_to_index(scores, -n)
+        means = scores.mean(axis=0)
+        score = means[index]
+        name = class_names[index]
+
+        if name == 'Snoring':
+            snoring_score = score
+        top_n_res += ' ' + format_float(score) + ' [' + truncate_str(name, 4) + '], '
+
+    snoring_tail = ('打鼾, ' + format_float(snoring_score)) if snoring_score > 0 else ''
+    result = top_n_res + snoring_tail + '\n'
+    if DEBUG: print(top_n_res)
+
+    return result, snoring_score
+
+
+def to_float32(data):
+    return np.float32(data)
+
+
+def predict_float32(idx, data):
+    return predict_waveform(idx, to_float32(data))
+
+
+def split_given_size(arr, size):
+    return np.split(arr, np.arange(size, len(arr), size))
+
+
+def predict_uri(mp3_uri):
+    result = ''
+    # result = ' mp3_uri: '
+    # result += mp3_uri + '\n'
+
+    mp3_input = mp3_uri
+    wav_input = audio_to_wav(mp3_input) if not mp3_input.endswith('.mp3') == True else mp3_input
+    predict_seconds = int(str(sys.argv[2])) if len(sys.argv) > 2 else 1
+
+    predict_samples = 15600 #OUT_SAMPLE_RATE * predict_seconds
+    single_channel, sc_sample_rate = read_single_channel(wav_input)
+    splits = split_given_size(single_channel, predict_samples)
+    result += ' sc_sample_rate: ' + str(sc_sample_rate) + '\n'
+
+    second_total = len(splits) * predict_seconds
+    result += (' second_total: ' + int_to_min_sec(second_total) + ', \n')
+    result += '\n'
+    snoring_scores = []
+
+    for idx in range(len(splits)):
+        split = splits[idx]
+        second_start = idx * predict_seconds
+        result += (int_to_min_sec(second_start) + ', ')
+        if len(split) == predict_samples:
+            print_result, snoring_score = predict_float32(idx, split)
+            result += print_result
+            snoring_scores.append(snoring_score)
+
+    # plt waveform
+    waveform_line = plt_line(single_channel)
+    # plt mfcc
+    mfcc_line = plt_mfcc(single_channel, OUT_SAMPLE_RATE)
+    # plt mfcc2
+    mfcc2_line = plt_mfcc2(wav_input, OUT_SAMPLE_RATE)
+    # plt snoring_booleans
+    snoring_booleans = list(map(lambda x: 1 if x > 0 else 0, snoring_scores))
+    # calc snoring frequency
+    snoring_sec = len(list(filter(lambda x: 1 if x > 0 else 0, snoring_scores)))
+    snoring_frequency = snoring_sec / second_total
+    apnea_sec = second_total - snoring_sec
+    apnea_frequency = (apnea_sec / 10) / second_total
+    ahi_result = str(
+        'snoring_sec:' + str(snoring_sec) + ', apnea_sec:' + str(apnea_sec) + ', second_total:' + str(second_total)
+        + ', snoring_frequency:' + format_float(snoring_frequency)
+        + ', apnea_frequency:' + format_float(apnea_frequency)
+    )
+
+    return waveform_line, mfcc_line, mfcc2_line, str(ahi_result), str(snoring_booleans), str(snoring_scores), str(result)
+
+
+# sys.argv
+if len(sys.argv) > 1 and len(sys.argv[1]) > 0:
+    res, plt = predict_uri(sys.argv[1])
+    plt.show()
+else:
+    print('usage: python test.py /path/to/audio_file [predict_seconds]')