add onnx vad

silero-vad.ipynb

@@ -1,21 +1,12 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "sVNOuHQQjsrp"
-   },
-   "source": [
-    "# PyTorch Examples"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
     "id": "FpMplOCA2Fwp"
    },
    "source": [
-    "## VAD"
+    "#VAD"
    ]
   },
   {
@@ -25,7 +16,7 @@
     "id": "62A6F_072Fwq"
    },
    "source": [
-    "### Install Dependencies"
+    "## Install Dependencies"
    ]
   },
   {
@@ -42,26 +33,39 @@
     "# this assumes that you have a relevant version of PyTorch installed\n",
     "!pip install -q torchaudio\n",
     "\n",
-    "SAMPLE_RATE = 16000\n",
+    "SAMPLING_RATE = 16000\n",
     "\n",
-    "import glob\n",
     "import torch\n",
     "torch.set_num_threads(1)\n",
     "\n",
     "from IPython.display import Audio\n",
     "from pprint import pprint\n",
-    "\n",
+    "# download example\n",
+    "torch.hub.download_url_to_file('https://models.silero.ai/vad_models/en.wav', 'en_example.wav')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "pSifus5IilRp"
+   },
+   "outputs": [],
+   "source": [
+    "USE_ONNX = False # change this to True if you want to test onnx model\n",
+    "if USE_ONNX:\n",
+    "    !pip install -q onnxruntime\n",
+    "  \n",
     "model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
     "                              model='silero_vad',\n",
-    "                              force_reload=True)\n",
+    "                              force_reload=True,\n",
+    "                              onnx=USE_ONNX)\n",
     "\n",
     "(get_speech_timestamps,\n",
     " save_audio,\n",
     " read_audio,\n",
     " VADIterator,\n",
-    " collect_chunks) = utils\n",
-    "\n",
-    "files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'"
+    " collect_chunks) = utils"
    ]
   },
   {
@@ -70,29 +74,7 @@
     "id": "fXbbaUO3jsrw"
    },
    "source": [
-    "### Full Audio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "RJRBksv39xf5"
-   },
-   "outputs": [],
-   "source": [
-    "wav = read_audio(f'{files_dir}/en.wav', sampling_rate=SAMPLE_RATE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "tEKb0YF_9y-i"
-   },
-   "outputs": [],
-   "source": [
-    "wav"
+    "## Full Audio"
    ]
   },
   {
@@ -112,9 +94,9 @@
    },
    "outputs": [],
    "source": [
-    "wav = read_audio(f'{files_dir}/en.wav', sampling_rate=SAMPLE_RATE)\n",
+    "wav = read_audio('en_example.wav', sampling_rate=SAMPLING_RATE)\n",
     "# get speech timestamps from full audio file\n",
-    "speech_timestamps = get_speech_timestamps(wav, model, sampling_rate=SAMPLE_RATE)\n",
+    "speech_timestamps = get_speech_timestamps(wav, model, sampling_rate=SAMPLING_RATE)\n",
     "pprint(speech_timestamps)"
    ]
   },
@@ -128,7 +110,7 @@
    "source": [
     "# merge all speech chunks to one audio\n",
     "save_audio('only_speech.wav',\n",
-    "           collect_chunks(speech_timestamps, wav), sampling_rate=16000) \n",
+    "           collect_chunks(speech_timestamps, wav), sampling_rate=SAMPLING_RATE) \n",
     "Audio('only_speech.wav')"
    ]
   },
@@ -138,7 +120,7 @@
     "id": "iDKQbVr8jsry"
    },
    "source": [
-    "### Stream imitation example"
+    "## Stream imitation example"
    ]
   },
   {
@@ -152,7 +134,7 @@
     "## using VADIterator class\n",
     "\n",
     "vad_iterator = VADIterator(model)\n",
-    "wav = read_audio(f'{files_dir}/en.wav', sampling_rate=SAMPLE_RATE)\n",
+    "wav = read_audio(f'en_example.wav', sampling_rate=SAMPLING_RATE)\n",
     "\n",
     "window_size_samples = 1536 # number of samples in a single audio chunk\n",
     "for i in range(0, len(wav), window_size_samples):\n",
@@ -172,14 +154,15 @@
    "source": [
     "## just probabilities\n",
     "\n",
-    "wav = read_audio(f'{files_dir}/en.wav', sampling_rate=SAMPLE_RATE)\n",
+    "wav = read_audio('en_example.wav', sampling_rate=SAMPLING_RATE)\n",
     "speech_probs = []\n",
     "window_size_samples = 1536\n",
     "for i in range(0, len(wav), window_size_samples):\n",
-    "    speech_prob = model(wav[i: i+ window_size_samples], SAMPLE_RATE).item()\n",
+    "    speech_prob = model(wav[i: i+ window_size_samples], SAMPLING_RATE).item()\n",
     "    speech_probs.append(speech_prob)\n",
+    "vad_iterator.reset_states() # reset model states after each audio\n",
     "\n",
-    "pprint(speech_probs[:100])"
+    "print(speech_probs[:10]) # first 10 chunks predicts"
    ]
   },
   {
@@ -189,7 +172,7 @@
     "id": "36jY0niD2Fww"
    },
    "source": [
-    "## Number detector"
+    "# Number detector"
    ]
   },
   {
@@ -200,7 +183,7 @@
     "id": "scd1DlS42Fwx"
    },
    "source": [
-    "### Install Dependencies"
+    "## Install Dependencies"
    ]
   },
   {
@@ -215,27 +198,41 @@
     "#@title Install and Import Dependencies\n",
     "\n",
     "# this assumes that you have a relevant version of PyTorch installed\n",
-    "!pip install -q torchaudio soundfile\n",
+    "!pip install -q torchaudio\n",
+    "\n",
+    "SAMPLING_RATE = 16000\n",
     "\n",
-    "import glob\n",
     "import torch\n",
     "torch.set_num_threads(1)\n",
     "\n",
     "from IPython.display import Audio\n",
     "from pprint import pprint\n",
-    "\n",
+    "# download example\n",
+    "torch.hub.download_url_to_file('https://models.silero.ai/vad_models/en_num.wav', 'en_number_example.wav')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "dPwCFHmFycUF"
+   },
+   "outputs": [],
+   "source": [
+    "USE_ONNX = False # change this to True if you want to test onnx model\n",
+    "if USE_ONNX:\n",
+    "    !pip install -q onnxruntime\n",
+    "  \n",
     "model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
     "                              model='silero_number_detector',\n",
-    "                              force_reload=True)\n",
+    "                              force_reload=True,\n",
+    "                              onnx=USE_ONNX)\n",
     "\n",
     "(get_number_ts,\n",
     " save_audio,\n",
     " read_audio,\n",
     " collect_chunks,\n",
-    " drop_chunks,\n",
-    " _) = utils\n",
-    "\n",
-    "files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'"
+    " drop_chunks) = utils\n"
    ]
   },
   {
@@ -246,7 +243,7 @@
     "id": "qhPa30ij2Fwy"
    },
    "source": [
-    "### Full audio"
+    "## Full audio"
    ]
   },
   {
@@ -258,7 +255,7 @@
    },
    "outputs": [],
    "source": [
-    "wav = read_audio(f'{files_dir}/en_num.wav')\n",
+    "wav = read_audio('en_number_example.wav', sampling_rate=SAMPLING_RATE)\n",
     "# get number timestamps from full audio file\n",
     "number_timestamps = get_number_ts(wav, model)\n",
     "pprint(number_timestamps)"
@@ -273,11 +270,10 @@
    },
    "outputs": [],
    "source": [
-    "sample_rate = 16000\n",
     "# convert ms in timestamps to samples\n",
     "for timestamp in number_timestamps:\n",
-    "    timestamp['start'] = int(timestamp['start'] * sample_rate / 1000)\n",
-    "    timestamp['end'] = int(timestamp['end'] * sample_rate / 1000)"
+    "    timestamp['start'] = int(timestamp['start'] * SAMPLING_RATE / 1000)\n",
+    "    timestamp['end'] = int(timestamp['end'] * SAMPLING_RATE / 1000)"
    ]
   },
   {
@@ -291,7 +287,7 @@
    "source": [
     "# merge all number chunks to one audio\n",
     "save_audio('only_numbers.wav',\n",
-    "           collect_chunks(number_timestamps, wav), sample_rate) \n",
+    "           collect_chunks(number_timestamps, wav), SAMPLING_RATE) \n",
     "Audio('only_numbers.wav')"
    ]
   },
@@ -306,7 +302,7 @@
    "source": [
     "# drop all number chunks from audio\n",
     "save_audio('no_numbers.wav',\n",
-    "           drop_chunks(number_timestamps, wav), sample_rate) \n",
+    "           drop_chunks(number_timestamps, wav), SAMPLING_RATE) \n",
     "Audio('no_numbers.wav')"
    ]
   },
@@ -317,7 +313,7 @@
     "id": "PnKtJKbq2Fwz"
    },
    "source": [
-    "## Language detector"
+    "# Language detector"
    ]
   },
   {
@@ -328,7 +324,7 @@
     "id": "F5cAmMbP2Fwz"
    },
    "source": [
-    "### Install Dependencies"
+    "## Install Dependencies"
    ]
   },
   {
@@ -343,24 +339,37 @@
     "#@title Install and Import Dependencies\n",
     "\n",
     "# this assumes that you have a relevant version of PyTorch installed\n",
-    "!pip install -q torchaudio soundfile\n",
+    "!pip install -q torchaudio\n",
+    "\n",
+    "SAMPLING_RATE = 16000\n",
     "\n",
-    "import glob\n",
     "import torch\n",
     "torch.set_num_threads(1)\n",
     "\n",
     "from IPython.display import Audio\n",
     "from pprint import pprint\n",
-    "\n",
+    "# download example\n",
+    "torch.hub.download_url_to_file('https://models.silero.ai/vad_models/en.wav', 'en_example.wav')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "JfRKDZiRztFe"
+   },
+   "outputs": [],
+   "source": [
+    "USE_ONNX = False # change this to True if you want to test onnx model\n",
+    "if USE_ONNX:\n",
+    "    !pip install -q onnxruntime\n",
+    "  \n",
     "model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
     "                              model='silero_lang_detector',\n",
-    "                              force_reload=True)\n",
+    "                              force_reload=True,\n",
+    "                              onnx=USE_ONNX)\n",
     "\n",
-    "(get_language,\n",
-    " read_audio,\n",
-    " _) = utils\n",
-    "\n",
-    "files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'"
+    "get_language, read_audio = utils"
    ]
   },
   {
@@ -371,7 +380,7 @@
     "id": "iC696eMX2Fwz"
    },
    "source": [
-    "### Full audio"
+    "## Full audio"
    ]
   },
   {
@@ -383,268 +392,10 @@
    },
    "outputs": [],
    "source": [
-    "wav = read_audio(f'{files_dir}/en.wav')\n",
+    "wav = read_audio('en_example.wav', sampling_rate=SAMPLING_RATE)\n",
     "lang = get_language(wav, model)\n",
     "print(lang)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "57avIBd6jsrz"
-   },
-   "source": [
-    "# ONNX Example"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "hEhnfORV2Fw0"
-   },
-   "source": [
-    "## VAD"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Cy7y-NAyALSe"
-   },
-   "source": [
-    "**TO BE DONE**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true,
-    "id": "7QMvUvpg2Fw4"
-   },
-   "source": [
-    "## Number detector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "id": "tBPDkpHr2Fw4"
-   },
-   "source": [
-    "### Install Dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "PdjGd56R2Fw5"
-   },
-   "outputs": [],
-   "source": [
-    "#@title Install and Import Dependencies\n",
-    "\n",
-    "# this assumes that you have a relevant version of PyTorch installed\n",
-    "!pip install -q torchaudio soundfile onnxruntime\n",
-    "\n",
-    "import glob\n",
-    "import torch\n",
-    "import onnxruntime\n",
-    "from pprint import pprint\n",
-    "\n",
-    "from IPython.display import Audio\n",
-    "\n",
-    "_, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
-    "                              model='silero_number_detector',\n",
-    "                              force_reload=True)\n",
-    "\n",
-    "(get_number_ts,\n",
-    " save_audio,\n",
-    " read_audio,\n",
-    " collect_chunks,\n",
-    " drop_chunks,\n",
-    " donwload_onnx_model) = utils\n",
-    "\n",
-    "files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'\n",
-    "donwload_onnx_model('number_detector')\n",
-    "\n",
-    "def init_onnx_model(model_path: str):\n",
-    "    return onnxruntime.InferenceSession(model_path)\n",
-    "\n",
-    "def validate_onnx(model, inputs):\n",
-    "    with torch.no_grad():\n",
-    "        ort_inputs = {'input': inputs.cpu().numpy()}\n",
-    "        outs = model.run(None, ort_inputs)\n",
-    "        outs = [torch.Tensor(x) for x in outs]\n",
-    "    return outs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "id": "I9QWSFZh2Fw5"
-   },
-   "source": [
-    "### Full Audio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "_r6QZiwu2Fw5"
-   },
-   "outputs": [],
-   "source": [
-    "model = init_onnx_model('number_detector.onnx')\n",
-    "wav = read_audio(f'{files_dir}/en_num.wav')\n",
-    "\n",
-    "# get number timestamps from full audio file\n",
-    "number_timestamps = get_number_ts(wav, model, run_function=validate_onnx)\n",
-    "pprint(number_timestamps)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "FN4aDwLV2Fw5"
-   },
-   "outputs": [],
-   "source": [
-    "sample_rate = 16000\n",
-    "# convert ms in timestamps to samples\n",
-    "for timestamp in number_timestamps:\n",
-    "    timestamp['start'] = int(timestamp['start'] * sample_rate / 1000)\n",
-    "    timestamp['end'] = int(timestamp['end'] * sample_rate / 1000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "JnvS6WTK2Fw5"
-   },
-   "outputs": [],
-   "source": [
-    "# merge all number chunks to one audio\n",
-    "save_audio('only_numbers.wav',\n",
-    "           collect_chunks(number_timestamps, wav), 16000) \n",
-    "Audio('only_numbers.wav')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "yUxOcOFG2Fw6"
-   },
-   "outputs": [],
-   "source": [
-    "# drop all number chunks from audio\n",
-    "save_audio('no_numbers.wav',\n",
-    "           drop_chunks(number_timestamps, wav), 16000) \n",
-    "Audio('no_numbers.wav')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true,
-    "id": "SR8Bgcd52Fw6"
-   },
-   "source": [
-    "## Language detector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "id": "PBnXPtKo2Fw6"
-   },
-   "source": [
-    "### Install Dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "iNkDWJ3H2Fw6"
-   },
-   "outputs": [],
-   "source": [
-    "#@title Install and Import Dependencies\n",
-    "\n",
-    "# this assumes that you have a relevant version of PyTorch installed\n",
-    "!pip install -q torchaudio soundfile onnxruntime\n",
-    "\n",
-    "import glob\n",
-    "import torch\n",
-    "import onnxruntime\n",
-    "from pprint import pprint\n",
-    "\n",
-    "from IPython.display import Audio\n",
-    "\n",
-    "_, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
-    "                              model='silero_lang_detector',\n",
-    "                              force_reload=True)\n",
-    "\n",
-    "(get_language,\n",
-    " read_audio,\n",
-    " donwload_onnx_model) = utils\n",
-    "\n",
-    "donwload_onnx_model('number_detector')\n",
-    "files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'\n",
-    "\n",
-    "def init_onnx_model(model_path: str):\n",
-    "    return onnxruntime.InferenceSession(model_path)\n",
-    "\n",
-    "def validate_onnx(model, inputs):\n",
-    "    with torch.no_grad():\n",
-    "        ort_inputs = {'input': inputs.cpu().numpy()}\n",
-    "        outs = model.run(None, ort_inputs)\n",
-    "        outs = [torch.Tensor(x) for x in outs]\n",
-    "    return outs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "hidden": true,
-    "id": "G8N8oP4q2Fw6"
-   },
-   "source": [
-    "### Full Audio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "WHXnh9IV2Fw6"
-   },
-   "outputs": [],
-   "source": [
-    "model = init_onnx_model('number_detector.onnx')\n",
-    "wav = read_audio(f'{files_dir}/en.wav')\n",
-    "\n",
-    "lang = get_language(wav, model, run_function=validate_onnx)\n",
-    "print(lang)"
-   ]
   }
  ],
  "metadata": {