add adaptive examples

README.md

@@ -114,6 +114,7 @@ model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',
                               force_reload=True)
 
 (get_speech_ts,
+ get_speech_ts_adaptive
  _, read_audio,
  _, _, _) = utils
 
@@ -122,9 +123,15 @@ files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'
 wav = read_audio(f'{files_dir}/en.wav')
 # full audio
 # get speech timestamps from full audio file
+
+# classic way
 speech_timestamps = get_speech_ts(wav, model,
                                   num_steps=4)
 pprint(speech_timestamps)
+
+# adaptive way
+speech_timestamps = get_speech_ts_adaptive(wav, model)
+pprint(speech_timestamps)
 ```
 
 #### Number Detector
@@ -195,6 +202,7 @@ _, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',
                               force_reload=True)
 
 (get_speech_ts,
+ get_speech_ts_adaptive
  _, read_audio,
  _, _, _) = utils
 
@@ -208,14 +216,20 @@ def validate_onnx(model, inputs):
         ort_inputs = {'input': inputs.cpu().numpy()}
         outs = model.run(None, ort_inputs)
         outs = [torch.Tensor(x) for x in outs]
-    return outs
+    return outs[0]
     
 model = init_onnx_model(f'{files_dir}/model.onnx')
 wav = read_audio(f'{files_dir}/en.wav')
 
 # get speech timestamps from full audio file
+
+# classic way
 speech_timestamps = get_speech_ts(wav, model, num_steps=4, run_function=validate_onnx) 
 pprint(speech_timestamps)
+
+# adaptive way
+speech_timestamps = get_speech_ts(wav, model, run_function=validate_onnx) 
+pprint(speech_timestamps)
 ```
 
 #### Number Detector
@@ -347,6 +361,9 @@ Since our VAD (only VAD, other networks are more flexible) was trained on chunks
 
 ### VAD Parameter Fine Tuning
 
+#### **Classic way**
+
+**This is straightforward classic method `get_speech_ts` where tresholds (`trig_sum` and `neg_trig_sum`) are specified by users**
 - Among others, we provide several [utils](https://github.com/snakers4/silero-vad/blob/8b28767292b424e3e505c55f15cd3c4b91e4804b/utils.py#L52-L59) to simplify working with VAD;
 - We provide sensible basic hyper-parameters that work for us, but your case can be different;
 - `trig_sum` - overlapping windows are used for each audio chunk, trig sum defines average probability among those windows for switching into triggered state (speech state);
@@ -365,6 +382,24 @@ speech_timestamps = get_speech_ts(wav, model,
                                   visualize_probs=True)
 ```
 
+#### **Adaptive way**
+
+**Adaptive algorythm (`get_speech_ts_adaptive`) automatically selects tresholds (`trig_sum` and `neg_trig_sum`) based on median speech probabilities over whole audio, SOME ARGUMENTS VARY FROM CLASSIC WAY FUNCTION ARGUMENTS**
+- `batch_size` - batch size to feed to silero VAD (default - `200`)
+- `step` - step size in samples, (default - `500`) (`num_samples_per_window` / `num_steps` from classic method)
+- `num_samples_per_window` -  number of samples in each window, our models were trained using `4000` samples (250 ms) per window, so this is preferable value (lesser values reduce [quality](https://github.com/snakers4/silero-vad/issues/2#issuecomment-750840434));
+- `min_speech_samples` - minimum speech chunk duration in samples (default - `10000`)
+- `min_silence_samples` - minimum silence duration in samples between to separate speech chunks (default - `4000`)
+- `speech_pad_samples` - widen speech by this amount of samples each side (default - `2000`)
+
+```
+speech_timestamps = get_speech_ts_adaptive(wav, model,
+                                  num_samples_per_window=4000,
+                                  step=500,
+                                  visualize_probs=True)
+```
+
+
 The chart should looks something like this:
 
 ![image](https://user-images.githubusercontent.com/12515440/106242896-79142580-6219-11eb-9add-fa7195d6fd26.png)

silero-vad.ipynb

@@ -3,6 +3,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "heading_collapsed": true,
     "id": "sVNOuHQQjsrp"
    },
    "source": [
@@ -12,7 +13,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "hidden": true
    },
    "source": [
     "## VAD"
@@ -57,6 +59,7 @@
     "                              force_reload=True)\n",
     "\n",
     "(get_speech_ts,\n",
+    " get_speech_ts_adaptive,\n",
     " save_audio,\n",
     " read_audio,\n",
     " state_generator,\n",
@@ -77,6 +80,15 @@
     "### Full Audio"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "**Classic way of getting speech chunks, you may need to select the tresholds yourself**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -116,6 +128,43 @@
     "Audio('only_speech.wav')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "**Experimental Adaptive method, algorythm selects tresholds itself (see readme for more information)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "wav = read_audio(f'{files_dir}/en.wav')\n",
+    "# get speech timestamps from full audio file\n",
+    "speech_timestamps = get_speech_ts_adaptive(wav, model, step=500, num_samples_per_window=4000)\n",
+    "pprint(speech_timestamps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "# merge all speech chunks to one audio\n",
+    "save_audio('only_speech.wav',\n",
+    "           collect_chunks(speech_timestamps, wav), 16000) \n",
+    "Audio('only_speech.wav')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -127,6 +176,19 @@
     "### Single Audio Stream"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-04-15T13:29:04.224833Z",
+     "start_time": "2021-04-15T13:29:04.220588Z"
+    },
+    "hidden": true
+   },
+   "source": [
+    "**Classic way of getting speech chunks, you may need to select the tresholds yourself**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -147,6 +209,30 @@
     "        print(batch)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "**Experimental Adaptive method, algorythm selects tresholds itself (see readme for more information)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "wav = f'{files_dir}/en.wav'\n",
+    "\n",
+    "for batch in single_audio_stream(model, wav, iterator_type='adaptive'):\n",
+    "    if batch:\n",
+    "        print(batch)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -196,7 +282,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "hidden": true
    },
    "source": [
     "## Number detector"
@@ -315,7 +402,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "hidden": true
    },
    "source": [
     "## Language detector"
@@ -387,6 +475,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "heading_collapsed": true,
     "id": "57avIBd6jsrz"
    },
    "source": [
@@ -396,7 +485,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "hidden": true
    },
    "source": [
     "## VAD"
@@ -415,13 +505,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-04-15T13:30:22.938755Z",
+     "start_time": "2021-04-15T13:30:20.970574Z"
+    },
     "cellView": "form",
     "hidden": true,
     "id": "Q4QIfSpprnkI"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'torch' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-ca9e92528117>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\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[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m _, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n\u001b[0m\u001b[1;32m     13\u001b[0m                               \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'silero_vad'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m                               force_reload=True)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'torch' is not defined"
+     ]
+    }
+   ],
    "source": [
     "#@title Install and Import Dependencies\n",
     "\n",
@@ -439,6 +545,7 @@
     "                              force_reload=True)\n",
     "\n",
     "(get_speech_ts,\n",
+    " get_speech_ts_adaptive,\n",
     " save_audio,\n",
     " read_audio,\n",
     " state_generator,\n",
@@ -470,17 +577,42 @@
    ]
   },
   {
-   "cell_type": "code",
+   "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-12-15T13:09:06.643812Z",
+     "end_time": "2021-04-15T13:34:22.554010Z",
-     "start_time": "2020-12-15T13:09:06.473386Z"
+     "start_time": "2021-04-15T13:34:22.550308Z"
+    },
+    "hidden": true
+   },
+   "source": [
+    "**Classic way of getting speech chunks, you may need to select the tresholds yourself**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-04-15T13:30:14.475412Z",
+     "start_time": "2021-04-15T13:30:14.427933Z"
     },
     "hidden": true,
     "id": "krnGoA6Kjsr0"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'init_onnx_model' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-65cde4c4cba8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minit_onnx_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'{files_dir}/model.onnx'\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      2\u001b[0m \u001b[0mwav\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_audio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'{files_dir}/en.wav'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# get speech timestamps from full audio file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mspeech_timestamps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_speech_ts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwav\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_function\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate_onnx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'init_onnx_model' is not defined"
+     ]
+    }
+   ],
    "source": [
     "model = init_onnx_model(f'{files_dir}/model.onnx')\n",
     "wav = read_audio(f'{files_dir}/en.wav')\n",
@@ -508,6 +640,60 @@
     "Audio('only_speech.wav')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "**Experimental Adaptive method, algorythm selects tresholds itself (see readme for more information)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "model = init_onnx_model(f'{files_dir}/model.onnx')\n",
+    "wav = read_audio(f'{files_dir}/en.wav')\n",
+    "\n",
+    "# get speech timestamps from full audio file\n",
+    "speech_timestamps = get_speech_ts_adaptive(wav, model, run_function=validate_onnx) \n",
+    "pprint(speech_timestamps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-04-15T13:34:41.375446Z",
+     "start_time": "2021-04-15T13:34:41.368055Z"
+    },
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'save_audio' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-5-713048adde74>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# merge all speech chunks to one audio\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[0msave_audio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'only_speech.wav'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcollect_chunks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspeech_timestamps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwav\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16000\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      3\u001b[0m \u001b[0mAudio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'only_speech.wav'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'save_audio' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# merge all speech chunks to one audio\n",
+    "save_audio('only_speech.wav', collect_chunks(speech_timestamps, wav), 16000)\n",
+    "Audio('only_speech.wav')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -519,6 +705,15 @@
     "### Single Audio Stream"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "**Classic way of getting speech chunks, you may need to select the tresholds yourself**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -554,6 +749,40 @@
     "        pprint(batch)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "**Experimental Adaptive method, algorythm selects tresholds itself (see readme for more information)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "model = init_onnx_model(f'{files_dir}/model.onnx')\n",
+    "wav = f'{files_dir}/en.wav'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "for batch in single_audio_stream(model, wav, iterator_type='adaptive', run_function=validate_onnx):\n",
+    "    if batch:\n",
+    "        pprint(batch)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -604,7 +833,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "hidden": true
    },
    "source": [
     "## Number detector"
@@ -753,7 +983,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "hidden": true
    },
    "source": [
     "## Language detector"
@@ -819,7 +1050,6 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true,
     "hidden": true,
     "id": "5JHErdB7jsr0"
    },
@@ -863,7 +1093,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.8.8"
   },
   "toc": {
    "base_numbering": 1,