diff --git a/README.md b/README.md
index e0c6a94..dd71db6 100644
--- a/README.md
+++ b/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
@@ -337,7 +351,7 @@ We use random 250 ms audio chunks for validation. Speech to non-speech ratio amo
 
 Since our VAD (only VAD, other networks are more flexible) was trained on chunks of the same length, model's output is just one float from 0 to 1 - **speech probability**. We use speech probabilities as thresholds for precision-recall curve. This can be extended to 100 - 150 ms. Less than 100 - 150 ms cannot be distinguished as speech with confidence.
 
-[Webrtc](https://github.com/wiseman/py-webrtcvad) splits audio into frames, each frame has corresponding number (0 **or** 1). We use 30ms frames for webrtc, so each 250 ms chunk is split into 8 frames, their **mean** value is used as a treshold for plot.
+[Webrtc](https://github.com/wiseman/py-webrtcvad) splits audio into frames, each frame has corresponding number (0 **or** 1). We use 30ms frames for webrtc, so each 250 ms chunk is split into 8 frames, their **mean** value is used as a threshold for plot.
 
 [Auditok](https://github.com/amsehili/auditok) - logic same as Webrtc, but we use 50ms frames.
 
@@ -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 thresholds (`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 algorithm (`get_speech_ts_adaptive`) automatically selects thresholds (`trig_sum` and `neg_trig_sum`) based on median speech probabilities over the whole audio, SOME ARGUMENTS VARY FROM THE 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)
@@ -390,7 +425,7 @@ Please see [Quality Metrics](#quality-metrics)
 ### How Number Detector Works
 
 - It is recommended to split long audio into short ones (< 15s) and apply model on each of them;
-- Number Detector can classify if whole audio contains a number, or if each audio frame contains a number;
+- Number Detector can classify if the whole audio contains a number, or if each audio frame contains a number;
 - Audio is splitted into frames in a certain way, so, having a per-frame output, we can restore timing bounds for a numbers with an accuracy of about 0.2s;
 
 ### How Language Classifier Works
diff --git a/silero-vad.ipynb b/silero-vad.ipynb
old mode 100755
new mode 100644
index 0cb0a9d..f841184
--- a/silero-vad.ipynb
+++ b/silero-vad.ipynb
@@ -12,7 +12,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "id": "FpMplOCA2Fwp"
    },
    "source": [
     "## VAD"
@@ -22,7 +22,7 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "id": "62A6F_072Fwq"
    },
    "source": [
     "### Install Dependencies"
@@ -32,11 +32,8 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-30T17:35:43.397137Z",
-     "start_time": "2020-12-30T17:33:10.962078Z"
-    },
-    "hidden": true
+    "hidden": true,
+    "id": "5w5AkskZ2Fwr"
    },
    "outputs": [],
    "source": [
@@ -57,6 +54,7 @@
     "                              force_reload=True)\n",
     "\n",
     "(get_speech_ts,\n",
+    " get_speech_ts_adaptive,\n",
     " save_audio,\n",
     " read_audio,\n",
     " state_generator,\n",
@@ -69,23 +67,25 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
     "id": "fXbbaUO3jsrw"
    },
    "source": [
     "### Full Audio"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dY2Us3_Q2Fws"
+   },
+   "source": [
+    "**Classic way of getting speech chunks, you may need to select the thresholds yourself**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-30T17:35:44.362860Z",
-     "start_time": "2020-12-30T17:35:43.398441Z"
-    },
-    "hidden": true,
     "id": "aI_eydBPjsrx"
    },
    "outputs": [],
@@ -101,11 +101,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-30T17:35:44.419280Z",
-     "start_time": "2020-12-30T17:35:44.364175Z"
-    },
-    "hidden": true,
     "id": "OuEobLchjsry"
    },
    "outputs": [],
@@ -119,23 +114,62 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
+    "id": "n8plzbJU2Fws"
+   },
+   "source": [
+    "**Experimental Adaptive method, algorithm selects thresholds itself (see readme for more information)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "SQOtu2Vl2Fwt"
+   },
+   "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": {
+    "id": "Lr6zCGXh2Fwt"
+   },
+   "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": {
     "id": "iDKQbVr8jsry"
    },
    "source": [
     "### Single Audio Stream"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xCM-HrUR2Fwu"
+   },
+   "source": [
+    "**Classic way of getting speech chunks, you may need to select the thresholds yourself**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-15T13:09:59.199321Z",
-     "start_time": "2020-12-15T13:09:59.196823Z"
-    },
-    "hidden": true,
     "id": "q-lql_2Wjsry"
    },
    "outputs": [],
@@ -147,11 +181,34 @@
     "        print(batch)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "t8TXtnvk2Fwv"
+   },
+   "source": [
+    "**Experimental Adaptive method, algorithm selects thresholds itself (see readme for more information)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "BX3UgwwB2Fwv"
+   },
+   "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": {
     "heading_collapsed": true,
-    "hidden": true,
     "id": "KBDVybJCjsrz"
    },
    "source": [
@@ -162,10 +219,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-15T13:10:03.590358Z",
-     "start_time": "2020-12-15T13:10:03.587071Z"
-    },
     "hidden": true,
     "id": "BK4tGfWgjsrz"
    },
@@ -179,10 +232,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-15T13:10:15.762491Z",
-     "start_time": "2020-12-15T13:10:03.591388Z"
-    },
     "hidden": true,
     "id": "v1l8sam1jsrz"
    },
@@ -196,7 +245,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "id": "36jY0niD2Fww"
    },
    "source": [
     "## Number detector"
@@ -206,7 +256,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "id": "scd1DlS42Fwx"
    },
    "source": [
     "### Install Dependencies"
@@ -216,7 +267,8 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "hidden": true
+    "hidden": true,
+    "id": "Kq5gQuYq2Fwx"
    },
    "outputs": [],
    "source": [
@@ -249,7 +301,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "id": "qhPa30ij2Fwy"
    },
    "source": [
     "### Full audio"
@@ -259,7 +312,8 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "hidden": true
+    "hidden": true,
+    "id": "EXpau6xq2Fwy"
    },
    "outputs": [],
    "source": [
@@ -273,7 +327,8 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "hidden": true
+    "hidden": true,
+    "id": "u-KfXRhZ2Fwy"
    },
    "outputs": [],
    "source": [
@@ -288,7 +343,8 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "hidden": true
+    "hidden": true,
+    "id": "iwYEC4aZ2Fwy"
    },
    "outputs": [],
    "source": [
@@ -302,7 +358,8 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "hidden": true
+    "hidden": true,
+    "id": "fHaYejX12Fwy"
    },
    "outputs": [],
    "source": [
@@ -315,7 +372,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "id": "PnKtJKbq2Fwz"
    },
    "source": [
     "## Language detector"
@@ -325,7 +383,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "id": "F5cAmMbP2Fwz"
    },
    "source": [
     "### Install Dependencies"
@@ -335,7 +394,8 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "hidden": true
+    "hidden": true,
+    "id": "Zu9D0t6n2Fwz"
    },
    "outputs": [],
    "source": [
@@ -365,7 +425,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "id": "iC696eMX2Fwz"
    },
    "source": [
     "### Full audio"
@@ -375,7 +436,8 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "hidden": true
+    "hidden": true,
+    "id": "c8UYnYBF2Fw0"
    },
    "outputs": [],
    "source": [
@@ -396,7 +458,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "id": "hEhnfORV2Fw0"
    },
    "source": [
     "## VAD"
@@ -406,7 +468,6 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true,
     "id": "bL4kn4KJrlyL"
    },
    "source": [
@@ -439,6 +500,7 @@
     "                              force_reload=True)\n",
     "\n",
     "(get_speech_ts,\n",
+    " get_speech_ts_adaptive,\n",
     " save_audio,\n",
     " read_audio,\n",
     " state_generator,\n",
@@ -461,23 +523,25 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
     "id": "5JHErdB7jsr0"
    },
    "source": [
     "### Full Audio"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "TNEtK5zi2Fw2"
+   },
+   "source": [
+    "**Classic way of getting speech chunks, you may need to select the thresholds yourself**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-15T13:09:06.643812Z",
-     "start_time": "2020-12-15T13:09:06.473386Z"
-    },
-    "hidden": true,
     "id": "krnGoA6Kjsr0"
    },
    "outputs": [],
@@ -494,11 +558,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-15T13:09:08.862421Z",
-     "start_time": "2020-12-15T13:09:08.820014Z"
-    },
-    "hidden": true,
     "id": "B176Lzfnjsr1"
    },
    "outputs": [],
@@ -511,23 +570,63 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
+    "id": "21RE8KEC2Fw2"
+   },
+   "source": [
+    "**Experimental Adaptive method, algorithm selects thresholds itself (see readme for more information)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "uIVs56rb2Fw2"
+   },
+   "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": null,
+   "metadata": {
+    "id": "cox6oumC2Fw3"
+   },
+   "outputs": [],
+   "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": {
     "id": "Rio9W50gjsr1"
    },
    "source": [
     "### Single Audio Stream"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "i8EZwtaA2Fw3"
+   },
+   "source": [
+    "**Classic way of getting speech chunks, you may need to select the thresholds yourself**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-15T13:09:09.606031Z",
-     "start_time": "2020-12-15T13:09:09.504239Z"
-    },
-    "hidden": true,
     "id": "IPkl8Yy1jsr1"
    },
    "outputs": [],
@@ -540,11 +639,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-15T13:09:11.453171Z",
-     "start_time": "2020-12-15T13:09:09.633435Z"
-    },
-    "hidden": true,
     "id": "NC6Jim0hjsr1"
    },
    "outputs": [],
@@ -554,11 +648,44 @@
     "        pprint(batch)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0pSKslpz2Fw3"
+   },
+   "source": [
+    "**Experimental Adaptive method, algorithm selects thresholds itself (see readme for more information)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "RZwc-Khk2Fw4"
+   },
+   "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": {
+    "id": "Z4lzFPs02Fw4"
+   },
+   "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": {
     "heading_collapsed": true,
-    "hidden": true,
     "id": "WNZ42u0ajsr1"
    },
    "source": [
@@ -569,10 +696,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-15T13:09:11.540423Z",
-     "start_time": "2020-12-15T13:09:11.455706Z"
-    },
     "hidden": true,
     "id": "XjhGQGppjsr1"
    },
@@ -587,10 +710,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-15T13:09:19.565434Z",
-     "start_time": "2020-12-15T13:09:11.552097Z"
-    },
     "hidden": true,
     "id": "QI7-arlqjsr2"
    },
@@ -604,7 +723,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "id": "7QMvUvpg2Fw4"
    },
    "source": [
     "## Number detector"
@@ -615,7 +735,7 @@
    "metadata": {
     "heading_collapsed": true,
     "hidden": true,
-    "id": "bL4kn4KJrlyL"
+    "id": "tBPDkpHr2Fw4"
    },
    "source": [
     "### Install Dependencies"
@@ -625,13 +745,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-30T17:25:19.107534Z",
-     "start_time": "2020-12-30T17:24:51.853293Z"
-    },
     "cellView": "form",
     "hidden": true,
-    "id": "Q4QIfSpprnkI"
+    "id": "PdjGd56R2Fw5"
    },
    "outputs": [],
    "source": [
@@ -675,7 +791,7 @@
    "metadata": {
     "heading_collapsed": true,
     "hidden": true,
-    "id": "5JHErdB7jsr0"
+    "id": "I9QWSFZh2Fw5"
    },
    "source": [
     "### Full Audio"
@@ -685,12 +801,8 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-15T13:09:06.643812Z",
-     "start_time": "2020-12-15T13:09:06.473386Z"
-    },
     "hidden": true,
-    "id": "krnGoA6Kjsr0"
+    "id": "_r6QZiwu2Fw5"
    },
    "outputs": [],
    "source": [
@@ -706,7 +818,8 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "hidden": true
+    "hidden": true,
+    "id": "FN4aDwLV2Fw5"
    },
    "outputs": [],
    "source": [
@@ -721,12 +834,8 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-15T13:09:08.862421Z",
-     "start_time": "2020-12-15T13:09:08.820014Z"
-    },
     "hidden": true,
-    "id": "B176Lzfnjsr1"
+    "id": "JnvS6WTK2Fw5"
    },
    "outputs": [],
    "source": [
@@ -740,7 +849,8 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "hidden": true
+    "hidden": true,
+    "id": "yUxOcOFG2Fw6"
    },
    "outputs": [],
    "source": [
@@ -753,7 +863,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "id": "SR8Bgcd52Fw6"
    },
    "source": [
     "## Language detector"
@@ -764,7 +875,7 @@
    "metadata": {
     "heading_collapsed": true,
     "hidden": true,
-    "id": "bL4kn4KJrlyL"
+    "id": "PBnXPtKo2Fw6"
    },
    "source": [
     "### Install Dependencies"
@@ -774,13 +885,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-30T17:25:19.107534Z",
-     "start_time": "2020-12-30T17:24:51.853293Z"
-    },
     "cellView": "form",
     "hidden": true,
-    "id": "Q4QIfSpprnkI"
+    "id": "iNkDWJ3H2Fw6"
    },
    "outputs": [],
    "source": [
@@ -819,9 +926,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true,
     "hidden": true,
-    "id": "5JHErdB7jsr0"
+    "id": "G8N8oP4q2Fw6"
    },
    "source": [
     "### Full Audio"
@@ -831,7 +937,8 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "hidden": true
+    "hidden": true,
+    "id": "WHXnh9IV2Fw6"
    },
    "outputs": [],
    "source": [
@@ -863,7 +970,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.8.8"
   },
   "toc": {
    "base_numbering": 1,
@@ -880,5 +987,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 0
 }