deepgeek/silero-vad
2
0
Fork 0
mirror of https://github.com/snakers4/silero-vad.git synced 2026-02-05 18:09:22 +08:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'master' of https://github.com/snakers4/silero-vad

Browse Source
This commit is contained in:
Kai Karren
2021-04-27 23:21:42 +02:00
parent 108c70fcce bd3e5f42e9
commit 4e8a5b761d
2 changed files with 260 additions and 118 deletions
Download Patch File Download Diff File Expand all files Collapse all files

41
README.md
View File

@@ -114,6 +114,7 @@ model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',
force_reload=True) force_reload=True)
(get_speech_ts, (get_speech_ts,
get_speech_ts_adaptive,
_, read_audio, _, read_audio,
_, _, _) = utils _, _, _) = utils
@@ -122,9 +123,15 @@ files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'
wav = read_audio(f'{files_dir}/en.wav') wav = read_audio(f'{files_dir}/en.wav')
# full audio # full audio
# get speech timestamps from full audio file # get speech timestamps from full audio file
# classic way
speech_timestamps = get_speech_ts(wav, model, speech_timestamps = get_speech_ts(wav, model,
num_steps=4) num_steps=4)
pprint(speech_timestamps) pprint(speech_timestamps)
# adaptive way
speech_timestamps = get_speech_ts_adaptive(wav, model)
pprint(speech_timestamps)
``` ```
#### Number Detector #### Number Detector
@@ -195,6 +202,7 @@ _, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',
force_reload=True) force_reload=True)
(get_speech_ts, (get_speech_ts,
get_speech_ts_adaptive,
_, read_audio, _, read_audio,
_, _, _) = utils _, _, _) = utils
@@ -208,14 +216,20 @@ def validate_onnx(model, inputs):
ort_inputs = {'input': inputs.cpu().numpy()} ort_inputs = {'input': inputs.cpu().numpy()}
outs = model.run(None, ort_inputs) outs = model.run(None, ort_inputs)
outs = [torch.Tensor(x) for x in outs] outs = [torch.Tensor(x) for x in outs]
return outs return outs[0]
model = init_onnx_model(f'{files_dir}/model.onnx') model = init_onnx_model(f'{files_dir}/model.onnx')
wav = read_audio(f'{files_dir}/en.wav') wav = read_audio(f'{files_dir}/en.wav')
# get speech timestamps from full audio file # get speech timestamps from full audio file
# classic way
speech_timestamps = get_speech_ts(wav, model, num_steps=4, run_function=validate_onnx) speech_timestamps = get_speech_ts(wav, model, num_steps=4, run_function=validate_onnx)
pprint(speech_timestamps) pprint(speech_timestamps)
# adaptive way
speech_timestamps = get_speech_ts(wav, model, run_function=validate_onnx)
pprint(speech_timestamps)
``` ```
#### Number Detector #### 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. 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. [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 ### 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; - 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; - 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); - `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) 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: The chart should looks something like this:
![image](https://user-images.githubusercontent.com/12515440/106242896-79142580-6219-11eb-9add-fa7195d6fd26.png) ![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 ### How Number Detector Works
- It is recommended to split long audio into short ones (< 15s) and apply model on each of them; - 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; - 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 ### How Language Classifier Works

337
silero-vad.ipynb Executable file → Normal file
View File

@@ -12,7 +12,7 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true "id": "FpMplOCA2Fwp"
}, },
"source": [ "source": [
"## VAD" "## VAD"
@@ -22,7 +22,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true, "heading_collapsed": true,
"hidden": true "id": "62A6F_072Fwq"
}, },
"source": [ "source": [
"### Install Dependencies" "### Install Dependencies"
@@ -32,11 +32,8 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"ExecuteTime": { "hidden": true,
"end_time": "2020-12-30T17:35:43.397137Z", "id": "5w5AkskZ2Fwr"
"start_time": "2020-12-30T17:33:10.962078Z"
},
"hidden": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -57,6 +54,7 @@
" force_reload=True)\n", " force_reload=True)\n",
"\n", "\n",
"(get_speech_ts,\n", "(get_speech_ts,\n",
" get_speech_ts_adaptive,\n",
" save_audio,\n", " save_audio,\n",
" read_audio,\n", " read_audio,\n",
" state_generator,\n", " state_generator,\n",
@@ -69,23 +67,25 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true,
"hidden": true,
"id": "fXbbaUO3jsrw" "id": "fXbbaUO3jsrw"
}, },
"source": [ "source": [
"### Full Audio" "### 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", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"ExecuteTime": {
"end_time": "2020-12-30T17:35:44.362860Z",
"start_time": "2020-12-30T17:35:43.398441Z"
},
"hidden": true,
"id": "aI_eydBPjsrx" "id": "aI_eydBPjsrx"
}, },
"outputs": [], "outputs": [],
@@ -101,11 +101,6 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"ExecuteTime": {
"end_time": "2020-12-30T17:35:44.419280Z",
"start_time": "2020-12-30T17:35:44.364175Z"
},
"hidden": true,
"id": "OuEobLchjsry" "id": "OuEobLchjsry"
}, },
"outputs": [], "outputs": [],
@@ -119,23 +114,62 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true, "id": "n8plzbJU2Fws"
"hidden": true, },
"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" "id": "iDKQbVr8jsry"
}, },
"source": [ "source": [
"### Single Audio Stream" "### 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", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"ExecuteTime": {
"end_time": "2020-12-15T13:09:59.199321Z",
"start_time": "2020-12-15T13:09:59.196823Z"
},
"hidden": true,
"id": "q-lql_2Wjsry" "id": "q-lql_2Wjsry"
}, },
"outputs": [], "outputs": [],
@@ -147,11 +181,34 @@
" print(batch)" " 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", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true, "heading_collapsed": true,
"hidden": true,
"id": "KBDVybJCjsrz" "id": "KBDVybJCjsrz"
}, },
"source": [ "source": [
@@ -162,10 +219,6 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"ExecuteTime": {
"end_time": "2020-12-15T13:10:03.590358Z",
"start_time": "2020-12-15T13:10:03.587071Z"
},
"hidden": true, "hidden": true,
"id": "BK4tGfWgjsrz" "id": "BK4tGfWgjsrz"
}, },
@@ -179,10 +232,6 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"ExecuteTime": {
"end_time": "2020-12-15T13:10:15.762491Z",
"start_time": "2020-12-15T13:10:03.591388Z"
},
"hidden": true, "hidden": true,
"id": "v1l8sam1jsrz" "id": "v1l8sam1jsrz"
}, },
@@ -196,7 +245,8 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true "heading_collapsed": true,
"id": "36jY0niD2Fww"
}, },
"source": [ "source": [
"## Number detector" "## Number detector"
@@ -206,7 +256,8 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true, "heading_collapsed": true,
"hidden": true "hidden": true,
"id": "scd1DlS42Fwx"
}, },
"source": [ "source": [
"### Install Dependencies" "### Install Dependencies"
@@ -216,7 +267,8 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"hidden": true "hidden": true,
"id": "Kq5gQuYq2Fwx"
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -249,7 +301,8 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true, "heading_collapsed": true,
"hidden": true "hidden": true,
"id": "qhPa30ij2Fwy"
}, },
"source": [ "source": [
"### Full audio" "### Full audio"
@@ -259,7 +312,8 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"hidden": true "hidden": true,
"id": "EXpau6xq2Fwy"
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -273,7 +327,8 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"hidden": true "hidden": true,
"id": "u-KfXRhZ2Fwy"
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -288,7 +343,8 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"hidden": true "hidden": true,
"id": "iwYEC4aZ2Fwy"
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -302,7 +358,8 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"hidden": true "hidden": true,
"id": "fHaYejX12Fwy"
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -315,7 +372,8 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true "heading_collapsed": true,
"id": "PnKtJKbq2Fwz"
}, },
"source": [ "source": [
"## Language detector" "## Language detector"
@@ -325,7 +383,8 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true, "heading_collapsed": true,
"hidden": true "hidden": true,
"id": "F5cAmMbP2Fwz"
}, },
"source": [ "source": [
"### Install Dependencies" "### Install Dependencies"
@@ -335,7 +394,8 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"hidden": true "hidden": true,
"id": "Zu9D0t6n2Fwz"
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -365,7 +425,8 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true, "heading_collapsed": true,
"hidden": true "hidden": true,
"id": "iC696eMX2Fwz"
}, },
"source": [ "source": [
"### Full audio" "### Full audio"
@@ -375,7 +436,8 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"hidden": true "hidden": true,
"id": "c8UYnYBF2Fw0"
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -396,7 +458,7 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true "id": "hEhnfORV2Fw0"
}, },
"source": [ "source": [
"## VAD" "## VAD"
@@ -406,7 +468,6 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true, "heading_collapsed": true,
"hidden": true,
"id": "bL4kn4KJrlyL" "id": "bL4kn4KJrlyL"
}, },
"source": [ "source": [
@@ -439,6 +500,7 @@
" force_reload=True)\n", " force_reload=True)\n",
"\n", "\n",
"(get_speech_ts,\n", "(get_speech_ts,\n",
" get_speech_ts_adaptive,\n",
" save_audio,\n", " save_audio,\n",
" read_audio,\n", " read_audio,\n",
" state_generator,\n", " state_generator,\n",
@@ -461,23 +523,25 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true,
"hidden": true,
"id": "5JHErdB7jsr0" "id": "5JHErdB7jsr0"
}, },
"source": [ "source": [
"### Full Audio" "### 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", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"ExecuteTime": {
"end_time": "2020-12-15T13:09:06.643812Z",
"start_time": "2020-12-15T13:09:06.473386Z"
},
"hidden": true,
"id": "krnGoA6Kjsr0" "id": "krnGoA6Kjsr0"
}, },
"outputs": [], "outputs": [],
@@ -494,11 +558,6 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"ExecuteTime": {
"end_time": "2020-12-15T13:09:08.862421Z",
"start_time": "2020-12-15T13:09:08.820014Z"
},
"hidden": true,
"id": "B176Lzfnjsr1" "id": "B176Lzfnjsr1"
}, },
"outputs": [], "outputs": [],
@@ -511,23 +570,63 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true, "id": "21RE8KEC2Fw2"
"hidden": true, },
"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" "id": "Rio9W50gjsr1"
}, },
"source": [ "source": [
"### Single Audio Stream" "### 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", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"ExecuteTime": {
"end_time": "2020-12-15T13:09:09.606031Z",
"start_time": "2020-12-15T13:09:09.504239Z"
},
"hidden": true,
"id": "IPkl8Yy1jsr1" "id": "IPkl8Yy1jsr1"
}, },
"outputs": [], "outputs": [],
@@ -540,11 +639,6 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"ExecuteTime": {
"end_time": "2020-12-15T13:09:11.453171Z",
"start_time": "2020-12-15T13:09:09.633435Z"
},
"hidden": true,
"id": "NC6Jim0hjsr1" "id": "NC6Jim0hjsr1"
}, },
"outputs": [], "outputs": [],
@@ -554,11 +648,44 @@
" pprint(batch)" " 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", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true, "heading_collapsed": true,
"hidden": true,
"id": "WNZ42u0ajsr1" "id": "WNZ42u0ajsr1"
}, },
"source": [ "source": [
@@ -569,10 +696,6 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"ExecuteTime": {
"end_time": "2020-12-15T13:09:11.540423Z",
"start_time": "2020-12-15T13:09:11.455706Z"
},
"hidden": true, "hidden": true,
"id": "XjhGQGppjsr1" "id": "XjhGQGppjsr1"
}, },
@@ -587,10 +710,6 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"ExecuteTime": {
"end_time": "2020-12-15T13:09:19.565434Z",
"start_time": "2020-12-15T13:09:11.552097Z"
},
"hidden": true, "hidden": true,
"id": "QI7-arlqjsr2" "id": "QI7-arlqjsr2"
}, },
@@ -604,7 +723,8 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true "heading_collapsed": true,
"id": "7QMvUvpg2Fw4"
}, },
"source": [ "source": [
"## Number detector" "## Number detector"
@@ -615,7 +735,7 @@
"metadata": { "metadata": {
"heading_collapsed": true, "heading_collapsed": true,
"hidden": true, "hidden": true,
"id": "bL4kn4KJrlyL" "id": "tBPDkpHr2Fw4"
}, },
"source": [ "source": [
"### Install Dependencies" "### Install Dependencies"
@@ -625,13 +745,9 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"ExecuteTime": {
"end_time": "2020-12-30T17:25:19.107534Z",
"start_time": "2020-12-30T17:24:51.853293Z"
},
"cellView": "form", "cellView": "form",
"hidden": true, "hidden": true,
"id": "Q4QIfSpprnkI" "id": "PdjGd56R2Fw5"
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -675,7 +791,7 @@
"metadata": { "metadata": {
"heading_collapsed": true, "heading_collapsed": true,
"hidden": true, "hidden": true,
"id": "5JHErdB7jsr0" "id": "I9QWSFZh2Fw5"
}, },
"source": [ "source": [
"### Full Audio" "### Full Audio"
@@ -685,12 +801,8 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"ExecuteTime": {
"end_time": "2020-12-15T13:09:06.643812Z",
"start_time": "2020-12-15T13:09:06.473386Z"
},
"hidden": true, "hidden": true,
"id": "krnGoA6Kjsr0" "id": "_r6QZiwu2Fw5"
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -706,7 +818,8 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"hidden": true "hidden": true,
"id": "FN4aDwLV2Fw5"
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -721,12 +834,8 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"ExecuteTime": {
"end_time": "2020-12-15T13:09:08.862421Z",
"start_time": "2020-12-15T13:09:08.820014Z"
},
"hidden": true, "hidden": true,
"id": "B176Lzfnjsr1" "id": "JnvS6WTK2Fw5"
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -740,7 +849,8 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"hidden": true "hidden": true,
"id": "yUxOcOFG2Fw6"
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -753,7 +863,8 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true "heading_collapsed": true,
"id": "SR8Bgcd52Fw6"
}, },
"source": [ "source": [
"## Language detector" "## Language detector"
@@ -764,7 +875,7 @@
"metadata": { "metadata": {
"heading_collapsed": true, "heading_collapsed": true,
"hidden": true, "hidden": true,
"id": "bL4kn4KJrlyL" "id": "PBnXPtKo2Fw6"
}, },
"source": [ "source": [
"### Install Dependencies" "### Install Dependencies"
@@ -774,13 +885,9 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"ExecuteTime": {
"end_time": "2020-12-30T17:25:19.107534Z",
"start_time": "2020-12-30T17:24:51.853293Z"
},
"cellView": "form", "cellView": "form",
"hidden": true, "hidden": true,
"id": "Q4QIfSpprnkI" "id": "iNkDWJ3H2Fw6"
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -819,9 +926,8 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true,
"hidden": true, "hidden": true,
"id": "5JHErdB7jsr0" "id": "G8N8oP4q2Fw6"
}, },
"source": [ "source": [
"### Full Audio" "### Full Audio"
@@ -831,7 +937,8 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"hidden": true "hidden": true,
"id": "WHXnh9IV2Fw6"
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -863,7 +970,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.8.3" "version": "3.8.8"
}, },
"toc": { "toc": {
"base_numbering": 1, "base_numbering": 1,
@@ -880,5 +987,5 @@
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 1 "nbformat_minor": 0
} }