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Merge pull request #60 from snakers4/adamnsandle

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Adamnsandle
This commit is contained in:
Alexander Veysov
2021-04-15 17:17:50 +03:00
committed by GitHub
parent 10ea4778bf ac86c5bc52
commit 131dd26f23
2 changed files with 302 additions and 36 deletions
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37
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
@@ -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 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; - 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 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: 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)

301
silero-vad.ipynb Executable file → Normal file
View File

@@ -3,6 +3,7 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true,
"id": "sVNOuHQQjsrp" "id": "sVNOuHQQjsrp"
}, },
"source": [ "source": [
@@ -12,7 +13,9 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true "heading_collapsed": true,
"hidden": true,
"id": "FpMplOCA2Fwp"
}, },
"source": [ "source": [
"## VAD" "## VAD"
@@ -22,7 +25,8 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true, "heading_collapsed": true,
"hidden": true "hidden": true,
"id": "62A6F_072Fwq"
}, },
"source": [ "source": [
"### Install Dependencies" "### Install Dependencies"
@@ -36,7 +40,8 @@
"end_time": "2020-12-30T17:35:43.397137Z", "end_time": "2020-12-30T17:35:43.397137Z",
"start_time": "2020-12-30T17:33:10.962078Z" "start_time": "2020-12-30T17:33:10.962078Z"
}, },
"hidden": true "hidden": true,
"id": "5w5AkskZ2Fwr"
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -57,6 +62,7 @@
" 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",
@@ -77,6 +83,16 @@
"### Full Audio" "### Full Audio"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {
"hidden": true,
"id": "dY2Us3_Q2Fws"
},
"source": [
"**Classic way of getting speech chunks, you may need to select the tresholds yourself**"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
@@ -116,6 +132,46 @@
"Audio('only_speech.wav')" "Audio('only_speech.wav')"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {
"hidden": true,
"id": "n8plzbJU2Fws"
},
"source": [
"**Experimental Adaptive method, algorythm selects tresholds itself (see readme for more information)**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"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": {
"hidden": true,
"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", "cell_type": "markdown",
"metadata": { "metadata": {
@@ -127,6 +183,20 @@
"### Single Audio Stream" "### 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,
"id": "xCM-HrUR2Fwu"
},
"source": [
"**Classic way of getting speech chunks, you may need to select the tresholds yourself**"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
@@ -147,6 +217,32 @@
" print(batch)" " print(batch)"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {
"hidden": true,
"id": "t8TXtnvk2Fwv"
},
"source": [
"**Experimental Adaptive method, algorythm selects tresholds itself (see readme for more information)**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"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": {
@@ -196,7 +292,9 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true "heading_collapsed": true,
"hidden": true,
"id": "36jY0niD2Fww"
}, },
"source": [ "source": [
"## Number detector" "## Number detector"
@@ -206,7 +304,8 @@
"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 +315,8 @@
"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 +349,8 @@
"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 +360,8 @@
"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 +375,8 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"hidden": true "hidden": true,
"id": "u-KfXRhZ2Fwy"
}, },
"outputs": [], "outputs": [],
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@@ -288,7 +391,8 @@
"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 +406,8 @@
"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 +420,9 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true "heading_collapsed": true,
"hidden": true,
"id": "PnKtJKbq2Fwz"
}, },
"source": [ "source": [
"## Language detector" "## Language detector"
@@ -325,7 +432,8 @@
"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 +443,8 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"hidden": true "hidden": true,
"id": "Zu9D0t6n2Fwz"
}, },
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"source": [ "source": [
@@ -365,7 +474,8 @@
"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 +485,8 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"hidden": true "hidden": true,
"id": "c8UYnYBF2Fw0"
}, },
"outputs": [], "outputs": [],
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@@ -387,6 +498,7 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true,
"id": "57avIBd6jsrz" "id": "57avIBd6jsrz"
}, },
"source": [ "source": [
@@ -396,7 +508,9 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true "heading_collapsed": true,
"hidden": true,
"id": "hEhnfORV2Fw0"
}, },
"source": [ "source": [
"## VAD" "## VAD"
@@ -417,6 +531,10 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"ExecuteTime": {
"end_time": "2021-04-15T13:30:22.938755Z",
"start_time": "2021-04-15T13:30:20.970574Z"
},
"cellView": "form", "cellView": "form",
"hidden": true, "hidden": true,
"id": "Q4QIfSpprnkI" "id": "Q4QIfSpprnkI"
@@ -439,6 +557,7 @@
" 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",
@@ -469,13 +588,27 @@
"### Full Audio" "### Full Audio"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-15T13:34:22.554010Z",
"start_time": "2021-04-15T13:34:22.550308Z"
},
"hidden": true,
"id": "TNEtK5zi2Fw2"
},
"source": [
"**Classic way of getting speech chunks, you may need to select the tresholds yourself**"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"ExecuteTime": { "ExecuteTime": {
"end_time": "2020-12-15T13:09:06.643812Z", "end_time": "2021-04-15T13:30:14.475412Z",
"start_time": "2020-12-15T13:09:06.473386Z" "start_time": "2021-04-15T13:30:14.427933Z"
}, },
"hidden": true, "hidden": true,
"id": "krnGoA6Kjsr0" "id": "krnGoA6Kjsr0"
@@ -508,6 +641,51 @@
"Audio('only_speech.wav')" "Audio('only_speech.wav')"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {
"hidden": true,
"id": "21RE8KEC2Fw2"
},
"source": [
"**Experimental Adaptive method, algorythm selects tresholds itself (see readme for more information)**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"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": {
"ExecuteTime": {
"end_time": "2021-04-15T13:34:41.375446Z",
"start_time": "2021-04-15T13:34:41.368055Z"
},
"hidden": true,
"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", "cell_type": "markdown",
"metadata": { "metadata": {
@@ -519,6 +697,16 @@
"### Single Audio Stream" "### Single Audio Stream"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {
"hidden": true,
"id": "i8EZwtaA2Fw3"
},
"source": [
"**Classic way of getting speech chunks, you may need to select the tresholds yourself**"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
@@ -554,6 +742,43 @@
" pprint(batch)" " pprint(batch)"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {
"hidden": true,
"id": "0pSKslpz2Fw3"
},
"source": [
"**Experimental Adaptive method, algorythm selects tresholds itself (see readme for more information)**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"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": {
"hidden": true,
"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": {
@@ -604,7 +829,9 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true "heading_collapsed": true,
"hidden": true,
"id": "7QMvUvpg2Fw4"
}, },
"source": [ "source": [
"## Number detector" "## Number detector"
@@ -615,7 +842,7 @@
"metadata": { "metadata": {
"heading_collapsed": true, "heading_collapsed": true,
"hidden": true, "hidden": true,
"id": "bL4kn4KJrlyL" "id": "tBPDkpHr2Fw4"
}, },
"source": [ "source": [
"### Install Dependencies" "### Install Dependencies"
@@ -631,7 +858,7 @@
}, },
"cellView": "form", "cellView": "form",
"hidden": true, "hidden": true,
"id": "Q4QIfSpprnkI" "id": "PdjGd56R2Fw5"
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -675,7 +902,7 @@
"metadata": { "metadata": {
"heading_collapsed": true, "heading_collapsed": true,
"hidden": true, "hidden": true,
"id": "5JHErdB7jsr0" "id": "I9QWSFZh2Fw5"
}, },
"source": [ "source": [
"### Full Audio" "### Full Audio"
@@ -690,7 +917,7 @@
"start_time": "2020-12-15T13:09:06.473386Z" "start_time": "2020-12-15T13:09:06.473386Z"
}, },
"hidden": true, "hidden": true,
"id": "krnGoA6Kjsr0" "id": "_r6QZiwu2Fw5"
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -706,7 +933,8 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"hidden": true "hidden": true,
"id": "FN4aDwLV2Fw5"
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -726,7 +954,7 @@
"start_time": "2020-12-15T13:09:08.820014Z" "start_time": "2020-12-15T13:09:08.820014Z"
}, },
"hidden": true, "hidden": true,
"id": "B176Lzfnjsr1" "id": "JnvS6WTK2Fw5"
}, },
"outputs": [], "outputs": [],
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@@ -740,7 +968,8 @@
"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 +982,9 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"heading_collapsed": true "heading_collapsed": true,
"hidden": true,
"id": "SR8Bgcd52Fw6"
}, },
"source": [ "source": [
"## Language detector" "## Language detector"
@@ -764,7 +995,7 @@
"metadata": { "metadata": {
"heading_collapsed": true, "heading_collapsed": true,
"hidden": true, "hidden": true,
"id": "bL4kn4KJrlyL" "id": "PBnXPtKo2Fw6"
}, },
"source": [ "source": [
"### Install Dependencies" "### Install Dependencies"
@@ -780,7 +1011,7 @@
}, },
"cellView": "form", "cellView": "form",
"hidden": true, "hidden": true,
"id": "Q4QIfSpprnkI" "id": "iNkDWJ3H2Fw6"
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -819,9 +1050,8 @@
{ {
"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 +1061,8 @@
"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 +1094,7 @@
"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 +1111,5 @@
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 1 "nbformat_minor": 0
} }