mirror of
https://github.com/snakers4/silero-vad.git
synced 2026-02-04 17:39:22 +08:00
Merge pull request #8 from snakers4/sontref
Add Number Detector + utils
This commit is contained in:
Alexander Veysov
GitHub
BIN
files/en_num.wav
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files/en_num.wav
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files/number_detector.jit
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files/number_detector.jit
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files/number_detector.onnx
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files/number_detector.onnx
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files/ru_num.wav
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files/ru_num.wav
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22
hubconf.py
22
hubconf.py
@@ -2,11 +2,13 @@ dependencies = ['torch', 'torchaudio']
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import torch
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from utils import (init_jit_model,
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get_speech_ts,
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get_number_ts,
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save_audio,
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read_audio,
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state_generator,
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single_audio_stream,
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collect_speeches)
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collect_chunks,
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drop_chunks)
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def silero_vad(**kwargs):
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@@ -21,6 +23,22 @@ def silero_vad(**kwargs):
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read_audio,
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state_generator,
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single_audio_stream,
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collect_speeches)
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collect_chunks)
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return model, utils
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def silero_number_detector(**kwargs):
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"""Silero Number Detector and Language Classifier
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Returns a model with a set of utils
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Please see https://github.com/snakers4/silero-vad for usage examples
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"""
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hub_dir = torch.hub.get_dir()
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model = init_jit_model(model_path=f'{hub_dir}/snakers4_silero-vad_master/files/number_detector.jit')
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utils = (get_number_ts,
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save_audio,
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read_audio,
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collect_chunks,
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drop_chunks)
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return model, utils
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354
silero-vad.ipynb
Normal file → Executable file
354
silero-vad.ipynb
Normal file → Executable file
@@ -6,16 +6,26 @@
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"id": "sVNOuHQQjsrp"
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},
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"source": [
|
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"# PyTorch Example"
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"# PyTorch Examples"
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]
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},
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{
|
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"cell_type": "markdown",
|
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"metadata": {
|
||||
"id": "9ZTzCtc5kYVg"
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"heading_collapsed": true
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},
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"source": [
|
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"## Install Dependencies"
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"## VAD"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
|
||||
"heading_collapsed": true,
|
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"hidden": true
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||||
},
|
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"source": [
|
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"### Install Dependencies"
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]
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},
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{
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@@ -23,12 +33,10 @@
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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||||
"end_time": "2020-12-15T14:00:15.701867Z",
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||||
"start_time": "2020-12-15T14:00:09.512876Z"
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"end_time": "2020-12-30T17:35:43.397137Z",
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"start_time": "2020-12-30T17:33:10.962078Z"
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||||
},
|
||||
"cellView": "form",
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||||
"collapsed": true,
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"id": "rllMjjsekbjt"
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"hidden": true
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||||
},
|
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"outputs": [],
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"source": [
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@@ -53,7 +61,7 @@
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" read_audio,\n",
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" state_generator,\n",
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" single_audio_stream,\n",
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" collect_speeches) = utils\n",
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" collect_chunks) = utils\n",
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"\n",
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"files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'"
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]
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@@ -61,10 +69,12 @@
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{
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||||
"cell_type": "markdown",
|
||||
"metadata": {
|
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"heading_collapsed": true,
|
||||
"hidden": true,
|
||||
"id": "fXbbaUO3jsrw"
|
||||
},
|
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"source": [
|
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"## Full Audio"
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"### Full Audio"
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]
|
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},
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{
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@@ -72,9 +82,10 @@
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-12-15T13:09:56.879818Z",
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"start_time": "2020-12-15T13:09:56.864765Z"
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"end_time": "2020-12-30T17:35:44.362860Z",
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"start_time": "2020-12-30T17:35:43.398441Z"
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},
|
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"hidden": true,
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"id": "aI_eydBPjsrx"
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},
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"outputs": [],
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@@ -91,26 +102,29 @@
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-12-15T13:09:58.941063Z",
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"start_time": "2020-12-15T13:09:58.887006Z"
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"end_time": "2020-12-30T17:35:44.419280Z",
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"start_time": "2020-12-30T17:35:44.364175Z"
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},
|
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"hidden": true,
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"id": "OuEobLchjsry"
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},
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"outputs": [],
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"source": [
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"# merge all speech chunks to one audio\n",
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"save_audio('only_speech.wav',\n",
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" collect_speeches(speech_timestamps, wav), 16000) \n",
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" collect_chunks(speech_timestamps, wav), 16000) \n",
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"Audio('only_speech.wav')"
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]
|
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},
|
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{
|
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"cell_type": "markdown",
|
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"metadata": {
|
||||
"heading_collapsed": true,
|
||||
"hidden": true,
|
||||
"id": "iDKQbVr8jsry"
|
||||
},
|
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"source": [
|
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"## Single Audio Stream"
|
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"### Single Audio Stream"
|
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]
|
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},
|
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{
|
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@@ -121,6 +135,7 @@
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"end_time": "2020-12-15T13:09:59.199321Z",
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"start_time": "2020-12-15T13:09:59.196823Z"
|
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},
|
||||
"hidden": true,
|
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"id": "q-lql_2Wjsry"
|
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},
|
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"outputs": [],
|
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@@ -135,10 +150,12 @@
|
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{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"heading_collapsed": true,
|
||||
"hidden": true,
|
||||
"id": "KBDVybJCjsrz"
|
||||
},
|
||||
"source": [
|
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"## Multiple Audio Streams"
|
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"### Multiple Audio Streams"
|
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]
|
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},
|
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{
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@@ -149,6 +166,7 @@
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"end_time": "2020-12-15T13:10:03.590358Z",
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"start_time": "2020-12-15T13:10:03.587071Z"
|
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},
|
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"hidden": true,
|
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"id": "BK4tGfWgjsrz"
|
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},
|
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"outputs": [],
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@@ -165,6 +183,7 @@
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"end_time": "2020-12-15T13:10:15.762491Z",
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"start_time": "2020-12-15T13:10:03.591388Z"
|
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},
|
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"hidden": true,
|
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"id": "v1l8sam1jsrz"
|
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},
|
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"outputs": [],
|
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@@ -174,6 +193,125 @@
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" pprint(batch)"
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]
|
||||
},
|
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{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"heading_collapsed": true
|
||||
},
|
||||
"source": [
|
||||
"## Number detector"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"heading_collapsed": true,
|
||||
"hidden": true
|
||||
},
|
||||
"source": [
|
||||
"### Install Dependencies"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"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\n",
|
||||
"\n",
|
||||
"import glob\n",
|
||||
"import torch\n",
|
||||
"torch.set_num_threads(1)\n",
|
||||
"\n",
|
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"from IPython.display import Audio\n",
|
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"from pprint import pprint\n",
|
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"\n",
|
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"model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
|
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" model='silero_number_detector',\n",
|
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" force_reload=True)\n",
|
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"\n",
|
||||
"(get_number_ts,\n",
|
||||
" save_audio,\n",
|
||||
" read_audio,\n",
|
||||
" collect_chunks,\n",
|
||||
" drop_chunks) = utils\n",
|
||||
"\n",
|
||||
"files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"heading_collapsed": true,
|
||||
"hidden": true
|
||||
},
|
||||
"source": [
|
||||
"### Full audio"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"wav = read_audio(f'{files_dir}/en_num.wav')\n",
|
||||
"# get number timestamps from full audio file\n",
|
||||
"number_timestamps = get_number_ts(wav, model)\n",
|
||||
"pprint(number_timestamps)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"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)\n",
|
||||
" timestamp['end'] = int(timestamp['end'] * sample_rate)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# merge all number chunks to one audio\n",
|
||||
"save_audio('only_numbers.wav',\n",
|
||||
" collect_chunks(number_timestamps, wav), sample_rate) \n",
|
||||
"Audio('only_numbers.wav')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# drop all number chunks from audio\n",
|
||||
"save_audio('no_numbers.wav',\n",
|
||||
" drop_chunks(number_timestamps, wav), sample_rate) \n",
|
||||
"Audio('no_numbers.wav')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
@@ -186,10 +324,21 @@
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"heading_collapsed": true
|
||||
},
|
||||
"source": [
|
||||
"## VAD"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"heading_collapsed": true,
|
||||
"hidden": true,
|
||||
"id": "bL4kn4KJrlyL"
|
||||
},
|
||||
"source": [
|
||||
"## Install Dependencies"
|
||||
"### Install Dependencies"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -197,6 +346,7 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"hidden": true,
|
||||
"id": "Q4QIfSpprnkI"
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -239,10 +389,12 @@
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"heading_collapsed": true,
|
||||
"hidden": true,
|
||||
"id": "5JHErdB7jsr0"
|
||||
},
|
||||
"source": [
|
||||
"## Full Audio"
|
||||
"### Full Audio"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -253,6 +405,7 @@
|
||||
"end_time": "2020-12-15T13:09:06.643812Z",
|
||||
"start_time": "2020-12-15T13:09:06.473386Z"
|
||||
},
|
||||
"hidden": true,
|
||||
"id": "krnGoA6Kjsr0"
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -273,22 +426,25 @@
|
||||
"end_time": "2020-12-15T13:09:08.862421Z",
|
||||
"start_time": "2020-12-15T13:09:08.820014Z"
|
||||
},
|
||||
"hidden": true,
|
||||
"id": "B176Lzfnjsr1"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# merge all speech chunks to one audio\n",
|
||||
"save_audio('only_speech.wav', collect_speeches(speech_timestamps, wav), 16000)\n",
|
||||
"save_audio('only_speech.wav', collect_chunks(speech_timestamps, wav), 16000)\n",
|
||||
"Audio('only_speech.wav')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"heading_collapsed": true,
|
||||
"hidden": true,
|
||||
"id": "Rio9W50gjsr1"
|
||||
},
|
||||
"source": [
|
||||
"## Single Audio Stream"
|
||||
"### Single Audio Stream"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -299,6 +455,7 @@
|
||||
"end_time": "2020-12-15T13:09:09.606031Z",
|
||||
"start_time": "2020-12-15T13:09:09.504239Z"
|
||||
},
|
||||
"hidden": true,
|
||||
"id": "IPkl8Yy1jsr1"
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -315,6 +472,7 @@
|
||||
"end_time": "2020-12-15T13:09:11.453171Z",
|
||||
"start_time": "2020-12-15T13:09:09.633435Z"
|
||||
},
|
||||
"hidden": true,
|
||||
"id": "NC6Jim0hjsr1"
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -327,10 +485,12 @@
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"heading_collapsed": true,
|
||||
"hidden": true,
|
||||
"id": "WNZ42u0ajsr1"
|
||||
},
|
||||
"source": [
|
||||
"## Multiple Audio Streams"
|
||||
"### Multiple Audio Streams"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -341,6 +501,7 @@
|
||||
"end_time": "2020-12-15T13:09:11.540423Z",
|
||||
"start_time": "2020-12-15T13:09:11.455706Z"
|
||||
},
|
||||
"hidden": true,
|
||||
"id": "XjhGQGppjsr1"
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -358,6 +519,7 @@
|
||||
"end_time": "2020-12-15T13:09:19.565434Z",
|
||||
"start_time": "2020-12-15T13:09:11.552097Z"
|
||||
},
|
||||
"hidden": true,
|
||||
"id": "QI7-arlqjsr2"
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -366,6 +528,154 @@
|
||||
" if batch:\n",
|
||||
" pprint(batch)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"heading_collapsed": true
|
||||
},
|
||||
"source": [
|
||||
"## Number detector"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"heading_collapsed": true,
|
||||
"hidden": true,
|
||||
"id": "bL4kn4KJrlyL"
|
||||
},
|
||||
"source": [
|
||||
"### Install Dependencies"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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"
|
||||
},
|
||||
"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) = utils\n",
|
||||
"\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": "5JHErdB7jsr0"
|
||||
},
|
||||
"source": [
|
||||
"### Full Audio"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": [],
|
||||
"source": [
|
||||
"model = init_onnx_model(f'{files_dir}/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
|
||||
},
|
||||
"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)\n",
|
||||
" timestamp['end'] = int(timestamp['end'] * sample_rate)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": [],
|
||||
"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
|
||||
},
|
||||
"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')"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
53
utils.py
53
utils.py
@@ -105,6 +105,39 @@ def get_speech_ts(wav: torch.Tensor,
|
||||
return speeches
|
||||
|
||||
|
||||
def get_number_ts(wav: torch.Tensor,
|
||||
model,
|
||||
model_stride=8,
|
||||
hop_length=160,
|
||||
sample_rate=16000,
|
||||
run_function=validate):
|
||||
wav = torch.unsqueeze(wav, dim=0)
|
||||
perframe_logits = run_function(model, wav)[0]
|
||||
perframe_preds = torch.argmax(torch.softmax(perframe_logits, dim=1), dim=1).squeeze() # (1, num_frames_strided)
|
||||
extended_preds = []
|
||||
for i in perframe_preds:
|
||||
extended_preds.extend([i.item()] * model_stride)
|
||||
# len(extended_preds) is *num_frames_real*; for each frame of audio we know if it has a number in it.
|
||||
triggered = False
|
||||
timings = []
|
||||
cur_timing = {}
|
||||
for i, pred in enumerate(extended_preds):
|
||||
if pred == 1:
|
||||
if not triggered:
|
||||
cur_timing['start'] = (i * hop_length) / sample_rate
|
||||
triggered = True
|
||||
elif pred == 0:
|
||||
if triggered:
|
||||
cur_timing['end'] = (i * hop_length) / sample_rate
|
||||
timings.append(cur_timing)
|
||||
cur_timing = {}
|
||||
triggered = False
|
||||
if cur_timing:
|
||||
cur_timing['end'] = len(wav) / sample_rate
|
||||
timings.append(cur_timing)
|
||||
return timings
|
||||
|
||||
|
||||
class VADiterator:
|
||||
def __init__(self,
|
||||
trig_sum: float = 0.26,
|
||||
@@ -252,9 +285,19 @@ def single_audio_stream(model,
|
||||
yield states
|
||||
|
||||
|
||||
def collect_speeches(tss: List[dict],
|
||||
wav: torch.Tensor):
|
||||
speech_chunks = []
|
||||
def collect_chunks(tss: List[dict],
|
||||
wav: torch.Tensor):
|
||||
chunks = []
|
||||
for i in tss:
|
||||
speech_chunks.append(wav[i['start']: i['end']])
|
||||
return torch.cat(speech_chunks)
|
||||
chunks.append(wav[i['start']: i['end']])
|
||||
return torch.cat(chunks)
|
||||
|
||||
|
||||
def drop_chunks(tss: List[dict],
|
||||
wav: torch.Tensor):
|
||||
chunks = []
|
||||
cur_start = 0
|
||||
for i in tss:
|
||||
chunks.append((wav[cur_start: i['start']]))
|
||||
cur_start = i['end']
|
||||
return torch.cat(chunks)
|
||||
|
||||
Reference in New Issue
Block a user