mirror of
https://github.com/snakers4/silero-vad.git
synced 2026-02-04 17:39:22 +08:00
delete onnx from utils
This commit is contained in:
adamnsandle
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files/model.jit
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files/model.jit
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283
silero-vad.ipynb
283
silero-vad.ipynb
@@ -1,30 +1,39 @@
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{
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||||
"cells": [
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||||
{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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||||
"# Jit example"
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]
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},
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{
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"cell_type": "code",
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||||
"execution_count": null,
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||||
"metadata": {
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||||
"ExecuteTime": {
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||||
"end_time": "2020-12-14T13:43:24.487521Z",
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||||
"start_time": "2020-12-14T13:43:23.780570Z"
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"end_time": "2020-12-15T11:54:25.940761Z",
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"start_time": "2020-12-15T11:54:25.933842Z"
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}
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},
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"outputs": [],
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"source": [
|
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"# imports\n",
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"import glob\n",
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"import torch\n",
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"from IPython.display import Audio\n",
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"torch.set_num_threads(1)\n",
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"from utils import (init_jit_model, get_speech_ts, \n",
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"\n",
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"from utils import (init_jit_model, get_speech_ts,\n",
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" save_audio, read_audio, \n",
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" state_generator, single_audio_stream, init_onnx_model)"
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" state_generator, single_audio_stream)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Full audio example"
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"## Full audio"
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]
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},
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{
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@@ -32,8 +41,8 @@
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-12-14T13:43:24.492506Z",
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"start_time": "2020-12-14T13:43:24.489440Z"
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"end_time": "2020-12-15T11:54:27.939388Z",
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"start_time": "2020-12-15T11:54:27.936636Z"
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}
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},
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"outputs": [],
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@@ -50,14 +59,13 @@
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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||||
"end_time": "2020-12-14T13:43:24.760714Z",
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"start_time": "2020-12-14T13:43:24.493992Z"
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"end_time": "2020-12-15T11:54:28.415177Z",
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"start_time": "2020-12-15T11:54:28.231677Z"
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}
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},
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"outputs": [],
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"source": [
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"model = init_jit_model('files/joint_VAD_just_RU_jit_cut_q.pth.tar', 'cpu') # from yml file\n",
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"model = init_onnx_model('files/joint_VAD_just_RU.onnx')"
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"model = init_jit_model('files/model.jit', 'cpu')"
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]
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},
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{
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@@ -65,14 +73,13 @@
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-12-14T13:43:24.793384Z",
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"start_time": "2020-12-14T13:43:24.762311Z"
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"end_time": "2020-12-15T11:54:28.560822Z",
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"start_time": "2020-12-15T11:54:28.549811Z"
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}
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},
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"outputs": [],
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"source": [
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"Audio('files/test_audio_2.wav')\n",
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"wav = read_audio('files/test_audio_2.wav')"
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"wav = read_audio('files/en.wav')"
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]
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},
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{
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@@ -80,13 +87,13 @@
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-12-14T13:43:25.320324Z",
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"start_time": "2020-12-14T13:43:24.808594Z"
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"end_time": "2020-12-15T11:54:30.088721Z",
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"start_time": "2020-12-15T11:54:29.019358Z"
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}
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},
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"outputs": [],
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"source": [
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"speech_timestamps = get_speech_ts(wav, model, num_steps=4) # kill extractor"
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"speech_timestamps = get_speech_ts(wav, model, num_steps=4) # get speech timestamps from full audio file"
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]
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},
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{
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@@ -94,8 +101,8 @@
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-12-14T13:43:25.324901Z",
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"start_time": "2020-12-14T13:43:25.321759Z"
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"end_time": "2020-12-15T11:54:30.198484Z",
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"start_time": "2020-12-15T11:54:30.188311Z"
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}
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},
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"outputs": [],
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@@ -108,13 +115,13 @@
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-12-14T13:43:25.344065Z",
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"start_time": "2020-12-14T13:43:25.326162Z"
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"end_time": "2020-12-15T11:54:30.816893Z",
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"start_time": "2020-12-15T11:54:30.782667Z"
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}
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},
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"outputs": [],
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"source": [
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"save_audio('only_speech.wav', collect_speeches(speech_timestamps, wav), 16000)\n",
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"save_audio('only_speech.wav', collect_speeches(speech_timestamps, wav), 16000) # merge all speech chunks to one audio\n",
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"Audio('only_speech.wav')"
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]
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},
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@@ -122,7 +129,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Single stream example"
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"## Single audio stream"
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]
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},
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{
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@@ -130,15 +137,14 @@
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-12-14T13:43:25.778585Z",
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"start_time": "2020-12-14T13:43:25.496583Z"
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"end_time": "2020-12-15T11:54:31.886189Z",
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"start_time": "2020-12-15T11:54:31.572194Z"
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}
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},
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"outputs": [],
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"source": [
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"model = init_jit_model('files/joint_VAD_just_RU_jit_cut_q.pth.tar', 'cpu') # from yml file\n",
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"#model = init_onnx_model('files/joint_VAD_just_RU.onnx')\n",
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"audio = 'files/test_audio_6.wav'"
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"model = init_jit_model('files/model.jit', 'cpu')\n",
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"wav = 'files/en.wav'"
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]
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},
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{
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@@ -146,13 +152,13 @@
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-12-14T13:43:29.402604Z",
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"start_time": "2020-12-14T13:43:25.780037Z"
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"end_time": "2020-12-15T11:54:35.624279Z",
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"start_time": "2020-12-15T11:54:32.049532Z"
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}
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},
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"outputs": [],
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"source": [
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"for i in single_audio_stream(model, audio):\n",
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"for i in single_audio_stream(model, wav):\n",
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" if i:\n",
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" print(i)"
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]
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@@ -161,7 +167,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Multiple stream example"
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"## Multiple audio stream"
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]
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},
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{
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@@ -169,14 +175,13 @@
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-12-14T13:43:29.674262Z",
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"start_time": "2020-12-14T13:43:29.403972Z"
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"end_time": "2020-12-15T11:40:13.406225Z",
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"start_time": "2020-12-15T11:40:13.206354Z"
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}
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},
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"outputs": [],
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"source": [
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"model = init_jit_model('files/joint_VAD_just_RU_jit_cut_q.pth.tar', 'cpu') # from yml file\n",
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"model = init_onnx_model('files/joint_VAD_just_RU.onnx')"
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"model = init_jit_model('files/model.jit', 'cpu')"
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]
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},
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{
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@@ -184,14 +189,14 @@
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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||||
"end_time": "2020-12-14T13:43:29.678449Z",
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"start_time": "2020-12-14T13:43:29.675519Z"
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"end_time": "2020-12-15T11:41:08.470917Z",
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"start_time": "2020-12-15T11:41:08.467369Z"
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}
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},
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"outputs": [],
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"source": [
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"audios_for_stream = glob.glob('files/test*.wav')\n",
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"len(audios_for_stream)"
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"audios_for_stream = glob.glob('files/*.wav')\n",
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"len(audios_for_stream) # total 4 audios"
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]
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},
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{
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@@ -199,29 +204,211 @@
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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||||
"end_time": "2020-12-14T13:43:40.236387Z",
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||||
"start_time": "2020-12-14T13:43:29.679274Z"
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||||
"end_time": "2020-12-15T11:41:25.685356Z",
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||||
"start_time": "2020-12-15T11:41:16.222672Z"
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}
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},
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"outputs": [],
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"source": [
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"for i in state_generator(model, audios_for_stream, audios_in_stream=2):\n",
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||||
"for i in state_generator(model, audios_for_stream, audios_in_stream=2): # 2 audio stream\n",
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" if i:\n",
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" print(i)"
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]
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||||
},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": []
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||||
},
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{
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"cell_type": "markdown",
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||||
"metadata": {},
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"source": [
|
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"# Onnx example"
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||||
]
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},
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{
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"cell_type": "code",
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||||
"execution_count": null,
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||||
"metadata": {
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||||
"ExecuteTime": {
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||||
"end_time": "2020-12-14T13:46:49.812052Z",
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||||
"start_time": "2020-12-14T13:46:49.586637Z"
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||||
"end_time": "2020-12-15T11:55:45.597504Z",
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||||
"start_time": "2020-12-15T11:55:45.582356Z"
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||||
}
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||||
},
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||||
"outputs": [],
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||||
"source": [
|
||||
"!cp ../silero-models-research/model_saves/joint_VAD_just_RU_jit_cut_q.pth.tar files/"
|
||||
"# imports\n",
|
||||
"import glob\n",
|
||||
"import torch\n",
|
||||
"from IPython.display import Audio\n",
|
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"torch.set_num_threads(1)\n",
|
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"import onnxruntime\n",
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"\n",
|
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"from utils import (get_speech_ts, save_audio, read_audio, \n",
|
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" state_generator, single_audio_stream)\n",
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"\n",
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||||
"def init_onnx_model(model_path: str):\n",
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" return onnxruntime.InferenceSession(model_path)\n",
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"\n",
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"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"
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||||
]
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||||
},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"## Full audio"
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]
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},
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{
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"cell_type": "code",
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||||
"execution_count": null,
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||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-12-15T11:55:56.874376Z",
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||||
"start_time": "2020-12-15T11:55:56.782230Z"
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||||
}
|
||||
},
|
||||
"outputs": [],
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||||
"source": [
|
||||
"model = init_onnx_model('files/model.onnx')\n",
|
||||
"wav = read_audio('files/en.wav')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-12-15T11:56:12.159463Z",
|
||||
"start_time": "2020-12-15T11:56:11.446991Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"speech_timestamps = get_speech_ts(wav, model, num_steps=4, run_function=validate_onnx) # get speech timestamps from full audio file"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-12-15T11:56:20.488863Z",
|
||||
"start_time": "2020-12-15T11:56:20.485485Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"speech_timestamps"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-12-15T11:56:27.908128Z",
|
||||
"start_time": "2020-12-15T11:56:27.870978Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"save_audio('only_speech.wav', collect_speeches(speech_timestamps, wav), 16000) # merge all speech chunks to one audio\n",
|
||||
"Audio('only_speech.wav')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Single audio stream"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-12-15T11:58:09.012892Z",
|
||||
"start_time": "2020-12-15T11:58:08.940907Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = init_onnx_model('files/model.onnx')\n",
|
||||
"wav = 'files/en.wav'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-12-15T11:58:11.562186Z",
|
||||
"start_time": "2020-12-15T11:58:09.949825Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for i in single_audio_stream(model, wav, run_function=validate_onnx):\n",
|
||||
" if i:\n",
|
||||
" print(i)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Multiple audio stream"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = init_onnx_model('files/model.onnx')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-12-15T11:59:09.381687Z",
|
||||
"start_time": "2020-12-15T11:59:09.378552Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"audios_for_stream = glob.glob('files/*.wav')\n",
|
||||
"len(audios_for_stream) # total 4 audios"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-12-15T11:59:27.712905Z",
|
||||
"start_time": "2020-12-15T11:59:21.608435Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for i in state_generator(model, audios_for_stream, audios_in_stream=2, run_function=validate_onnx): # 2 audio stream\n",
|
||||
" if i:\n",
|
||||
" print(i)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
58
utils.py
58
utils.py
@@ -1,15 +1,16 @@
|
||||
import torch
|
||||
import torchaudio
|
||||
import onnxruntime
|
||||
import numpy as np
|
||||
from typing import List
|
||||
from itertools import repeat
|
||||
from collections import deque
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
torchaudio.set_audio_backend("soundfile") # switch backend
|
||||
|
||||
def validate(model, inputs):
|
||||
with torch.no_grad():
|
||||
outs = model(inputs)
|
||||
return outs
|
||||
|
||||
def read_audio(path: str,
|
||||
target_sr: int = 16000):
|
||||
@@ -43,14 +44,9 @@ def init_jit_model(model_path: str,
|
||||
model.eval()
|
||||
return model
|
||||
|
||||
|
||||
def init_onnx_model(model_path: str):
|
||||
return onnxruntime.InferenceSession(model_path)
|
||||
|
||||
|
||||
def get_speech_ts(wav, model,
|
||||
trig_sum=0.25, neg_trig_sum=0.01,
|
||||
num_steps=8, batch_size=200):
|
||||
trig_sum=0.25, neg_trig_sum=0.02,
|
||||
num_steps=8, batch_size=200, run_function=validate):
|
||||
|
||||
num_samples = 4000
|
||||
assert num_samples % num_steps == 0
|
||||
@@ -62,16 +58,16 @@ def get_speech_ts(wav, model,
|
||||
chunk = wav[i: i+num_samples]
|
||||
if len(chunk) < num_samples:
|
||||
chunk = F.pad(chunk, (0, num_samples - len(chunk)))
|
||||
to_concat.append(chunk)
|
||||
to_concat.append(chunk.unsqueeze(0))
|
||||
if len(to_concat) >= batch_size:
|
||||
chunks = torch.Tensor(torch.vstack(to_concat))
|
||||
out = validate(model, chunks)[-2]
|
||||
chunks = torch.Tensor(torch.cat(to_concat, dim=0))
|
||||
out = run_function(model, chunks)[-2]
|
||||
outs.append(out)
|
||||
to_concat = []
|
||||
|
||||
if to_concat:
|
||||
chunks = torch.Tensor(torch.vstack(to_concat))
|
||||
out = validate(model, chunks)[-2]
|
||||
chunks = torch.Tensor(torch.cat(to_concat, dim=0))
|
||||
out = run_function(model, chunks)[-2]
|
||||
outs.append(out)
|
||||
|
||||
outs = torch.cat(outs, dim=0)
|
||||
@@ -101,7 +97,7 @@ def get_speech_ts(wav, model,
|
||||
|
||||
class VADiterator:
|
||||
def __init__(self,
|
||||
trig_sum=0.26, neg_trig_sum=0.01,
|
||||
trig_sum=0.26, neg_trig_sum=0.02,
|
||||
num_steps=8):
|
||||
self.num_samples = 4000
|
||||
self.num_steps = num_steps
|
||||
@@ -133,11 +129,11 @@ class VADiterator:
|
||||
wav_chunk = F.pad(wav_chunk, (0, self.num_samples - len(wav_chunk))) # assume that short chunk means end of audio
|
||||
self.last = True
|
||||
|
||||
stacked = torch.hstack([self.prev, wav_chunk])
|
||||
stacked = torch.cat([self.prev, wav_chunk])
|
||||
self.prev = wav_chunk
|
||||
|
||||
overlap_chunks = [stacked[i:i+self.num_samples] for i in range(self.step, self.num_samples+1, self.step)] # 500 step is good enough
|
||||
return torch.vstack(overlap_chunks)
|
||||
overlap_chunks = [stacked[i:i+self.num_samples].unsqueeze(0) for i in range(self.step, self.num_samples+1, self.step)] # 500 step is good enough
|
||||
return torch.cat(overlap_chunks, dim=0)
|
||||
|
||||
def state(self, model_out):
|
||||
current_speech = {}
|
||||
@@ -159,14 +155,14 @@ class VADiterator:
|
||||
|
||||
def state_generator(model, audios,
|
||||
onnx=False,
|
||||
trig_sum=0.26, neg_trig_sum=0.01,
|
||||
num_steps=8, audios_in_stream=5):
|
||||
trig_sum=0.26, neg_trig_sum=0.02,
|
||||
num_steps=8, audios_in_stream=5, run_function=validate):
|
||||
VADiters = [VADiterator(trig_sum, neg_trig_sum, num_steps) for i in range(audios_in_stream)]
|
||||
for i, current_pieces in enumerate(stream_imitator(audios, audios_in_stream)):
|
||||
for_batch = [x.prepare_batch(*y) for x, y in zip(VADiters, current_pieces)]
|
||||
batch = torch.cat(for_batch)
|
||||
|
||||
outs = validate(model, batch)
|
||||
outs = run_function(model, batch)
|
||||
vad_outs = np.split(outs[-2].numpy(), audios_in_stream)
|
||||
|
||||
states = []
|
||||
@@ -212,7 +208,7 @@ def stream_imitator(audios, audios_in_stream):
|
||||
|
||||
|
||||
def single_audio_stream(model, audio, onnx=False, trig_sum=0.26,
|
||||
neg_trig_sum=0.01, num_steps=8):
|
||||
neg_trig_sum=0.02, num_steps=8, run_function=validate):
|
||||
num_samples = 4000
|
||||
VADiter = VADiterator(trig_sum, neg_trig_sum, num_steps)
|
||||
wav = read_audio(audio)
|
||||
@@ -220,7 +216,7 @@ def single_audio_stream(model, audio, onnx=False, trig_sum=0.26,
|
||||
for chunk in wav_chunks:
|
||||
batch = VADiter.prepare_batch(chunk)
|
||||
|
||||
outs = validate(model, batch)
|
||||
outs = run_function(model, batch)
|
||||
vad_outs = outs[-2] # this is very misleading
|
||||
|
||||
states = []
|
||||
@@ -228,17 +224,3 @@ def single_audio_stream(model, audio, onnx=False, trig_sum=0.26,
|
||||
if state[0]:
|
||||
states.append(state[0])
|
||||
yield states
|
||||
|
||||
|
||||
def validate(model, inputs):
|
||||
onnx = False
|
||||
if type(model) == onnxruntime.capi.session.InferenceSession:
|
||||
onnx = True
|
||||
with torch.no_grad():
|
||||
if onnx:
|
||||
ort_inputs = {'input': inputs.cpu().numpy()}
|
||||
outs = model.run(None, ort_inputs)
|
||||
outs = [torch.Tensor(x) for x in outs]
|
||||
else:
|
||||
outs = model(inputs)
|
||||
return outs
|
||||
|
||||
Reference in New Issue
Block a user