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Fangjun Kuang
Committed by GitHub
ed06ced1 1 parent ac6825ff

Add WebAssembly for NodeJS. (#628)

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正在显示 39 个修改的文件 包含 1484 行增加 和 1921 行删除
... ... @@ -9,6 +9,7 @@ concurrency:
permissions:
contents: read
id-token: write
jobs:
nodejs:
... ... @@ -20,10 +21,20 @@ jobs:
python-version: ["3.8"]
steps:
- uses: actions/checkout@v2
- uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Install emsdk
uses: mymindstorm/setup-emsdk@v14
- name: View emsdk version
shell: bash
run: |
emcc -v
echo "--------------------"
emcc --check
- name: Setup Python ${{ matrix.python-version }}
uses: actions/setup-python@v5
with:
... ... @@ -31,28 +42,38 @@ jobs:
- uses: actions/setup-node@v4
with:
node-version: 13
registry-url: 'https://registry.npmjs.org'
- name: Display node version
shell: bash
run: |
node --version
npm --version
cd nodejs-examples
npm install npm@6.14.4 -g
npm install npm@6.14.4
npm --version
- name: Build nodejs package
shell: bash
env:
NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
run: |
./build-wasm-simd-nodejs.sh
cp -v build-wasm-simd-nodejs/install/bin/wasm/nodejs/*.js ./scripts/nodejs/
cp -v build-wasm-simd-nodejs/install/bin/wasm/nodejs/*.wasm ./scripts/nodejs/
SHERPA_ONNX_VERSION=$(grep "SHERPA_ONNX_VERSION" ./CMakeLists.txt | cut -d " " -f 2 | cut -d '"' -f 2)
echo "SHERPA_ONNX_VERSION $SHERPA_ONNX_VERSION"
cd scripts/nodejs
./run.sh
owner=${{ github.repository_owner }}
echo "owner: $owner"
sed -i.bak s/SHERPA_ONNX_VERSION/$SHERPA_ONNX_VERSION/g ./package.json
sed -i.bak s/k2-fsa/$owner/g ./package.json
rm package.json.bak
git diff
npm install
rm run.sh
npm ci
# see https://docs.npmjs.com/generating-provenance-statements
npm publish --provenance --access public
... ...
... ... @@ -40,7 +40,6 @@ jobs:
- uses: actions/setup-node@v4
with:
node-version: 13
registry-url: 'https://registry.npmjs.org'
- name: Display node version
... ...
... ... @@ -24,7 +24,7 @@ jobs:
strategy:
fail-fast: false
matrix:
os: [ubuntu-latest, macos-latest] #, windows-2019]
os: [ubuntu-latest] #, macos-latest] #, windows-2019]
python-version: ["3.8"]
steps:
... ... @@ -32,49 +32,38 @@ jobs:
with:
fetch-depth: 0
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2
with:
key: ${{ matrix.os }}-Release-ON
- name: Install emsdk
uses: mymindstorm/setup-emsdk@v14
- name: Configure CMake
- name: View emsdk version
shell: bash
run: |
export CMAKE_CXX_COMPILER_LAUNCHER=ccache
export PATH="/usr/lib/ccache:/usr/local/opt/ccache/libexec:$PATH"
cmake --version
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=Release -D BUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX=./install ..
cmake --build . --target install --config Release
ls -lh install/lib
emcc -v
echo "--------------------"
emcc --check
- name: Setup Python ${{ matrix.python-version }}
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- name: Copy files
- uses: actions/setup-node@v4
with:
registry-url: 'https://registry.npmjs.org'
- name: Display node version
shell: bash
run: |
node --version
- name: Build nodejs package
shell: bash
env:
NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
run: |
os=${{ matrix.os }}
if [[ $os == 'ubuntu-latest' ]]; then
mkdir -p scripts/nodejs/lib/linux-x64
dst=scripts/nodejs/lib/linux-x64
elif [[ $os == 'macos-latest' ]]; then
mkdir -p scripts/nodejs/lib/osx-x64
dst=scripts/nodejs/lib/osx-x64
elif [[ $os == 'windows-2019' ]]; then
mkdir -p scripts/nodejs/lib/win-x64
dst=scripts/nodejs/lib/win-x64
fi
ls -lh build/install/lib/
rm -rf build/install/lib/pkgconfig
cp -v build/install/lib/* $dst/
./build-wasm-simd-nodejs.sh
cp -v build-wasm-simd-nodejs/install/bin/wasm/nodejs/*.js ./scripts/nodejs/
cp -v build-wasm-simd-nodejs/install/bin/wasm/nodejs/*.wasm ./scripts/nodejs/
- name: replace files
shell: bash
... ... @@ -89,17 +78,6 @@ jobs:
git diff
cp *.js ../scripts/nodejs
- uses: actions/setup-node@v4
with:
node-version: 13
registry-url: 'https://registry.npmjs.org'
- name: Display node version
shell: bash
run: |
node --version
npm --version
- name: Run tests
shell: bash
run: |
... ...
... ... @@ -23,6 +23,7 @@ option(SHERPA_ONNX_ENABLE_GPU "Enable ONNX Runtime GPU support" OFF)
option(SHERPA_ONNX_ENABLE_WASM "Whether to enable WASM" OFF)
option(SHERPA_ONNX_ENABLE_WASM_TTS "Whether to enable WASM for TTS" OFF)
option(SHERPA_ONNX_ENABLE_WASM_ASR "Whether to enable WASM for ASR" OFF)
option(SHERPA_ONNX_ENABLE_WASM_NODEJS "Whether to enable WASM for NodeJS" OFF)
option(SHERPA_ONNX_ENABLE_BINARY "Whether to build binaries" ON)
option(SHERPA_ONNX_LINK_LIBSTDCPP_STATICALLY "True to link libstdc++ statically. Used only when BUILD_SHARED_LIBS is OFF on Linux" ON)
... ... @@ -108,6 +109,7 @@ message(STATUS "SHERPA_ONNX_ENABLE_GPU ${SHERPA_ONNX_ENABLE_GPU}")
message(STATUS "SHERPA_ONNX_ENABLE_WASM ${SHERPA_ONNX_ENABLE_WASM}")
message(STATUS "SHERPA_ONNX_ENABLE_WASM_TTS ${SHERPA_ONNX_ENABLE_WASM_TTS}")
message(STATUS "SHERPA_ONNX_ENABLE_WASM_ASR ${SHERPA_ONNX_ENABLE_WASM_ASR}")
message(STATUS "SHERPA_ONNX_ENABLE_WASM_NODEJS ${SHERPA_ONNX_ENABLE_WASM_NODEJS}")
if(SHERPA_ONNX_ENABLE_WASM_TTS)
if(NOT SHERPA_ONNX_ENABLE_WASM)
... ... @@ -121,6 +123,12 @@ if(SHERPA_ONNX_ENABLE_WASM_ASR)
endif()
endif()
if(SHERPA_ONNX_ENABLE_WASM_NODEJS)
if(NOT SHERPA_ONNX_ENABLE_WASM)
message(FATAL_ERROR "Please set SHERPA_ONNX_ENABLE_WASM to ON if you enable WASM for NodeJS")
endif()
endif()
if(SHERPA_ONNX_ENABLE_WASM)
add_definitions(-DSHERPA_ONNX_ENABLE_WASM=1)
endif()
... ...
#!/usr/bin/env bash
# Copyright (c) 2024 Xiaomi Corporation
#
# This script is to build sherpa-onnx for WebAssembly (NodeJS)
#
# Please use NodeJS >= 18
set -ex
if [ x"$EMSCRIPTEN" == x"" ]; then
if ! command -v emcc &> /dev/null; then
echo "Please install emscripten first"
echo ""
echo "You can use the following commands to install it:"
echo ""
echo "git clone https://github.com/emscripten-core/emsdk.git"
echo "cd emsdk"
echo "git pull"
echo "./emsdk install latest"
echo "./emsdk activate latest"
echo "source ./emsdk_env.sh"
exit 1
else
EMSCRIPTEN=$(dirname $(realpath $(which emcc)))
fi
fi
export EMSCRIPTEN=$EMSCRIPTEN
echo "EMSCRIPTEN: $EMSCRIPTEN"
if [ ! -f $EMSCRIPTEN/cmake/Modules/Platform/Emscripten.cmake ]; then
echo "Cannot find $EMSCRIPTEN/cmake/Modules/Platform/Emscripten.cmake"
echo "Please make sure you have installed emsdk correctly"
exit 1
fi
mkdir -p build-wasm-simd-nodejs
pushd build-wasm-simd-nodejs
export SHERPA_ONNX_IS_USING_BUILD_WASM_SH=ON
cmake \
-DCMAKE_INSTALL_PREFIX=./install \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_TOOLCHAIN_FILE=$EMSCRIPTEN/cmake/Modules/Platform/Emscripten.cmake \
\
-DSHERPA_ONNX_ENABLE_PYTHON=OFF \
-DSHERPA_ONNX_ENABLE_TESTS=OFF \
-DSHERPA_ONNX_ENABLE_CHECK=OFF \
-DBUILD_SHARED_LIBS=OFF \
-DSHERPA_ONNX_ENABLE_PORTAUDIO=OFF \
-DSHERPA_ONNX_ENABLE_JNI=OFF \
-DSHERPA_ONNX_ENABLE_C_API=ON \
-DSHERPA_ONNX_ENABLE_WEBSOCKET=OFF \
-DSHERPA_ONNX_ENABLE_GPU=OFF \
-DSHERPA_ONNX_ENABLE_WASM=ON \
-DSHERPA_ONNX_ENABLE_WASM_NODEJS=ON \
-DSHERPA_ONNX_ENABLE_BINARY=OFF \
-DSHERPA_ONNX_LINK_LIBSTDCPP_STATICALLY=OFF \
..
make -j10
make install
ls -lh install/bin/wasm/nodejs
... ...
node_modules
lib
package-lock.json
*.tar.bz2
... ...
... ... @@ -2,38 +2,18 @@
This directory contains nodejs examples for [sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx).
Before you continue, please first install the npm package `sherpa-onnx` by
Before you continue, please first run
```bash
npm install sherpa-onnx
cd ./nodejs-examples
npm i
```
In the following, we describe how to use [sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx)
for text-to-speech and speech-to-text.
**Caution**: If you get the following error:
```
/Users/fangjun/open-source/sherpa-onnx/nodejs-examples/node_modules/ffi-napi/lib/dynamic_library.js:67
if (match = err.match(/^(([^ \t()])+\.so([^ \t:()])*):([ \t])*/)) {
^
TypeError: Cannot read properties of null (reading 'match')
at new DynamicLibrary (/Users/fangjun/open-source/sherpa-onnx/nodejs-examples/node_modules/ffi-napi/lib/dynamic_library.js:67:21)
at Object.Library (/Users/fangjun/open-source/sherpa-onnx/nodejs-examples/node_modules/ffi-napi/lib/library.js:47:10)
at Object.<anonymous> (/Users/fangjun/open-source/sherpa-onnx/nodejs-examples/node_modules/sherpa-onnx3/index.js:268:28)
at Module._compile (node:internal/modules/cjs/loader:1376:14)
at Module._extensions..js (node:internal/modules/cjs/loader:1435:10)
at Module.load (node:internal/modules/cjs/loader:1207:32)
at Module._load (node:internal/modules/cjs/loader:1023:12)
at Module.require (node:internal/modules/cjs/loader:1235:19)
at require (node:internal/modules/helpers:176:18)
at Object.<anonymous> (/Users/fangjun/open-source/sherpa-onnx/nodejs-examples/test-offline-tts-zh.js:3:21)
```
Please downgrade your node to version v13.14.0. See also
https://github.com/node-ffi-napi/node-ffi-napi/issues/244
and
https://github.com/node-ffi-napi/node-ffi-napi/issues/97 .
Note: You need `Node >= 18`.
# Text-to-speech
... ... @@ -71,13 +51,7 @@ node ./test-offline-tts-zh.js
# Speech-to-text
In the following, we demonstrate how to decode files and how to perform
speech recognition with a microphone with `nodejs`. We need to install two additional
npm packages:
```bash
npm install wav naudiodon2
```
speech recognition with a microphone with `nodejs`.
## ./test-offline-nemo-ctc.js
... ... @@ -200,60 +174,3 @@ wget -q https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherp
tar xvf sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13.tar.bz2
node ./test-online-zipformer2-ctc.js
```
## ./test-vad-microphone-offline-paraformer.js
[./test-vad-microphone-offline-paraformer.js](./test-vad-microphone-offline-paraformer.js)
demonstrates how to use [silero-vad](https://github.com/snakers4/silero-vad)
with non-streaming Paraformer for speech recognition from microphone.
You can use the following command to run it:
```bash
wget -q https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
wget -q https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-paraformer-zh-2023-03-28.tar.bz2
tar xvf sherpa-onnx-paraformer-zh-2023-03-28.tar.bz2
node ./test-vad-microphone-offline-paraformer.js
```
## ./test-vad-microphone-offline-transducer.js
[./test-vad-microphone-offline-transducer.js](./test-vad-microphone-offline-transducer.js)
demonstrates how to use [silero-vad](https://github.com/snakers4/silero-vad)
with a non-streaming transducer model for speech recognition from microphone.
You can use the following command to run it:
```bash
wget -q https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
wget -q https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-en-2023-06-26.tar.bz2
tar xvf sherpa-onnx-zipformer-en-2023-06-26.tar.bz2
node ./test-vad-microphone-offline-transducer.js
```
## ./test-vad-microphone-offline-whisper.js
[./test-vad-microphone-offline-whisper.js](./test-vad-microphone-offline-whisper.js)
demonstrates how to use [silero-vad](https://github.com/snakers4/silero-vad)
with whisper for speech recognition from microphone.
You can use the following command to run it:
```bash
wget -q https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
wget -q https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-whisper-tiny.en.tar.bz2
tar xvf sherpa-onnx-whisper-tiny.en.tar.bz2
node ./test-vad-microphone-offline-whisper.js
```
## ./test-vad-microphone.js
[./test-vad-microphone.js](./test-vad-microphone.js)
demonstrates how to use [silero-vad](https://github.com/snakers4/silero-vad).
You can use the following command to run it:
```bash
wget -q https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
node ./test-vad-microphone.js
```
... ...
{
"dependencies": {
"naudiodon2": "^2.4.0",
"sherpa-onnx": "^1.8.12",
"sherpa-onnx": "*",
"wav": "^1.0.2"
}
}
... ...
// Copyright (c) 2023 Xiaomi Corporation (authors: Fangjun Kuang)
// Copyright (c) 2023-2024 Xiaomi Corporation (authors: Fangjun Kuang)
//
const fs = require('fs');
const {Readable} = require('stream');
... ... @@ -6,32 +6,58 @@ const wav = require('wav');
const sherpa_onnx = require('sherpa-onnx');
function createRecognizer() {
const featConfig = new sherpa_onnx.FeatureConfig();
featConfig.sampleRate = 16000;
featConfig.featureDim = 80;
// test online recognizer
const nemoCtc = new sherpa_onnx.OfflineNemoEncDecCtcModelConfig();
nemoCtc.model = './sherpa-onnx-nemo-ctc-en-conformer-small/model.int8.onnx';
const tokens = './sherpa-onnx-nemo-ctc-en-conformer-small/tokens.txt';
const modelConfig = new sherpa_onnx.OfflineModelConfig();
modelConfig.nemoCtc = nemoCtc;
modelConfig.tokens = tokens;
modelConfig.modelType = 'nemo_ctc';
const recognizerConfig = new sherpa_onnx.OfflineRecognizerConfig();
recognizerConfig.featConfig = featConfig;
recognizerConfig.modelConfig = modelConfig;
recognizerConfig.decodingMethod = 'greedy_search';
const recognizer = new sherpa_onnx.OfflineRecognizer(recognizerConfig);
return recognizer;
function createOfflineRecognizer() {
let featConfig = {
sampleRate: 16000,
featureDim: 80,
};
let modelConfig = {
transducer: {
encoder: '',
decoder: '',
joiner: '',
},
paraformer: {
model: '',
},
nemoCtc: {
model: './sherpa-onnx-nemo-ctc-en-conformer-small/model.int8.onnx',
},
whisper: {
encoder: '',
decoder: '',
},
tdnn: {
model: '',
},
tokens: './sherpa-onnx-nemo-ctc-en-conformer-small/tokens.txt',
numThreads: 1,
debug: 0,
provider: 'cpu',
modelType: 'nemo_ctc',
};
let lmConfig = {
model: '',
scale: 1.0,
};
let config = {
featConfig: featConfig,
modelConfig: modelConfig,
lmConfig: lmConfig,
decodingMethod: 'greedy_search',
maxActivePaths: 4,
hotwordsFile: '',
hotwordsScore: 1.5,
};
return sherpa_onnx.createOfflineRecognizer(config);
}
recognizer = createRecognizer();
stream = recognizer.createStream();
const recognizer = createOfflineRecognizer();
const stream = recognizer.createStream();
const waveFilename =
'./sherpa-onnx-nemo-ctc-en-conformer-small/test_wavs/0.wav';
... ... @@ -72,8 +98,8 @@ fs.createReadStream(waveFilename, {highWaterMark: 4096})
stream.acceptWaveform(recognizer.config.featConfig.sampleRate, flattened);
recognizer.decode(stream);
const r = recognizer.getResult(stream);
console.log(r.text);
const text = recognizer.getResult(stream);
console.log(text);
stream.free();
recognizer.free();
... ...
... ... @@ -6,32 +6,59 @@ const wav = require('wav');
const sherpa_onnx = require('sherpa-onnx');
function createRecognizer() {
const featConfig = new sherpa_onnx.FeatureConfig();
featConfig.sampleRate = 16000;
featConfig.featureDim = 80;
// test online recognizer
const paraformer = new sherpa_onnx.OfflineParaformerModelConfig();
paraformer.model = './sherpa-onnx-paraformer-zh-2023-03-28/model.onnx';
const tokens = './sherpa-onnx-paraformer-zh-2023-03-28/tokens.txt';
const modelConfig = new sherpa_onnx.OfflineModelConfig();
modelConfig.paraformer = paraformer;
modelConfig.tokens = tokens;
modelConfig.modelType = 'paraformer';
const recognizerConfig = new sherpa_onnx.OfflineRecognizerConfig();
recognizerConfig.featConfig = featConfig;
recognizerConfig.modelConfig = modelConfig;
recognizerConfig.decodingMethod = 'greedy_search';
const recognizer = new sherpa_onnx.OfflineRecognizer(recognizerConfig);
return recognizer;
function createOfflineRecognizer() {
let featConfig = {
sampleRate: 16000,
featureDim: 80,
};
let modelConfig = {
transducer: {
encoder: '',
decoder: '',
joiner: '',
},
paraformer: {
model: './sherpa-onnx-paraformer-zh-2023-03-28/model.int8.onnx',
},
nemoCtc: {
model: '',
},
whisper: {
encoder: '',
decoder: '',
},
tdnn: {
model: '',
},
tokens: './sherpa-onnx-paraformer-zh-2023-03-28/tokens.txt',
numThreads: 1,
debug: 0,
provider: 'cpu',
modelType: 'paraformer',
};
let lmConfig = {
model: '',
scale: 1.0,
};
let config = {
featConfig: featConfig,
modelConfig: modelConfig,
lmConfig: lmConfig,
decodingMethod: 'greedy_search',
maxActivePaths: 4,
hotwordsFile: '',
hotwordsScore: 1.5,
};
return sherpa_onnx.createOfflineRecognizer(config);
}
recognizer = createRecognizer();
stream = recognizer.createStream();
const recognizer = createOfflineRecognizer();
const stream = recognizer.createStream();
const waveFilename = './sherpa-onnx-paraformer-zh-2023-03-28/test_wavs/0.wav';
... ... @@ -71,8 +98,8 @@ fs.createReadStream(waveFilename, {'highWaterMark': 4096})
stream.acceptWaveform(recognizer.config.featConfig.sampleRate, flattened);
recognizer.decode(stream);
const r = recognizer.getResult(stream);
console.log(r.text);
const text = recognizer.getResult(stream);
console.log(text);
stream.free();
recognizer.free();
... ...
... ... @@ -6,37 +6,60 @@ const wav = require('wav');
const sherpa_onnx = require('sherpa-onnx');
function createRecognizer() {
const featConfig = new sherpa_onnx.FeatureConfig();
featConfig.sampleRate = 16000;
featConfig.featureDim = 80;
// test online recognizer
const transducer = new sherpa_onnx.OfflineTransducerModelConfig();
transducer.encoder =
'./sherpa-onnx-zipformer-en-2023-06-26/encoder-epoch-99-avg-1.onnx';
transducer.decoder =
'./sherpa-onnx-zipformer-en-2023-06-26/decoder-epoch-99-avg-1.onnx';
transducer.joiner =
'./sherpa-onnx-zipformer-en-2023-06-26/joiner-epoch-99-avg-1.onnx';
const tokens = './sherpa-onnx-zipformer-en-2023-06-26/tokens.txt';
const modelConfig = new sherpa_onnx.OfflineModelConfig();
modelConfig.transducer = transducer;
modelConfig.tokens = tokens;
modelConfig.modelType = 'transducer';
const recognizerConfig = new sherpa_onnx.OfflineRecognizerConfig();
recognizerConfig.featConfig = featConfig;
recognizerConfig.modelConfig = modelConfig;
recognizerConfig.decodingMethod = 'greedy_search';
const recognizer = new sherpa_onnx.OfflineRecognizer(recognizerConfig);
return recognizer;
function createOfflineRecognizer() {
let featConfig = {
sampleRate: 16000,
featureDim: 80,
};
let modelConfig = {
transducer: {
encoder:
'./sherpa-onnx-zipformer-en-2023-06-26/encoder-epoch-99-avg-1.int8.onnx',
decoder:
'./sherpa-onnx-zipformer-en-2023-06-26/decoder-epoch-99-avg-1.onnx',
joiner:
'./sherpa-onnx-zipformer-en-2023-06-26/joiner-epoch-99-avg-1.int8.onnx',
},
paraformer: {
model: '',
},
nemoCtc: {
model: '',
},
whisper: {
encoder: '',
decoder: '',
},
tdnn: {
model: '',
},
tokens: './sherpa-onnx-zipformer-en-2023-06-26/tokens.txt',
numThreads: 1,
debug: 0,
provider: 'cpu',
modelType: 'transducer',
};
let lmConfig = {
model: '',
scale: 1.0,
};
let config = {
featConfig: featConfig,
modelConfig: modelConfig,
lmConfig: lmConfig,
decodingMethod: 'greedy_search',
maxActivePaths: 4,
hotwordsFile: '',
hotwordsScore: 1.5,
};
return sherpa_onnx.createOfflineRecognizer(config);
}
recognizer = createRecognizer();
stream = recognizer.createStream();
const recognizer = createOfflineRecognizer();
const stream = recognizer.createStream();
const waveFilename = './sherpa-onnx-zipformer-en-2023-06-26/test_wavs/0.wav';
... ... @@ -76,8 +99,8 @@ fs.createReadStream(waveFilename, {'highWaterMark': 4096})
stream.acceptWaveform(recognizer.config.featConfig.sampleRate, flattened);
recognizer.decode(stream);
const r = recognizer.getResult(stream);
console.log(r.text);
const text = recognizer.getResult(stream);
console.log(text);
stream.free();
recognizer.free();
... ...
// Copyright (c) 2023 Xiaomi Corporation (authors: Fangjun Kuang)
// Copyright (c) 2023-2024 Xiaomi Corporation (authors: Fangjun Kuang)
const sherpa_onnx = require('sherpa-onnx');
function createOfflineTts() {
const vits = new sherpa_onnx.OfflineTtsVitsModelConfig();
vits.model = 'vits-piper-en_US-amy-low/en_US-amy-low.onnx'
vits.tokens = './vits-piper-en_US-amy-low/tokens.txt';
vits.dataDir = './vits-piper-en_US-amy-low/espeak-ng-data'
let offlineTtsVitsModelConfig = {
model: './vits-piper-en_US-amy-low/en_US-amy-low.onnx',
lexicon: '',
tokens: './vits-piper-en_US-amy-low/tokens.txt',
dataDir: './vits-piper-en_US-amy-low/espeak-ng-data',
noiseScale: 0.667,
noiseScaleW: 0.8,
lengthScale: 1.0,
};
let offlineTtsModelConfig = {
offlineTtsVitsModelConfig: offlineTtsVitsModelConfig,
numThreads: 1,
debug: 1,
provider: 'cpu',
};
const modelConfig = new sherpa_onnx.OfflineTtsModelConfig();
modelConfig.vits = vits;
let offlineTtsConfig = {
offlineTtsModelConfig: offlineTtsModelConfig,
ruleFsts: '',
maxNumSentences: 1,
};
const config = new sherpa_onnx.OfflineTtsConfig();
config.model = modelConfig;
return new sherpa_onnx.OfflineTts(config);
return sherpa_onnx.createOfflineTts(offlineTtsConfig);
}
const tts = createOfflineTts();
const speakerId = 0;
const speed = 1.0;
const audio = tts.generate(
'“Today as always, men fall into two groups: slaves and free men. Whoever does not have two-thirds of his day for himself, is a slave, whatever he may be: a statesman, a businessman, an official, or a scholar.”',
speakerId, speed);
audio.save('./test-en.wav');
const audio = tts.generate({
text:
'“Today as always, men fall into two groups: slaves and free men. Whoever does not have two-thirds of his day for himself, is a slave, whatever he may be: a statesman, a businessman, an official, or a scholar.”',
sid: speakerId,
speed: speed
});
tts.save('./test-en.wav', audio);
console.log('Saved to test-en.wav successfully.');
tts.free();
... ...
... ... @@ -3,25 +3,37 @@
const sherpa_onnx = require('sherpa-onnx');
function createOfflineTts() {
const vits = new sherpa_onnx.OfflineTtsVitsModelConfig();
vits.model = './vits-zh-aishell3/vits-aishell3.onnx';
vits.lexicon = './vits-zh-aishell3/lexicon.txt';
vits.tokens = './vits-zh-aishell3/tokens.txt';
let offlineTtsVitsModelConfig = {
model: './vits-zh-aishell3/vits-aishell3.onnx',
lexicon: './vits-zh-aishell3/lexicon.txt',
tokens: './vits-zh-aishell3/tokens.txt',
dataDir: '',
noiseScale: 0.667,
noiseScaleW: 0.8,
lengthScale: 1.0,
};
let offlineTtsModelConfig = {
offlineTtsVitsModelConfig: offlineTtsVitsModelConfig,
numThreads: 1,
debug: 1,
provider: 'cpu',
};
const modelConfig = new sherpa_onnx.OfflineTtsModelConfig();
modelConfig.vits = vits;
let offlineTtsConfig = {
offlineTtsModelConfig: offlineTtsModelConfig,
ruleFsts: './vits-zh-aishell3/rule.fst',
maxNumSentences: 1,
};
const config = new sherpa_onnx.OfflineTtsConfig();
config.model = modelConfig;
config.ruleFsts = './vits-zh-aishell3/rule.fst';
return new sherpa_onnx.OfflineTts(config);
return sherpa_onnx.createOfflineTts(offlineTtsConfig);
}
const tts = createOfflineTts();
const speakerId = 66;
const speed = 1.0;
const audio = tts.generate('3年前中国总人口是1411778724人', speakerId, speed);
audio.save('./test-zh.wav');
const audio = tts.generate(
{text: '3年前中国总人口是1411778724人', sid: speakerId, speed: speed});
tts.save('./test-zh.wav', audio);
console.log('Saved to test-zh.wav successfully.');
tts.free();
... ...
... ... @@ -6,32 +6,58 @@ const wav = require('wav');
const sherpa_onnx = require('sherpa-onnx');
function createRecognizer() {
const featConfig = new sherpa_onnx.FeatureConfig();
featConfig.sampleRate = 16000;
featConfig.featureDim = 80;
// test online recognizer
const whisper = new sherpa_onnx.OfflineWhisperModelConfig();
whisper.encoder = './sherpa-onnx-whisper-tiny.en/tiny.en-encoder.int8.onnx';
whisper.decoder = './sherpa-onnx-whisper-tiny.en/tiny.en-decoder.int8.onnx';
const tokens = './sherpa-onnx-whisper-tiny.en/tiny.en-tokens.txt';
const modelConfig = new sherpa_onnx.OfflineModelConfig();
modelConfig.whisper = whisper;
modelConfig.tokens = tokens;
modelConfig.modelType = 'whisper';
const recognizerConfig = new sherpa_onnx.OfflineRecognizerConfig();
recognizerConfig.featConfig = featConfig;
recognizerConfig.modelConfig = modelConfig;
recognizerConfig.decodingMethod = 'greedy_search';
const recognizer = new sherpa_onnx.OfflineRecognizer(recognizerConfig);
return recognizer;
function createOfflineRecognizer() {
let featConfig = {
sampleRate: 16000,
featureDim: 80,
};
let modelConfig = {
transducer: {
encoder: '',
decoder: '',
joiner: '',
},
paraformer: {
model: '',
},
nemoCtc: {
model: '',
},
whisper: {
encoder: './sherpa-onnx-whisper-tiny.en/tiny.en-encoder.int8.onnx',
decoder: './sherpa-onnx-whisper-tiny.en/tiny.en-decoder.int8.onnx',
},
tdnn: {
model: '',
},
tokens: './sherpa-onnx-whisper-tiny.en/tiny.en-tokens.txt',
numThreads: 1,
debug: 0,
provider: 'cpu',
modelType: 'whisper',
};
let lmConfig = {
model: '',
scale: 1.0,
};
let config = {
featConfig: featConfig,
modelConfig: modelConfig,
lmConfig: lmConfig,
decodingMethod: 'greedy_search',
maxActivePaths: 4,
hotwordsFile: '',
hotwordsScore: 1.5,
};
return sherpa_onnx.createOfflineRecognizer(config);
}
recognizer = createRecognizer();
recognizer = createOfflineRecognizer();
stream = recognizer.createStream();
const waveFilename = './sherpa-onnx-whisper-tiny.en/test_wavs/0.wav';
... ... @@ -72,8 +98,8 @@ fs.createReadStream(waveFilename, {'highWaterMark': 4096})
stream.acceptWaveform(recognizer.config.featConfig.sampleRate, flattened);
recognizer.decode(stream);
const r = recognizer.getResult(stream);
console.log(r.text);
const text = recognizer.getResult(stream);
console.log(text);
stream.free();
recognizer.free();
... ...
... ... @@ -5,37 +5,58 @@ console.log(portAudio.getDevices());
const sherpa_onnx = require('sherpa-onnx');
function createRecognizer() {
const featConfig = new sherpa_onnx.FeatureConfig();
featConfig.sampleRate = 16000;
featConfig.featureDim = 80;
const paraformer = new sherpa_onnx.OnlineParaformerModelConfig();
paraformer.encoder =
'./sherpa-onnx-streaming-paraformer-bilingual-zh-en/encoder.int8.onnx';
paraformer.decoder =
'./sherpa-onnx-streaming-paraformer-bilingual-zh-en/decoder.int8.onnx';
const tokens =
'./sherpa-onnx-streaming-paraformer-bilingual-zh-en/tokens.txt';
const modelConfig = new sherpa_onnx.OnlineModelConfig();
modelConfig.paraformer = paraformer;
modelConfig.tokens = tokens;
modelConfig.modelType = 'paraformer';
const recognizerConfig = new sherpa_onnx.OnlineRecognizerConfig();
recognizerConfig.featConfig = featConfig;
recognizerConfig.modelConfig = modelConfig;
recognizerConfig.decodingMethod = 'greedy_search';
recognizerConfig.enableEndpoint = 1;
const recognizer = new sherpa_onnx.OnlineRecognizer(recognizerConfig);
return recognizer;
function createOnlineRecognizer() {
let onlineTransducerModelConfig = {
encoder: '',
decoder: '',
joiner: '',
};
let onlineParaformerModelConfig = {
encoder:
'./sherpa-onnx-streaming-paraformer-bilingual-zh-en/encoder.int8.onnx',
decoder:
'./sherpa-onnx-streaming-paraformer-bilingual-zh-en/decoder.int8.onnx',
};
let onlineZipformer2CtcModelConfig = {
model: '',
};
let onlineModelConfig = {
transducer: onlineTransducerModelConfig,
paraformer: onlineParaformerModelConfig,
zipformer2Ctc: onlineZipformer2CtcModelConfig,
tokens: './sherpa-onnx-streaming-paraformer-bilingual-zh-en/tokens.txt',
numThreads: 1,
provider: 'cpu',
debug: 1,
modelType: 'paraformer',
};
let featureConfig = {
sampleRate: 16000,
featureDim: 80,
};
let recognizerConfig = {
featConfig: featureConfig,
modelConfig: onlineModelConfig,
decodingMethod: 'greedy_search',
maxActivePaths: 4,
enableEndpoint: 1,
rule1MinTrailingSilence: 2.4,
rule2MinTrailingSilence: 1.2,
rule3MinUtteranceLength: 20,
hotwordsFile: '',
hotwordsScore: 1.5,
};
return sherpa_onnx.createOnlineRecognizer(recognizerConfig);
}
recognizer = createRecognizer();
stream = recognizer.createStream();
display = new sherpa_onnx.Display(50);
const recognizer = createOnlineRecognizer();
const stream = recognizer.createStream();
let lastText = '';
let segmentIndex = 0;
... ... @@ -61,11 +82,11 @@ ai.on('data', data => {
}
const isEndpoint = recognizer.isEndpoint(stream);
const text = recognizer.getResult(stream).text;
const text = recognizer.getResult(stream);
if (text.length > 0 && lastText != text) {
lastText = text;
display.print(segmentIndex, lastText);
console.log(segmentIndex, lastText);
}
if (isEndpoint) {
if (text.length > 0) {
... ...
... ... @@ -6,34 +6,58 @@ const wav = require('wav');
const sherpa_onnx = require('sherpa-onnx');
function createRecognizer() {
const featConfig = new sherpa_onnx.FeatureConfig();
featConfig.sampleRate = 16000;
featConfig.featureDim = 80;
const paraformer = new sherpa_onnx.OnlineParaformerModelConfig();
paraformer.encoder =
'./sherpa-onnx-streaming-paraformer-bilingual-zh-en/encoder.onnx';
paraformer.decoder =
'./sherpa-onnx-streaming-paraformer-bilingual-zh-en/decoder.onnx';
const tokens =
'./sherpa-onnx-streaming-paraformer-bilingual-zh-en/tokens.txt';
const modelConfig = new sherpa_onnx.OnlineModelConfig();
modelConfig.paraformer = paraformer;
modelConfig.tokens = tokens;
modelConfig.modelType = 'paraformer';
const recognizerConfig = new sherpa_onnx.OnlineRecognizerConfig();
recognizerConfig.featConfig = featConfig;
recognizerConfig.modelConfig = modelConfig;
recognizerConfig.decodingMethod = 'greedy_search';
const recognizer = new sherpa_onnx.OnlineRecognizer(recognizerConfig);
return recognizer;
function createOnlineRecognizer() {
let onlineTransducerModelConfig = {
encoder: '',
decoder: '',
joiner: '',
};
let onlineParaformerModelConfig = {
encoder:
'./sherpa-onnx-streaming-paraformer-bilingual-zh-en/encoder.int8.onnx',
decoder:
'./sherpa-onnx-streaming-paraformer-bilingual-zh-en/decoder.int8.onnx',
};
let onlineZipformer2CtcModelConfig = {
model: '',
};
let onlineModelConfig = {
transducer: onlineTransducerModelConfig,
paraformer: onlineParaformerModelConfig,
zipformer2Ctc: onlineZipformer2CtcModelConfig,
tokens: './sherpa-onnx-streaming-paraformer-bilingual-zh-en/tokens.txt',
numThreads: 1,
provider: 'cpu',
debug: 1,
modelType: 'paraformer',
};
let featureConfig = {
sampleRate: 16000,
featureDim: 80,
};
let recognizerConfig = {
featConfig: featureConfig,
modelConfig: onlineModelConfig,
decodingMethod: 'greedy_search',
maxActivePaths: 4,
enableEndpoint: 1,
rule1MinTrailingSilence: 2.4,
rule2MinTrailingSilence: 1.2,
rule3MinUtteranceLength: 20,
hotwordsFile: '',
hotwordsScore: 1.5,
};
return sherpa_onnx.createOnlineRecognizer(recognizerConfig);
}
recognizer = createRecognizer();
stream = recognizer.createStream();
const recognizer = createOnlineRecognizer();
const stream = recognizer.createStream();
const waveFilename =
'./sherpa-onnx-streaming-paraformer-bilingual-zh-en/test_wavs/0.wav';
... ... @@ -47,8 +71,8 @@ function decode(samples) {
while (recognizer.isReady(stream)) {
recognizer.decode(stream);
}
const r = recognizer.getResult(stream);
console.log(r.text);
const text = recognizer.getResult(stream);
console.log(text);
}
reader.on('format', ({audioFormat, bitDepth, channels, sampleRate}) => {
... ...
... ... @@ -5,39 +5,60 @@ const portAudio = require('naudiodon2');
const sherpa_onnx = require('sherpa-onnx');
function createRecognizer() {
const featConfig = new sherpa_onnx.FeatureConfig();
featConfig.sampleRate = 16000;
featConfig.featureDim = 80;
// test online recognizer
const transducer = new sherpa_onnx.OnlineTransducerModelConfig();
transducer.encoder =
'./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/encoder-epoch-99-avg-1.int8.onnx';
transducer.decoder =
'./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/decoder-epoch-99-avg-1.onnx';
transducer.joiner =
'./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/joiner-epoch-99-avg-1.onnx';
const tokens =
'./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/tokens.txt';
const modelConfig = new sherpa_onnx.OnlineModelConfig();
modelConfig.transducer = transducer;
modelConfig.tokens = tokens;
modelConfig.modelType = 'zipformer';
const recognizerConfig = new sherpa_onnx.OnlineRecognizerConfig();
recognizerConfig.featConfig = featConfig;
recognizerConfig.modelConfig = modelConfig;
recognizerConfig.decodingMethod = 'greedy_search';
recognizerConfig.enableEndpoint = 1;
const recognizer = new sherpa_onnx.OnlineRecognizer(recognizerConfig);
return recognizer;
function createOnlineRecognizer() {
let onlineTransducerModelConfig = {
encoder:
'./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/encoder-epoch-99-avg-1.int8.onnx',
decoder:
'./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/decoder-epoch-99-avg-1.onnx',
joiner:
'./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/joiner-epoch-99-avg-1.int8.onnx',
};
let onlineParaformerModelConfig = {
encoder: '',
decoder: '',
};
let onlineZipformer2CtcModelConfig = {
model: '',
};
let onlineModelConfig = {
transducer: onlineTransducerModelConfig,
paraformer: onlineParaformerModelConfig,
zipformer2Ctc: onlineZipformer2CtcModelConfig,
tokens:
'./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/tokens.txt',
numThreads: 1,
provider: 'cpu',
debug: 1,
modelType: 'zipformer',
};
let featureConfig = {
sampleRate: 16000,
featureDim: 80,
};
let recognizerConfig = {
featConfig: featureConfig,
modelConfig: onlineModelConfig,
decodingMethod: 'greedy_search',
maxActivePaths: 4,
enableEndpoint: 1,
rule1MinTrailingSilence: 2.4,
rule2MinTrailingSilence: 1.2,
rule3MinUtteranceLength: 20,
hotwordsFile: '',
hotwordsScore: 1.5,
};
return sherpa_onnx.createOnlineRecognizer(recognizerConfig);
}
recognizer = createRecognizer();
stream = recognizer.createStream();
display = new sherpa_onnx.Display(50);
const recognizer = createOnlineRecognizer();
const stream = recognizer.createStream();
let lastText = '';
let segmentIndex = 0;
... ... @@ -63,11 +84,11 @@ ai.on('data', data => {
}
const isEndpoint = recognizer.isEndpoint(stream);
const text = recognizer.getResult(stream).text;
const text = recognizer.getResult(stream);
if (text.length > 0 && lastText != text) {
lastText = text;
display.print(segmentIndex, lastText);
console.log(segmentIndex, lastText);
}
if (isEndpoint) {
if (text.length > 0) {
... ...
... ... @@ -6,37 +6,60 @@ const wav = require('wav');
const sherpa_onnx = require('sherpa-onnx');
function createRecognizer() {
const featConfig = new sherpa_onnx.FeatureConfig();
featConfig.sampleRate = 16000;
featConfig.featureDim = 80;
// test online recognizer
const transducer = new sherpa_onnx.OnlineTransducerModelConfig();
transducer.encoder =
'./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/encoder-epoch-99-avg-1.int8.onnx';
transducer.decoder =
'./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/decoder-epoch-99-avg-1.onnx';
transducer.joiner =
'./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/joiner-epoch-99-avg-1.onnx';
const tokens =
'./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/tokens.txt';
const modelConfig = new sherpa_onnx.OnlineModelConfig();
modelConfig.transducer = transducer;
modelConfig.tokens = tokens;
modelConfig.modelType = 'zipformer';
const recognizerConfig = new sherpa_onnx.OnlineRecognizerConfig();
recognizerConfig.featConfig = featConfig;
recognizerConfig.modelConfig = modelConfig;
recognizerConfig.decodingMethod = 'greedy_search';
recognizer = new sherpa_onnx.OnlineRecognizer(recognizerConfig);
return recognizer;
function createOnlineRecognizer() {
let onlineTransducerModelConfig = {
encoder:
'./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/encoder-epoch-99-avg-1.int8.onnx',
decoder:
'./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/decoder-epoch-99-avg-1.onnx',
joiner:
'./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/joiner-epoch-99-avg-1.int8.onnx',
};
let onlineParaformerModelConfig = {
encoder: '',
decoder: '',
};
let onlineZipformer2CtcModelConfig = {
model: '',
};
let onlineModelConfig = {
transducer: onlineTransducerModelConfig,
paraformer: onlineParaformerModelConfig,
zipformer2Ctc: onlineZipformer2CtcModelConfig,
tokens:
'./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/tokens.txt',
numThreads: 1,
provider: 'cpu',
debug: 1,
modelType: 'zipformer',
};
let featureConfig = {
sampleRate: 16000,
featureDim: 80,
};
let recognizerConfig = {
featConfig: featureConfig,
modelConfig: onlineModelConfig,
decodingMethod: 'greedy_search',
maxActivePaths: 4,
enableEndpoint: 1,
rule1MinTrailingSilence: 2.4,
rule2MinTrailingSilence: 1.2,
rule3MinUtteranceLength: 20,
hotwordsFile: '',
hotwordsScore: 1.5,
};
return sherpa_onnx.createOnlineRecognizer(recognizerConfig);
}
recognizer = createRecognizer();
stream = recognizer.createStream();
const recognizer = createOnlineRecognizer();
const stream = recognizer.createStream();
const waveFilename =
'./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/test_wavs/0.wav';
... ... @@ -50,8 +73,8 @@ function decode(samples) {
while (recognizer.isReady(stream)) {
recognizer.decode(stream);
}
const r = recognizer.getResult(stream);
console.log(r.text);
const text = recognizer.getResult(stream);
console.log(text);
}
reader.on('format', ({audioFormat, bitDepth, channels, sampleRate}) => {
... ...
... ... @@ -6,32 +6,58 @@ const wav = require('wav');
const sherpa_onnx = require('sherpa-onnx');
function createRecognizer() {
const featConfig = new sherpa_onnx.FeatureConfig();
featConfig.sampleRate = 16000;
featConfig.featureDim = 80;
// test online recognizer
const zipformer2Ctc = new sherpa_onnx.OnlineZipformer2CtcModelConfig();
zipformer2Ctc.model =
'./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/ctc-epoch-20-avg-1-chunk-16-left-128.onnx';
const tokens =
'./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/tokens.txt';
const modelConfig = new sherpa_onnx.OnlineModelConfig();
modelConfig.zipformer2Ctc = zipformer2Ctc;
modelConfig.tokens = tokens;
const recognizerConfig = new sherpa_onnx.OnlineRecognizerConfig();
recognizerConfig.featConfig = featConfig;
recognizerConfig.modelConfig = modelConfig;
recognizerConfig.decodingMethod = 'greedy_search';
recognizer = new sherpa_onnx.OnlineRecognizer(recognizerConfig);
return recognizer;
function createOnlineRecognizer() {
let onlineTransducerModelConfig = {
encoder: '',
decoder: '',
joiner: '',
};
let onlineParaformerModelConfig = {
encoder: '',
decoder: '',
};
let onlineZipformer2CtcModelConfig = {
model:
'./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/ctc-epoch-20-avg-1-chunk-16-left-128.onnx',
};
let onlineModelConfig = {
transducer: onlineTransducerModelConfig,
paraformer: onlineParaformerModelConfig,
zipformer2Ctc: onlineZipformer2CtcModelConfig,
tokens:
'./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/tokens.txt',
numThreads: 1,
provider: 'cpu',
debug: 1,
modelType: '',
};
let featureConfig = {
sampleRate: 16000,
featureDim: 80,
};
let recognizerConfig = {
featConfig: featureConfig,
modelConfig: onlineModelConfig,
decodingMethod: 'greedy_search',
maxActivePaths: 4,
enableEndpoint: 1,
rule1MinTrailingSilence: 2.4,
rule2MinTrailingSilence: 1.2,
rule3MinUtteranceLength: 20,
hotwordsFile: '',
hotwordsScore: 1.5,
};
return sherpa_onnx.createOnlineRecognizer(recognizerConfig);
}
recognizer = createRecognizer();
stream = recognizer.createStream();
const recognizer = createOnlineRecognizer();
const stream = recognizer.createStream();
const waveFilename =
'./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/test_wavs/DEV_T0000000000.wav';
... ... @@ -45,8 +71,8 @@ function decode(samples) {
while (recognizer.isReady(stream)) {
recognizer.decode(stream);
}
const r = recognizer.getResult(stream);
console.log(r.text);
const text = recognizer.getResult(stream);
console.log(text);
}
reader.on('format', ({audioFormat, bitDepth, channels, sampleRate}) => {
... ...
// Copyright (c) 2023 Xiaomi Corporation (authors: Fangjun Kuang)
//
const sherpa_onnx = require('sherpa-onnx3');
const portAudio = require('naudiodon2');
console.log(portAudio.getDevices());
function createOfflineRecognizer() {
const featConfig = new sherpa_onnx.FeatureConfig();
featConfig.sampleRate = 16000;
featConfig.featureDim = 80;
// test online recognizer
const paraformer = new sherpa_onnx.OfflineParaformerModelConfig();
paraformer.model = './sherpa-onnx-paraformer-zh-2023-03-28/model.int8.onnx';
const tokens = './sherpa-onnx-paraformer-zh-2023-03-28/tokens.txt';
const modelConfig = new sherpa_onnx.OfflineModelConfig();
modelConfig.paraformer = paraformer;
modelConfig.tokens = tokens;
modelConfig.modelType = 'paraformer';
const recognizerConfig = new sherpa_onnx.OfflineRecognizerConfig();
recognizerConfig.featConfig = featConfig;
recognizerConfig.modelConfig = modelConfig;
recognizerConfig.decodingMethod = 'greedy_search';
const recognizer = new sherpa_onnx.OfflineRecognizer(recognizerConfig);
return recognizer
}
function createVad() {
const sileroVadModelConfig = new sherpa_onnx.SileroVadModelConfig();
sileroVadModelConfig.model = './silero_vad.onnx';
sileroVadModelConfig.minSpeechDuration = 0.3; // seconds
sileroVadModelConfig.minSilenceDuration = 0.3; // seconds
sileroVadModelConfig.windowSize = 512;
const vadModelConfig = new sherpa_onnx.VadModelConfig();
vadModelConfig.sileroVad = sileroVadModelConfig;
vadModelConfig.sampleRate = 16000;
const bufferSizeInSeconds = 60;
const vad = new sherpa_onnx.VoiceActivityDetector(
vadModelConfig, bufferSizeInSeconds);
return vad;
}
const recognizer = createOfflineRecognizer();
const vad = createVad();
const bufferSizeInSeconds = 30;
const buffer =
new sherpa_onnx.CircularBuffer(bufferSizeInSeconds * vad.config.sampleRate);
var ai = new portAudio.AudioIO({
inOptions: {
channelCount: 1,
sampleFormat: portAudio.SampleFormatFloat32,
sampleRate: vad.config.sampleRate,
deviceId: -1, // Use -1 or omit the deviceId to select the default device
closeOnError: true // Close the stream if an audio error is detected, if
// set false then just log the error
}
});
let printed = false;
let index = 0;
ai.on('data', data => {
const windowSize = vad.config.sileroVad.windowSize;
buffer.push(new Float32Array(data.buffer));
while (buffer.size() > windowSize) {
const samples = buffer.get(buffer.head(), windowSize);
buffer.pop(windowSize);
vad.acceptWaveform(samples)
}
while (!vad.isEmpty()) {
const segment = vad.front();
vad.pop();
const stream = recognizer.createStream();
stream.acceptWaveform(
recognizer.config.featConfig.sampleRate, segment.samples);
recognizer.decode(stream);
const r = recognizer.getResult(stream);
stream.free();
if (r.text.length > 0) {
console.log(`${index}: ${r.text}`);
index += 1;
}
}
});
ai.on('close', () => {
console.log('Free resources');
recognizer.free();
vad.free();
buffer.free();
});
ai.start();
console.log('Started! Please speak')
// Copyright (c) 2023 Xiaomi Corporation (authors: Fangjun Kuang)
//
const sherpa_onnx = require('sherpa-onnx');
const portAudio = require('naudiodon2');
console.log(portAudio.getDevices());
function createOfflineRecognizer() {
const featConfig = new sherpa_onnx.FeatureConfig();
featConfig.sampleRate = 16000;
featConfig.featureDim = 80;
// test online recognizer
const transducer = new sherpa_onnx.OfflineTransducerModelConfig();
transducer.encoder =
'./sherpa-onnx-zipformer-en-2023-06-26/encoder-epoch-99-avg-1.onnx';
transducer.decoder =
'./sherpa-onnx-zipformer-en-2023-06-26/decoder-epoch-99-avg-1.onnx';
transducer.joiner =
'./sherpa-onnx-zipformer-en-2023-06-26/joiner-epoch-99-avg-1.onnx';
const tokens = './sherpa-onnx-zipformer-en-2023-06-26/tokens.txt';
const modelConfig = new sherpa_onnx.OfflineModelConfig();
modelConfig.transducer = transducer;
modelConfig.tokens = tokens;
modelConfig.modelType = 'transducer';
const recognizerConfig = new sherpa_onnx.OfflineRecognizerConfig();
recognizerConfig.featConfig = featConfig;
recognizerConfig.modelConfig = modelConfig;
recognizerConfig.decodingMethod = 'greedy_search';
const recognizer = new sherpa_onnx.OfflineRecognizer(recognizerConfig);
return recognizer;
}
function createVad() {
const sileroVadModelConfig = new sherpa_onnx.SileroVadModelConfig();
sileroVadModelConfig.model = './silero_vad.onnx';
sileroVadModelConfig.minSpeechDuration = 0.3; // seconds
sileroVadModelConfig.minSilenceDuration = 0.3; // seconds
sileroVadModelConfig.windowSize = 512;
const vadModelConfig = new sherpa_onnx.VadModelConfig();
vadModelConfig.sileroVad = sileroVadModelConfig;
vadModelConfig.sampleRate = 16000;
const bufferSizeInSeconds = 60;
const vad = new sherpa_onnx.VoiceActivityDetector(
vadModelConfig, bufferSizeInSeconds);
return vad;
}
const recognizer = createOfflineRecognizer();
const vad = createVad();
const bufferSizeInSeconds = 30;
const buffer =
new sherpa_onnx.CircularBuffer(bufferSizeInSeconds * vad.config.sampleRate);
const ai = new portAudio.AudioIO({
inOptions: {
channelCount: 1,
closeOnError: true, // Close the stream if an audio error is detected, if
// set false then just log the error
deviceId: -1, // Use -1 or omit the deviceId to select the default device
sampleFormat: portAudio.SampleFormatFloat32,
sampleRate: vad.config.sampleRate
}
});
let printed = false;
let index = 0;
ai.on('data', data => {
const windowSize = vad.config.sileroVad.windowSize;
buffer.push(new Float32Array(data.buffer));
while (buffer.size() > windowSize) {
const samples = buffer.get(buffer.head(), windowSize);
buffer.pop(windowSize);
vad.acceptWaveform(samples)
}
while (!vad.isEmpty()) {
const segment = vad.front();
vad.pop();
const stream = recognizer.createStream();
stream.acceptWaveform(
recognizer.config.featConfig.sampleRate, segment.samples);
recognizer.decode(stream);
const r = recognizer.getResult(stream);
stream.free();
if (r.text.length > 0) {
console.log(`${index}: ${r.text}`);
index += 1;
}
}
});
ai.on('close', () => {
console.log('Free resources');
recognizer.free();
vad.free();
buffer.free();
});
ai.start();
console.log('Started! Please speak')
// Copyright (c) 2023 Xiaomi Corporation (authors: Fangjun Kuang)
//
const sherpa_onnx = require('sherpa-onnx');
const portAudio = require('naudiodon2');
console.log(portAudio.getDevices());
function createOfflineRecognizer() {
const featConfig = new sherpa_onnx.FeatureConfig();
featConfig.sampleRate = 16000;
featConfig.featureDim = 80;
// test online recognizer
const whisper = new sherpa_onnx.OfflineWhisperModelConfig();
whisper.encoder = './sherpa-onnx-whisper-tiny.en/tiny.en-encoder.int8.onnx';
whisper.decoder = './sherpa-onnx-whisper-tiny.en/tiny.en-decoder.int8.onnx';
const tokens = './sherpa-onnx-whisper-tiny.en/tiny.en-tokens.txt';
const modelConfig = new sherpa_onnx.OfflineModelConfig();
modelConfig.whisper = whisper;
modelConfig.tokens = tokens;
modelConfig.modelType = 'whisper';
const recognizerConfig = new sherpa_onnx.OfflineRecognizerConfig();
recognizerConfig.featConfig = featConfig;
recognizerConfig.modelConfig = modelConfig;
recognizerConfig.decodingMethod = 'greedy_search';
const recognizer = new sherpa_onnx.OfflineRecognizer(recognizerConfig);
return recognizer;
}
function createVad() {
const sileroVadModelConfig = new sherpa_onnx.SileroVadModelConfig();
sileroVadModelConfig.model = './silero_vad.onnx';
sileroVadModelConfig.minSpeechDuration = 0.3; // seconds
sileroVadModelConfig.minSilenceDuration = 0.3; // seconds
sileroVadModelConfig.windowSize = 512;
const vadModelConfig = new sherpa_onnx.VadModelConfig();
vadModelConfig.sileroVad = sileroVadModelConfig;
vadModelConfig.sampleRate = 16000;
const bufferSizeInSeconds = 60;
const vad = new sherpa_onnx.VoiceActivityDetector(
vadModelConfig, bufferSizeInSeconds);
return vad;
}
const recognizer = createOfflineRecognizer();
const vad = createVad();
const bufferSizeInSeconds = 30;
const buffer =
new sherpa_onnx.CircularBuffer(bufferSizeInSeconds * vad.config.sampleRate);
const ai = new portAudio.AudioIO({
inOptions: {
channelCount: 1,
closeOnError: true, // Close the stream if an audio error is detected, if
// set false then just log the error
deviceId: -1, // Use -1 or omit the deviceId to select the default device
sampleFormat: portAudio.SampleFormatFloat32,
sampleRate: vad.config.sampleRate
}
});
let printed = false;
let index = 0;
ai.on('data', data => {
const windowSize = vad.config.sileroVad.windowSize;
buffer.push(new Float32Array(data.buffer));
while (buffer.size() > windowSize) {
const samples = buffer.get(buffer.head(), windowSize);
buffer.pop(windowSize);
vad.acceptWaveform(samples)
}
while (!vad.isEmpty()) {
const segment = vad.front();
vad.pop();
const stream = recognizer.createStream();
stream.acceptWaveform(
recognizer.config.featConfig.sampleRate, segment.samples);
recognizer.decode(stream);
const r = recognizer.getResult(stream);
stream.free();
if (r.text.length > 0) {
console.log(`${index}: ${r.text}`);
index += 1;
}
}
});
ai.on('close', () => {
console.log('Free resources');
recognizer.free();
vad.free();
buffer.free();
});
ai.start();
console.log('Started! Please speak')
// Copyright (c) 2023 Xiaomi Corporation (authors: Fangjun Kuang)
const sherpa_onnx = require('sherpa-onnx');
const portAudio = require('naudiodon2');
console.log(portAudio.getDevices());
function createVad() {
const sileroVadModelConfig = new sherpa_onnx.SileroVadModelConfig();
sileroVadModelConfig.model = './silero_vad.onnx';
sileroVadModelConfig.minSpeechDuration = 0.3; // seconds
sileroVadModelConfig.minSilenceDuration = 0.3; // seconds
sileroVadModelConfig.windowSize = 512;
const vadModelConfig = new sherpa_onnx.VadModelConfig();
vadModelConfig.sileroVad = sileroVadModelConfig;
vadModelConfig.sampleRate = 16000;
const bufferSizeInSeconds = 60;
const vad = new sherpa_onnx.VoiceActivityDetector(
vadModelConfig, bufferSizeInSeconds);
return vad;
}
vad = createVad();
const bufferSizeInSeconds = 30;
const buffer =
new sherpa_onnx.CircularBuffer(bufferSizeInSeconds * vad.config.sampleRate);
const ai = new portAudio.AudioIO({
inOptions: {
channelCount: 1,
closeOnError: true, // Close the stream if an audio error is detected, if
// set false then just log the error
deviceId: -1, // Use -1 or omit the deviceId to select the default device
sampleFormat: portAudio.SampleFormatFloat32,
sampleRate: vad.config.sampleRate
}
});
let printed = false;
let index = 0;
ai.on('data', data => {
const windowSize = vad.config.sileroVad.windowSize;
buffer.push(new Float32Array(data.buffer));
while (buffer.size() > windowSize) {
const samples = buffer.get(buffer.head(), windowSize);
buffer.pop(windowSize);
vad.acceptWaveform(samples)
if (vad.isDetected() && !printed) {
console.log(`${index}: Detected speech`)
printed = true;
}
if (!vad.isDetected()) {
printed = false;
}
while (!vad.isEmpty()) {
const segment = vad.front();
vad.pop();
const duration = segment.samples.length / vad.config.sampleRate;
console.log(`${index} End of speech. Duration: ${duration} seconds`);
index += 1;
}
}
});
ai.on('close', () => {
console.log('Free resources');
vad.free();
buffer.free();
});
ai.start();
console.log('Started! Please speak')
Language: JavaScript
JavaScriptQuotes: Double
... ... @@ -7,3 +7,5 @@ It processes everything locally without accessing the Internet.
Please refer to
https://github.com/k2-fsa/sherpa-onnx/tree/master/nodejs-examples
for examples.
You need Node >= 18 for this package.
... ...
// Copyright (c) 2023 Xiaomi Corporation (authors: Fangjun Kuang)
//
// Please use
//
// npm install ffi-napi ref-struct-napi
//
// before you use this file
//
//
// Please use node 13. node 16, 18, 20, and 21 are known not working.
// See also
// https://github.com/node-ffi-napi/node-ffi-napi/issues/244
// and
// https://github.com/node-ffi-napi/node-ffi-napi/issues/97
"use strict"
// Copyright (c) 2023-2024 Xiaomi Corporation (authors: Fangjun Kuang)
'use strict'
const debug = require("debug")("sherpa-onnx");
const os = require("os");
const path = require("path");
const ffi = require("ffi-napi");
const ref = require("ref-napi");
const fs = require("fs");
var ArrayType = require("ref-array-napi");
const wasmModule = require('./sherpa-onnx-wasm-nodejs.js')();
const sherpa_onnx_asr = require('./sherpa-onnx-asr.js');
const sherpa_onnx_tts = require('./sherpa-onnx-tts.js');
const FloatArray = ArrayType(ref.types.float);
const StructType = require("ref-struct-napi");
const cstring = ref.types.CString;
const cstringPtr = ref.refType(cstring);
const int32_t = ref.types.int32;
const float = ref.types.float;
const floatPtr = ref.refType(float);
const SherpaOnnxOnlineTransducerModelConfig = StructType({
"encoder" : cstring,
"decoder" : cstring,
"joiner" : cstring,
});
const SherpaOnnxOnlineParaformerModelConfig = StructType({
"encoder" : cstring,
"decoder" : cstring,
});
const SherpaOnnxOnlineZipformer2CtcModelConfig = StructType({
"model" : cstring,
});
const SherpaOnnxOnlineModelConfig = StructType({
"transducer" : SherpaOnnxOnlineTransducerModelConfig,
"paraformer" : SherpaOnnxOnlineParaformerModelConfig,
"zipformer2Ctc" : SherpaOnnxOnlineZipformer2CtcModelConfig,
"tokens" : cstring,
"numThreads" : int32_t,
"provider" : cstring,
"debug" : int32_t,
"modelType" : cstring,
});
const SherpaOnnxFeatureConfig = StructType({
"sampleRate" : int32_t,
"featureDim" : int32_t,
});
const SherpaOnnxOnlineRecognizerConfig = StructType({
"featConfig" : SherpaOnnxFeatureConfig,
"modelConfig" : SherpaOnnxOnlineModelConfig,
"decodingMethod" : cstring,
"maxActivePaths" : int32_t,
"enableEndpoint" : int32_t,
"rule1MinTrailingSilence" : float,
"rule2MinTrailingSilence" : float,
"rule3MinUtteranceLength" : float,
"hotwordsFile" : cstring,
"hotwordsScore" : float,
});
const SherpaOnnxOnlineRecognizerResult = StructType({
"text" : cstring,
"tokens" : cstring,
"tokensArr" : cstringPtr,
"timestamps" : floatPtr,
"count" : int32_t,
"json" : cstring,
});
const SherpaOnnxOnlineRecognizerPtr = ref.refType(ref.types.void);
const SherpaOnnxOnlineStreamPtr = ref.refType(ref.types.void);
const SherpaOnnxOnlineStreamPtrPtr = ref.refType(SherpaOnnxOnlineStreamPtr);
const SherpaOnnxOnlineRecognizerResultPtr =
ref.refType(SherpaOnnxOnlineRecognizerResult);
const SherpaOnnxOnlineRecognizerConfigPtr =
ref.refType(SherpaOnnxOnlineRecognizerConfig);
const SherpaOnnxOfflineTransducerModelConfig = StructType({
"encoder" : cstring,
"decoder" : cstring,
"joiner" : cstring,
});
const SherpaOnnxOfflineParaformerModelConfig = StructType({
"model" : cstring,
});
const SherpaOnnxOfflineNemoEncDecCtcModelConfig = StructType({
"model" : cstring,
});
const SherpaOnnxOfflineWhisperModelConfig = StructType({
"encoder" : cstring,
"decoder" : cstring,
});
const SherpaOnnxOfflineTdnnModelConfig = StructType({
"model" : cstring,
});
const SherpaOnnxOfflineLMConfig = StructType({
"model" : cstring,
"scale" : float,
});
const SherpaOnnxOfflineModelConfig = StructType({
"transducer" : SherpaOnnxOfflineTransducerModelConfig,
"paraformer" : SherpaOnnxOfflineParaformerModelConfig,
"nemoCtc" : SherpaOnnxOfflineNemoEncDecCtcModelConfig,
"whisper" : SherpaOnnxOfflineWhisperModelConfig,
"tdnn" : SherpaOnnxOfflineTdnnModelConfig,
"tokens" : cstring,
"numThreads" : int32_t,
"debug" : int32_t,
"provider" : cstring,
"modelType" : cstring,
});
const SherpaOnnxOfflineRecognizerConfig = StructType({
"featConfig" : SherpaOnnxFeatureConfig,
"modelConfig" : SherpaOnnxOfflineModelConfig,
"lmConfig" : SherpaOnnxOfflineLMConfig,
"decodingMethod" : cstring,
"maxActivePaths" : int32_t,
"hotwordsFile" : cstring,
"hotwordsScore" : float,
});
const SherpaOnnxOfflineRecognizerResult = StructType({
"text" : cstring,
"timestamps" : floatPtr,
"count" : int32_t,
});
const SherpaOnnxOfflineRecognizerPtr = ref.refType(ref.types.void);
const SherpaOnnxOfflineStreamPtr = ref.refType(ref.types.void);
const SherpaOnnxOfflineStreamPtrPtr = ref.refType(SherpaOnnxOfflineStreamPtr);
const SherpaOnnxOfflineRecognizerResultPtr =
ref.refType(SherpaOnnxOfflineRecognizerResult);
const SherpaOnnxOfflineRecognizerConfigPtr =
ref.refType(SherpaOnnxOfflineRecognizerConfig);
// vad
const SherpaOnnxSileroVadModelConfig = StructType({
"model" : cstring,
"threshold" : float,
"minSilenceDuration" : float,
"minSpeechDuration" : float,
"windowSize" : int32_t,
});
const SherpaOnnxVadModelConfig = StructType({
"sileroVad" : SherpaOnnxSileroVadModelConfig,
"sampleRate" : int32_t,
"numThreads" : int32_t,
"provider" : cstring,
"debug" : int32_t,
});
const SherpaOnnxSpeechSegment = StructType({
"start" : int32_t,
"samples" : FloatArray,
"n" : int32_t,
});
const SherpaOnnxVadModelConfigPtr = ref.refType(SherpaOnnxVadModelConfig);
const SherpaOnnxSpeechSegmentPtr = ref.refType(SherpaOnnxSpeechSegment);
const SherpaOnnxCircularBufferPtr = ref.refType(ref.types.void);
const SherpaOnnxVoiceActivityDetectorPtr = ref.refType(ref.types.void);
// tts
const SherpaOnnxOfflineTtsVitsModelConfig = StructType({
"model" : cstring,
"lexicon" : cstring,
"tokens" : cstring,
"dataDir" : cstring,
"noiseScale" : float,
"noiseScaleW" : float,
"lengthScale" : float,
});
const SherpaOnnxOfflineTtsModelConfig = StructType({
"vits" : SherpaOnnxOfflineTtsVitsModelConfig,
"numThreads" : int32_t,
"debug" : int32_t,
"provider" : cstring,
});
const SherpaOnnxOfflineTtsConfig = StructType({
"model" : SherpaOnnxOfflineTtsModelConfig,
"ruleFsts" : cstring,
"maxNumSentences" : int32_t,
});
const SherpaOnnxGeneratedAudio = StructType({
"samples" : FloatArray,
"n" : int32_t,
"sampleRate" : int32_t,
});
const SherpaOnnxOfflineTtsVitsModelConfigPtr =
ref.refType(SherpaOnnxOfflineTtsVitsModelConfig);
const SherpaOnnxOfflineTtsConfigPtr = ref.refType(SherpaOnnxOfflineTtsConfig);
const SherpaOnnxGeneratedAudioPtr = ref.refType(SherpaOnnxGeneratedAudio);
const SherpaOnnxOfflineTtsPtr = ref.refType(ref.types.void);
const SherpaOnnxDisplayPtr = ref.refType(ref.types.void);
let soname;
if (os.platform() == "win32") {
// see https://nodejs.org/api/process.html#processarch
if (process.arch == "x64") {
let currentPath = process.env.Path;
let dllDirectory = path.resolve(path.join(__dirname, "lib", "win-x64"));
process.env.Path = currentPath + path.delimiter + dllDirectory;
soname = path.join(__dirname, "lib", "win-x64", "sherpa-onnx-c-api.dll")
} else if (process.arch == "ia32") {
let currentPath = process.env.Path;
let dllDirectory = path.resolve(path.join(__dirname, "lib", "win-x86"));
process.env.Path = currentPath + path.delimiter + dllDirectory;
soname = path.join(__dirname, "lib", "win-x86", "sherpa-onnx-c-api.dll")
} else {
throw new Error(
`Support only Windows x86 and x64 for now. Given ${process.arch}`);
}
} else if (os.platform() == "darwin") {
if (process.arch == "x64") {
soname =
path.join(__dirname, "lib", "osx-x64", "libsherpa-onnx-c-api.dylib");
} else if (process.arch == "arm64") {
soname =
path.join(__dirname, "lib", "osx-arm64", "libsherpa-onnx-c-api.dylib");
} else {
throw new Error(
`Support only macOS x64 and arm64 for now. Given ${process.arch}`);
}
} else if (os.platform() == "linux") {
if (process.arch == "x64") {
soname =
path.join(__dirname, "lib", "linux-x64", "libsherpa-onnx-c-api.so");
} else {
throw new Error(`Support only Linux x64 for now. Given ${process.arch}`);
}
} else {
throw new Error(`Unsupported platform ${os.platform()}`);
function createOnlineRecognizer(config) {
return sherpa_onnx_asr.createOnlineRecognizer(wasmModule, config);
}
if (!fs.existsSync(soname)) {
throw new Error(`Cannot find file ${soname}. Please make sure you have run
./build.sh`);
function createOfflineRecognizer(config) {
return new sherpa_onnx_asr.OfflineRecognizer(config, wasmModule);
}
debug("soname ", soname)
const libsherpa_onnx = ffi.Library(soname, {
// online asr
"CreateOnlineRecognizer" : [
SherpaOnnxOnlineRecognizerPtr, [ SherpaOnnxOnlineRecognizerConfigPtr ]
],
"DestroyOnlineRecognizer" : [ "void", [ SherpaOnnxOnlineRecognizerPtr ] ],
"CreateOnlineStream" :
[ SherpaOnnxOnlineStreamPtr, [ SherpaOnnxOnlineRecognizerPtr ] ],
"CreateOnlineStreamWithHotwords" :
[ SherpaOnnxOnlineStreamPtr, [ SherpaOnnxOnlineRecognizerPtr, cstring ] ],
"DestroyOnlineStream" : [ "void", [ SherpaOnnxOnlineStreamPtr ] ],
"AcceptWaveform" :
[ "void", [ SherpaOnnxOnlineStreamPtr, int32_t, floatPtr, int32_t ] ],
"IsOnlineStreamReady" :
[ int32_t, [ SherpaOnnxOnlineRecognizerPtr, SherpaOnnxOnlineStreamPtr ] ],
"DecodeOnlineStream" :
[ "void", [ SherpaOnnxOnlineRecognizerPtr, SherpaOnnxOnlineStreamPtr ] ],
"DecodeMultipleOnlineStreams" : [
"void",
[ SherpaOnnxOnlineRecognizerPtr, SherpaOnnxOnlineStreamPtrPtr, int32_t ]
],
"GetOnlineStreamResult" : [
SherpaOnnxOnlineRecognizerResultPtr,
[ SherpaOnnxOnlineRecognizerPtr, SherpaOnnxOnlineStreamPtr ]
],
"DestroyOnlineRecognizerResult" :
[ "void", [ SherpaOnnxOnlineRecognizerResultPtr ] ],
"Reset" :
[ "void", [ SherpaOnnxOnlineRecognizerPtr, SherpaOnnxOnlineStreamPtr ] ],
"InputFinished" : [ "void", [ SherpaOnnxOnlineStreamPtr ] ],
"IsEndpoint" :
[ int32_t, [ SherpaOnnxOnlineRecognizerPtr, SherpaOnnxOnlineStreamPtr ] ],
// offline asr
"CreateOfflineRecognizer" : [
SherpaOnnxOfflineRecognizerPtr, [ SherpaOnnxOfflineRecognizerConfigPtr ]
],
"DestroyOfflineRecognizer" : [ "void", [ SherpaOnnxOfflineRecognizerPtr ] ],
"CreateOfflineStream" :
[ SherpaOnnxOfflineStreamPtr, [ SherpaOnnxOfflineRecognizerPtr ] ],
"DestroyOfflineStream" : [ "void", [ SherpaOnnxOfflineStreamPtr ] ],
"AcceptWaveformOffline" :
[ "void", [ SherpaOnnxOfflineStreamPtr, int32_t, floatPtr, int32_t ] ],
"DecodeOfflineStream" : [
"void", [ SherpaOnnxOfflineRecognizerPtr, SherpaOnnxOfflineStreamPtr ]
],
"DecodeMultipleOfflineStreams" : [
"void",
[ SherpaOnnxOfflineRecognizerPtr, SherpaOnnxOfflineStreamPtrPtr, int32_t ]
],
"GetOfflineStreamResult" :
[ SherpaOnnxOfflineRecognizerResultPtr, [ SherpaOnnxOfflineStreamPtr ] ],
"DestroyOfflineRecognizerResult" :
[ "void", [ SherpaOnnxOfflineRecognizerResultPtr ] ],
// vad
"SherpaOnnxCreateCircularBuffer" :
[ SherpaOnnxCircularBufferPtr, [ int32_t ] ],
"SherpaOnnxDestroyCircularBuffer" :
[ "void", [ SherpaOnnxCircularBufferPtr ] ],
"SherpaOnnxCircularBufferPush" :
[ "void", [ SherpaOnnxCircularBufferPtr, floatPtr, int32_t ] ],
"SherpaOnnxCircularBufferGet" :
[ FloatArray, [ SherpaOnnxCircularBufferPtr, int32_t, int32_t ] ],
"SherpaOnnxCircularBufferFree" : [ "void", [ FloatArray ] ],
"SherpaOnnxCircularBufferPop" :
[ "void", [ SherpaOnnxCircularBufferPtr, int32_t ] ],
"SherpaOnnxCircularBufferSize" : [ int32_t, [ SherpaOnnxCircularBufferPtr ] ],
"SherpaOnnxCircularBufferHead" : [ int32_t, [ SherpaOnnxCircularBufferPtr ] ],
"SherpaOnnxCircularBufferReset" : [ "void", [ SherpaOnnxCircularBufferPtr ] ],
"SherpaOnnxCreateVoiceActivityDetector" : [
SherpaOnnxVoiceActivityDetectorPtr, [ SherpaOnnxVadModelConfigPtr, float ]
],
"SherpaOnnxDestroyVoiceActivityDetector" :
[ "void", [ SherpaOnnxVoiceActivityDetectorPtr ] ],
"SherpaOnnxVoiceActivityDetectorAcceptWaveform" :
[ "void", [ SherpaOnnxVoiceActivityDetectorPtr, floatPtr, int32_t ] ],
"SherpaOnnxVoiceActivityDetectorEmpty" :
[ int32_t, [ SherpaOnnxVoiceActivityDetectorPtr ] ],
"SherpaOnnxVoiceActivityDetectorDetected" :
[ int32_t, [ SherpaOnnxVoiceActivityDetectorPtr ] ],
"SherpaOnnxVoiceActivityDetectorPop" :
[ "void", [ SherpaOnnxVoiceActivityDetectorPtr ] ],
"SherpaOnnxVoiceActivityDetectorClear" :
[ "void", [ SherpaOnnxVoiceActivityDetectorPtr ] ],
"SherpaOnnxVoiceActivityDetectorFront" :
[ SherpaOnnxSpeechSegmentPtr, [ SherpaOnnxVoiceActivityDetectorPtr ] ],
"SherpaOnnxDestroySpeechSegment" : [ "void", [ SherpaOnnxSpeechSegmentPtr ] ],
"SherpaOnnxVoiceActivityDetectorReset" :
[ "void", [ SherpaOnnxVoiceActivityDetectorPtr ] ],
// tts
"SherpaOnnxCreateOfflineTts" :
[ SherpaOnnxOfflineTtsPtr, [ SherpaOnnxOfflineTtsConfigPtr ] ],
"SherpaOnnxDestroyOfflineTts" : [ "void", [ SherpaOnnxOfflineTtsPtr ] ],
"SherpaOnnxOfflineTtsGenerate" : [
SherpaOnnxGeneratedAudioPtr,
[ SherpaOnnxOfflineTtsPtr, cstring, int32_t, float ]
],
"SherpaOnnxDestroyOfflineTtsGeneratedAudio" :
[ "void", [ SherpaOnnxGeneratedAudioPtr ] ],
"SherpaOnnxWriteWave" : [ "void", [ floatPtr, int32_t, int32_t, cstring ] ],
// display
"CreateDisplay" : [ SherpaOnnxDisplayPtr, [ int32_t ] ],
"DestroyDisplay" : [ "void", [ SherpaOnnxDisplayPtr ] ],
"SherpaOnnxPrint" : [ "void", [ SherpaOnnxDisplayPtr, int32_t, cstring ] ],
});
class Display {
constructor(maxWordPerLine) {
this.handle = libsherpa_onnx.CreateDisplay(maxWordPerLine);
}
free() {
if (this.handle) {
libsherpa_onnx.DestroyDisplay(this.handle);
this.handle = null;
}
}
print(idx, s) { libsherpa_onnx.SherpaOnnxPrint(this.handle, idx, s); }
};
class OnlineResult {
constructor(text) { this.text = Buffer.from(text, "utf-8").toString(); }
};
class OnlineStream {
constructor(handle) { this.handle = handle }
free() {
if (this.handle) {
libsherpa_onnx.DestroyOnlineStream(this.handle);
this.handle = null;
}
}
/**
* @param sampleRate {Number}
* @param samples {Float32Array} Containing samples in the range [-1, 1]
*/
acceptWaveform(sampleRate, samples) {
libsherpa_onnx.AcceptWaveform(this.handle, sampleRate, samples,
samples.length);
}
};
class OnlineRecognizer {
constructor(config) {
this.config = config;
this.recognizer_handle =
libsherpa_onnx.CreateOnlineRecognizer(config.ref());
}
free() {
if (this.recognizer_handle) {
libsherpa_onnx.DestroyOnlineRecognizer(this.recognizer_handle);
this.recognizer_handle = null;
}
}
createStream() {
let handle = libsherpa_onnx.CreateOnlineStream(this.recognizer_handle);
return new OnlineStream(handle);
}
isReady(stream) {
return libsherpa_onnx.IsOnlineStreamReady(this.recognizer_handle,
stream.handle)
}
isEndpoint(stream) {
return libsherpa_onnx.IsEndpoint(this.recognizer_handle, stream.handle);
}
reset(stream) { libsherpa_onnx.Reset(this.recognizer_handle, stream.handle); }
decode(stream) {
libsherpa_onnx.DecodeOnlineStream(this.recognizer_handle, stream.handle)
}
getResult(stream) {
let handle = libsherpa_onnx.GetOnlineStreamResult(this.recognizer_handle,
stream.handle);
let r = handle.deref();
let ans = new OnlineResult(r.text);
libsherpa_onnx.DestroyOnlineRecognizerResult(handle);
return ans
}
};
class OfflineResult {
constructor(text) { this.text = Buffer.from(text, "utf-8").toString(); }
};
class OfflineStream {
constructor(handle) { this.handle = handle }
free() {
if (this.handle) {
libsherpa_onnx.DestroyOfflineStream(this.handle);
this.handle = null;
}
}
/**
* @param sampleRate {Number}
* @param samples {Float32Array} Containing samples in the range [-1, 1]
*/
acceptWaveform(sampleRate, samples) {
libsherpa_onnx.AcceptWaveformOffline(this.handle, sampleRate, samples,
samples.length);
}
};
class OfflineRecognizer {
constructor(config) {
this.config = config;
this.recognizer_handle =
libsherpa_onnx.CreateOfflineRecognizer(config.ref());
}
free() {
if (this.recognizer_handle) {
libsherpa_onnx.DestroyOfflineRecognizer(this.recognizer_handle);
this.recognizer_handle = null;
}
}
createStream() {
let handle = libsherpa_onnx.CreateOfflineStream(this.recognizer_handle);
return new OfflineStream(handle);
}
decode(stream) {
libsherpa_onnx.DecodeOfflineStream(this.recognizer_handle, stream.handle)
}
getResult(stream) {
let handle = libsherpa_onnx.GetOfflineStreamResult(stream.handle);
let r = handle.deref();
let ans = new OfflineResult(r.text);
libsherpa_onnx.DestroyOfflineRecognizerResult(handle);
return ans
}
};
class SpeechSegment {
constructor(start, samples) {
this.start = start;
this.samples = samples;
}
};
// this buffer holds only float entries.
class CircularBuffer {
/**
* @param capacity {int} The capacity of the circular buffer.
*/
constructor(capacity) {
this.handle = libsherpa_onnx.SherpaOnnxCreateCircularBuffer(capacity);
}
free() {
if (this.handle) {
libsherpa_onnx.SherpaOnnxDestroyCircularBuffer(this.handle);
this.handle = null;
}
}
/**
* @param samples {Float32Array}
*/
push(samples) {
libsherpa_onnx.SherpaOnnxCircularBufferPush(this.handle, samples,
samples.length);
}
get(startIndex, n) {
let data =
libsherpa_onnx.SherpaOnnxCircularBufferGet(this.handle, startIndex, n);
// https://tootallnate.github.io/ref/#exports-reinterpret
const buffer = data.buffer.reinterpret(n * ref.sizeof.float).buffer;
// create a copy since we are going to free the buffer at the end
let s = new Float32Array(buffer).slice(0);
libsherpa_onnx.SherpaOnnxCircularBufferFree(data);
return s;
}
pop(n) { libsherpa_onnx.SherpaOnnxCircularBufferPop(this.handle, n); }
size() { return libsherpa_onnx.SherpaOnnxCircularBufferSize(this.handle); }
head() { return libsherpa_onnx.SherpaOnnxCircularBufferHead(this.handle); }
reset() { libsherpa_onnx.SherpaOnnxCircularBufferReset(this.handle); }
};
class VoiceActivityDetector {
constructor(config, bufferSizeInSeconds) {
this.config = config;
this.handle = libsherpa_onnx.SherpaOnnxCreateVoiceActivityDetector(
config.ref(), bufferSizeInSeconds);
}
free() {
if (this.handle) {
libsherpa_onnx.SherpaOnnxDestroyVoiceActivityDetector(this.handle);
}
}
acceptWaveform(samples) {
libsherpa_onnx.SherpaOnnxVoiceActivityDetectorAcceptWaveform(
this.handle, samples, samples.length);
}
isEmpty() {
return libsherpa_onnx.SherpaOnnxVoiceActivityDetectorEmpty(this.handle);
}
isDetected() {
return libsherpa_onnx.SherpaOnnxVoiceActivityDetectorDetected(this.handle);
}
pop() { libsherpa_onnx.SherpaOnnxVoiceActivityDetectorPop(this.handle); }
clear() { libsherpa_onnx.SherpaOnnxVoiceActivityDetectorClear(this.handle); }
reset() { libsherpa_onnx.SherpaOnnxVoiceActivityDetectorReset(this.handle); }
front() {
let segment =
libsherpa_onnx.SherpaOnnxVoiceActivityDetectorFront(this.handle);
let buffer =
segment.deref()
.samples.buffer.reinterpret(segment.deref().n * ref.sizeof.float)
.buffer;
let samples = new Float32Array(buffer).slice(0);
let ans = new SpeechSegment(segment.deref().start, samples);
libsherpa_onnx.SherpaOnnxDestroySpeechSegment(segment);
return ans;
}
};
class GeneratedAudio {
constructor(sampleRate, samples) {
this.sampleRate = sampleRate;
this.samples = samples;
}
save(filename) {
libsherpa_onnx.SherpaOnnxWriteWave(this.samples, this.samples.length,
this.sampleRate, filename);
}
};
class OfflineTts {
constructor(config) {
this.config = config;
this.handle = libsherpa_onnx.SherpaOnnxCreateOfflineTts(config.ref());
}
free() {
if (this.handle) {
libsherpa_onnx.SherpaOnnxDestroyOfflineTts(this.handle);
this.handle = null;
}
}
generate(text, sid, speed) {
let r = libsherpa_onnx.SherpaOnnxOfflineTtsGenerate(this.handle, text, sid,
speed);
const buffer =
r.deref()
.samples.buffer.reinterpret(r.deref().n * ref.sizeof.float)
.buffer;
let samples = new Float32Array(buffer).slice(0);
let sampleRate = r.deref().sampleRate;
let generatedAudio = new GeneratedAudio(sampleRate, samples);
libsherpa_onnx.SherpaOnnxDestroyOfflineTtsGeneratedAudio(r);
return generatedAudio;
}
};
// online asr
const OnlineTransducerModelConfig = SherpaOnnxOnlineTransducerModelConfig;
const OnlineModelConfig = SherpaOnnxOnlineModelConfig;
const FeatureConfig = SherpaOnnxFeatureConfig;
const OnlineRecognizerConfig = SherpaOnnxOnlineRecognizerConfig;
const OnlineParaformerModelConfig = SherpaOnnxOnlineParaformerModelConfig;
const OnlineZipformer2CtcModelConfig = SherpaOnnxOnlineZipformer2CtcModelConfig;
// offline asr
const OfflineTransducerModelConfig = SherpaOnnxOfflineTransducerModelConfig;
const OfflineModelConfig = SherpaOnnxOfflineModelConfig;
const OfflineRecognizerConfig = SherpaOnnxOfflineRecognizerConfig;
const OfflineParaformerModelConfig = SherpaOnnxOfflineParaformerModelConfig;
const OfflineWhisperModelConfig = SherpaOnnxOfflineWhisperModelConfig;
const OfflineNemoEncDecCtcModelConfig =
SherpaOnnxOfflineNemoEncDecCtcModelConfig;
const OfflineTdnnModelConfig = SherpaOnnxOfflineTdnnModelConfig;
// vad
const SileroVadModelConfig = SherpaOnnxSileroVadModelConfig;
const VadModelConfig = SherpaOnnxVadModelConfig;
// tts
const OfflineTtsVitsModelConfig = SherpaOnnxOfflineTtsVitsModelConfig;
const OfflineTtsModelConfig = SherpaOnnxOfflineTtsModelConfig;
const OfflineTtsConfig = SherpaOnnxOfflineTtsConfig;
function createOfflineTts(config) {
return sherpa_onnx_tts.createOfflineTts(wasmModule, config);
}
// Note: online means streaming and offline means non-streaming here.
// Both of them don't require internet connection.
module.exports = {
// online asr
OnlineTransducerModelConfig,
OnlineModelConfig,
FeatureConfig,
OnlineRecognizerConfig,
OnlineRecognizer,
OnlineStream,
OnlineParaformerModelConfig,
OnlineZipformer2CtcModelConfig,
// offline asr
OfflineRecognizer,
OfflineStream,
OfflineTransducerModelConfig,
OfflineModelConfig,
OfflineRecognizerConfig,
OfflineParaformerModelConfig,
OfflineWhisperModelConfig,
OfflineNemoEncDecCtcModelConfig,
OfflineTdnnModelConfig,
// vad
SileroVadModelConfig,
VadModelConfig,
CircularBuffer,
VoiceActivityDetector,
// tts
OfflineTtsVitsModelConfig,
OfflineTtsModelConfig,
OfflineTtsConfig,
OfflineTts,
//
Display,
createOnlineRecognizer,
createOfflineRecognizer,
createOfflineTts,
};
... ...
{
"name": "sherpa-onnx2",
"version": "1.8.10",
"description": "Real-time speech recognition with Next-gen Kaldi",
"name": "sherpa-onnx",
"version": "SHERPA_ONNX_VERSION",
"description": "Speech-to-text and text-to-speech using Next-gen Kaldi without internet connection",
"main": "index.js",
"scripts": {
"test": "echo \"Error: no test specified\" && exit 1"
... ... @@ -11,15 +11,30 @@
"url": "git+https://github.com/k2-fsa/sherpa-onnx.git"
},
"keywords": [
"speech-to-text",
"text-to-speech",
"speech to text",
"text to speech",
"transcription",
"real-time speech recognition",
"without internet connect",
"without internet connection",
"embedded systems",
"open source",
"zipformer",
"asr",
"speech"
"tts",
"stt",
"c++",
"onnxruntime",
"onnx",
"ai",
"next-gen kaldi",
"offline",
"privacy",
"open source",
"streaming speech recognition",
"speech",
"recognition",
"WebAssembly",
"wasm"
],
"author": "The next-gen Kaldi team",
"license": "Apache-2.0",
... ... @@ -28,10 +43,5 @@
},
"homepage": "https://github.com/k2-fsa/sherpa-onnx#readme",
"dependencies": {
"ffi-napi": "^4.0.3",
"npm": "^6.14.18",
"ref-array-napi": "^1.2.2",
"ref-napi": "^3.0.3",
"ref-struct-napi": "^1.1.1"
}
}
... ...
{
"name": "sherpa-onnx",
"version": "SHERPA_ONNX_VERSION",
"description": "Real-time speech recognition with Next-gen Kaldi",
"main": "index.js",
"scripts": {
"test": "echo \"Error: no test specified\" && exit 1"
},
"repository": {
"type": "git",
"url": "git+https://github.com/k2-fsa/sherpa-onnx.git"
},
"keywords": [
"speech to text",
"text to speech",
"transcription",
"real-time speech recognition",
"without internet connect",
"embedded systems",
"open source",
"zipformer",
"asr",
"tts",
"stt",
"c++",
"onnxruntime",
"onnx",
"ai",
"next-gen kaldi",
"offline",
"privacy",
"open source",
"streaming speech recognition",
"speech",
"recognition"
],
"author": "The next-gen Kaldi team",
"license": "Apache-2.0",
"bugs": {
"url": "https://github.com/k2-fsa/sherpa-onnx/issues"
},
"homepage": "https://github.com/k2-fsa/sherpa-onnx#readme",
"dependencies": {
"ffi-napi": "^4.0.3",
"npm": "^6.14.18",
"ref-array-napi": "^1.2.2",
"ref-napi": "^3.0.3",
"ref-struct-napi": "^1.1.1"
}
}
#!/usr/bin/env bash
set -ex
SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
SHERPA_ONNX_DIR=$(realpath $SCRIPT_DIR/../..)
echo "SCRIPT_DIR: $SCRIPT_DIR"
echo "SHERPA_ONNX_DIR: $SHERPA_ONNX_DIR"
SHERPA_ONNX_VERSION=$(grep "SHERPA_ONNX_VERSION" $SHERPA_ONNX_DIR/CMakeLists.txt | cut -d " " -f 2 | cut -d '"' -f 2)
echo "SHERPA_ONNX_VERSION $SHERPA_ONNX_VERSION"
sed -i.bak s/SHERPA_ONNX_VERSION/$SHERPA_ONNX_VERSION/g ./package.json.in
cp package.json.in package.json
rm package.json.in
rm package.json.in.bak
rm .clang-format
function windows_x64() {
echo "Process Windows (x64)"
mkdir -p lib/win-x64
dst=$(realpath lib/win-x64)
mkdir t
cd t
wget -q https://huggingface.co/csukuangfj/sherpa-onnx-wheels/resolve/main/sherpa_onnx-${SHERPA_ONNX_VERSION}-cp38-cp38-win_amd64.whl
unzip ./sherpa_onnx-${SHERPA_ONNX_VERSION}-cp38-cp38-win_amd64.whl
cp -v sherpa_onnx-${SHERPA_ONNX_VERSION}.data/data/bin/*.dll $dst
cp -v sherpa_onnx-${SHERPA_ONNX_VERSION}.data/data/bin/*.lib $dst
rm -fv $dst/sherpa-onnx-portaudio.dll
cd ..
rm -rf t
}
function windows_x86() {
echo "Process Windows (x86)"
mkdir -p lib/win-x86
dst=$(realpath lib/win-x86)
mkdir t
cd t
wget -q https://huggingface.co/csukuangfj/sherpa-onnx-wheels/resolve/main/sherpa_onnx-${SHERPA_ONNX_VERSION}-cp38-cp38-win32.whl
unzip ./sherpa_onnx-${SHERPA_ONNX_VERSION}-cp38-cp38-win32.whl
cp -v sherpa_onnx-${SHERPA_ONNX_VERSION}.data/data/bin/*.dll $dst
cp -v sherpa_onnx-${SHERPA_ONNX_VERSION}.data/data/bin/*.lib $dst
rm -fv $dst/sherpa-onnx-portaudio.dll
cd ..
rm -rf t
}
function linux_x64() {
echo "Process Linux (x64)"
mkdir -p lib/linux-x64
dst=$(realpath lib/linux-x64)
mkdir t
cd t
wget -q https://huggingface.co/csukuangfj/sherpa-onnx-wheels/resolve/main/sherpa_onnx-${SHERPA_ONNX_VERSION}-cp38-cp38-manylinux_2_28_x86_64.whl
unzip ./sherpa_onnx-${SHERPA_ONNX_VERSION}-cp38-cp38-manylinux_2_28_x86_64.whl
cp -v sherpa_onnx/lib/*.so* $dst
rm -v $dst/libcargs.so
rm -v $dst/libsherpa-onnx-portaudio.so
rm -v $dst/libsherpa-onnx-fst.so
rm -v $dst/libonnxruntime.so
cd ..
rm -rf t
}
function osx_x64() {
echo "Process osx-x64"
mkdir -p lib/osx-x64
dst=$(realpath lib/osx-x64)
mkdir t
cd t
wget -q https://huggingface.co/csukuangfj/sherpa-onnx-wheels/resolve/main/sherpa_onnx-${SHERPA_ONNX_VERSION}-cp38-cp38-macosx_11_0_x86_64.whl
unzip ./sherpa_onnx-${SHERPA_ONNX_VERSION}-cp38-cp38-macosx_11_0_x86_64.whl
cp -v sherpa_onnx/lib/*.dylib $dst/
rm -v $dst/libonnxruntime.dylib
rm -v $dst/libcargs.dylib
rm -v $dst/libsherpa-onnx-fst.dylib
rm -v $dst/libsherpa-onnx-portaudio.dylib
cd ..
rm -rf t
}
function osx_arm64() {
echo "Process osx-arm64"
mkdir -p lib/osx-arm64
dst=$(realpath lib/osx-arm64)
mkdir t
cd t
wget -q https://huggingface.co/csukuangfj/sherpa-onnx-wheels/resolve/main/sherpa_onnx-${SHERPA_ONNX_VERSION}-cp38-cp38-macosx_11_0_arm64.whl
unzip ./sherpa_onnx-${SHERPA_ONNX_VERSION}-cp38-cp38-macosx_11_0_arm64.whl
cp -v sherpa_onnx/lib/*.dylib $dst/
rm -v $dst/libonnxruntime.dylib
rm -v $dst/libcargs.dylib
rm -v $dst/libsherpa-onnx-fst.dylib
rm -v $dst/libsherpa-onnx-portaudio.dylib
cd ..
rm -rf t
}
windows_x64
ls -lh lib/win-x64
windows_x86
ls -lh lib/win-x86
linux_x64
ls -lh lib/linux-x64
osx_x64
ls -lh lib/osx-x64
osx_arm64
ls -lh lib/osx-arm64
... ... @@ -94,6 +94,11 @@ SherpaOnnxOnlineRecognizer *CreateOnlineRecognizer(
SHERPA_ONNX_LOGE("%s\n", recognizer_config.ToString().c_str());
}
if (!recognizer_config.Validate()) {
SHERPA_ONNX_LOGE("Errors in config!");
return nullptr;
}
SherpaOnnxOnlineRecognizer *recognizer = new SherpaOnnxOnlineRecognizer;
recognizer->impl =
... ... @@ -324,6 +329,11 @@ SherpaOnnxOfflineRecognizer *CreateOfflineRecognizer(
SHERPA_ONNX_LOGE("%s", recognizer_config.ToString().c_str());
}
if (!recognizer_config.Validate()) {
SHERPA_ONNX_LOGE("Errors in config");
return nullptr;
}
SherpaOnnxOfflineRecognizer *recognizer = new SherpaOnnxOfflineRecognizer;
recognizer->impl =
... ... @@ -480,6 +490,11 @@ SherpaOnnxVoiceActivityDetector *SherpaOnnxCreateVoiceActivityDetector(
SHERPA_ONNX_LOGE("%s", vad_config.ToString().c_str());
}
if (!vad_config.Validate()) {
SHERPA_ONNX_LOGE("Errors in config");
return nullptr;
}
SherpaOnnxVoiceActivityDetector *p = new SherpaOnnxVoiceActivityDetector;
p->impl = std::make_unique<sherpa_onnx::VoiceActivityDetector>(
vad_config, buffer_size_in_seconds);
... ... @@ -570,6 +585,11 @@ SherpaOnnxOfflineTts *SherpaOnnxCreateOfflineTts(
SHERPA_ONNX_LOGE("%s\n", tts_config.ToString().c_str());
}
if (!tts_config.Validate()) {
SHERPA_ONNX_LOGE("Errors in config");
return nullptr;
}
SherpaOnnxOfflineTts *tts = new SherpaOnnxOfflineTts;
tts->impl = std::make_unique<sherpa_onnx::OfflineTts>(tts_config);
... ...
... ... @@ -5,3 +5,7 @@ endif()
if(SHERPA_ONNX_ENABLE_WASM_ASR)
add_subdirectory(asr)
endif()
if(SHERPA_ONNX_ENABLE_WASM_NODEJS)
add_subdirectory(nodejs)
endif()
... ...
... ... @@ -45,7 +45,7 @@ Module.onRuntimeInitialized = function() {
startBtn.disabled = false;
recognizer = createRecognizer();
recognizer = createOnlineRecognizer(Module);
console.log('recognizer is created!', recognizer);
};
... ...
function freeConfig(config) {
function freeConfig(config, Module) {
if ('buffer' in config) {
_free(config.buffer);
Module._free(config.buffer);
}
if ('config' in config) {
freeConfig(config.config)
freeConfig(config.config, Module)
}
if ('transducer' in config) {
freeConfig(config.transducer)
freeConfig(config.transducer, Module)
}
if ('paraformer' in config) {
freeConfig(config.paraformer)
freeConfig(config.paraformer, Module)
}
if ('ctc' in config) {
freeConfig(config.ctc)
freeConfig(config.ctc, Module)
}
if ('feat' in config) {
freeConfig(config.feat)
freeConfig(config.feat, Module)
}
if ('model' in config) {
freeConfig(config.model)
freeConfig(config.model, Module)
}
_free(config.ptr);
if ('nemoCtc' in config) {
freeConfig(config.nemoCtc, Module)
}
if ('whisper' in config) {
freeConfig(config.whisper, Module)
}
if ('tdnn' in config) {
freeConfig(config.tdnn, Module)
}
if ('lm' in config) {
freeConfig(config.lm, Module)
}
Module._free(config.ptr);
}
// The user should free the returned pointers
function initSherpaOnnxOnlineTransducerModelConfig(config) {
let encoderLen = lengthBytesUTF8(config.encoder) + 1;
let decoderLen = lengthBytesUTF8(config.decoder) + 1;
let joinerLen = lengthBytesUTF8(config.joiner) + 1;
function initSherpaOnnxOnlineTransducerModelConfig(config, Module) {
const encoderLen = Module.lengthBytesUTF8(config.encoder) + 1;
const decoderLen = Module.lengthBytesUTF8(config.decoder) + 1;
const joinerLen = Module.lengthBytesUTF8(config.joiner) + 1;
let n = encoderLen + decoderLen + joinerLen;
const n = encoderLen + decoderLen + joinerLen;
let buffer = _malloc(n);
const buffer = Module._malloc(n);
let len = 3 * 4; // 3 pointers
let ptr = _malloc(len);
const len = 3 * 4; // 3 pointers
const ptr = Module._malloc(len);
let offset = 0;
stringToUTF8(config.encoder, buffer + offset, encoderLen);
Module.stringToUTF8(config.encoder, buffer + offset, encoderLen);
offset += encoderLen;
stringToUTF8(config.decoder, buffer + offset, decoderLen);
Module.stringToUTF8(config.decoder, buffer + offset, decoderLen);
offset += decoderLen;
stringToUTF8(config.joiner, buffer + offset, joinerLen);
Module.stringToUTF8(config.joiner, buffer + offset, joinerLen);
offset = 0;
setValue(ptr, buffer + offset, 'i8*');
Module.setValue(ptr, buffer + offset, 'i8*');
offset += encoderLen;
setValue(ptr + 4, buffer + offset, 'i8*');
Module.setValue(ptr + 4, buffer + offset, 'i8*');
offset += decoderLen;
setValue(ptr + 8, buffer + offset, 'i8*');
Module.setValue(ptr + 8, buffer + offset, 'i8*');
return {
buffer: buffer, ptr: ptr, len: len,
}
}
function initSherpaOnnxOnlineParaformerModelConfig(config) {
let encoderLen = lengthBytesUTF8(config.encoder) + 1;
let decoderLen = lengthBytesUTF8(config.decoder) + 1;
function initSherpaOnnxOnlineParaformerModelConfig(config, Module) {
const encoderLen = Module.lengthBytesUTF8(config.encoder) + 1;
const decoderLen = Module.lengthBytesUTF8(config.decoder) + 1;
let n = encoderLen + decoderLen;
let buffer = _malloc(n);
const n = encoderLen + decoderLen;
const buffer = Module._malloc(n);
let len = 2 * 4; // 2 pointers
let ptr = _malloc(len);
const len = 2 * 4; // 2 pointers
const ptr = Module._malloc(len);
let offset = 0;
stringToUTF8(config.encoder, buffer + offset, encoderLen);
Module.stringToUTF8(config.encoder, buffer + offset, encoderLen);
offset += encoderLen;
stringToUTF8(config.decoder, buffer + offset, decoderLen);
Module.stringToUTF8(config.decoder, buffer + offset, decoderLen);
offset = 0;
setValue(ptr, buffer + offset, 'i8*');
Module.setValue(ptr, buffer + offset, 'i8*');
offset += encoderLen;
setValue(ptr + 4, buffer + offset, 'i8*');
Module.setValue(ptr + 4, buffer + offset, 'i8*');
return {
buffer: buffer, ptr: ptr, len: len,
}
}
function initSherpaOnnxOnlineZipformer2CtcModelConfig(config) {
let n = lengthBytesUTF8(config.model) + 1;
let buffer = _malloc(n);
function initSherpaOnnxOnlineZipformer2CtcModelConfig(config, Module) {
const n = Module.lengthBytesUTF8(config.model) + 1;
const buffer = Module._malloc(n);
let len = 1 * 4; // 1 pointer
let ptr = _malloc(len);
const len = 1 * 4; // 1 pointer
const ptr = Module._malloc(len);
stringToUTF8(config.model, buffer, n);
Module.stringToUTF8(config.model, buffer, n);
setValue(ptr, buffer, 'i8*');
Module.setValue(ptr, buffer, 'i8*');
return {
buffer: buffer, ptr: ptr, len: len,
}
}
function initSherpaOnnxOnlineModelConfig(config) {
let transducer = initSherpaOnnxOnlineTransducerModelConfig(config.transducer);
let paraformer = initSherpaOnnxOnlineParaformerModelConfig(config.paraformer);
let ctc = initSherpaOnnxOnlineZipformer2CtcModelConfig(config.zipformer2Ctc);
function initSherpaOnnxOnlineModelConfig(config, Module) {
const transducer =
initSherpaOnnxOnlineTransducerModelConfig(config.transducer, Module);
const paraformer =
initSherpaOnnxOnlineParaformerModelConfig(config.paraformer, Module);
const ctc = initSherpaOnnxOnlineZipformer2CtcModelConfig(
config.zipformer2Ctc, Module);
let len = transducer.len + paraformer.len + ctc.len + 5 * 4;
let ptr = _malloc(len);
const len = transducer.len + paraformer.len + ctc.len + 5 * 4;
const ptr = Module._malloc(len);
let offset = 0;
_CopyHeap(transducer.ptr, transducer.len, ptr + offset);
Module._CopyHeap(transducer.ptr, transducer.len, ptr + offset);
offset += transducer.len;
_CopyHeap(paraformer.ptr, paraformer.len, ptr + offset);
Module._CopyHeap(paraformer.ptr, paraformer.len, ptr + offset);
offset += paraformer.len;
_CopyHeap(ctc.ptr, ctc.len, ptr + offset);
Module._CopyHeap(ctc.ptr, ctc.len, ptr + offset);
offset += ctc.len;
let tokensLen = lengthBytesUTF8(config.tokens) + 1;
let providerLen = lengthBytesUTF8(config.provider) + 1;
let modelTypeLen = lengthBytesUTF8(config.modelType) + 1;
let bufferLen = tokensLen + providerLen + modelTypeLen;
let buffer = _malloc(bufferLen);
const tokensLen = Module.lengthBytesUTF8(config.tokens) + 1;
const providerLen = Module.lengthBytesUTF8(config.provider) + 1;
const modelTypeLen = Module.lengthBytesUTF8(config.modelType) + 1;
const bufferLen = tokensLen + providerLen + modelTypeLen;
const buffer = Module._malloc(bufferLen);
offset = 0;
stringToUTF8(config.tokens, buffer, tokensLen);
Module.stringToUTF8(config.tokens, buffer, tokensLen);
offset += tokensLen;
stringToUTF8(config.provider, buffer + offset, providerLen);
Module.stringToUTF8(config.provider, buffer + offset, providerLen);
offset += providerLen;
stringToUTF8(config.modelType, buffer + offset, modelTypeLen);
Module.stringToUTF8(config.modelType, buffer + offset, modelTypeLen);
offset = transducer.len + paraformer.len + ctc.len;
setValue(ptr + offset, buffer, 'i8*'); // tokens
Module.setValue(ptr + offset, buffer, 'i8*'); // tokens
offset += 4;
setValue(ptr + offset, config.numThreads, 'i32');
Module.setValue(ptr + offset, config.numThreads, 'i32');
offset += 4;
setValue(ptr + offset, buffer + tokensLen, 'i8*'); // provider
Module.setValue(ptr + offset, buffer + tokensLen, 'i8*'); // provider
offset += 4;
setValue(ptr + offset, config.debug, 'i32');
Module.setValue(ptr + offset, config.debug, 'i32');
offset += 4;
setValue(ptr + offset, buffer + tokensLen + providerLen, 'i8*'); // modelType
Module.setValue(
ptr + offset, buffer + tokensLen + providerLen, 'i8*'); // modelType
offset += 4;
return {
... ... @@ -164,63 +184,63 @@ function initSherpaOnnxOnlineModelConfig(config) {
}
}
function initSherpaOnnxFeatureConfig(config) {
let len = 2 * 4; // 2 pointers
let ptr = _malloc(len);
function initSherpaOnnxFeatureConfig(config, Module) {
const len = 2 * 4; // 2 pointers
const ptr = Module._malloc(len);
setValue(ptr, config.sampleRate, 'i32');
setValue(ptr + 4, config.featureDim, 'i32');
Module.setValue(ptr, config.sampleRate, 'i32');
Module.setValue(ptr + 4, config.featureDim, 'i32');
return {ptr: ptr, len: len};
}
function initSherpaOnnxOnlineRecognizerConfig(config) {
let feat = initSherpaOnnxFeatureConfig(config.featConfig);
let model = initSherpaOnnxOnlineModelConfig(config.modelConfig);
function initSherpaOnnxOnlineRecognizerConfig(config, Module) {
const feat = initSherpaOnnxFeatureConfig(config.featConfig, Module);
const model = initSherpaOnnxOnlineModelConfig(config.modelConfig, Module);
let len = feat.len + model.len + 8 * 4;
let ptr = _malloc(len);
const len = feat.len + model.len + 8 * 4;
const ptr = Module._malloc(len);
let offset = 0;
_CopyHeap(feat.ptr, feat.len, ptr + offset);
Module._CopyHeap(feat.ptr, feat.len, ptr + offset);
offset += feat.len;
_CopyHeap(model.ptr, model.len, ptr + offset);
Module._CopyHeap(model.ptr, model.len, ptr + offset);
offset += model.len;
let decodingMethodLen = lengthBytesUTF8(config.decodingMethod) + 1;
let hotwordsFileLen = lengthBytesUTF8(config.hotwordsFile) + 1;
let bufferLen = decodingMethodLen + hotwordsFileLen;
let buffer = _malloc(bufferLen);
const decodingMethodLen = Module.lengthBytesUTF8(config.decodingMethod) + 1;
const hotwordsFileLen = Module.lengthBytesUTF8(config.hotwordsFile) + 1;
const bufferLen = decodingMethodLen + hotwordsFileLen;
const buffer = Module._malloc(bufferLen);
offset = 0;
stringToUTF8(config.decodingMethod, buffer, decodingMethodLen);
Module.stringToUTF8(config.decodingMethod, buffer, decodingMethodLen);
offset += decodingMethodLen;
stringToUTF8(config.hotwordsFile, buffer + offset, hotwordsFileLen);
Module.stringToUTF8(config.hotwordsFile, buffer + offset, hotwordsFileLen);
offset = feat.len + model.len;
setValue(ptr + offset, buffer, 'i8*'); // decoding method
Module.setValue(ptr + offset, buffer, 'i8*'); // decoding method
offset += 4;
setValue(ptr + offset, config.maxActivePaths, 'i32');
Module.setValue(ptr + offset, config.maxActivePaths, 'i32');
offset += 4;
setValue(ptr + offset, config.enableEndpoint, 'i32');
Module.setValue(ptr + offset, config.enableEndpoint, 'i32');
offset += 4;
setValue(ptr + offset, config.rule1MinTrailingSilence, 'float');
Module.setValue(ptr + offset, config.rule1MinTrailingSilence, 'float');
offset += 4;
setValue(ptr + offset, config.rule2MinTrailingSilence, 'float');
Module.setValue(ptr + offset, config.rule2MinTrailingSilence, 'float');
offset += 4;
setValue(ptr + offset, config.rule3MinUtteranceLength, 'float');
Module.setValue(ptr + offset, config.rule3MinUtteranceLength, 'float');
offset += 4;
setValue(ptr + offset, buffer + decodingMethodLen, 'i8*');
Module.setValue(ptr + offset, buffer + decodingMethodLen, 'i8*');
offset += 4;
setValue(ptr + offset, config.hotwordsScore, 'float');
Module.setValue(ptr + offset, config.hotwordsScore, 'float');
offset += 4;
return {
... ... @@ -229,21 +249,21 @@ function initSherpaOnnxOnlineRecognizerConfig(config) {
}
function createRecognizer() {
let onlineTransducerModelConfig = {
function createOnlineRecognizer(Module, myConfig) {
const onlineTransducerModelConfig = {
encoder: '',
decoder: '',
joiner: '',
}
};
let onlineParaformerModelConfig = {
const onlineParaformerModelConfig = {
encoder: '',
decoder: '',
}
};
let onlineZipformer2CtcModelConfig = {
const onlineZipformer2CtcModelConfig = {
model: '',
}
};
let type = 0;
... ... @@ -266,7 +286,7 @@ function createRecognizer() {
}
let onlineModelConfig = {
const onlineModelConfig = {
transducer: onlineTransducerModelConfig,
paraformer: onlineParaformerModelConfig,
zipformer2Ctc: onlineZipformer2CtcModelConfig,
... ... @@ -275,12 +295,12 @@ function createRecognizer() {
provider: 'cpu',
debug: 1,
modelType: '',
}
};
let featureConfig = {
const featureConfig = {
sampleRate: 16000,
featureDim: 80,
}
};
let recognizerConfig = {
featConfig: featureConfig,
... ... @@ -293,23 +313,336 @@ function createRecognizer() {
rule3MinUtteranceLength: 20,
hotwordsFile: '',
hotwordsScore: 1.5,
};
if (myConfig) {
recognizerConfig = myConfig;
}
return new OnlineRecognizer(recognizerConfig, Module);
}
function initSherpaOnnxOfflineTransducerModelConfig(config, Module) {
const encoderLen = Module.lengthBytesUTF8(config.encoder) + 1;
const decoderLen = Module.lengthBytesUTF8(config.decoder) + 1;
const joinerLen = Module.lengthBytesUTF8(config.joiner) + 1;
const n = encoderLen + decoderLen + joinerLen;
const buffer = Module._malloc(n);
const len = 3 * 4; // 3 pointers
const ptr = Module._malloc(len);
let offset = 0;
Module.stringToUTF8(config.encoder, buffer + offset, encoderLen);
offset += encoderLen;
Module.stringToUTF8(config.decoder, buffer + offset, decoderLen);
offset += decoderLen;
Module.stringToUTF8(config.joiner, buffer + offset, joinerLen);
offset = 0;
Module.setValue(ptr, buffer + offset, 'i8*');
offset += encoderLen;
Module.setValue(ptr + 4, buffer + offset, 'i8*');
offset += decoderLen;
Module.setValue(ptr + 8, buffer + offset, 'i8*');
return {
buffer: buffer, ptr: ptr, len: len,
}
}
function initSherpaOnnxOfflineParaformerModelConfig(config, Module) {
const n = Module.lengthBytesUTF8(config.model) + 1;
const buffer = Module._malloc(n);
const len = 1 * 4; // 1 pointer
const ptr = Module._malloc(len);
Module.stringToUTF8(config.model, buffer, n);
Module.setValue(ptr, buffer, 'i8*');
return {
buffer: buffer, ptr: ptr, len: len,
}
}
function initSherpaOnnxOfflineNemoEncDecCtcModelConfig(config, Module) {
const n = Module.lengthBytesUTF8(config.model) + 1;
const buffer = Module._malloc(n);
const len = 1 * 4; // 1 pointer
const ptr = Module._malloc(len);
Module.stringToUTF8(config.model, buffer, n);
Module.setValue(ptr, buffer, 'i8*');
return {
buffer: buffer, ptr: ptr, len: len,
}
}
function initSherpaOnnxOfflineWhisperModelConfig(config, Module) {
const encoderLen = Module.lengthBytesUTF8(config.encoder) + 1;
const decoderLen = Module.lengthBytesUTF8(config.decoder) + 1;
const n = encoderLen + decoderLen;
const buffer = Module._malloc(n);
const len = 2 * 4; // 2 pointers
const ptr = Module._malloc(len);
let offset = 0;
Module.stringToUTF8(config.encoder, buffer + offset, encoderLen);
offset += encoderLen;
Module.stringToUTF8(config.decoder, buffer + offset, decoderLen);
offset = 0;
Module.setValue(ptr, buffer + offset, 'i8*');
offset += encoderLen;
Module.setValue(ptr + 4, buffer + offset, 'i8*');
return {
buffer: buffer, ptr: ptr, len: len,
}
}
function initSherpaOnnxOfflineTdnnModelConfig(config, Module) {
const n = Module.lengthBytesUTF8(config.model) + 1;
const buffer = Module._malloc(n);
const len = 1 * 4; // 1 pointer
const ptr = Module._malloc(len);
Module.stringToUTF8(config.model, buffer, n);
Module.setValue(ptr, buffer, 'i8*');
return {
buffer: buffer, ptr: ptr, len: len,
}
}
function initSherpaOnnxOfflineLMConfig(config, Module) {
const n = Module.lengthBytesUTF8(config.model) + 1;
const buffer = Module._malloc(n);
const len = 2 * 4;
const ptr = Module._malloc(len);
Module.stringToUTF8(config.model, buffer, n);
Module.setValue(ptr, buffer, 'i8*');
Module.setValue(ptr + 4, config.scale, 'float');
return {
buffer: buffer, ptr: ptr, len: len,
}
}
function initSherpaOnnxOfflineModelConfig(config, Module) {
const transducer =
initSherpaOnnxOfflineTransducerModelConfig(config.transducer, Module);
const paraformer =
initSherpaOnnxOfflineParaformerModelConfig(config.paraformer, Module);
const nemoCtc =
initSherpaOnnxOfflineNemoEncDecCtcModelConfig(config.nemoCtc, Module);
const whisper =
initSherpaOnnxOfflineWhisperModelConfig(config.whisper, Module);
const tdnn = initSherpaOnnxOfflineTdnnModelConfig(config.tdnn, Module);
const len = transducer.len + paraformer.len + nemoCtc.len + whisper.len +
tdnn.len + 5 * 4;
const ptr = Module._malloc(len);
let offset = 0;
Module._CopyHeap(transducer.ptr, transducer.len, ptr + offset);
offset += transducer.len;
Module._CopyHeap(paraformer.ptr, paraformer.len, ptr + offset);
offset += paraformer.len;
Module._CopyHeap(nemoCtc.ptr, nemoCtc.len, ptr + offset);
offset += nemoCtc.len;
Module._CopyHeap(whisper.ptr, whisper.len, ptr + offset);
offset += whisper.len;
Module._CopyHeap(tdnn.ptr, tdnn.len, ptr + offset);
offset += tdnn.len;
const tokensLen = Module.lengthBytesUTF8(config.tokens) + 1;
const providerLen = Module.lengthBytesUTF8(config.provider) + 1;
const modelTypeLen = Module.lengthBytesUTF8(config.modelType) + 1;
const bufferLen = tokensLen + providerLen + modelTypeLen;
const buffer = Module._malloc(bufferLen);
offset = 0;
Module.stringToUTF8(config.tokens, buffer, tokensLen);
offset += tokensLen;
Module.stringToUTF8(config.provider, buffer + offset, providerLen);
offset += providerLen;
Module.stringToUTF8(config.modelType, buffer + offset, modelTypeLen);
offset =
transducer.len + paraformer.len + nemoCtc.len + whisper.len + tdnn.len;
Module.setValue(ptr + offset, buffer, 'i8*'); // tokens
offset += 4;
Module.setValue(ptr + offset, config.numThreads, 'i32');
offset += 4;
Module.setValue(ptr + offset, config.debug, 'i32');
offset += 4;
Module.setValue(ptr + offset, buffer + tokensLen, 'i8*'); // provider
offset += 4;
Module.setValue(
ptr + offset, buffer + tokensLen + providerLen, 'i8*'); // modelType
offset += 4;
return {
buffer: buffer, ptr: ptr, len: len, transducer: transducer,
paraformer: paraformer, nemoCtc: nemoCtc, whisper: whisper, tdnn: tdnn
}
}
function initSherpaOnnxOfflineRecognizerConfig(config, Module) {
const feat = initSherpaOnnxFeatureConfig(config.featConfig, Module);
const model = initSherpaOnnxOfflineModelConfig(config.modelConfig, Module);
const lm = initSherpaOnnxOfflineLMConfig(config.lmConfig, Module);
const len = feat.len + model.len + lm.len + 4 * 4;
const ptr = Module._malloc(len);
let offset = 0;
Module._CopyHeap(feat.ptr, feat.len, ptr + offset);
offset += feat.len;
Module._CopyHeap(model.ptr, model.len, ptr + offset);
offset += model.len;
Module._CopyHeap(lm.ptr, lm.len, ptr + offset);
offset += lm.len;
const decodingMethodLen = Module.lengthBytesUTF8(config.decodingMethod) + 1;
const hotwordsFileLen = Module.lengthBytesUTF8(config.hotwordsFile) + 1;
const bufferLen = decodingMethodLen + hotwordsFileLen;
const buffer = Module._malloc(bufferLen);
offset = 0;
Module.stringToUTF8(config.decodingMethod, buffer, decodingMethodLen);
offset += decodingMethodLen;
Module.stringToUTF8(config.hotwordsFile, buffer + offset, hotwordsFileLen);
offset = feat.len + model.len + lm.len;
Module.setValue(ptr + offset, buffer, 'i8*'); // decoding method
offset += 4;
Module.setValue(ptr + offset, config.maxActivePaths, 'i32');
offset += 4;
return new OnlineRecognizer(recognizerConfig);
Module.setValue(ptr + offset, buffer + decodingMethodLen, 'i8*');
offset += 4;
Module.setValue(ptr + offset, config.hotwordsScore, 'float');
offset += 4;
return {
buffer: buffer, ptr: ptr, len: len, feat: feat, model: model, lm: lm
}
}
class OfflineStream {
constructor(handle, Module) {
this.handle = handle;
this.Module = Module;
}
free() {
if (this.handle) {
this.Module._DestroyOfflineStream(this.handle);
this.handle = null;
}
}
/**
* @param sampleRate {Number}
* @param samples {Float32Array} Containing samples in the range [-1, 1]
*/
acceptWaveform(sampleRate, samples) {
const pointer =
this.Module._malloc(samples.length * samples.BYTES_PER_ELEMENT);
this.Module.HEAPF32.set(samples, pointer / samples.BYTES_PER_ELEMENT);
this.Module._AcceptWaveformOffline(
this.handle, sampleRate, pointer, samples.length);
this.Module._free(pointer);
}
};
class OfflineRecognizer {
constructor(configObj, Module) {
this.config = configObj;
const config = initSherpaOnnxOfflineRecognizerConfig(configObj, Module);
const handle = Module._CreateOfflineRecognizer(config.ptr);
freeConfig(config, Module);
this.handle = handle;
this.Module = Module;
}
free() {
this.Module._DestroyOfflineRecognizer(this.handle);
this.handle = 0
}
createStream() {
const handle = this.Module._CreateOfflineStream(this.handle);
return new OfflineStream(handle, this.Module);
}
decode(stream) {
this.Module._DecodeOfflineStream(this.handle, stream.handle);
}
getResult(stream) {
const r = this.Module._GetOfflineStreamResult(stream.handle);
const textPtr = this.Module.getValue(r, 'i8*');
const text = this.Module.UTF8ToString(textPtr);
this.Module._DestroyOfflineRecognizerResult(r);
return text;
}
};
class OnlineStream {
constructor(handle) {
constructor(handle, Module) {
this.handle = handle;
this.pointer = null; // buffer
this.n = 0; // buffer size
this.Module = Module;
}
free() {
if (this.handle) {
_DestroyOnlineStream(this.handle);
this.Module._DestroyOnlineStream(this.handle);
this.handle = null;
_free(this.pointer);
this.Module._free(this.pointer);
this.pointer = null;
this.n = 0;
}
... ... @@ -321,61 +654,73 @@ class OnlineStream {
*/
acceptWaveform(sampleRate, samples) {
if (this.n < samples.length) {
_free(this.pointer);
this.pointer = _malloc(samples.length * samples.BYTES_PER_ELEMENT);
this.Module._free(this.pointer);
this.pointer =
this.Module._malloc(samples.length * samples.BYTES_PER_ELEMENT);
this.n = samples.length
}
Module.HEAPF32.set(samples, this.pointer / samples.BYTES_PER_ELEMENT);
_AcceptWaveform(this.handle, sampleRate, this.pointer, samples.length);
this.Module.HEAPF32.set(samples, this.pointer / samples.BYTES_PER_ELEMENT);
this.Module._AcceptWaveform(
this.handle, sampleRate, this.pointer, samples.length);
}
inputFinished() {
_InputFinished(this.handle);
this.Module._InputFinished(this.handle);
}
};
class OnlineRecognizer {
constructor(configObj) {
let config = initSherpaOnnxOnlineRecognizerConfig(configObj)
let handle = _CreateOnlineRecognizer(config.ptr);
constructor(configObj, Module) {
this.config = configObj;
const config = initSherpaOnnxOnlineRecognizerConfig(configObj, Module)
const handle = Module._CreateOnlineRecognizer(config.ptr);
freeConfig(config);
freeConfig(config, Module);
this.handle = handle;
this.Module = Module;
}
free() {
_DestroyOnlineRecognizer(this.handle);
this.Module._DestroyOnlineRecognizer(this.handle);
this.handle = 0
}
createStream() {
let handle = _CreateOnlineStream(this.handle);
return new OnlineStream(handle);
const handle = this.Module._CreateOnlineStream(this.handle);
return new OnlineStream(handle, this.Module);
}
isReady(stream) {
return _IsOnlineStreamReady(this.handle, stream.handle) == 1;
return this.Module._IsOnlineStreamReady(this.handle, stream.handle) == 1;
}
decode(stream) {
return _DecodeOnlineStream(this.handle, stream.handle);
this.Module._DecodeOnlineStream(this.handle, stream.handle);
}
isEndpoint(stream) {
return _IsEndpoint(this.handle, stream.handle) == 1;
return this.Module._IsEndpoint(this.handle, stream.handle) == 1;
}
reset(stream) {
_Reset(this.handle, stream.handle);
this.Module._Reset(this.handle, stream.handle);
}
getResult(stream) {
let r = _GetOnlineStreamResult(this.handle, stream.handle);
let textPtr = getValue(r, 'i8*');
let text = UTF8ToString(textPtr);
_DestroyOnlineRecognizerResult(r);
const r = this.Module._GetOnlineStreamResult(this.handle, stream.handle);
const textPtr = this.Module.getValue(r, 'i8*');
const text = this.Module.UTF8ToString(textPtr);
this.Module._DestroyOnlineRecognizerResult(r);
return text;
}
}
if (typeof process == 'object' && typeof process.versions == 'object' &&
typeof process.versions.node == 'string') {
module.exports = {
createOnlineRecognizer,
OfflineRecognizer,
};
}
... ...
// wasm/sherpa-onnx-wasm-asr-main.cc
// wasm/sherpa-onnx-wasm-main-asr.cc
//
// Copyright (c) 2024 Xiaomi Corporation
#include <stdio.h>
... ...
if(NOT $ENV{SHERPA_ONNX_IS_USING_BUILD_WASM_SH})
message(FATAL_ERROR "Please use ./build-wasm-simd-nodejs.sh to build for wasm NodeJS")
endif()
set(exported_functions
#tts
PrintOfflineTtsConfig
SherpaOnnxCreateOfflineTts
SherpaOnnxDestroyOfflineTts
SherpaOnnxDestroyOfflineTtsGeneratedAudio
SherpaOnnxOfflineTtsGenerate
SherpaOnnxOfflineTtsGenerateWithCallback
SherpaOnnxOfflineTtsNumSpeakers
SherpaOnnxOfflineTtsSampleRate
SherpaOnnxWriteWave
# streaming asr
AcceptWaveform
CreateOnlineRecognizer
CreateOnlineStream
DecodeOnlineStream
DestroyOnlineRecognizer
DestroyOnlineRecognizerResult
DestroyOnlineStream
GetOnlineStreamResult
InputFinished
IsEndpoint
IsOnlineStreamReady
Reset
# non-streaming ASR
PrintOfflineRecognizerConfig
CreateOfflineRecognizer
DestroyOfflineRecognizer
CreateOfflineStream
DestroyOfflineStream
AcceptWaveformOffline
DecodeOfflineStream
DecodeMultipleOfflineStreams
GetOfflineStreamResult
DestroyOfflineRecognizerResult
)
set(mangled_exported_functions)
foreach(x IN LISTS exported_functions)
list(APPEND mangled_exported_functions "_${x}")
endforeach()
list(JOIN mangled_exported_functions "," all_exported_functions)
include_directories(${CMAKE_SOURCE_DIR})
set(MY_FLAGS " -s FORCE_FILESYSTEM=1 -s INITIAL_MEMORY=512MB -s ALLOW_MEMORY_GROWTH=1")
string(APPEND MY_FLAGS " -sSTACK_SIZE=10485760 ") # 10MB
string(APPEND MY_FLAGS " -sEXPORTED_FUNCTIONS=[_CopyHeap,_malloc,_free,${all_exported_functions}] ")
string(APPEND MY_FLAGS " -sEXPORTED_RUNTIME_METHODS=['ccall','stringToUTF8','setValue','getValue'] ")
string(APPEND MY_FLAGS " -sNODERAWFS=1 ")
string(APPEND MY_FLAGS " -sEXPORTED_RUNTIME_METHODS=['ccall','stringToUTF8','setValue','getValue','lengthBytesUTF8','UTF8ToString'] ")
string(APPEND MY_FLAGS " -sMODULARIZE=1 -sWASM_ASYNC_COMPILATION=0 ")
message(STATUS "MY_FLAGS: ${MY_FLAGS}")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${MY_FLAGS}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${MY_FLAGS}")
set(CMAKE_EXECUTBLE_LINKER_FLAGS "${CMAKE_EXECUTBLE_LINKER_FLAGS} ${MY_FLAGS}")
add_executable(sherpa-onnx-wasm-nodejs sherpa-onnx-wasm-nodejs.cc)
target_link_libraries(sherpa-onnx-wasm-nodejs sherpa-onnx-core sherpa-onnx-c-api)
install(TARGETS sherpa-onnx-wasm-nodejs DESTINATION bin/wasm/nodejs)
install(
FILES
${CMAKE_SOURCE_DIR}/wasm/asr/sherpa-onnx-asr.js
${CMAKE_SOURCE_DIR}/wasm/tts/sherpa-onnx-tts.js
"$<TARGET_FILE_DIR:sherpa-onnx-wasm-nodejs>/sherpa-onnx-wasm-nodejs.js"
"$<TARGET_FILE_DIR:sherpa-onnx-wasm-nodejs>/sherpa-onnx-wasm-nodejs.wasm"
DESTINATION
bin/wasm/nodejs
)
... ...
// wasm/sherpa-onnx-wasm-main-nodejs.cc
//
// Copyright (c) 2024 Xiaomi Corporation
#include <stdio.h>
#include <algorithm>
#include <memory>
#include "sherpa-onnx/c-api/c-api.h"
extern "C" {
static_assert(sizeof(SherpaOnnxOfflineTransducerModelConfig) == 3 * 4, "");
static_assert(sizeof(SherpaOnnxOfflineParaformerModelConfig) == 4, "");
static_assert(sizeof(SherpaOnnxOfflineNemoEncDecCtcModelConfig) == 4, "");
static_assert(sizeof(SherpaOnnxOfflineWhisperModelConfig) == 2 * 4, "");
static_assert(sizeof(SherpaOnnxOfflineTdnnModelConfig) == 4, "");
static_assert(sizeof(SherpaOnnxOfflineLMConfig) == 2 * 4, "");
static_assert(sizeof(SherpaOnnxOfflineModelConfig) ==
sizeof(SherpaOnnxOfflineTransducerModelConfig) +
sizeof(SherpaOnnxOfflineParaformerModelConfig) +
sizeof(SherpaOnnxOfflineNemoEncDecCtcModelConfig) +
sizeof(SherpaOnnxOfflineWhisperModelConfig) +
sizeof(SherpaOnnxOfflineTdnnModelConfig) + 5 * 4,
"");
static_assert(sizeof(SherpaOnnxFeatureConfig) == 2 * 4, "");
static_assert(sizeof(SherpaOnnxOfflineRecognizerConfig) ==
sizeof(SherpaOnnxFeatureConfig) +
sizeof(SherpaOnnxOfflineLMConfig) +
sizeof(SherpaOnnxOfflineModelConfig) + 4 * 4,
"");
void PrintOfflineTtsConfig(SherpaOnnxOfflineTtsConfig *tts_config) {
auto tts_model_config = &tts_config->model;
auto vits_model_config = &tts_model_config->vits;
fprintf(stdout, "----------vits model config----------\n");
fprintf(stdout, "model: %s\n", vits_model_config->model);
fprintf(stdout, "lexicon: %s\n", vits_model_config->lexicon);
fprintf(stdout, "tokens: %s\n", vits_model_config->tokens);
fprintf(stdout, "data_dir: %s\n", vits_model_config->data_dir);
fprintf(stdout, "noise scale: %.3f\n", vits_model_config->noise_scale);
fprintf(stdout, "noise scale w: %.3f\n", vits_model_config->noise_scale_w);
fprintf(stdout, "length scale: %.3f\n", vits_model_config->length_scale);
fprintf(stdout, "----------tts model config----------\n");
fprintf(stdout, "num threads: %d\n", tts_model_config->num_threads);
fprintf(stdout, "debug: %d\n", tts_model_config->debug);
fprintf(stdout, "provider: %s\n", tts_model_config->provider);
fprintf(stdout, "----------tts config----------\n");
fprintf(stdout, "rule_fsts: %s\n", tts_config->rule_fsts);
fprintf(stdout, "max num sentences: %d\n", tts_config->max_num_sentences);
}
void PrintOfflineRecognizerConfig(SherpaOnnxOfflineRecognizerConfig *config) {
auto model_config = &config->model_config;
auto feat = &config->feat_config;
auto transducer = &model_config->transducer;
auto paraformer = &model_config->paraformer;
auto nemo_ctc = &model_config->nemo_ctc;
auto whisper = &model_config->whisper;
auto tdnn = &model_config->tdnn;
fprintf(stdout, "----------offline transducer model config----------\n");
fprintf(stdout, "encoder: %s\n", transducer->encoder);
fprintf(stdout, "decoder: %s\n", transducer->decoder);
fprintf(stdout, "joiner: %s\n", transducer->joiner);
fprintf(stdout, "----------offline paraformer model config----------\n");
fprintf(stdout, "model: %s\n", paraformer->model);
fprintf(stdout, "----------offline nemo_ctc model config----------\n");
fprintf(stdout, "model: %s\n", nemo_ctc->model);
fprintf(stdout, "----------offline whisper model config----------\n");
fprintf(stdout, "encoder: %s\n", whisper->encoder);
fprintf(stdout, "decoder: %s\n", whisper->decoder);
fprintf(stdout, "----------offline tdnn model config----------\n");
fprintf(stdout, "model: %s\n", tdnn->model);
fprintf(stdout, "tokens: %s\n", model_config->tokens);
fprintf(stdout, "num_threads: %d\n", model_config->num_threads);
fprintf(stdout, "provider: %s\n", model_config->provider);
fprintf(stdout, "debug: %d\n", model_config->debug);
fprintf(stdout, "model type: %s\n", model_config->model_type);
fprintf(stdout, "----------feat config----------\n");
fprintf(stdout, "sample rate: %d\n", feat->sample_rate);
fprintf(stdout, "feat dim: %d\n", feat->feature_dim);
fprintf(stdout, "----------recognizer config----------\n");
fprintf(stdout, "decoding method: %s\n", config->decoding_method);
fprintf(stdout, "max active paths: %d\n", config->max_active_paths);
fprintf(stdout, "hotwords_file: %s\n", config->hotwords_file);
fprintf(stdout, "hotwords_score: %.2f\n", config->hotwords_score);
}
void CopyHeap(const char *src, int32_t num_bytes, char *dst) {
std::copy(src, src + num_bytes, dst);
}
}
... ...
... ... @@ -22,7 +22,7 @@ Module.onRuntimeInitialized = function() {
console.log('Model files downloaded!');
console.log('Initializing tts ......');
tts = initSherpaOnnxOfflineTts()
tts = createOfflineTts(Module)
if (tts.numSpeakers > 1) {
speakerIdLabel.innerHTML = `Speaker ID (0 - ${tts.numSpeakers - 1}):`;
}
... ...
function freeConfig(config) {
function freeConfig(config, Module) {
if ('buffer' in config) {
_free(config.buffer);
Module._free(config.buffer);
}
if ('config' in config) {
freeConfig(config.config)
freeConfig(config.config, Module)
}
_free(config.ptr);
Module._free(config.ptr);
}
// The user should free the returned pointers
function initSherpaOnnxOfflineTtsVitsModelConfig(config) {
let modelLen = lengthBytesUTF8(config.model) + 1;
let lexiconLen = lengthBytesUTF8(config.lexicon) + 1;
let tokensLen = lengthBytesUTF8(config.tokens) + 1;
let dataDirLen = lengthBytesUTF8(config.dataDir) + 1;
function initSherpaOnnxOfflineTtsVitsModelConfig(config, Module) {
const modelLen = Module.lengthBytesUTF8(config.model) + 1;
const lexiconLen = Module.lengthBytesUTF8(config.lexicon) + 1;
const tokensLen = Module.lengthBytesUTF8(config.tokens) + 1;
const dataDirLen = Module.lengthBytesUTF8(config.dataDir) + 1;
let n = modelLen + lexiconLen + tokensLen + dataDirLen;
const n = modelLen + lexiconLen + tokensLen + dataDirLen;
let buffer = _malloc(n);
const buffer = Module._malloc(n);
let len = 7 * 4;
let ptr = _malloc(len);
const len = 7 * 4;
const ptr = Module._malloc(len);
let offset = 0;
stringToUTF8(config.model, buffer + offset, modelLen);
Module.stringToUTF8(config.model, buffer + offset, modelLen);
offset += modelLen;
stringToUTF8(config.lexicon, buffer + offset, lexiconLen);
Module.stringToUTF8(config.lexicon, buffer + offset, lexiconLen);
offset += lexiconLen;
stringToUTF8(config.tokens, buffer + offset, tokensLen);
Module.stringToUTF8(config.tokens, buffer + offset, tokensLen);
offset += tokensLen;
stringToUTF8(config.dataDir, buffer + offset, dataDirLen);
Module.stringToUTF8(config.dataDir, buffer + offset, dataDirLen);
offset += dataDirLen;
offset = 0;
setValue(ptr, buffer + offset, 'i8*');
Module.setValue(ptr, buffer + offset, 'i8*');
offset += modelLen;
setValue(ptr + 4, buffer + offset, 'i8*');
Module.setValue(ptr + 4, buffer + offset, 'i8*');
offset += lexiconLen;
setValue(ptr + 8, buffer + offset, 'i8*');
Module.setValue(ptr + 8, buffer + offset, 'i8*');
offset += tokensLen;
setValue(ptr + 12, buffer + offset, 'i8*');
Module.setValue(ptr + 12, buffer + offset, 'i8*');
offset += dataDirLen;
setValue(ptr + 16, config.noiseScale, 'float');
setValue(ptr + 20, config.noiseScaleW, 'float');
setValue(ptr + 24, config.lengthScale, 'float');
Module.setValue(ptr + 16, config.noiseScale, 'float');
Module.setValue(ptr + 20, config.noiseScaleW, 'float');
Module.setValue(ptr + 24, config.lengthScale, 'float');
return {
buffer: buffer, ptr: ptr, len: len,
}
}
function initSherpaOnnxOfflineTtsModelConfig(config) {
let vitsModelConfig =
initSherpaOnnxOfflineTtsVitsModelConfig(config.offlineTtsVitsModelConfig);
function initSherpaOnnxOfflineTtsModelConfig(config, Module) {
const vitsModelConfig = initSherpaOnnxOfflineTtsVitsModelConfig(
config.offlineTtsVitsModelConfig, Module);
let len = vitsModelConfig.len + 3 * 4;
let ptr = _malloc(len);
const len = vitsModelConfig.len + 3 * 4;
const ptr = Module._malloc(len);
let offset = 0;
_CopyHeap(vitsModelConfig.ptr, vitsModelConfig.len, ptr + offset);
Module._CopyHeap(vitsModelConfig.ptr, vitsModelConfig.len, ptr + offset);
offset += vitsModelConfig.len;
setValue(ptr + offset, config.numThreads, 'i32');
Module.setValue(ptr + offset, config.numThreads, 'i32');
offset += 4;
setValue(ptr + offset, config.debug, 'i32');
Module.setValue(ptr + offset, config.debug, 'i32');
offset += 4;
let providerLen = lengthBytesUTF8(config.provider) + 1;
let buffer = _malloc(providerLen);
stringToUTF8(config.provider, buffer, providerLen);
setValue(ptr + offset, buffer, 'i8*');
const providerLen = Module.lengthBytesUTF8(config.provider) + 1;
const buffer = Module._malloc(providerLen);
Module.stringToUTF8(config.provider, buffer, providerLen);
Module.setValue(ptr + offset, buffer, 'i8*');
return {
buffer: buffer, ptr: ptr, len: len, config: vitsModelConfig,
}
}
function initSherpaOnnxOfflineTtsConfig(config) {
let modelConfig =
initSherpaOnnxOfflineTtsModelConfig(config.offlineTtsModelConfig);
let len = modelConfig.len + 2 * 4;
let ptr = _malloc(len);
function initSherpaOnnxOfflineTtsConfig(config, Module) {
const modelConfig =
initSherpaOnnxOfflineTtsModelConfig(config.offlineTtsModelConfig, Module);
const len = modelConfig.len + 2 * 4;
const ptr = Module._malloc(len);
let offset = 0;
_CopyHeap(modelConfig.ptr, modelConfig.len, ptr + offset);
Module._CopyHeap(modelConfig.ptr, modelConfig.len, ptr + offset);
offset += modelConfig.len;
let ruleFstsLen = lengthBytesUTF8(config.ruleFsts) + 1;
let buffer = _malloc(ruleFstsLen);
stringToUTF8(config.ruleFsts, buffer, ruleFstsLen);
setValue(ptr + offset, buffer, 'i8*');
const ruleFstsLen = Module.lengthBytesUTF8(config.ruleFsts) + 1;
const buffer = Module._malloc(ruleFstsLen);
Module.stringToUTF8(config.ruleFsts, buffer, ruleFstsLen);
Module.setValue(ptr + offset, buffer, 'i8*');
offset += 4;
setValue(ptr + offset, config.maxNumSentences, 'i32');
Module.setValue(ptr + offset, config.maxNumSentences, 'i32');
return {
buffer: buffer, ptr: ptr, len: len, config: modelConfig,
... ... @@ -111,19 +111,21 @@ function initSherpaOnnxOfflineTtsConfig(config) {
}
class OfflineTts {
constructor(configObj) {
let config = initSherpaOnnxOfflineTtsConfig(configObj)
let handle = _SherpaOnnxCreateOfflineTts(config.ptr);
constructor(configObj, Module) {
console.log(configObj)
const config = initSherpaOnnxOfflineTtsConfig(configObj, Module)
const handle = Module._SherpaOnnxCreateOfflineTts(config.ptr);
freeConfig(config);
freeConfig(config, Module);
this.handle = handle;
this.sampleRate = _SherpaOnnxOfflineTtsSampleRate(this.handle);
this.numSpeakers = _SherpaOnnxOfflineTtsNumSpeakers(this.handle);
this.sampleRate = Module._SherpaOnnxOfflineTtsSampleRate(this.handle);
this.numSpeakers = Module._SherpaOnnxOfflineTtsNumSpeakers(this.handle);
this.Module = Module
}
free() {
_SherpaOnnxDestroyOfflineTts(this.handle);
this.Module._SherpaOnnxDestroyOfflineTts(this.handle);
this.handle = 0
}
... ... @@ -133,29 +135,44 @@ class OfflineTts {
// speed: 1.0
// }
generate(config) {
let textLen = lengthBytesUTF8(config.text) + 1;
let textPtr = _malloc(textLen);
stringToUTF8(config.text, textPtr, textLen);
const textLen = this.Module.lengthBytesUTF8(config.text) + 1;
const textPtr = this.Module._malloc(textLen);
this.Module.stringToUTF8(config.text, textPtr, textLen);
let h = _SherpaOnnxOfflineTtsGenerate(
const h = this.Module._SherpaOnnxOfflineTtsGenerate(
this.handle, textPtr, config.sid, config.speed);
let numSamples = HEAP32[h / 4 + 1];
let sampleRate = HEAP32[h / 4 + 2];
const numSamples = this.Module.HEAP32[h / 4 + 1];
const sampleRate = this.Module.HEAP32[h / 4 + 2];
let samplesPtr = HEAP32[h / 4] / 4;
let samples = new Float32Array(numSamples);
const samplesPtr = this.Module.HEAP32[h / 4] / 4;
const samples = new Float32Array(numSamples);
for (let i = 0; i < numSamples; i++) {
samples[i] = HEAPF32[samplesPtr + i];
samples[i] = this.Module.HEAPF32[samplesPtr + i];
}
_SherpaOnnxDestroyOfflineTtsGeneratedAudio(h);
this.Module._SherpaOnnxDestroyOfflineTtsGeneratedAudio(h);
return {samples: samples, sampleRate: sampleRate};
}
save(filename, audio) {
const samples = audio.samples;
const sampleRate = audio.sampleRate;
const ptr = this.Module._malloc(samples.length * 4);
for (let i = 0; i < samples.length; i++) {
this.Module.HEAPF32[ptr / 4 + i] = samples[i];
}
const filenameLen = this.Module.lengthBytesUTF8(filename) + 1;
const buffer = this.Module._malloc(filenameLen);
this.Module.stringToUTF8(filename, buffer, filenameLen);
this.Module._SherpaOnnxWriteWave(ptr, samples.length, sampleRate, buffer);
this.Module._free(buffer);
this.Module._free(ptr);
}
}
function initSherpaOnnxOfflineTts() {
let offlineTtsVitsModelConfig = {
function createOfflineTts(Module, myConfig) {
const offlineTtsVitsModelConfig = {
model: './model.onnx',
lexicon: '',
tokens: './tokens.txt',
... ... @@ -164,7 +181,7 @@ function initSherpaOnnxOfflineTts() {
noiseScaleW: 0.8,
lengthScale: 1.0,
};
let offlineTtsModelConfig = {
const offlineTtsModelConfig = {
offlineTtsVitsModelConfig: offlineTtsVitsModelConfig,
numThreads: 1,
debug: 1,
... ... @@ -176,5 +193,16 @@ function initSherpaOnnxOfflineTts() {
maxNumSentences: 1,
}
return new OfflineTts(offlineTtsConfig);
if (myConfig) {
offlineTtsConfig = myConfig;
}
return new OfflineTts(offlineTtsConfig, Module);
}
if (typeof process == 'object' && typeof process.versions == 'object' &&
typeof process.versions.node == 'string') {
module.exports = {
createOfflineTts,
};
}
... ...
// wasm/sherpa-onnx-wasm-main.cc
// wasm/sherpa-onnx-wasm-main-tts.cc
//
// Copyright (c) 2024 Xiaomi Corporation
#include <stdio.h>
... ...