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Add go-api-examples (#219)

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This commit is contained in:
Fangjun Kuang
2023-07-21 17:56:09 +08:00
committed by GitHub
parent 6125d9e063
commit e63d205b3d
28 changed files with 1624 additions and 14 deletions
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146
scripts/go/release.sh Executable file
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#!/usr/bin/env bash
set -ex
git config --global user.email "csukuangfj@gmail.com"
git config --global user.name "Fangjun Kuang"
SHERPA_ONNX_VERSION=v$(grep "SHERPA_ONNX_VERSION" ./CMakeLists.txt | cut -d " " -f 2 | cut -d '"' -f 2)
echo "========================================================================="
git clone git@github.com:k2-fsa/sherpa-onnx-go-linux.git
echo "Copy libs for Linux x86_64"
rm -rf sherpa-onnx-go-linux/lib/x86_64-unknown-linux-gnu/lib*
cp -v ./linux/sherpa_onnx/lib/libkaldi-native-fbank-core.so sherpa-onnx-go-linux/lib/x86_64-unknown-linux-gnu/
cp -v ./linux/sherpa_onnx/lib/libonnxruntime* sherpa-onnx-go-linux/lib/x86_64-unknown-linux-gnu/
cp -v ./linux/sherpa_onnx/lib/libsherpa-onnx-c-api.so sherpa-onnx-go-linux/lib/x86_64-unknown-linux-gnu/
cp -v ./linux/sherpa_onnx/lib/libsherpa-onnx-core.so sherpa-onnx-go-linux/lib/x86_64-unknown-linux-gnu/
echo "Copy sources for Linux x86_64"
cp sherpa-onnx/c-api/c-api.h sherpa-onnx-go-linux/
cp scripts/go/sherpa_onnx.go sherpa-onnx-go-linux/
pushd sherpa-onnx-go-linux
tag=$(git describe --abbrev=0 --tags)
if [[ x"$VERSION" == x"auto" ]]; then
# this is a pre-release
if [[ $tag == ${SHERPA_ONNX_VERSION}* ]]; then
# echo we have already release pre-release before, so just increment it
last=$(echo $tag | rev | cut -d'.' -f 1 | rev)
new_last=$((last+1))
new_tag=${SHERPA_ONNX_VERSION}-alpha.${new_last}
else
new_tag=${SHERPA_ONNX_VERSION}-alpha.1
fi
else
new_tag=$VERSION
fi
echo "new_tag: $new_tag"
git add .
git status
git commit -m "Release $new_tag" && \
git tag $new_tag && \
git push origin $new_tag || true
popd
echo "========================================================================="
git clone git@github.com:k2-fsa/sherpa-onnx-go-macos.git
echo "Copy libs for macOS x86_64"
rm -rf sherpa-onnx-go-macos/lib/x86_64-apple-darwin/lib*
cp -v ./macos-x86_64/libkaldi-native-fbank-core.dylib sherpa-onnx-go-macos/lib/x86_64-apple-darwin
cp -v ./macos-x86_64/libonnxruntime* sherpa-onnx-go-macos/lib/x86_64-apple-darwin
cp -v ./macos-x86_64/libsherpa-onnx-c-api.dylib sherpa-onnx-go-macos/lib/x86_64-apple-darwin
cp -v ./macos-x86_64/libsherpa-onnx-core.dylib sherpa-onnx-go-macos/lib/x86_64-apple-darwin
echo "Copy libs for macOS arm64"
rm -rf sherpa-onnx-go-macos/lib/aarch64-apple-darwin/lib*
cp -v ./macos-arm64/libkaldi-native-fbank-core.dylib sherpa-onnx-go-macos/lib/aarch64-apple-darwin
cp -v ./macos-arm64/libonnxruntime* sherpa-onnx-go-macos/lib/aarch64-apple-darwin
cp -v ./macos-arm64/libsherpa-onnx-c-api.dylib sherpa-onnx-go-macos/lib/aarch64-apple-darwin
cp -v ./macos-arm64/libsherpa-onnx-core.dylib sherpa-onnx-go-macos/lib/aarch64-apple-darwin
echo "Copy sources for macOS"
cp sherpa-onnx/c-api/c-api.h sherpa-onnx-go-macos/
cp scripts/go/sherpa_onnx.go sherpa-onnx-go-macos/
pushd sherpa-onnx-go-macos
tag=$(git describe --abbrev=0 --tags)
if [[ x"$VERSION" == x"auto" ]]; then
# this is a pre-release
if [[ $tag == ${SHERPA_ONNX_VERSION}* ]]; then
# echo we have already release pre-release before, so just increment it
last=$(echo $tag | rev | cut -d'.' -f 1 | rev)
new_last=$((last+1))
new_tag=${SHERPA_ONNX_VERSION}-alpha.${new_last}
else
new_tag=${SHERPA_ONNX_VERSION}-alpha.1
fi
else
new_tag=$VERSION
fi
echo "new_tag: $new_tag"
git add .
git status
git commit -m "Release $new_tag" && \
git tag $new_tag && \
git push origin $new_tag || true
popd
echo "========================================================================="
git clone git@github.com:k2-fsa/sherpa-onnx-go-windows.git
echo "Copy libs for Windows x86_64"
rm -fv sherpa-onnx-go-windows/lib/x86_64-pc-windows-gnu/*
cp -v ./windows-x64/kaldi-native-fbank-core.dll sherpa-onnx-go-windows/lib/x86_64-pc-windows-gnu
cp -v ./windows-x64/onnxruntime.dll sherpa-onnx-go-windows/lib/x86_64-pc-windows-gnu
cp -v ./windows-x64/sherpa-onnx-c-api.dll sherpa-onnx-go-windows/lib/x86_64-pc-windows-gnu
cp -v ./windows-x64/sherpa-onnx-core.dll sherpa-onnx-go-windows/lib/x86_64-pc-windows-gnu
echo "Copy libs for Windows x86"
rm -fv sherpa-onnx-go-windows/lib/i686-pc-windows-gnu/*
cp -v ./windows-win32/kaldi-native-fbank-core.dll sherpa-onnx-go-windows/lib/i686-pc-windows-gnu
cp -v ./windows-win32/onnxruntime.dll sherpa-onnx-go-windows/lib/i686-pc-windows-gnu
cp -v ./windows-win32/sherpa-onnx-c-api.dll sherpa-onnx-go-windows/lib/i686-pc-windows-gnu
cp -v ./windows-win32/sherpa-onnx-core.dll sherpa-onnx-go-windows/lib/i686-pc-windows-gnu
echo "Copy sources for Windows"
cp sherpa-onnx/c-api/c-api.h sherpa-onnx-go-windows/
cp scripts/go/sherpa_onnx.go sherpa-onnx-go-windows/
pushd sherpa-onnx-go-windows
tag=$(git describe --abbrev=0 --tags)
if [[ x"$VERSION" == x"auto" ]]; then
# this is a pre-release
if [[ $tag == ${SHERPA_ONNX_VERSION}* ]]; then
# echo we have already release pre-release before, so just increment it
last=$(echo $tag | rev | cut -d'.' -f 1 | rev)
new_last=$((last+1))
new_tag=${SHERPA_ONNX_VERSION}-alpha.${new_last}
else
new_tag=${SHERPA_ONNX_VERSION}-alpha.1
fi
else
new_tag=$VERSION
fi
echo "new_tag: $new_tag"
git add .
git status
git commit -m "Release $new_tag" && \
git tag $new_tag && \
git push origin $new_tag || true
popd
echo "========================================================================="
rm -fv ~/.ssh/github

443
scripts/go/sherpa_onnx.go Normal file
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/*
Speech recognition with [Next-gen Kaldi].
[sherpa-onnx] is an open-source speech recognition framework for [Next-gen Kaldi].
It depends only on [onnxruntime], supporting both streaming and non-streaming
speech recognition.
It does not need to access the network during recognition and everything
runs locally.
It supports a variety of platforms, such as Linux (x86_64, aarch64, arm),
Windows (x86_64, x86), macOS (x86_64, arm64), etc.
Usage examples:
1. Real-time speech recognition from a microphone
Please see
https://github.com/k2-fsa/sherpa-onnx/tree/master/go-api-examples/real-time-speech-recognition-from-microphone
2. Decode files using a non-streaming model
Please see
https://github.com/k2-fsa/sherpa-onnx/tree/master/go-api-examples/non-streaming-decode-files
3. Decode files using a streaming model
Please see
https://github.com/k2-fsa/sherpa-onnx/tree/master/go-api-examples/streaming-decode-files
[sherpa-onnx]: https://github.com/k2-fsa/sherpa-onnx
[onnxruntime]: https://github.com/microsoft/onnxruntime
[Next-gen Kaldi]: https://github.com/k2-fsa/
*/
package sherpa_onnx
// #include <stdlib.h>
// #include "c-api.h"
import "C"
import "unsafe"
// Configuration for online/streaming transducer models
//
// Please refer to
// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/online-transducer/index.html
// to download pre-trained models
type OnlineTransducerModelConfig struct {
Encoder string // Path to the encoder model, e.g., encoder.onnx or encoder.int8.onnx
Decoder string // Path to the decoder model.
Joiner string // Path to the joiner model.
Tokens string // Path to tokens.txt
NumThreads int // Number of threads to use for neural network computation
Provider string // Optional. Valid values are: cpu, cuda, coreml
Debug int // 1 to show model meta information while loading it.
ModelType string // Optional. You can specify it for faster model initialization
}
// Configuration for the feature extractor
type FeatureConfig struct {
// Sample rate expected by the model. It is 16000 for all
// pre-trained models provided by us
SampleRate int
// Feature dimension expected by the model. It is 80 for all
// pre-trained models provided by us
FeatureDim int
}
// Configuration for the online/streaming recognizer.
type OnlineRecognizerConfig struct {
FeatConfig FeatureConfig
ModelConfig OnlineTransducerModelConfig
// Valid decoding methods: greedy_search, modified_beam_search
DecodingMethod string
// Used only when DecodingMethod is modified_beam_search. It specifies
// the maximum number of paths to keep during the search
MaxActivePaths int
EnableEndpoint int // 1 to enable endpoint detection.
// Please see
// https://k2-fsa.github.io/sherpa/ncnn/endpoint.html
// for the meaning of Rule1MinTrailingSilence, Rule2MinTrailingSilence
// and Rule3MinUtteranceLength.
Rule1MinTrailingSilence float32
Rule2MinTrailingSilence float32
Rule3MinUtteranceLength float32
}
// It contains the recognition result for a online stream.
type OnlineRecognizerResult struct {
Text string
}
// The online recognizer class. It wraps a pointer from C.
type OnlineRecognizer struct {
impl *C.struct_SherpaOnnxOnlineRecognizer
}
// The online stream class. It wraps a pointer from C.
type OnlineStream struct {
impl *C.struct_SherpaOnnxOnlineStream
}
// Free the internal pointer inside the recognizer to avoid memory leak.
func DeleteOnlineRecognizer(recognizer *OnlineRecognizer) {
C.DestroyOnlineRecognizer(recognizer.impl)
recognizer.impl = nil
}
// The user is responsible to invoke [DeleteOnlineRecognizer]() to free
// the returned recognizer to avoid memory leak
func NewOnlineRecognizer(config *OnlineRecognizerConfig) *OnlineRecognizer {
c := C.struct_SherpaOnnxOnlineRecognizerConfig{}
c.feat_config.sample_rate = C.int(config.FeatConfig.SampleRate)
c.feat_config.feature_dim = C.int(config.FeatConfig.FeatureDim)
c.model_config.encoder = C.CString(config.ModelConfig.Encoder)
defer C.free(unsafe.Pointer(c.model_config.encoder))
c.model_config.decoder = C.CString(config.ModelConfig.Decoder)
defer C.free(unsafe.Pointer(c.model_config.decoder))
c.model_config.joiner = C.CString(config.ModelConfig.Joiner)
defer C.free(unsafe.Pointer(c.model_config.joiner))
c.model_config.tokens = C.CString(config.ModelConfig.Tokens)
defer C.free(unsafe.Pointer(c.model_config.tokens))
c.model_config.num_threads = C.int(config.ModelConfig.NumThreads)
c.model_config.provider = C.CString(config.ModelConfig.Provider)
defer C.free(unsafe.Pointer(c.model_config.provider))
c.model_config.debug = C.int(config.ModelConfig.Debug)
c.model_config.model_type = C.CString(config.ModelConfig.ModelType)
defer C.free(unsafe.Pointer(c.model_config.model_type))
c.decoding_method = C.CString(config.DecodingMethod)
defer C.free(unsafe.Pointer(c.decoding_method))
c.max_active_paths = C.int(config.MaxActivePaths)
c.enable_endpoint = C.int(config.EnableEndpoint)
c.rule1_min_trailing_silence = C.float(config.Rule1MinTrailingSilence)
c.rule2_min_trailing_silence = C.float(config.Rule2MinTrailingSilence)
c.rule3_min_utterance_length = C.float(config.Rule3MinUtteranceLength)
recognizer := &OnlineRecognizer{}
recognizer.impl = C.CreateOnlineRecognizer(&c)
return recognizer
}
// Delete the internal pointer inside the stream to avoid memory leak.
func DeleteOnlineStream(stream *OnlineStream) {
C.DestroyOnlineStream(stream.impl)
stream.impl = nil
}
// The user is responsible to invoke [DeleteOnlineStream]() to free
// the returned stream to avoid memory leak
func NewOnlineStream(recognizer *OnlineRecognizer) *OnlineStream {
stream := &OnlineStream{}
stream.impl = C.CreateOnlineStream(recognizer.impl)
return stream
}
// Input audio samples for the stream.
//
// sampleRate is the actual sample rate of the input audio samples. If it
// is different from the sample rate expected by the feature extractor, we will
// do resampling inside.
//
// samples contains audio samples. Each sample is in the range [-1, 1]
func (s *OnlineStream) AcceptWaveform(sampleRate int, samples []float32) {
C.AcceptWaveform(s.impl, C.int(sampleRate), (*C.float)(&samples[0]), C.int(len(samples)))
}
// Signal that there will be no incoming audio samples.
// After calling this function, you cannot call [OnlineStream.AcceptWaveform] any longer.
//
// The main purpose of this function is to flush the remaining audio samples
// buffered inside for feature extraction.
func (s *OnlineStream) InputFinished() {
C.InputFinished(s.impl)
}
// Check whether the stream has enough feature frames for decoding.
// Return true if this stream is ready for decoding. Return false otherwise.
//
// You will usually use it like below:
//
// for recognizer.IsReady(s) {
// recognizer.Decode(s)
// }
func (recognizer *OnlineRecognizer) IsReady(s *OnlineStream) bool {
return C.IsOnlineStreamReady(recognizer.impl, s.impl) == 1
}
// Return true if an endpoint is detected.
//
// You usually use it like below:
//
// if recognizer.IsEndpoint(s) {
// // do your own stuff after detecting an endpoint
//
// recognizer.Reset(s)
// }
func (recognizer *OnlineRecognizer) IsEndpoint(s *OnlineStream) bool {
return C.IsEndpoint(recognizer.impl, s.impl) == 1
}
// After calling this function, the internal neural network model states
// are reset and IsEndpoint(s) would return false. GetResult(s) would also
// return an empty string.
func (recognizer *OnlineRecognizer) Reset(s *OnlineStream) {
C.Reset(recognizer.impl, s.impl)
}
// Decode the stream. Before calling this function, you have to ensure
// that recognizer.IsReady(s) returns true. Otherwise, you will be SAD.
//
// You usually use it like below:
//
// for recognizer.IsReady(s) {
// recognizer.Decode(s)
// }
func (recognizer *OnlineRecognizer) Decode(s *OnlineStream) {
C.DecodeOnlineStream(recognizer.impl, s.impl)
}
// Decode multiple streams in parallel, i.e., in batch.
// You have to ensure that each stream is ready for decoding. Otherwise,
// you will be SAD.
func (recognizer *OnlineRecognizer) DecodeStreams(s []*OnlineStream) {
ss := make([]*C.struct_SherpaOnnxOnlineStream, len(s))
for i, v := range s {
ss[i] = v.impl
}
C.DecodeMultipleOnlineStreams(recognizer.impl, &ss[0], C.int(len(s)))
}
// Get the current result of stream since the last invoke of Reset()
func (recognizer *OnlineRecognizer) GetResult(s *OnlineStream) *OnlineRecognizerResult {
p := C.GetOnlineStreamResult(recognizer.impl, s.impl)
defer C.DestroyOnlineRecognizerResult(p)
result := &OnlineRecognizerResult{}
result.Text = C.GoString(p.text)
return result
}
// Configuration for offline/non-streaming transducer.
//
// Please refer to
// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-transducer/index.html
// to download pre-trained models
type OfflineTransducerModelConfig struct {
Encoder string // Path to the encoder model, i.e., encoder.onnx or encoder.int8.onnx
Decoder string // Path to the decoder model
Joiner string // Path to the joiner model
}
// Configuration for offline/non-streaming paraformer.
//
// please refer to
// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-paraformer/index.html
// to download pre-trained models
type OfflineParaformerModelConfig struct {
Model string // Path to the model, e.g., model.onnx or model.int8.onnx
}
// Configuration for offline/non-streaming NeMo CTC models.
//
// Please refer to
// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/index.html
// to download pre-trained models
type OfflineNemoEncDecCtcModelConfig struct {
Model string // Path to the model, e.g., model.onnx or model.int8.onnx
}
// Configuration for offline LM.
type OfflineLMConfig struct {
Model string // Path to the model
Scale float32 // scale for LM score
}
type OfflineModelConfig struct {
Transducer OfflineTransducerModelConfig
Paraformer OfflineParaformerModelConfig
NemoCTC OfflineNemoEncDecCtcModelConfig
Tokens string // Path to tokens.txt
// Number of threads to use for neural network computation
NumThreads int
// 1 to print model meta information while loading
Debug int
// Optional. Valid values: cpu, cuda, coreml
Provider string
// Optional. Specify it for faster model initialization.
ModelType string
}
// Configuration for the offline/non-streaming recognizer.
type OfflineRecognizerConfig struct {
FeatConfig FeatureConfig
ModelConfig OfflineModelConfig
LmConfig OfflineLMConfig
// Valid decoding method: greedy_search, modified_beam_search
DecodingMethod string
// Used only when DecodingMethod is modified_beam_search.
MaxActivePaths int
}
// It wraps a pointer from C
type OfflineRecognizer struct {
impl *C.struct_SherpaOnnxOfflineRecognizer
}
// It wraps a pointer from C
type OfflineStream struct {
impl *C.struct_SherpaOnnxOfflineStream
}
// It contains recognition result of an offline stream.
type OfflineRecognizerResult struct {
Text string
}
// Frees the internal pointer of the recognition to avoid memory leak.
func DeleteOfflineRecognizer(recognizer *OfflineRecognizer) {
C.DestroyOfflineRecognizer(recognizer.impl)
recognizer.impl = nil
}
// The user is responsible to invoke [DeleteOfflineRecognizer]() to free
// the returned recognizer to avoid memory leak
func NewOfflineRecognizer(config *OfflineRecognizerConfig) *OfflineRecognizer {
c := C.struct_SherpaOnnxOfflineRecognizerConfig{}
c.feat_config.sample_rate = C.int(config.FeatConfig.SampleRate)
c.feat_config.feature_dim = C.int(config.FeatConfig.FeatureDim)
c.model_config.transducer.encoder = C.CString(config.ModelConfig.Transducer.Encoder)
defer C.free(unsafe.Pointer(c.model_config.transducer.encoder))
c.model_config.transducer.decoder = C.CString(config.ModelConfig.Transducer.Decoder)
defer C.free(unsafe.Pointer(c.model_config.transducer.decoder))
c.model_config.transducer.joiner = C.CString(config.ModelConfig.Transducer.Joiner)
defer C.free(unsafe.Pointer(c.model_config.transducer.joiner))
c.model_config.paraformer.model = C.CString(config.ModelConfig.Paraformer.Model)
defer C.free(unsafe.Pointer(c.model_config.paraformer.model))
c.model_config.nemo_ctc.model = C.CString(config.ModelConfig.NemoCTC.Model)
defer C.free(unsafe.Pointer(c.model_config.nemo_ctc.model))
c.model_config.tokens = C.CString(config.ModelConfig.Tokens)
defer C.free(unsafe.Pointer(c.model_config.tokens))
c.model_config.num_threads = C.int(config.ModelConfig.NumThreads)
c.model_config.debug = C.int(config.ModelConfig.Debug)
c.model_config.provider = C.CString(config.ModelConfig.Provider)
defer C.free(unsafe.Pointer(c.model_config.provider))
c.model_config.model_type = C.CString(config.ModelConfig.ModelType)
defer C.free(unsafe.Pointer(c.model_config.model_type))
c.lm_config.model = C.CString(config.LmConfig.Model)
defer C.free(unsafe.Pointer(c.lm_config.model))
c.lm_config.scale = C.float(config.LmConfig.Scale)
c.decoding_method = C.CString(config.DecodingMethod)
defer C.free(unsafe.Pointer(c.decoding_method))
c.max_active_paths = C.int(config.MaxActivePaths)
recognizer := &OfflineRecognizer{}
recognizer.impl = C.CreateOfflineRecognizer(&c)
return recognizer
}
// Frees the internal pointer of the stream to avoid memory leak.
func DeleteOfflineStream(stream *OfflineStream) {
C.DestroyOfflineStream(stream.impl)
stream.impl = nil
}
// The user is responsible to invoke [DeleteOfflineStream]() to free
// the returned stream to avoid memory leak
func NewOfflineStream(recognizer *OfflineRecognizer) *OfflineStream {
stream := &OfflineStream{}
stream.impl = C.CreateOfflineStream(recognizer.impl)
return stream
}
// Input audio samples for the offline stream.
// Please only call it once. That is, input all samples at once.
//
// sampleRate is the sample rate of the input audio samples. If it is different
// from the value expected by the feature extractor, we will do resampling inside.
//
// samples contains the actual audio samples. Each sample is in the range [-1, 1].
func (s *OfflineStream) AcceptWaveform(sampleRate int, samples []float32) {
C.AcceptWaveformOffline(s.impl, C.int(sampleRate), (*C.float)(&samples[0]), C.int(len(samples)))
}
// Decode the offline stream.
func (recognizer *OfflineRecognizer) Decode(s *OfflineStream) {
C.DecodeOfflineStream(recognizer.impl, s.impl)
}
// Decode multiple streams in parallel, i.e., in batch.
func (recognizer *OfflineRecognizer) DecodeStreams(s []*OfflineStream) {
ss := make([]*C.struct_SherpaOnnxOfflineStream, len(s))
for i, v := range s {
ss[i] = v.impl
}
C.DecodeMultipleOfflineStreams(recognizer.impl, &ss[0], C.int(len(s)))
}
// Get the recognition result of the offline stream.
func (s *OfflineStream) GetResult() *OfflineRecognizerResult {
p := C.GetOfflineStreamResult(s.impl)
defer C.DestroyOfflineRecognizerResult(p)
result := &OfflineRecognizerResult{}
result.Text = C.GoString(p.text)
return result
}

5
scripts/go/ssh_config Normal file
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Host github.com
Hostname github.com
User git
IdentityFile ~/.ssh/github
StrictHostKeyChecking no