d01682d968
* Add vad clear api for better performance * rename to make naming consistent and remove macro * Fix linker error * Fix Vad.kt
139 lines
3.7 KiB
C++
139 lines
3.7 KiB
C++
// sherpa-onnx/csrc/voice-activity-detector.cc
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//
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// Copyright (c) 2023 Xiaomi Corporation
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#include "sherpa-onnx/csrc/voice-activity-detector.h"
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#include <queue>
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#include <utility>
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#include "sherpa-onnx/csrc/circular-buffer.h"
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#include "sherpa-onnx/csrc/vad-model.h"
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namespace sherpa_onnx {
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class VoiceActivityDetector::Impl {
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public:
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explicit Impl(const VadModelConfig &config, float buffer_size_in_seconds = 60)
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: model_(VadModel::Create(config)),
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config_(config),
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buffer_(buffer_size_in_seconds * config.sample_rate) {}
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#if __ANDROID_API__ >= 9
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Impl(AAssetManager *mgr, const VadModelConfig &config,
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float buffer_size_in_seconds = 60)
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: model_(VadModel::Create(mgr, config)),
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config_(config),
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buffer_(buffer_size_in_seconds * config.sample_rate) {}
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#endif
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void AcceptWaveform(const float *samples, int32_t n) {
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int32_t window_size = model_->WindowSize();
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// note n is usally window_size and there is no need to use
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// an extra buffer here
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last_.insert(last_.end(), samples, samples + n);
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int32_t k = static_cast<int32_t>(last_.size()) / window_size;
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const float *p = last_.data();
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bool is_speech = false;
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for (int32_t i = 0; i != k; ++i, p += window_size) {
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buffer_.Push(p, window_size);
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is_speech = model_->IsSpeech(p, window_size);
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}
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last_ = std::vector<float>(
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p, static_cast<const float *>(last_.data()) + last_.size());
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if (is_speech) {
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if (start_ == -1) {
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// beginning of speech
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start_ = buffer_.Tail() - 2 * model_->WindowSize() -
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model_->MinSpeechDurationSamples();
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}
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} else {
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// non-speech
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if (start_ != -1 && buffer_.Size()) {
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// end of speech, save the speech segment
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int32_t end = buffer_.Tail() - model_->MinSilenceDurationSamples();
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std::vector<float> s = buffer_.Get(start_, end - start_);
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SpeechSegment segment;
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segment.start = start_;
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segment.samples = std::move(s);
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segments_.push(std::move(segment));
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buffer_.Pop(end - buffer_.Head());
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}
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start_ = -1;
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}
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}
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bool Empty() const { return segments_.empty(); }
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void Pop() { segments_.pop(); }
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void Clear() { std::queue<SpeechSegment>().swap(segments_); }
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const SpeechSegment &Front() const { return segments_.front(); }
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void Reset() {
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std::queue<SpeechSegment>().swap(segments_);
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model_->Reset();
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buffer_.Reset();
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start_ = -1;
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}
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bool IsSpeechDetected() const { return start_ != -1; }
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private:
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std::queue<SpeechSegment> segments_;
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std::unique_ptr<VadModel> model_;
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VadModelConfig config_;
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CircularBuffer buffer_;
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std::vector<float> last_;
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int32_t start_ = -1;
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};
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VoiceActivityDetector::VoiceActivityDetector(
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const VadModelConfig &config, float buffer_size_in_seconds /*= 60*/)
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: impl_(std::make_unique<Impl>(config, buffer_size_in_seconds)) {}
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#if __ANDROID_API__ >= 9
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VoiceActivityDetector::VoiceActivityDetector(
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AAssetManager *mgr, const VadModelConfig &config,
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float buffer_size_in_seconds /*= 60*/)
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: impl_(std::make_unique<Impl>(mgr, config, buffer_size_in_seconds)) {}
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#endif
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VoiceActivityDetector::~VoiceActivityDetector() = default;
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void VoiceActivityDetector::AcceptWaveform(const float *samples, int32_t n) {
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impl_->AcceptWaveform(samples, n);
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}
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bool VoiceActivityDetector::Empty() const { return impl_->Empty(); }
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void VoiceActivityDetector::Pop() { impl_->Pop(); }
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void VoiceActivityDetector::Clear() { impl_->Clear(); }
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const SpeechSegment &VoiceActivityDetector::Front() const {
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return impl_->Front();
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}
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void VoiceActivityDetector::Reset() { impl_->Reset(); }
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bool VoiceActivityDetector::IsSpeechDetected() const {
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return impl_->IsSpeechDetected();
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}
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} // namespace sherpa_onnx
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