// sherpa-onnx/csrc/voice-activity-detector.cc
//
// Copyright (c)  2023  Xiaomi Corporation

#include "sherpa-onnx/csrc/voice-activity-detector.h"

#include <queue>
#include <utility>

#include "sherpa-onnx/csrc/circular-buffer.h"
#include "sherpa-onnx/csrc/vad-model.h"

namespace sherpa_onnx {

class VoiceActivityDetector::Impl {
 public:
  explicit Impl(const VadModelConfig &config, float buffer_size_in_seconds = 60)
      : model_(VadModel::Create(config)),
        config_(config),
        buffer_(buffer_size_in_seconds * config.sample_rate) {}

#if __ANDROID_API__ >= 9
  Impl(AAssetManager *mgr, const VadModelConfig &config,
       float buffer_size_in_seconds = 60)
      : model_(VadModel::Create(mgr, config)),
        config_(config),
        buffer_(buffer_size_in_seconds * config.sample_rate) {}
#endif

  void AcceptWaveform(const float *samples, int32_t n) {
    int32_t window_size = model_->WindowSize();

    // note n is usally window_size and there is no need to use
    // an extra buffer here
    last_.insert(last_.end(), samples, samples + n);
    int32_t k = static_cast<int32_t>(last_.size()) / window_size;
    const float *p = last_.data();
    bool is_speech = false;

    for (int32_t i = 0; i != k; ++i, p += window_size) {
      buffer_.Push(p, window_size);
      is_speech = model_->IsSpeech(p, window_size);
    }

    last_ = std::vector<float>(
        p, static_cast<const float *>(last_.data()) + last_.size());

    if (is_speech) {
      if (start_ == -1) {
        // beginning of speech
        start_ = buffer_.Tail() - 2 * model_->WindowSize() -
                 model_->MinSpeechDurationSamples();
      }
    } else {
      // non-speech
      if (start_ != -1 && buffer_.Size()) {
        // end of speech, save the speech segment
        int32_t end = buffer_.Tail() - model_->MinSilenceDurationSamples();

        std::vector<float> s = buffer_.Get(start_, end - start_);
        SpeechSegment segment;

        segment.start = start_;
        segment.samples = std::move(s);

        segments_.push(std::move(segment));

        buffer_.Pop(end - buffer_.Head());
      }

      start_ = -1;
    }
  }

  bool Empty() const { return segments_.empty(); }

  void Pop() { segments_.pop(); }

  void Clear() { std::queue<SpeechSegment>().swap(segments_); }

  const SpeechSegment &Front() const { return segments_.front(); }

  void Reset() {
    std::queue<SpeechSegment>().swap(segments_);

    model_->Reset();
    buffer_.Reset();

    start_ = -1;
  }

  bool IsSpeechDetected() const { return start_ != -1; }

 private:
  std::queue<SpeechSegment> segments_;

  std::unique_ptr<VadModel> model_;
  VadModelConfig config_;
  CircularBuffer buffer_;
  std::vector<float> last_;

  int32_t start_ = -1;
};

VoiceActivityDetector::VoiceActivityDetector(
    const VadModelConfig &config, float buffer_size_in_seconds /*= 60*/)
    : impl_(std::make_unique<Impl>(config, buffer_size_in_seconds)) {}

#if __ANDROID_API__ >= 9
VoiceActivityDetector::VoiceActivityDetector(
    AAssetManager *mgr, const VadModelConfig &config,
    float buffer_size_in_seconds /*= 60*/)
    : impl_(std::make_unique<Impl>(mgr, config, buffer_size_in_seconds)) {}
#endif

VoiceActivityDetector::~VoiceActivityDetector() = default;

void VoiceActivityDetector::AcceptWaveform(const float *samples, int32_t n) {
  impl_->AcceptWaveform(samples, n);
}

bool VoiceActivityDetector::Empty() const { return impl_->Empty(); }

void VoiceActivityDetector::Pop() { impl_->Pop(); }

void VoiceActivityDetector::Clear() { impl_->Clear(); }

const SpeechSegment &VoiceActivityDetector::Front() const {
  return impl_->Front();
}

void VoiceActivityDetector::Reset() { impl_->Reset(); }

bool VoiceActivityDetector::IsSpeechDetected() const {
  return impl_->IsSpeechDetected();
}

}  // namespace sherpa_onnx