// Package vad turns a stream of raw audio chunks into bounded speech
// segments using a simple energy-threshold heuristic — no ML model. See
// transcriptor-ai/CLAUDE.md for why: no Go/Swift ML ecosystem was mature
// enough to justify the integration cost for this. Revisit only if
// segmentation quality proves to be the actual bottleneck.
package vad

import (
	"encoding/binary"
	"math"
	"time"

	"github.com/stephanetailland/transcriptor-ai/internal/capture"
)

// Segment is a bounded span of speech audio ready for ASR.
type Segment struct {
	Track capture.Track
	Data  []byte // raw PCM, concatenation of the contributing chunks
	Start time.Time
	End   time.Time
	// IsFinal is true when silence closed the utterance, false when the
	// segment was force-cut at MaxSegmentDuration and more audio for the
	// same utterance follows in the next segment (which starts with
	// Config.Overlap worth of audio repeated for ASR context continuity).
	IsFinal bool
}

// Config controls the energy-threshold heuristic. All durations are
// expressed in wall-clock time; SampleRate converts them to byte counts.
type Config struct {
	SampleRate int

	// SilenceThresholdDB is the RMS level (dBFS, 0 = full scale) below which
	// a chunk is considered silence. Real speech in testing sits around -40
	// to -20 dBFS; captured silence sits around -90 dBFS. Needs empirical
	// tuning against real meeting audio — this default is a starting point,
	// not a validated value.
	SilenceThresholdDB float64

	// SilenceHangover is how long silence must persist after speech before
	// the segment is closed as final (IsFinal: true).
	SilenceHangover time.Duration

	// MinSpeechDuration is the minimum contiguous voiced audio required
	// before a segment starts accumulating, to filter transient noise
	// (clicks, pops) rather than opening a segment on every blip.
	MinSpeechDuration time.Duration

	// MaxSegmentDuration force-cuts a segment even mid-speech, so a long
	// monologue still produces incremental, timely output instead of one
	// unbounded segment.
	MaxSegmentDuration time.Duration

	// Overlap is how much trailing audio is carried into the next segment
	// when force-cut, for ASR context continuity across the cut. Tradeoff:
	// the carried-over audio gets transcribed again, and both copies are
	// published — a nonzero Overlap produces visible duplicate text at
	// every force-cut boundary (confirmed in real testing: this is worse
	// than the word occasionally getting cut in half at a hard boundary,
	// which is why DefaultConfig uses 0). Only raise this if paired with
	// dedup logic on the published text — never on its own.
	Overlap time.Duration
}

// DefaultConfig returns starting-point thresholds; tune against real audio.
func DefaultConfig(sampleRate int) Config {
	return Config{
		SampleRate:         sampleRate,
		SilenceThresholdDB: -40,
		SilenceHangover:    600 * time.Millisecond,
		MinSpeechDuration:  200 * time.Millisecond,
		MaxSegmentDuration: 4 * time.Second,
		Overlap:            0,
	}
}

func (c Config) bytesForDuration(d time.Duration) int {
	// 16-bit mono samples: 2 bytes/sample.
	samples := int(float64(c.SampleRate) * d.Seconds())
	n := samples * 2
	if n%2 != 0 {
		n++
	}
	return n
}

// Detector runs the heuristic independently per track (system and mic have
// separate state) over a shared, tagged chunk stream.
type Detector struct {
	cfg    Config
	states map[capture.Track]*trackState
}

func New(cfg Config) *Detector {
	return &Detector{cfg: cfg, states: make(map[capture.Track]*trackState)}
}

type trackState struct {
	inSpeech bool
	buffer   []byte
	segStart time.Time

	// candidate holds voiced audio not yet promoted to a real segment,
	// while we wait to confirm MinSpeechDuration.
	candidate      []byte
	candidateStart time.Time

	silenceSince time.Time // zero value means "not currently in a silence run"
	lastChunkAt  time.Time // timestamp of the most recently processed chunk
}

// Run consumes chunks until the channel closes, emitting Segments on the
// returned channel. Any in-progress segment is flushed (as final, since
// capture ended) before the output channel closes.
func (d *Detector) Run(chunks <-chan capture.Chunk) <-chan Segment {
	out := make(chan Segment, 16)
	go func() {
		defer close(out)
		for chunk := range chunks {
			d.process(chunk, out)
		}
		for track, st := range d.states {
			if st.inSpeech && len(st.buffer) > 0 {
				out <- Segment{Track: track, Data: st.buffer, Start: st.segStart, End: st.lastChunkAt, IsFinal: true}
			}
		}
	}()
	return out
}

func (d *Detector) process(chunk capture.Chunk, out chan<- Segment) {
	st, ok := d.states[chunk.Track]
	if !ok {
		st = &trackState{}
		d.states[chunk.Track] = st
	}

	speech := dbFS(chunk.Data) >= d.cfg.SilenceThresholdDB
	st.lastChunkAt = chunk.Timestamp

	switch {
	case speech && !st.inSpeech:
		// Possible start of an utterance — buffer as a candidate until
		// MinSpeechDuration is confirmed, to filter transient noise.
		if len(st.candidate) == 0 {
			st.candidateStart = chunk.Timestamp
		}
		st.candidate = append(st.candidate, chunk.Data...)
		if chunk.Timestamp.Sub(st.candidateStart) >= d.cfg.MinSpeechDuration {
			st.inSpeech = true
			st.segStart = st.candidateStart
			st.buffer = st.candidate
			st.candidate = nil
			st.silenceSince = time.Time{}
		}

	case speech && st.inSpeech:
		st.buffer = append(st.buffer, chunk.Data...)
		st.silenceSince = time.Time{} // speech resumed, cancel any pending hangover
		if chunk.Timestamp.Sub(st.segStart) >= d.cfg.MaxSegmentDuration {
			out <- Segment{Track: chunk.Track, Data: st.buffer, Start: st.segStart, End: chunk.Timestamp, IsFinal: false}
			st.buffer = tail(st.buffer, d.cfg.bytesForDuration(d.cfg.Overlap))
			st.segStart = chunk.Timestamp.Add(-d.cfg.Overlap)
		}

	case !speech && st.inSpeech:
		if st.silenceSince.IsZero() {
			st.silenceSince = chunk.Timestamp
		}
		st.buffer = append(st.buffer, chunk.Data...) // keep brief trailing silence for natural cutoff
		if chunk.Timestamp.Sub(st.silenceSince) >= d.cfg.SilenceHangover {
			out <- Segment{Track: chunk.Track, Data: st.buffer, Start: st.segStart, End: chunk.Timestamp, IsFinal: true}
			st.inSpeech = false
			st.buffer = nil
			st.silenceSince = time.Time{}
		}

	default: // !speech && !st.inSpeech
		st.candidate = nil
	}
}

// dbFS returns the RMS level of a 16-bit little-endian PCM buffer in dBFS
// (0 = full scale for int16). Silence (all-zero input) returns -infinity,
// which compares correctly against any real threshold.
func dbFS(data []byte) float64 {
	n := len(data) / 2
	if n == 0 {
		return math.Inf(-1)
	}
	var sumSquares float64
	for i := 0; i+1 < len(data); i += 2 {
		s := int16(binary.LittleEndian.Uint16(data[i : i+2]))
		v := float64(s)
		sumSquares += v * v
	}
	rms := math.Sqrt(sumSquares / float64(n))
	if rms == 0 {
		return math.Inf(-1)
	}
	return 20 * math.Log10(rms/32768)
}

func tail(data []byte, n int) []byte {
	if n <= 0 {
		return nil
	}
	if n >= len(data) {
		out := make([]byte, len(data))
		copy(out, data)
		return out
	}
	out := make([]byte, n)
	copy(out, data[len(data)-n:])
	return out
}