diarization.py

from __future__ import annotations

import threading
from collections import Counter
from typing import Any

try:
    from .audio import round_sec, seconds_from_samples
    from .config import VoiceRuntimeConfig
except ImportError:  # HF flat-root execution fallback
    from audio import round_sec, seconds_from_samples
    from config import VoiceRuntimeConfig


class DiarizationRuntime:
    _lock = threading.Lock()
    _pipeline = None
    _loaded_id: str | None = None

    @classmethod
    def get_pipeline(cls, config: VoiceRuntimeConfig):
        with cls._lock:
            if cls._pipeline is not None and cls._loaded_id == config.diarization_model_id:
                return cls._pipeline

            if not config.hf_token:
                raise RuntimeError("HF_TOKEN is required for diarization model download/use.")

            try:
                from pyannote.audio import Pipeline
            except Exception as exc:
                raise RuntimeError("pyannote.audio is not installed; install it to enable diarization.") from exc

            pipeline = Pipeline.from_pretrained(config.diarization_model_id, token=config.hf_token)
            try:
                pipeline.to("cpu")
            except Exception:
                pass

            cls._pipeline = pipeline
            cls._loaded_id = config.diarization_model_id
            return cls._pipeline


def _segment_to_payload(start_sec: float, end_sec: float, speaker: str, sample_rate: int) -> dict[str, Any]:
    start_sample = int(round(start_sec * sample_rate))
    end_sample = int(round(end_sec * sample_rate))
    end_sample = max(start_sample + 1, end_sample)
    return {
        "speaker": speaker,
        "start_sec": round_sec(start_sec),
        "end_sec": round_sec(end_sec),
        "duration_sec": round_sec(max(0.0, end_sec - start_sec)),
        "start_sample": start_sample,
        "end_sample": end_sample,
    }


def _resolve_annotation(diarization_output: Any) -> Any:
    """Return an object exposing itertracks(yield_label=True)."""
    if hasattr(diarization_output, "itertracks"):
        return diarization_output

    # Newer pyannote pipelines may return wrappers like DiarizeOutput.
    for attr in ("speaker_diarization", "annotation", "diarization"):
        candidate = getattr(diarization_output, attr, None)
        if candidate is not None and hasattr(candidate, "itertracks"):
            return candidate

    if isinstance(diarization_output, dict):
        for key in ("speaker_diarization", "annotation", "diarization"):
            candidate = diarization_output.get(key)
            if candidate is not None and hasattr(candidate, "itertracks"):
                return candidate

    raise RuntimeError(
        "Unsupported diarization output type "
        f"{type(diarization_output).__name__}; expected Annotation-compatible object."
    )


def run_diarization(wav_path: str, config: VoiceRuntimeConfig, sample_rate: int) -> list[dict[str, Any]]:
    if not config.diarization_enabled:
        return []

    pipeline = DiarizationRuntime.get_pipeline(config)

    kwargs: dict[str, Any] = {}
    if config.diarization_min_speakers > 0:
        kwargs["min_speakers"] = config.diarization_min_speakers
    if config.diarization_max_speakers > 0:
        kwargs["max_speakers"] = config.diarization_max_speakers

    diarization_output = pipeline(wav_path, **kwargs) if kwargs else pipeline(wav_path)
    annotation = _resolve_annotation(diarization_output)

    diarization_segments: list[dict[str, Any]] = []
    for turn, _, speaker in annotation.itertracks(yield_label=True):
        diarization_segments.append(
            _segment_to_payload(
                start_sec=float(turn.start),
                end_sec=float(turn.end),
                speaker=str(speaker),
                sample_rate=sample_rate,
            )
        )

    diarization_segments.sort(key=lambda item: item["start_sec"])
    return diarization_segments


def _find_speaker_for_time(timestamp_sec: float, diarization_segments: list[dict[str, Any]]) -> str | None:
    for segment in diarization_segments:
        if segment["start_sec"] <= timestamp_sec < segment["end_sec"]:
            return segment["speaker"]
    return None


def attach_speakers_to_words(words: list[dict[str, Any]], diarization_segments: list[dict[str, Any]]) -> None:
    if not diarization_segments:
        return

    for word in words:
        midpoint = (float(word["start_sec"]) + float(word["end_sec"])) / 2.0
        speaker = _find_speaker_for_time(midpoint, diarization_segments)
        word["speaker"] = speaker or "unknown"


def attach_speakers_to_segments(

    segments: list[dict[str, Any]],

    words: list[dict[str, Any]],

    sample_rate: int,

) -> None:
    del sample_rate
    if not words:
        for segment in segments:
            segment["speaker"] = "unknown"
        return

    words_by_segment: dict[int, list[str]] = {}
    for word in words:
        seg_idx = int(word.get("segment_index", -1))
        if seg_idx < 0:
            continue
        words_by_segment.setdefault(seg_idx, []).append(str(word.get("speaker", "unknown")))

    for segment in segments:
        idx = int(segment.get("index", -1))
        labels = words_by_segment.get(idx, [])
        if not labels:
            segment["speaker"] = "unknown"
            continue
        top = Counter(labels).most_common(1)[0][0]
        segment["speaker"] = top


def build_diarization_summary(diarization_segments: list[dict[str, Any]]) -> dict[str, Any]:
    speakers = sorted({item["speaker"] for item in diarization_segments})
    total_speech_sec = round_sec(sum(float(item["duration_sec"]) for item in diarization_segments))
    return {
        "speaker_count": len(speakers),
        "speakers": speakers,
        "segment_count": len(diarization_segments),
        "total_speech_sec": total_speech_sec,
    }