path: root/converter/tts.py

"""Client wrappers for the TTS backends.

QwenTTSClient talks to the Qwen3-TTS Gradio demos (custom voice / voice clone).
FasterTTSClient talks to the OpenAI-compatible server from the
faster-qwen3-tts repository (voice cloning only; the reference voice is
configured server-side — see the "Faster backend" section of the README).
"""

import contextlib
import io
import json
import logging
import shutil
import sys
import tempfile
import threading
import time
import urllib.error
import urllib.request
import wave
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple

from . import config
from .audio import concat_audio_files, probe_duration_ms
from .chunking import split_into_chunks

logger = logging.getLogger(__name__)

# Voice modes (re-exported for the CLI and the converter orchestrator).
VOICE_MODE_CUSTOM = "custom_voice"
VOICE_MODE_CLONE = "voice_clone"
VOICE_MODES = (VOICE_MODE_CUSTOM, VOICE_MODE_CLONE)

# Languages understood by the Qwen3-TTS API. Display names must match the
# demo dropdown exactly (the demo silently falls back to "Auto" for
# unrecognized values, so languages are validated client-side first).
TTS_LANGUAGES = (
    "Auto",
    "Chinese",
    "English",
    "German",
    "Italian",
    "Portuguese",
    "Spanish",
    "Japanese",
    "Korean",
    "French",
    "Russian",
)

# Short aliases accepted on the command line (ISO 639-1 codes and common
# shorthands), mapped to the display names above.
TTS_LANGUAGE_ALIASES = {
    "zh": "Chinese",
    "en": "English",
    "de": "German",
    "it": "Italian",
    "pt": "Portuguese",
    "es": "Spanish",
    "ja": "Japanese",
    "ko": "Korean",
    "fr": "French",
    "ru": "Russian",
    "zh-cn": "Chinese",
    "zh-tw": "Chinese",
    "pt-br": "Portuguese",
    "en-us": "English",
    "en-gb": "English",
}

# Canonical speaker names -> display names used by the qwen-tts demo.
SPEAKER_DISPLAY_NAMES = {
    "ryan": "Ryan",
    "serena": "Serena",
    "vivian": "Vivian",
    "uncle_fu": "Uncle Fu",
    "aiden": "Aiden",
    "ono_anna": "Ono Anna",
    "sohee": "Sohee",
    "eric": "Eric",
    "dylan": "Dylan",
}

# Fixed model facts: both demos run the 1.7B model (the CustomVoice demo
# takes its full HuggingFace id), and the 12Hz codec outputs 24 kHz audio.
MODEL_SIZE = "1.7B"
CUSTOM_VOICE_MODEL_ID = "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice"
SAMPLE_RATE = 24000

CHUNKS_FOLDER = Path(__file__).resolve().parent.parent / "chunks"


def speaker_display_name() -> str:
    """Return the Gradio display name for the configured custom speaker."""
    return SPEAKER_DISPLAY_NAMES.get(
        config.CUSTOM_VOICE_SPEAKER.lower(), config.CUSTOM_VOICE_SPEAKER)


def normalize_language(value: Optional[str]) -> str:
    """Normalize a user-provided language name to a Qwen3-TTS display name.

    Accepts the display names in TTS_LANGUAGES case-insensitively as
    well as the short aliases in TTS_LANGUAGE_ALIASES (ISO 639-1 codes
    and common shorthands). Raises ValueError for anything else, since the
    Qwen3-TTS demo silently falls back to "Auto" for unrecognized languages.
    """
    if value is None:
        raise ValueError("Language must not be None")
    candidate = value.strip()
    if not candidate:
        raise ValueError("Language must not be empty")
    for name in TTS_LANGUAGES:
        if candidate.lower() == name.lower():
            return name
    alias = TTS_LANGUAGE_ALIASES.get(candidate.lower())
    if alias:
        return alias
    raise ValueError(
        f"Unknown language: {value!r}. Expected one of "
        f"{', '.join(TTS_LANGUAGES)} (or an alias: "
        f"{', '.join(sorted(TTS_LANGUAGE_ALIASES))})."
    )


def transcribe_reference_audio(audio_path: str, model_name: str = "base") -> Optional[str]:
    """Transcribe reference audio locally using an optional Whisper backend.

    The current qwen-tts demo does not expose a transcription endpoint, so
    transcription is done client-side when a Whisper package is available.
    Returns None if no backend is installed.
    """
    for backend in ("faster_whisper", "whisper"):
        try:
            if backend == "faster_whisper":
                from faster_whisper import WhisperModel
                model = WhisperModel(model_name, device="cpu", compute_type="int8")
                segments, _ = model.transcribe(audio_path)
                text = " ".join(seg.text.strip() for seg in segments).strip()
            else:
                import whisper
                model = whisper.load_model(model_name)
                result = model.transcribe(audio_path)
                text = (result.get("text") or "").strip()
            if text:
                logger.info("Transcription complete via %s: %s", backend, text)
                return text
        except ImportError:
            continue
        except Exception as exc:
            logger.warning("%s transcription failed: %s", backend, exc)
    logger.warning("No Whisper backend available; transcription skipped.")
    return None


def check_for_truncation(text: str, actual_seconds: Optional[float], label: str) -> None:
    """Raise RuntimeError when audio is far shorter than its text implies.

    Both backends silently truncate audio when a single generation hits an
    internal cap (no error is reported to the client), so grossly short
    audio must be detected client-side: failing the request lets the retry
    logic re-run it, and persistent failures surface as failed chunks
    instead of a "successful" run with missing audio. ``actual_seconds``
    is None when the duration could not be determined, in which case the
    check is skipped. Requests shorter than
    ``config.MIN_WORDS_FOR_DURATION_CHECK`` words are not checked (their
    duration estimates are too noisy).
    """
    words = len(text.split())
    if actual_seconds is None or words < config.MIN_WORDS_FOR_DURATION_CHECK:
        return
    expected_seconds = 60.0 * words / config.ESTIMATED_WORDS_PER_MINUTE
    if actual_seconds < expected_seconds * config.MIN_AUDIO_DURATION_RATIO:
        raise RuntimeError(
            f"{label}: audio is far shorter than the text implies "
            f"({actual_seconds:.1f}s of audio for {words} words, expected at "
            f"least {expected_seconds * config.MIN_AUDIO_DURATION_RATIO:.0f}s); "
            "the TTS server likely truncated the generation silently"
        )


def _audio_duration_seconds(path: Path) -> Optional[float]:
    """Return an audio file's duration in seconds, or None when unknown."""
    try:
        with wave.open(str(path), "rb") as wav_file:
            framerate = wav_file.getframerate()
            if framerate > 0:
                return wav_file.getnframes() / float(framerate)
    except (wave.Error, EOFError, OSError):
        pass
    if shutil.which("ffprobe") is None:
        return None
    milliseconds = probe_duration_ms(path)
    if milliseconds <= 0:
        return None
    return milliseconds / 1000.0


class _BaseTTSClient:
    """Shared chunk retry logic, heartbeat, and chunk file bookkeeping."""

    def generate_chunk(self, text: str, chunk_num: int) -> Optional[str]:
        """Generate one audio chunk; returns its path in the chunks folder."""
        raise NotImplementedError

    def _chunk_path(self, chunk_num: int, suffix: str) -> Path:
        """Resolve the target path for a chunk, removing stale files first.

        Any stale chunk file for this index is removed so a retry or extension
        change can never leave two files matching chunk_NNNN.*.
        """
        for stale in CHUNKS_FOLDER.glob(f"chunk_{chunk_num:04d}.*"):
            try:
                stale.unlink()
            except OSError as exc:
                logger.debug("Could not remove stale chunk file %s: %s", stale, exc)
        return CHUNKS_FOLDER / f"chunk_{chunk_num:04d}{suffix}"

    def process_chunk_with_retry(self, chunk_num: int, text: str) -> Optional[Path]:
        """Process a chunk with retry logic and rate limiting.

        Returns the generated chunk file's path, or None when all attempts
        failed.
        """
        # Optional pause between API calls (rate limiting on hosted demos)
        if chunk_num > 1 and config.MIN_DELAY_BETWEEN_CHUNKS > 0:
            time.sleep(config.MIN_DELAY_BETWEEN_CHUNKS)

        for attempt in range(config.MAX_RETRIES):
            try:
                result = self.generate_chunk(text, chunk_num)
                if result and Path(result).exists():
                    return Path(result)
                logger.warning("Chunk %d attempt %d failed", chunk_num, attempt + 1)
            except Exception as exc:
                logger.warning("Chunk %d attempt %d error: %s", chunk_num, attempt + 1, exc)

            if attempt < config.MAX_RETRIES - 1:
                sleep_time = 5 + (2 ** attempt)
                logger.info("Waiting %ds before retry...", sleep_time)
                time.sleep(sleep_time)

        logger.error("Chunk %d failed after %d attempts", chunk_num, config.MAX_RETRIES)
        return None

    @contextlib.contextmanager
    def _chunk_heartbeat(self, chunk_num: int):
        """Print a periodic "still working" message while a chunk generates."""
        stop = threading.Event()

        def _beat():
            start = time.time()
            while not stop.wait(config.HEARTBEAT_INTERVAL_SECONDS):
                elapsed = time.time() - start
                print(f"[...] Chunk {chunk_num} still generating — "
                      f"{int(elapsed // 60)}m {int(elapsed % 60)}s elapsed", flush=True)

        thread = threading.Thread(target=_beat, daemon=True)
        thread.start()
        try:
            yield
        finally:
            stop.set()
            thread.join()


class QwenTTSClient(_BaseTTSClient):
    """Generates audio chunks through a Qwen3-TTS Gradio server."""

    def __init__(self, voice_mode: str = "custom_voice", voice_clone_ref_audio: Optional[str] = None,
                 voice_clone_ref_text: Optional[str] = None, skip_transcription: bool = False,
                 language: Optional[str] = None):
        if voice_mode not in VOICE_MODES:
            raise ValueError(
                f"Unknown voice mode: {voice_mode!r} (expected one of {VOICE_MODES})"
            )
        self.voice_mode = voice_mode
        self.voice_clone_ref_audio = voice_clone_ref_audio
        self.voice_clone_ref_text = (voice_clone_ref_text or "").strip()
        self.skip_transcription = skip_transcription
        if language is None:
            language = config.LANGUAGE
        # Validate before connecting so bad values fail fast without a server.
        self.language = normalize_language(language)
        self.client = None
        self.api_info: Dict[str, Any] = {}
        self.clone_client = None
        self.clone_api_info: Dict[str, Any] = {}
        self._ref_audio_filedata: Optional[Dict[str, Any]] = None
        self._connect()

    # ------------------------------------------------------------------
    # Connection
    # ------------------------------------------------------------------

    def _connect(self) -> None:
        api_url = config.VOICE_CLONE_API_URL if self.voice_mode == VOICE_MODE_CLONE else config.QWEN_API_URL
        try:
            if self.voice_mode == VOICE_MODE_CLONE:
                # Voice clone uses the Base-model demo, which is a separate server
                # from the CustomVoice demo (that one only exposes /run_instruct).
                self._init_client(config.VOICE_CLONE_API_URL, clone=True)
                print(f"[OK] Connected to Voice Clone API at {config.VOICE_CLONE_API_URL}")
                self._resolve_reference_text()
            else:
                self._init_client(config.QWEN_API_URL, clone=False)
                print("[OK] Connected to Qwen API")
        except Exception as exc:
            raise RuntimeError(
                f"Qwen API initialization failed at {api_url}: {exc}. "
                "Make sure the Qwen Gradio server is running and reachable, and that your "
                "installed Qwen3-TTS version matches this converter's API expectations "
                "(voice clone requires the Base-model demo: Qwen/Qwen3-TTS-12Hz-1.7B-Base)."
            ) from exc

    def _resolve_reference_text(self) -> None:
        """Resolve the reference transcript: explicit text, then local
        transcription, then x-vector-only mode."""
        if not self.voice_clone_ref_text and self.voice_clone_ref_audio:
            if self.skip_transcription:
                print("[INFO] Skipping reference audio transcription (--no-transcription).")
            else:
                print("[INFO] Transcribing reference audio for voice cloning...")
                self.voice_clone_ref_text = self.transcribe_audio(self.voice_clone_ref_audio) or ""
        if not self.voice_clone_ref_text:
            print("[WARNING] No reference text available; using x-vector-only clone mode (lower quality).")
            print('          Pass --transcription "..." for higher-quality in-context cloning.')
        else:
            print(f"[OK] Reference text:\n{self.voice_clone_ref_text}")

    def _init_client(self, url: str, clone: bool = False) -> None:
        """Initialize a Gradio client and store its API metadata.

        gradio_client prints its usage info directly to stdout while the
        client is created and its API metadata loaded, so stdout is swapped
        for a buffer for the whole process; the captured text is re-emitted
        at DEBUG level for troubleshooting.
        """
        from gradio_client import Client

        logger.info("Connecting to Qwen API at %s...", url)
        old_stdout = sys.stdout
        captured = io.StringIO()
        sys.stdout = captured
        try:
            try:
                client = Client(url, httpx_kwargs={"timeout": config.API_TIMEOUT})
            except TypeError:
                # Older gradio_client versions don't support httpx_kwargs.
                client = Client(url)
            if clone:
                self.clone_client = client
                self.clone_api_info = self._load_api_info(client)
            else:
                self.client = client
                self.api_info = self._load_api_info(client)
        finally:
            sys.stdout = old_stdout
        usage_info = captured.getvalue().strip()
        if usage_info:
            logger.debug("Gradio client output for %s:\n%s", url, usage_info)
        logger.info("Connected to Qwen API")

    @staticmethod
    def _load_api_info(client) -> Dict[str, Any]:
        """Load available API metadata from the Gradio app."""
        try:
            return client.view_api(return_format="dict")
        except Exception as exc:
            logger.warning("Unable to read API metadata: %s", exc)
            return {}

    def _resolve_api_name(self, *candidates: str, api_info: Optional[Dict[str, Any]] = None) -> str:
        """Return the first available api_name from candidate list."""
        info = api_info if api_info is not None else self.api_info
        named_endpoints = info.get("named_endpoints", {})
        for candidate in candidates:
            if candidate in named_endpoints:
                return candidate
        return candidates[0]

    def _endpoint_accepts_param(self, api_name: str, param_name: str,
                                api_info: Optional[Dict[str, Any]] = None) -> bool:
        """Check whether endpoint input schema includes the given parameter."""
        info = api_info if api_info is not None else self.api_info
        endpoint = info.get("named_endpoints", {}).get(api_name, {})
        parameters = endpoint.get("parameters", [])
        return any(parameter.get("parameter_name") == param_name for parameter in parameters)

    # ------------------------------------------------------------------
    # Reference audio transcription (voice clone)
    # ------------------------------------------------------------------

    def transcribe_audio(self, audio_path: str) -> Optional[str]:
        """Transcribe reference audio locally using an optional Whisper backend."""
        return transcribe_reference_audio(audio_path)

    # ------------------------------------------------------------------
    # Chunk generation
    # ------------------------------------------------------------------

    def generate_chunk(self, text: str, chunk_num: int) -> Optional[str]:
        """Generate one audio chunk; returns its path in the chunks folder.

        The text is split into sub-requests of at most
        ``config.MAX_REQUEST_WORDS`` words each (the book-level chunker
        normally guarantees this already; the split is defense in depth
        against pathological input such as a punctuation-free run of
        text), and the audio files returned for the sub-requests are
        concatenated into one chunk file.
        """
        try:
            sub_texts = split_into_chunks(text, max_words=config.MAX_REQUEST_WORDS)
            if not sub_texts:
                raise RuntimeError("No text to synthesize")

            output_path: Optional[Path] = None
            with tempfile.TemporaryDirectory(prefix="tts_parts_") as parts_dir, \
                    self._chunk_heartbeat(chunk_num):
                part_paths = [
                    self._generate_sub_request(sub_text, parts_dir, sub_num,
                                                len(sub_texts), chunk_num)
                    for sub_num, sub_text in enumerate(sub_texts, 1)
                ]
                if len(part_paths) == 1:
                    suffix = part_paths[0].suffix or ".wav"
                    output_path = self._chunk_path(chunk_num, suffix)
                    shutil.copy2(part_paths[0], output_path)
                else:
                    output_path = self._chunk_path(chunk_num, ".wav")
                    concat_audio_files(part_paths, output_path)

            logger.debug("Chunk %d generated successfully (%d sub-request(s))",
                         chunk_num, len(sub_texts))
            return str(output_path)

        except Exception as exc:
            logger.error("Qwen chunk processing failed for chunk %d: %s", chunk_num, exc)
            return None

    def _generate_sub_request(self, text: str, parts_dir: str, sub_num: int,
                              sub_total: int, chunk_num: int) -> Path:
        """Run one API generation for ``text``; returns the downloaded audio."""
        if sub_total > 1:
            logger.info("Chunk %d: oversized input split into %d requests "
                        "(sub-request %d/%d)", chunk_num, sub_total, sub_num, sub_total)
        if self.voice_mode == VOICE_MODE_CUSTOM:
            result = self._generate_custom_voice(text)
        elif self.voice_mode == VOICE_MODE_CLONE:
            result = self._generate_voice_clone(text)
        else:
            raise ValueError(f"Unknown voice mode: {self.voice_mode}")

        if not isinstance(result, (tuple, list)) or not result:
            raise RuntimeError("Qwen API returned an invalid result")

        audio_path = result[0]  # First element is the audio file path
        if not isinstance(audio_path, (str, Path)) or not audio_path:
            raise RuntimeError("Qwen API did not return an audio file path")

        source = Path(audio_path)
        if not source.exists():
            raise RuntimeError(f"Generated audio file not found: {audio_path}")

        destination = Path(parts_dir) / f"part_{sub_num:02d}{source.suffix or '.wav'}"
        shutil.copy2(source, destination)

        check_for_truncation(
            text, _audio_duration_seconds(destination),
            f"Chunk {chunk_num} sub-request {sub_num}/{sub_total}")
        return destination

    # ------------------------------------------------------------------
    # API payloads
    # ------------------------------------------------------------------

    def _generate_custom_voice(self, text: str) -> Tuple:
        """Generate audio using CustomVoice mode."""
        custom_api = self._resolve_api_name("/run_instruct", "/run_custom_voice", "/generate_custom_voice")
        if custom_api == "/run_instruct":
            payload = dict(
                text=text,
                lang_disp=self.language,
                spk_disp=speaker_display_name(),
                instruct=config.CUSTOM_VOICE_INSTRUCT,
            )
        else:
            payload = dict(
                text=text,
                language=self.language,
                speaker=config.CUSTOM_VOICE_SPEAKER,
                instruct=config.CUSTOM_VOICE_INSTRUCT,
            )
            if self._endpoint_accepts_param(custom_api, "model_id_cv"):
                payload["model_id_cv"] = CUSTOM_VOICE_MODEL_ID
            elif self._endpoint_accepts_param(custom_api, "model_size"):
                payload["model_size"] = MODEL_SIZE

            if self._endpoint_accepts_param(custom_api, "seed"):
                payload["seed"] = config.SEED

        return self.client.predict(**payload, api_name=custom_api)

    def _ref_audio_payload(self) -> Dict[str, Any]:
        """Gradio file payload for the reference audio (built once, reused)."""
        if self._ref_audio_filedata is None:
            from gradio_client import handle_file
            self._ref_audio_filedata = handle_file(self.voice_clone_ref_audio)
        return self._ref_audio_filedata

    def _generate_voice_clone(self, text: str) -> Tuple:
        """Generate audio using Voice Clone mode."""
        if not Path(self.voice_clone_ref_audio).exists():
            raise FileNotFoundError(f"Reference audio not found: {self.voice_clone_ref_audio}")

        if self.clone_client is None:
            raise RuntimeError("Voice Clone client is not initialized. Is the Base-model demo running?")

        clone_api = self._resolve_api_name("/run_voice_clone", "/generate_voice_clone",
                                           api_info=self.clone_api_info)
        use_xvector = config.VOICE_CLONE_USE_XVECTOR_ONLY or not self.voice_clone_ref_text

        if clone_api == "/run_voice_clone":
            payload = dict(
                ref_aud=self._ref_audio_payload(),
                ref_txt=self.voice_clone_ref_text,
                use_xvec=use_xvector,
                text=text,
                lang_disp=self.language,
            )
        else:
            payload = dict(
                ref_audio=self._ref_audio_payload(),
                ref_text=self.voice_clone_ref_text,
                target_text=text,
                language=self.language,
                use_xvector_only=use_xvector,
            )
            optional_params = {
                "model_size": MODEL_SIZE,
                "max_chunk_chars": config.VOICE_CLONE_MAX_CHUNK_CHARS,
                "chunk_gap": config.VOICE_CLONE_CHUNK_GAP,
                "seed": config.SEED,
            }
            for name, value in optional_params.items():
                if self._endpoint_accepts_param(clone_api, name, api_info=self.clone_api_info):
                    payload[name] = value

        return self.clone_client.predict(**payload, api_name=clone_api)


class FasterTTSClient(_BaseTTSClient):
    """Generates audio chunks through a faster-qwen3-tts server.

    Talks to the OpenAI-compatible server shipped in the faster-qwen3-tts
    repository (examples/openai_server.py). The reference voice (ref audio,
    ref text) and language are configured on the server itself via
    --ref-audio/--ref-text or a --voices JSON file; this client only sends
    text. Unlike the Gradio demo, the server performs one generation per
    request, so long chunks are sub-chunked client-side.
    """

    def __init__(self, voice: Optional[str] = None, api_url: Optional[str] = None):
        self.voice = voice or config.FASTER_TTS_VOICE
        self.api_url = (api_url or config.FASTER_TTS_API_URL).rstrip("/")
        self._check_health()

    def _check_health(self) -> None:
        """Verify the server is reachable and its model is loaded."""
        url = f"{self.api_url}/health"
        try:
            with urllib.request.urlopen(url, timeout=10) as response:
                payload = json.loads(response.read().decode("utf-8"))
        except Exception as exc:
            raise RuntimeError(
                f"Faster TTS server not reachable at {url}: {exc}. "
                "Start the faster-qwen3-tts OpenAI-compatible server first "
                "(see the 'Faster backend' section of the README)."
            ) from exc
        if not payload.get("model_loaded"):
            raise RuntimeError(
                "The faster TTS server is running but its model is not loaded yet; "
                "wait for model download and startup to finish, then retry."
            )
        print(f"[OK] Connected to faster TTS API at {self.api_url} (voice '{self.voice}')")
        print(f"[INFO] The server silently falls back to its first configured voice if "
              f"'{self.voice}' is not defined in its voice config (see README).")

    # ------------------------------------------------------------------
    # HTTP requests
    # ------------------------------------------------------------------

    def _request_pcm(self, text: str) -> bytes:
        """POST one sub-chunk and return raw 16-bit mono PCM bytes."""
        url = f"{self.api_url}/v1/audio/speech"
        payload = json.dumps({
            "model": "tts-1",
            "input": text,
            "voice": self.voice,
            "response_format": "pcm",
        }).encode("utf-8")
        request = urllib.request.Request(
            url, data=payload, headers={"Content-Type": "application/json"}, method="POST")
        try:
            with urllib.request.urlopen(request, timeout=config.API_TIMEOUT) as response:
                pcm = response.read()
        except urllib.error.HTTPError as exc:
            detail = ""
            try:
                detail = exc.read().decode("utf-8", errors="replace")[:200]
            except Exception:
                pass
            raise RuntimeError(f"Faster TTS server returned HTTP {exc.code}: {detail}") from exc
        except urllib.error.URLError as exc:
            raise RuntimeError(f"Faster TTS request failed: {exc.reason}") from exc
        if not pcm:
            raise RuntimeError("Faster TTS server returned empty audio")
        return pcm

    def _request_pcm_with_retry(self, text: str, chunk_num: int, sub_num: int,
                                sub_total: int) -> bytes:
        """Request one sub-chunk, retrying transient failures."""
        for attempt in range(config.MAX_RETRIES):
            try:
                return self._request_pcm(text)
            except Exception as exc:
                logger.warning("Chunk %d sub-chunk %d/%d attempt %d failed: %s",
                               chunk_num, sub_num, sub_total, attempt + 1, exc)
                if attempt < config.MAX_RETRIES - 1:
                    time.sleep(2 + 2 * attempt)
        raise RuntimeError(f"Sub-chunk {sub_num}/{sub_total} failed after "
                           f"{config.MAX_RETRIES} attempts")

    # ------------------------------------------------------------------
    # Chunk generation
    # ------------------------------------------------------------------

    def generate_chunk(self, text: str, chunk_num: int) -> Optional[str]:
        """Generate one audio chunk; returns its path in the chunks folder."""
        try:
            sub_chunks = split_into_chunks(text, max_words=config.MAX_REQUEST_WORDS)
            if not sub_chunks:
                raise RuntimeError("No text to synthesize")

            pcm_parts: List[bytes] = []
            with self._chunk_heartbeat(chunk_num):
                for sub_num, sub_text in enumerate(sub_chunks, 1):
                    pcm = self._request_pcm_with_retry(
                        sub_text, chunk_num, sub_num, len(sub_chunks))
                    check_for_truncation(
                        sub_text, len(pcm) / (2 * SAMPLE_RATE),
                        f"Chunk {chunk_num} sub-chunk {sub_num}/{len(sub_chunks)}")
                    pcm_parts.append(pcm)

            output_path = self._chunk_path(chunk_num, ".wav")
            with wave.open(str(output_path), "wb") as wav_file:
                wav_file.setnchannels(1)
                wav_file.setsampwidth(2)
                wav_file.setframerate(SAMPLE_RATE)
                wav_file.writeframes(b"".join(pcm_parts))

            logger.debug("Chunk %d generated (%d sub-chunks)", chunk_num, len(sub_chunks))
            return str(output_path)

        except Exception as exc:
            logger.error("Faster chunk processing failed for chunk %d: %s", chunk_num, exc)
            return None