mirror of
https://github.com/denizsafak/abogen.git
synced 2026-07-18 21:50:28 +02:00
feat: Implement speaker analysis and EPUB 3 export functionality
- Added speaker analysis module to infer speaker identities from text chunks. - Introduced SpeakerGuess and SpeakerAnalysis data classes for managing speaker data. - Developed functions for analyzing speaker occurrences and confidence levels. - Created EPUB 3 exporter to generate EPUB packages with synchronized narration and media overlays. - Implemented configurable chunking options for TTS synthesis and EPUB alignment. - Enhanced JavaScript for speaker preview functionality in the web interface. - Added comprehensive tests for chunking and EPUB exporting features. - Documented upgrade plan for transitioning to EPUB 3 with multi-speaker support.
This commit is contained in:
JB
+430
-4
@@ -9,7 +9,7 @@ import threading
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import time
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import uuid
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from pathlib import Path
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from typing import Any, Dict, Iterable, List, Optional, Tuple, cast
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from typing import Any, Dict, Iterable, List, Mapping, Optional, Tuple, cast
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from flask import (
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Blueprint,
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@@ -35,6 +35,7 @@ from abogen.constants import (
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SUPPORTED_SOUND_FORMATS,
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VOICES_INTERNAL,
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)
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from abogen.chunking import ChunkLevel, build_chunks_for_chapters
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from abogen.utils import (
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calculate_text_length,
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clean_text,
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@@ -57,6 +58,7 @@ from abogen.voice_profiles import (
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)
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from abogen.voice_formulas import get_new_voice
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from abogen.speaker_analysis import analyze_speakers
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from abogen.text_extractor import extract_from_path
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from .conversion_runner import SPLIT_PATTERN, SAMPLE_RATE, _select_device, _to_float32
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from .service import ConversionService, Job, JobStatus, PendingJob
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@@ -69,6 +71,174 @@ _preview_pipeline_lock = threading.RLock()
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_preview_pipelines: Dict[Tuple[str, str], Any] = {}
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_CHUNK_LEVEL_OPTIONS = [
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{"value": "paragraph", "label": "Paragraphs"},
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{"value": "sentence", "label": "Sentences"},
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]
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_SPEAKER_MODE_OPTIONS = [
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{"value": "single", "label": "Single Speaker"},
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{"value": "multi", "label": "Multi-Speaker"},
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]
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_CHUNK_LEVEL_VALUES = {option["value"] for option in _CHUNK_LEVEL_OPTIONS}
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_SPEAKER_MODE_VALUES = {option["value"] for option in _SPEAKER_MODE_OPTIONS}
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_DEFAULT_ANALYSIS_THRESHOLD = 3
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_MAX_ANALYSIS_SPEAKERS = 6
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def _build_narrator_roster(
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voice: str,
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voice_profile: Optional[str],
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existing: Optional[Mapping[str, Any]] = None,
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) -> Dict[str, Any]:
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roster: Dict[str, Any] = {
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"narrator": {
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"id": "narrator",
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"label": "Narrator",
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"voice": voice,
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}
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}
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if voice_profile:
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roster["narrator"]["voice_profile"] = voice_profile
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existing_entry: Optional[Mapping[str, Any]] = None
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if existing is not None:
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existing_entry = existing.get("narrator") if isinstance(existing, Mapping) else None
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if isinstance(existing_entry, Mapping):
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roster_entry = roster["narrator"]
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for key in ("label", "voice", "voice_profile", "voice_formula", "pronunciation"):
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value = existing_entry.get(key)
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if value is not None and value != "":
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roster_entry[key] = value
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return roster
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def _build_speaker_roster(
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analysis: Dict[str, Any],
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base_voice: str,
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voice_profile: Optional[str],
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existing: Optional[Mapping[str, Any]] = None,
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) -> Dict[str, Any]:
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roster = _build_narrator_roster(base_voice, voice_profile, existing)
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existing_map: Dict[str, Any] = dict(existing) if isinstance(existing, Mapping) else {}
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speakers = analysis.get("speakers", {}) if isinstance(analysis, dict) else {}
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for speaker_id, payload in speakers.items():
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if speaker_id == "narrator":
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continue
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if payload.get("suppressed"):
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continue
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previous = existing_map.get(speaker_id)
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roster[speaker_id] = {
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"id": speaker_id,
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"label": payload.get("label") or speaker_id.replace("_", " ").title(),
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"voice": base_voice,
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"analysis_confidence": payload.get("confidence"),
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"analysis_count": payload.get("count"),
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}
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if isinstance(previous, Mapping):
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for key in ("voice", "voice_profile", "voice_formula", "resolved_voice", "pronunciation"):
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value = previous.get(key)
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if value is not None and value != "":
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roster[speaker_id][key] = value
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return roster
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def _prepare_speaker_metadata(
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*,
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chapters: List[Dict[str, Any]],
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chunks: List[Dict[str, Any]],
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speaker_mode: str,
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voice: str,
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voice_profile: Optional[str],
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threshold: int,
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existing_roster: Optional[Mapping[str, Any]] = None,
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) -> tuple[List[Dict[str, Any]], Dict[str, Any], Dict[str, Any]]:
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chunk_list = [dict(chunk) for chunk in chunks]
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threshold_value = max(1, int(threshold))
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if speaker_mode != "multi":
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for chunk in chunk_list:
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chunk["speaker_id"] = "narrator"
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chunk["speaker_label"] = "Narrator"
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analysis_payload = {
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"version": "1.0",
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"narrator": "narrator",
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"assignments": {str(chunk.get("id")): "narrator" for chunk in chunk_list},
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"speakers": {
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"narrator": {
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"id": "narrator",
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"label": "Narrator",
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"count": len(chunk_list),
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"confidence": "low",
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"sample_quotes": [],
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"suppressed": False,
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}
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},
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"suppressed": [],
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"stats": {
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"total_chunks": len(chunk_list),
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"explicit_chunks": 0,
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"active_speakers": 0,
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"unique_speakers": 1,
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"suppressed": 0,
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},
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}
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roster = _build_narrator_roster(voice, voice_profile, existing_roster)
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narrator_pron = roster["narrator"].get("pronunciation")
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if narrator_pron:
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analysis_payload["speakers"]["narrator"]["pronunciation"] = narrator_pron
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return chunk_list, roster, analysis_payload
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analysis_result = analyze_speakers(
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chapters, chunk_list, threshold=threshold_value, max_speakers=_MAX_ANALYSIS_SPEAKERS
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)
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analysis_payload = analysis_result.to_dict()
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assignments = analysis_payload.get("assignments", {})
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suppressed_ids = analysis_payload.get("suppressed", [])
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suppressed_details: List[Dict[str, Any]] = []
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speakers_payload = analysis_payload.get("speakers", {})
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if isinstance(suppressed_ids, Iterable):
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for suppressed_id in suppressed_ids:
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speaker_meta = speakers_payload.get(suppressed_id) if isinstance(speakers_payload, dict) else None
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if isinstance(speaker_meta, dict):
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suppressed_details.append(
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{
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"id": suppressed_id,
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"label": speaker_meta.get("label")
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or str(suppressed_id).replace("_", " ").title(),
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"pronunciation": speaker_meta.get("pronunciation"),
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}
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)
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else:
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suppressed_details.append(
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{
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"id": suppressed_id,
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"label": str(suppressed_id).replace("_", " ").title(),
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"pronunciation": None,
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}
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)
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analysis_payload["suppressed_details"] = suppressed_details
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roster = _build_speaker_roster(analysis_payload, voice, voice_profile, existing=existing_roster)
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speakers_payload = analysis_payload.get("speakers")
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if isinstance(speakers_payload, dict):
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for roster_id, roster_payload in roster.items():
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if roster_id in speakers_payload and isinstance(roster_payload, dict):
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pronunciation_value = roster_payload.get("pronunciation")
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if pronunciation_value:
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speakers_payload[roster_id]["pronunciation"] = pronunciation_value
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for chunk in chunk_list:
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chunk_id = str(chunk.get("id"))
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speaker_id = assignments.get(chunk_id, "narrator")
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chunk["speaker_id"] = speaker_id
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speaker_meta = roster.get(speaker_id)
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chunk["speaker_label"] = speaker_meta.get("label") if isinstance(speaker_meta, dict) else speaker_id
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return chunk_list, roster, analysis_payload
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_SUPPLEMENT_TITLE_PATTERNS: List[tuple[re.Pattern[str], float]] = [
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(re.compile(r"\btitle\s+page\b"), 3.0),
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(re.compile(r"\bcopyright\b"), 2.4),
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@@ -196,6 +366,7 @@ def _build_voice_catalog() -> List[Dict[str, str]]:
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def _template_options() -> Dict[str, Any]:
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current_settings = _load_settings()
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profiles = serialize_profiles()
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ordered_profiles = sorted(profiles.items())
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profile_options = []
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@@ -219,6 +390,14 @@ def _template_options() -> Dict[str, Any]:
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"voice_catalog": _build_voice_catalog(),
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"sample_voice_texts": SAMPLE_VOICE_TEXTS,
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"voice_profiles_data": profiles,
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"chunk_levels": _CHUNK_LEVEL_OPTIONS,
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"speaker_modes": _SPEAKER_MODE_OPTIONS,
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"speaker_analysis_threshold": current_settings.get(
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"speaker_analysis_threshold", _DEFAULT_ANALYSIS_THRESHOLD
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),
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"speaker_pronunciation_sentence": current_settings.get(
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"speaker_pronunciation_sentence", _settings_defaults()["speaker_pronunciation_sentence"]
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),
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}
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@@ -237,10 +416,11 @@ BOOLEAN_SETTINGS = {
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"save_chapters_separately",
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"merge_chapters_at_end",
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"save_as_project",
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"generate_epub3",
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}
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FLOAT_SETTINGS = {"silence_between_chapters", "chapter_intro_delay"}
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INT_SETTINGS = {"max_subtitle_words"}
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INT_SETTINGS = {"max_subtitle_words", "speaker_analysis_threshold"}
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def _has_output_override() -> bool:
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@@ -262,6 +442,11 @@ def _settings_defaults() -> Dict[str, Any]:
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"silence_between_chapters": 2.0,
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"chapter_intro_delay": 0.5,
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"max_subtitle_words": 50,
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"chunk_level": "paragraph",
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"speaker_mode": "single",
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"generate_epub3": False,
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"speaker_analysis_threshold": _DEFAULT_ANALYSIS_THRESHOLD,
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"speaker_pronunciation_sentence": "This is {{name}} speaking.",
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}
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@@ -323,6 +508,14 @@ def _normalize_setting_value(key: str, value: Any, defaults: Dict[str, Any]) ->
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if isinstance(value, str) and value in VOICES_INTERNAL:
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return value
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return defaults[key]
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if key == "chunk_level":
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if isinstance(value, str) and value in _CHUNK_LEVEL_VALUES:
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return value
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return defaults[key]
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if key == "speaker_mode":
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if isinstance(value, str) and value in _SPEAKER_MODE_VALUES:
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return value
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return defaults[key]
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return value if value is not None else defaults.get(key)
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@@ -519,6 +712,12 @@ def settings_page() -> Response | str:
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)
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for key in sorted(BOOLEAN_SETTINGS):
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updated[key] = _coerce_bool(form.get(key), False)
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updated["chunk_level"] = _normalize_setting_value(
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"chunk_level", form.get("chunk_level"), defaults
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)
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updated["speaker_mode"] = _normalize_setting_value(
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"speaker_mode", form.get("speaker_mode"), defaults
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)
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updated["separate_chapters_format"] = _normalize_setting_value(
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"separate_chapters_format", form.get("separate_chapters_format"), defaults
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)
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@@ -531,6 +730,16 @@ def settings_page() -> Response | str:
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updated["max_subtitle_words"] = _coerce_int(
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form.get("max_subtitle_words"), defaults["max_subtitle_words"]
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)
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updated["speaker_analysis_threshold"] = _coerce_int(
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form.get("speaker_analysis_threshold"),
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defaults["speaker_analysis_threshold"],
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minimum=1,
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maximum=25,
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)
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sentence_value = (form.get("speaker_pronunciation_sentence") or "").strip()
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if not sentence_value:
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sentence_value = defaults["speaker_pronunciation_sentence"]
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updated["speaker_pronunciation_sentence"] = sentence_value
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cfg = load_config() or {}
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cfg.update(updated)
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@@ -800,6 +1009,102 @@ def api_preview_voice_mix() -> Response:
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return response
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@api_bp.post("/speaker-preview")
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def api_speaker_preview() -> Response:
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payload = request.get_json(force=True, silent=False)
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text = (payload.get("text") or "").strip()
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voice_spec = (payload.get("voice") or "").strip()
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language = (payload.get("language") or "a").strip() or "a"
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speed_input = payload.get("speed", 1.0)
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try:
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speed = float(speed_input)
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except (TypeError, ValueError):
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speed = 1.0
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max_seconds_input = payload.get("max_seconds", 8.0)
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try:
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max_seconds = max(1.0, min(15.0, float(max_seconds_input)))
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except (TypeError, ValueError):
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max_seconds = 8.0
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if not text:
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abort(400, "Preview text is required")
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if not voice_spec:
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abort(400, "Voice selection is required")
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settings = _load_settings()
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use_gpu_default = settings.get("use_gpu", True)
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if "use_gpu" in payload:
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use_gpu = _coerce_bool(payload.get("use_gpu"), use_gpu_default)
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else:
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use_gpu = use_gpu_default
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device = "cpu"
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if use_gpu:
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try:
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device = _select_device()
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except Exception: # pragma: no cover - fallback
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device = "cpu"
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use_gpu = False
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try:
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pipeline = _get_preview_pipeline(language, device)
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except Exception as exc: # pragma: no cover - defensive guard
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abort(500, f"Failed to initialise preview pipeline: {exc}")
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if pipeline is None: # pragma: no cover - defensive double-check
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abort(500, "Preview pipeline initialisation failed")
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voice_choice: Any = voice_spec
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if "*" in voice_spec:
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try:
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voice_choice = get_new_voice(pipeline, voice_spec, use_gpu)
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except ValueError as exc:
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abort(400, str(exc))
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segments = pipeline(
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text,
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voice=voice_choice,
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speed=speed,
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split_pattern=SPLIT_PATTERN,
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)
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audio_chunks: List[np.ndarray] = []
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accumulated = 0
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max_samples = int(max_seconds * SAMPLE_RATE)
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for segment in segments:
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graphemes = getattr(segment, "graphemes", "").strip()
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if not graphemes:
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continue
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audio = _to_float32(getattr(segment, "audio", None))
|
||||
if audio.size == 0:
|
||||
continue
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remaining = max_samples - accumulated
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if remaining <= 0:
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||||
break
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if audio.shape[0] > remaining:
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audio = audio[:remaining]
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audio_chunks.append(audio)
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accumulated += audio.shape[0]
|
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if accumulated >= max_samples:
|
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break
|
||||
|
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if not audio_chunks:
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abort(500, "Preview could not be generated")
|
||||
|
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audio_data = np.concatenate(audio_chunks)
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buffer = io.BytesIO()
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sf.write(buffer, audio_data, SAMPLE_RATE, format="WAV")
|
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buffer.seek(0)
|
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response = send_file(
|
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buffer,
|
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mimetype="audio/wav",
|
||||
as_attachment=False,
|
||||
download_name="speaker_preview.wav",
|
||||
)
|
||||
response.headers["Cache-Control"] = "no-store"
|
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return response
|
||||
|
||||
|
||||
@web_bp.post("/jobs")
|
||||
def enqueue_job() -> Response:
|
||||
service = _service()
|
||||
@@ -921,6 +1226,41 @@ def enqueue_job() -> Response:
|
||||
chapter_intro_delay = settings["chapter_intro_delay"]
|
||||
max_subtitle_words = settings["max_subtitle_words"]
|
||||
|
||||
chunk_level_default = str(settings.get("chunk_level", "paragraph")).strip().lower()
|
||||
raw_chunk_level = (request.form.get("chunk_level") or chunk_level_default).strip().lower()
|
||||
if raw_chunk_level not in _CHUNK_LEVEL_VALUES:
|
||||
raw_chunk_level = chunk_level_default if chunk_level_default in _CHUNK_LEVEL_VALUES else "paragraph"
|
||||
chunk_level_value = raw_chunk_level
|
||||
chunk_level_literal = cast(ChunkLevel, chunk_level_value)
|
||||
|
||||
speaker_mode_default = str(settings.get("speaker_mode", "single")).strip().lower()
|
||||
raw_speaker_mode = (request.form.get("speaker_mode") or speaker_mode_default).strip().lower()
|
||||
if raw_speaker_mode not in _SPEAKER_MODE_VALUES:
|
||||
raw_speaker_mode = "single"
|
||||
speaker_mode_value = raw_speaker_mode
|
||||
|
||||
generate_epub3_default = bool(settings.get("generate_epub3", False))
|
||||
generate_epub3 = _coerce_bool(request.form.get("generate_epub3"), generate_epub3_default)
|
||||
|
||||
selected_chapter_sources = [entry for entry in chapters_payload if entry.get("enabled")]
|
||||
raw_chunks = build_chunks_for_chapters(selected_chapter_sources, level=chunk_level_literal)
|
||||
|
||||
analysis_threshold = _coerce_int(
|
||||
settings.get("speaker_analysis_threshold"),
|
||||
_DEFAULT_ANALYSIS_THRESHOLD,
|
||||
minimum=1,
|
||||
maximum=25,
|
||||
)
|
||||
|
||||
processed_chunks, speakers, analysis_payload = _prepare_speaker_metadata(
|
||||
chapters=selected_chapter_sources,
|
||||
chunks=raw_chunks,
|
||||
speaker_mode=speaker_mode_value,
|
||||
voice=voice,
|
||||
voice_profile=selected_profile or None,
|
||||
threshold=analysis_threshold,
|
||||
)
|
||||
|
||||
pending = PendingJob(
|
||||
id=uuid.uuid4().hex,
|
||||
original_filename=original_name,
|
||||
@@ -941,7 +1281,7 @@ def enqueue_job() -> Response:
|
||||
separate_chapters_format=separate_chapters_format,
|
||||
silence_between_chapters=silence_between_chapters,
|
||||
save_as_project=save_as_project,
|
||||
voice_profile=selected_profile,
|
||||
voice_profile=selected_profile or None,
|
||||
max_subtitle_words=max_subtitle_words,
|
||||
metadata_tags=metadata_tags,
|
||||
chapters=chapters_payload,
|
||||
@@ -949,6 +1289,13 @@ def enqueue_job() -> Response:
|
||||
cover_image_path=cover_path,
|
||||
cover_image_mime=cover_mime,
|
||||
chapter_intro_delay=chapter_intro_delay,
|
||||
chunk_level=chunk_level_value,
|
||||
speaker_mode=speaker_mode_value,
|
||||
generate_epub3=generate_epub3,
|
||||
chunks=processed_chunks,
|
||||
speakers=speakers,
|
||||
speaker_analysis=analysis_payload,
|
||||
speaker_analysis_threshold=analysis_threshold,
|
||||
)
|
||||
|
||||
service.store_pending_job(pending)
|
||||
@@ -972,6 +1319,62 @@ def finalize_job(pending_id: str) -> Response:
|
||||
abort(404)
|
||||
pending = cast(PendingJob, pending)
|
||||
|
||||
raw_chunk_level = (request.form.get("chunk_level") or pending.chunk_level or "paragraph").strip().lower()
|
||||
if raw_chunk_level not in _CHUNK_LEVEL_VALUES:
|
||||
raw_chunk_level = pending.chunk_level if pending.chunk_level in _CHUNK_LEVEL_VALUES else "paragraph"
|
||||
pending.chunk_level = raw_chunk_level
|
||||
chunk_level_literal = cast(ChunkLevel, pending.chunk_level)
|
||||
|
||||
raw_speaker_mode = (request.form.get("speaker_mode") or pending.speaker_mode or "single").strip().lower()
|
||||
if raw_speaker_mode not in _SPEAKER_MODE_VALUES:
|
||||
raw_speaker_mode = "single"
|
||||
pending.speaker_mode = raw_speaker_mode
|
||||
|
||||
pending.generate_epub3 = _coerce_bool(request.form.get("generate_epub3"), False)
|
||||
|
||||
threshold_default = getattr(pending, "speaker_analysis_threshold", _DEFAULT_ANALYSIS_THRESHOLD)
|
||||
raw_threshold = request.form.get("speaker_analysis_threshold")
|
||||
if raw_threshold is not None:
|
||||
pending.speaker_analysis_threshold = _coerce_int(
|
||||
raw_threshold,
|
||||
threshold_default,
|
||||
minimum=1,
|
||||
maximum=25,
|
||||
)
|
||||
else:
|
||||
pending.speaker_analysis_threshold = threshold_default
|
||||
|
||||
if not pending.speakers:
|
||||
narrator: Dict[str, Any] = {
|
||||
"id": "narrator",
|
||||
"label": "Narrator",
|
||||
"voice": pending.voice,
|
||||
}
|
||||
if pending.voice_profile:
|
||||
narrator["voice_profile"] = pending.voice_profile
|
||||
pending.speakers = {"narrator": narrator}
|
||||
else:
|
||||
existing_narrator = pending.speakers.get("narrator")
|
||||
if isinstance(existing_narrator, dict):
|
||||
existing_narrator.setdefault("id", "narrator")
|
||||
existing_narrator["label"] = existing_narrator.get("label", "Narrator")
|
||||
existing_narrator["voice"] = pending.voice
|
||||
if pending.voice_profile:
|
||||
existing_narrator["voice_profile"] = pending.voice_profile
|
||||
pending.speakers["narrator"] = existing_narrator
|
||||
|
||||
if isinstance(pending.speakers, dict):
|
||||
for speaker_id, payload in list(pending.speakers.items()):
|
||||
if not isinstance(payload, dict):
|
||||
continue
|
||||
field_key = f"speaker-{speaker_id}-pronunciation"
|
||||
raw_value = request.form.get(field_key, "")
|
||||
pronunciation = raw_value.strip()
|
||||
if pronunciation:
|
||||
payload["pronunciation"] = pronunciation
|
||||
else:
|
||||
payload.pop("pronunciation", None)
|
||||
|
||||
profiles = serialize_profiles()
|
||||
delay_value = pending.chapter_intro_delay
|
||||
raw_delay = request.form.get("chapter_intro_delay")
|
||||
@@ -1038,9 +1441,25 @@ def finalize_job(pending_id: str) -> Response:
|
||||
overrides.append(entry)
|
||||
pending.chapters[index] = dict(entry)
|
||||
|
||||
if not any(item.get("enabled") for item in overrides):
|
||||
enabled_overrides = [entry for entry in overrides if entry.get("enabled")]
|
||||
if not enabled_overrides:
|
||||
pending.chunks = []
|
||||
return _render_prepare_page(pending, error="Select at least one chapter to convert.")
|
||||
|
||||
raw_chunks = build_chunks_for_chapters(enabled_overrides, level=chunk_level_literal)
|
||||
processed_chunks, roster, analysis_payload = _prepare_speaker_metadata(
|
||||
chapters=enabled_overrides,
|
||||
chunks=raw_chunks,
|
||||
speaker_mode=pending.speaker_mode,
|
||||
voice=pending.voice,
|
||||
voice_profile=pending.voice_profile,
|
||||
threshold=pending.speaker_analysis_threshold,
|
||||
existing_roster=pending.speakers,
|
||||
)
|
||||
pending.chunks = processed_chunks
|
||||
pending.speakers = roster
|
||||
pending.speaker_analysis = analysis_payload
|
||||
|
||||
if errors:
|
||||
return _render_prepare_page(pending, error=" ".join(errors))
|
||||
|
||||
@@ -1074,6 +1493,13 @@ def finalize_job(pending_id: str) -> Response:
|
||||
cover_image_path=pending.cover_image_path,
|
||||
cover_image_mime=pending.cover_image_mime,
|
||||
chapter_intro_delay=pending.chapter_intro_delay,
|
||||
chunk_level=pending.chunk_level,
|
||||
chunks=processed_chunks,
|
||||
speakers=roster,
|
||||
speaker_mode=pending.speaker_mode,
|
||||
speaker_analysis=analysis_payload,
|
||||
speaker_analysis_threshold=pending.speaker_analysis_threshold,
|
||||
generate_epub3=pending.generate_epub3,
|
||||
)
|
||||
|
||||
return redirect(url_for("web.queue_page"))
|
||||
|
||||
Reference in New Issue
Block a user