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https://github.com/denizsafak/abogen.git
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Port voice marker and word substitution features to upstream refactored structure
The upstream project moved PyQt code to abogen/pyqt/ subdirectory, making the original feature commits non-mergeable. This commit re-applies both features to the new file locations. Voice Marker feature (<<VOICE:voice_name>> syntax): - subtitle_utils.py: Added _VOICE_MARKER_PATTERN, _VOICE_MARKER_SEARCH_PATTERN, validate_voice_name(), split_text_by_voice_markers() (with valid/invalid counts) - pyqt/conversion.py: Added load_voice_cached(), voice marker pre-processing before chapter loop, inner voice segment loop wrapping spaCy+TTS block, updated imports - pyqt/gui.py: Added Insert Voice Marker button and insert_voice_marker() to TextboxDialog Word Substitution feature (text preprocessing before TTS): - word_substitution.py: New module (word replacements, ALL CAPS, numerals, punctuation) - pyqt/conversion.py: apply_word_substitutions() call after clean_text() - pyqt/gui.py: WordSubstitutionsDialog, word_sub_combo, Settings button, on_word_sub_changed(), show_word_sub_dialog(), config persistence, queue restore - pyqt/queued_item.py: 6 new word substitution fields - pyqt/queue_manager_gui.py: 6 fields added to OVERRIDE_FIELDS and get_current_attributes() Note: num2words>=0.5.13 was already added to pyproject.toml by upstream. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
olandir
co-authored by
Claude Sonnet 4.6
Claude Sonnet 4.6
parent
8322f7f416
commit
2223f46c9e
+356
-260
@@ -42,6 +42,10 @@ from abogen.subtitle_utils import (
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get_sample_voice_text,
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sanitize_name_for_os,
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_CHAPTER_MARKER_SEARCH_PATTERN,
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_VOICE_MARKER_PATTERN,
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_VOICE_MARKER_SEARCH_PATTERN,
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split_text_by_voice_markers,
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validate_voice_name,
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)
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class CountdownDialog(QDialog):
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@@ -296,6 +300,31 @@ class ConversionThread(QThread):
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self.use_spacy_segmentation = True # Default, will be overridden from GUI
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# Set split pattern based on language and subtitle mode
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self.split_pattern = self._get_split_pattern(lang_code, subtitle_mode)
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self.voice_cache = {} # Cache for loaded voices
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def load_voice_cached(self, voice_name, tts):
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"""Load voice with caching to avoid reloading same voice.
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Args:
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voice_name: Voice name or formula string
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tts: TTS pipeline instance
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Returns:
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Loaded voice tensor or voice name string
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"""
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# Check cache first
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if voice_name in self.voice_cache:
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return self.voice_cache[voice_name]
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# Load voice
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if "*" in voice_name:
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loaded_voice = get_new_voice(tts, voice_name, self.use_gpu)
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else:
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loaded_voice = voice_name
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# Cache it
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self.voice_cache[voice_name] = loaded_voice
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return loaded_voice
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def _stream_audio_in_chunks(
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self, segments, process_func, progress_prefix="Processing"
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@@ -524,6 +553,26 @@ class ConversionThread(QThread):
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# Clean up text using utility function
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text = clean_text(text)
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# Apply word substitutions if enabled
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if getattr(self, "word_substitutions_enabled", False):
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from abogen.word_substitution import apply_word_substitutions
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self.log_updated.emit("Applying word substitutions...")
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substitutions_list = getattr(self, "word_substitutions_list", "")
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case_sensitive = getattr(self, "case_sensitive_substitutions", False)
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replace_caps = getattr(self, "replace_all_caps", False)
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replace_nums = getattr(self, "replace_numerals", False)
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fix_punct = getattr(self, "fix_nonstandard_punctuation", False)
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text = apply_word_substitutions(
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text,
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substitutions_list,
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case_sensitive,
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replace_caps,
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replace_nums,
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fix_punct,
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)
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# --- Chapter splitting logic ---
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# Use pre-compiled pattern for better performance
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@@ -550,6 +599,42 @@ class ConversionThread(QThread):
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chapters = [("text", text)]
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total_chapters = len(chapters)
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# --- Voice marker splitting logic ---
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# Split each chapter by voice markers, preserving voice state across chapters
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chapters_with_voices = []
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current_voice = self.voice # Start with default voice
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total_valid_markers = 0
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total_invalid_markers = 0
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for chapter_name, chapter_text in chapters:
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# Use current_voice as the starting voice for this chapter
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voice_segments, last_voice, valid_count, invalid_count = split_text_by_voice_markers(chapter_text, current_voice)
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chapters_with_voices.append((chapter_name, voice_segments))
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# Update current_voice so next chapter continues with this voice
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current_voice = last_voice
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# Track total valid/invalid markers
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total_valid_markers += valid_count
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total_invalid_markers += invalid_count
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# Log voice marker information with accurate counts
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total_markers = total_valid_markers + total_invalid_markers
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if total_markers > 0:
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if total_invalid_markers == 0:
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# All markers were valid
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self.log_updated.emit(
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(f"\nDetected {total_markers} voice marker(s) - all valid", "grey")
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)
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else:
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# Some markers were invalid
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self.log_updated.emit(
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(f"\nDetected {total_markers} voice marker(s) - {total_valid_markers} valid, {total_invalid_markers} invalid (using previous voice)", "orange")
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)
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# Replace chapters with the new structure
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chapters = chapters_with_voices
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# For text files with chapters, prompt user for options if not already set
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is_txt_file = not self.is_direct_text and (
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self.file_name.lower().endswith(".txt")
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@@ -842,7 +927,7 @@ class ConversionThread(QThread):
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]
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srt_index = 1 # SRT numbering fix for chapter-only mode
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# Instead of processing the whole text, process by chapter
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for chapter_idx, (chapter_name, chapter_text) in enumerate(chapters, 1):
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for chapter_idx, (chapter_name, voice_segments) in enumerate(chapters, 1):
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chapter_out_path = None
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chapter_out_file = None
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chapter_ffmpeg_proc = None
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@@ -862,11 +947,6 @@ class ConversionThread(QThread):
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if merge_chapters_at_end:
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chapter_time["start"] = current_time
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# Check if the voice is a formula and load it if necessary
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if "*" in self.voice:
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loaded_voice = get_new_voice(tts, self.voice, self.use_gpu)
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else:
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loaded_voice = self.voice
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# Prepare per-chapter output file if needed
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if save_chapters_separately and total_chapters > 1:
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# First pass: keep alphanumeric, spaces, hyphens, and underscores
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@@ -986,286 +1066,302 @@ class ConversionThread(QThread):
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chapter_subtitle_path = None
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chapter_subtitle_file = None
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# Determine if spaCy segmentation should be used for PRE-TTS segmentation
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# Only non-English languages use spaCy for pre-segmentation
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# English uses spaCy only for subtitle generation (post-TTS)
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# spaCy is disabled when subtitle mode is "Disabled" or "Line"
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# spaCy is also disabled when input is a subtitle file
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is_subtitle_input = (
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not self.is_direct_text
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and self.file_name
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and os.path.splitext(self.file_name)[1].lower()
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in [".srt", ".ass", ".vtt"]
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)
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use_spacy = (
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getattr(self, "use_spacy_segmentation", False)
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and self.subtitle_mode not in ["Disabled", "Line"]
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and not is_subtitle_input
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)
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spacy_sentences = None
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active_split_pattern = self.split_pattern
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spacing_pattern = r"\s*" if self.lang_code in ["z", "j"] else r"\s+"
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# Pre-load spaCy model for English if it will be needed for subtitle generation
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if (
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use_spacy
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and self.lang_code in ["a", "b"]
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and self.subtitle_mode in ["Sentence", "Sentence + Comma"]
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):
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from abogen.spacy_utils import get_spacy_model
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nlp = get_spacy_model(
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self.lang_code,
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log_callback=lambda msg: self.log_updated.emit(msg),
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)
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if nlp:
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# Process each voice segment within the chapter
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for segment_idx, (voice_name, segment_text) in enumerate(voice_segments):
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# Load voice for this segment (with caching)
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try:
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loaded_voice = self.load_voice_cached(voice_name, tts)
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if segment_idx > 0:
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voice_display = voice_name if len(voice_name) < 50 else voice_name[:47] + "..."
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self.log_updated.emit((f" → Voice: {voice_display}", "grey"))
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except Exception:
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self.log_updated.emit(
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(
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"\nUsing spaCy for sentence segmentation (only for subtitles)...",
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"grey",
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)
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(f"⚠ Voice loading error for '{voice_name}', continuing with previous", "orange")
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)
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if segment_idx == 0:
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loaded_voice = self.load_voice_cached(self.voice, tts)
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if use_spacy and self.lang_code not in ["a", "b"]:
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# Non-English: use spaCy for pre-TTS segmentation
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self.log_updated.emit(
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("\nUsing spaCy for sentence segmentation (pre-TTS)...", "grey")
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# Determine if spaCy segmentation should be used for PRE-TTS segmentation
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# Only non-English languages use spaCy for pre-segmentation
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# English uses spaCy only for subtitle generation (post-TTS)
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# spaCy is disabled when subtitle mode is "Disabled" or "Line"
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# spaCy is also disabled when input is a subtitle file
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is_subtitle_input = (
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not self.is_direct_text
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and self.file_name
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and os.path.splitext(self.file_name)[1].lower()
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in [".srt", ".ass", ".vtt"]
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)
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from abogen.spacy_utils import segment_sentences
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spacy_sentences = segment_sentences(
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chapter_text,
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self.lang_code,
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log_callback=lambda msg: self.log_updated.emit(msg),
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use_spacy = (
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getattr(self, "use_spacy_segmentation", False)
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and self.subtitle_mode not in ["Disabled", "Line"]
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and not is_subtitle_input
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)
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if spacy_sentences:
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self.log_updated.emit(
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(
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f"\nspaCy: Text segmented into {len(spacy_sentences)} sentences...",
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"grey",
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)
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)
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# For Sentence + Comma mode, still split on commas within spaCy sentences
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if self.subtitle_mode == "Sentence + Comma":
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active_split_pattern = r"(?<=[{}]){}|\n+".format(
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self.PUNCTUATION_COMMAS, spacing_pattern
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)
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else:
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active_split_pattern = (
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"\n" # Use newline splitting for Sentence mode
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)
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else:
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self.log_updated.emit(
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("\nspaCy: Fallback to default segmentation...", "grey")
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)
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spacy_sentences = None
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active_split_pattern = self.split_pattern
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spacing_pattern = r"\s*" if self.lang_code in ["z", "j"] else r"\s+"
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# Process text - either as spaCy sentences or as single text
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text_segments = spacy_sentences if spacy_sentences else [chapter_text]
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# Print active split pattern used by the TTS engine once for this batch
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try:
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print(f"Using split pattern: {active_split_pattern!r}")
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except Exception:
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# Print must never break processing
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print("Using split pattern: (unprintable)")
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for text_segment in text_segments:
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for result in tts(
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text_segment,
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voice=loaded_voice,
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speed=self.speed,
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split_pattern=active_split_pattern,
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# Pre-load spaCy model for English if it will be needed for subtitle generation
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if (
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use_spacy
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and self.lang_code in ["a", "b"]
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and self.subtitle_mode in ["Sentence", "Sentence + Comma"]
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):
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# Print the result for debugging
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# print(f"Result: {result}")
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if self.cancel_requested:
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if chapter_out_file:
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chapter_out_file.close()
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if merged_out_file:
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merged_out_file.close()
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self.conversion_finished.emit("Cancelled", None)
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return
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current_segment += 1
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grapheme_len = len(result.graphemes)
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self.processed_char_count += grapheme_len
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# Log progress with both character counts and the graphemes content
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self.log_updated.emit(
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f"\n{self.processed_char_count:,}/{self.total_char_count:,}: {result.graphemes}"
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from abogen.spacy_utils import get_spacy_model
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nlp = get_spacy_model(
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self.lang_code,
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log_callback=lambda msg: self.log_updated.emit(msg),
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)
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if nlp:
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self.log_updated.emit(
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(
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"\nUsing spaCy for sentence segmentation (only for subtitles)...",
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"grey",
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)
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)
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chunk_dur = len(result.audio) / rate
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chunk_start = current_time
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# Write audio directly to merged file ONLY if merging
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if merge_chapters_at_end and merged_out_file:
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merged_out_file.write(result.audio)
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elif merge_chapters_at_end and ffmpeg_proc:
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if hasattr(result.audio, "numpy"):
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audio_bytes = (
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result.audio.numpy().astype("float32").tobytes()
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if use_spacy and self.lang_code not in ["a", "b"]:
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# Non-English: use spaCy for pre-TTS segmentation
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self.log_updated.emit(
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("\nUsing spaCy for sentence segmentation (pre-TTS)...", "grey")
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)
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from abogen.spacy_utils import segment_sentences
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spacy_sentences = segment_sentences(
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segment_text,
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self.lang_code,
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log_callback=lambda msg: self.log_updated.emit(msg),
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)
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if spacy_sentences:
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self.log_updated.emit(
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(
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f"\nspaCy: Text segmented into {len(spacy_sentences)} sentences...",
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"grey",
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)
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)
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# For Sentence + Comma mode, still split on commas within spaCy sentences
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if self.subtitle_mode == "Sentence + Comma":
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active_split_pattern = r"(?<=[{}]){}|\n+".format(
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self.PUNCTUATION_COMMAS, spacing_pattern
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)
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else:
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audio_bytes = result.audio.astype("float32").tobytes()
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ffmpeg_proc.stdin.write(audio_bytes)
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if chapter_out_file:
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chapter_out_file.write(result.audio)
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elif chapter_ffmpeg_proc:
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if hasattr(result.audio, "numpy"):
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audio_bytes = (
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result.audio.numpy().astype("float32").tobytes()
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active_split_pattern = (
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"\n" # Use newline splitting for Sentence mode
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)
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else:
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audio_bytes = result.audio.astype("float32").tobytes()
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chapter_ffmpeg_proc.stdin.write(audio_bytes)
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# Subtitle logic
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if self.subtitle_mode != "Disabled":
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tokens_list = getattr(result, "tokens", [])
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else:
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self.log_updated.emit(
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("\nspaCy: Fallback to default segmentation...", "grey")
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)
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# Fallback for languages without token support (non-English)
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# Create a single token representing the entire segment duration
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if not tokens_list and result.graphemes:
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# Process text - either as spaCy sentences or as single text
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text_segments = spacy_sentences if spacy_sentences else [segment_text]
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class FakeToken:
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def __init__(self, text, start, end):
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self.text = text
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self.start_ts = start
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self.end_ts = end
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self.whitespace = ""
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# Print active split pattern used by the TTS engine once for this batch
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try:
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print(f"Using split pattern: {active_split_pattern!r}")
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except Exception:
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# Print must never break processing
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print("Using split pattern: (unprintable)")
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tokens_list = [
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FakeToken(result.graphemes, 0, chunk_dur)
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]
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for text_segment in text_segments:
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for result in tts(
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text_segment,
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voice=loaded_voice,
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speed=self.speed,
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split_pattern=active_split_pattern,
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):
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# Print the result for debugging
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# print(f"Result: {result}")
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if self.cancel_requested:
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if chapter_out_file:
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chapter_out_file.close()
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if merged_out_file:
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merged_out_file.close()
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self.conversion_finished.emit("Cancelled", None)
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return
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current_segment += 1
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grapheme_len = len(result.graphemes)
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self.processed_char_count += grapheme_len
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# Log progress with both character counts and the graphemes content
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self.log_updated.emit(
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f"\n{self.processed_char_count:,}/{self.total_char_count:,}: {result.graphemes}"
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)
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tokens_with_timestamps = []
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chapter_tokens_with_timestamps = []
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chunk_dur = len(result.audio) / rate
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chunk_start = current_time
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# Write audio directly to merged file ONLY if merging
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if merge_chapters_at_end and merged_out_file:
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merged_out_file.write(result.audio)
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elif merge_chapters_at_end and ffmpeg_proc:
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if hasattr(result.audio, "numpy"):
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audio_bytes = (
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result.audio.numpy().astype("float32").tobytes()
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)
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else:
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audio_bytes = result.audio.astype("float32").tobytes()
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ffmpeg_proc.stdin.write(audio_bytes)
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if chapter_out_file:
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chapter_out_file.write(result.audio)
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elif chapter_ffmpeg_proc:
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if hasattr(result.audio, "numpy"):
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audio_bytes = (
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result.audio.numpy().astype("float32").tobytes()
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)
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else:
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audio_bytes = result.audio.astype("float32").tobytes()
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chapter_ffmpeg_proc.stdin.write(audio_bytes)
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# Subtitle logic
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if self.subtitle_mode != "Disabled":
|
||||
tokens_list = getattr(result, "tokens", [])
|
||||
|
||||
# Process every token, regardless of text or timestamps
|
||||
for tok in tokens_list:
|
||||
tokens_with_timestamps.append(
|
||||
{
|
||||
"start": chunk_start + (tok.start_ts or 0),
|
||||
"end": chunk_start + (tok.end_ts or 0),
|
||||
"text": tok.text,
|
||||
"whitespace": tok.whitespace,
|
||||
}
|
||||
)
|
||||
if chapter_out_file or chapter_ffmpeg_proc:
|
||||
chapter_tokens_with_timestamps.append(
|
||||
# Fallback for languages without token support (non-English)
|
||||
# Create a single token representing the entire segment duration
|
||||
if not tokens_list and result.graphemes:
|
||||
|
||||
class FakeToken:
|
||||
def __init__(self, text, start, end):
|
||||
self.text = text
|
||||
self.start_ts = start
|
||||
self.end_ts = end
|
||||
self.whitespace = ""
|
||||
|
||||
tokens_list = [
|
||||
FakeToken(result.graphemes, 0, chunk_dur)
|
||||
]
|
||||
|
||||
tokens_with_timestamps = []
|
||||
chapter_tokens_with_timestamps = []
|
||||
|
||||
# Process every token, regardless of text or timestamps
|
||||
for tok in tokens_list:
|
||||
tokens_with_timestamps.append(
|
||||
{
|
||||
"start": chapter_current_time
|
||||
+ (tok.start_ts or 0),
|
||||
"end": chapter_current_time
|
||||
+ (tok.end_ts or 0),
|
||||
"start": chunk_start + (tok.start_ts or 0),
|
||||
"end": chunk_start + (tok.end_ts or 0),
|
||||
"text": tok.text,
|
||||
"whitespace": tok.whitespace,
|
||||
}
|
||||
)
|
||||
# Process tokens according to subtitle mode
|
||||
# Global subtitle processing ONLY if merging
|
||||
if chapter_out_file or chapter_ffmpeg_proc:
|
||||
chapter_tokens_with_timestamps.append(
|
||||
{
|
||||
"start": chapter_current_time
|
||||
+ (tok.start_ts or 0),
|
||||
"end": chapter_current_time
|
||||
+ (tok.end_ts or 0),
|
||||
"text": tok.text,
|
||||
"whitespace": tok.whitespace,
|
||||
}
|
||||
)
|
||||
# Process tokens according to subtitle mode
|
||||
# Global subtitle processing ONLY if merging
|
||||
if merge_chapters_at_end:
|
||||
# Incremental subtitle writing for merged output
|
||||
new_entries = []
|
||||
self._process_subtitle_tokens(
|
||||
tokens_with_timestamps,
|
||||
new_entries,
|
||||
self.max_subtitle_words,
|
||||
fallback_end_time=chunk_start + chunk_dur,
|
||||
)
|
||||
if merged_subtitle_file:
|
||||
subtitle_format = getattr(
|
||||
self, "subtitle_format", "srt"
|
||||
)
|
||||
if "ass" in subtitle_format:
|
||||
for start, end, text in new_entries:
|
||||
start_time = self._ass_time(start)
|
||||
end_time = self._ass_time(end)
|
||||
# Use karaoke effect for highlighting mode
|
||||
effect = (
|
||||
"karaoke"
|
||||
if self.subtitle_mode
|
||||
== "Sentence + Highlighting"
|
||||
else ""
|
||||
)
|
||||
merged_subtitle_file.write(
|
||||
f"Dialogue: 0,{start_time},{end_time},Default,,{merged_subtitle_margin},{merged_subtitle_margin},0,{effect},{merged_subtitle_alignment_tag}{text}\n"
|
||||
)
|
||||
else:
|
||||
for entry in new_entries:
|
||||
start, end, text = entry
|
||||
merged_subtitle_file.write(
|
||||
f"{merged_srt_index}\n{self._srt_time(start)} --> {self._srt_time(end)}\n{text}\n\n"
|
||||
)
|
||||
merged_srt_index += 1
|
||||
# Per-chapter subtitle processing for both file and ffmpeg_proc
|
||||
if chapter_out_file or chapter_ffmpeg_proc:
|
||||
new_chapter_entries = []
|
||||
self._process_subtitle_tokens(
|
||||
chapter_tokens_with_timestamps,
|
||||
new_chapter_entries,
|
||||
self.max_subtitle_words,
|
||||
fallback_end_time=chapter_current_time + chunk_dur,
|
||||
)
|
||||
if chapter_subtitle_file:
|
||||
subtitle_format = getattr(
|
||||
self, "subtitle_format", "srt"
|
||||
)
|
||||
if "ass" in subtitle_format:
|
||||
for start, end, text in new_chapter_entries:
|
||||
start_time = self._ass_time(start)
|
||||
end_time = self._ass_time(end)
|
||||
# Use karaoke effect for highlighting mode
|
||||
effect = (
|
||||
"karaoke"
|
||||
if self.subtitle_mode
|
||||
== "Sentence + Highlighting"
|
||||
else ""
|
||||
)
|
||||
chapter_subtitle_file.write(
|
||||
f"Dialogue: 0,{start_time},{end_time},Default,,{chapter_subtitle_margin},{chapter_subtitle_margin},0,{effect},{chapter_subtitle_alignment_tag}{text}\n"
|
||||
)
|
||||
else:
|
||||
for entry in new_chapter_entries:
|
||||
start, end, text = entry
|
||||
chapter_subtitle_file.write(
|
||||
f"{chapter_srt_index}\n{self._srt_time(start)} --> {self._srt_time(end)}\n{text}\n\n"
|
||||
)
|
||||
chapter_srt_index += 1
|
||||
if merge_chapters_at_end:
|
||||
# Incremental subtitle writing for merged output
|
||||
new_entries = []
|
||||
self._process_subtitle_tokens(
|
||||
tokens_with_timestamps,
|
||||
new_entries,
|
||||
self.max_subtitle_words,
|
||||
fallback_end_time=chunk_start + chunk_dur,
|
||||
)
|
||||
if merged_subtitle_file:
|
||||
subtitle_format = getattr(
|
||||
self, "subtitle_format", "srt"
|
||||
)
|
||||
if "ass" in subtitle_format:
|
||||
for start, end, text in new_entries:
|
||||
start_time = self._ass_time(start)
|
||||
end_time = self._ass_time(end)
|
||||
# Use karaoke effect for highlighting mode
|
||||
effect = (
|
||||
"karaoke"
|
||||
if self.subtitle_mode
|
||||
== "Sentence + Highlighting"
|
||||
else ""
|
||||
)
|
||||
merged_subtitle_file.write(
|
||||
f"Dialogue: 0,{start_time},{end_time},Default,,{merged_subtitle_margin},{merged_subtitle_margin},0,{effect},{merged_subtitle_alignment_tag}{text}\n"
|
||||
)
|
||||
else:
|
||||
for entry in new_entries:
|
||||
start, end, text = entry
|
||||
merged_subtitle_file.write(
|
||||
f"{merged_srt_index}\n{self._srt_time(start)} --> {self._srt_time(end)}\n{text}\n\n"
|
||||
)
|
||||
merged_srt_index += 1
|
||||
# Per-chapter subtitle processing for both file and ffmpeg_proc
|
||||
if chapter_out_file or chapter_ffmpeg_proc:
|
||||
new_chapter_entries = []
|
||||
self._process_subtitle_tokens(
|
||||
chapter_tokens_with_timestamps,
|
||||
new_chapter_entries,
|
||||
self.max_subtitle_words,
|
||||
fallback_end_time=chapter_current_time + chunk_dur,
|
||||
)
|
||||
if chapter_subtitle_file:
|
||||
subtitle_format = getattr(
|
||||
self, "subtitle_format", "srt"
|
||||
)
|
||||
if "ass" in subtitle_format:
|
||||
for start, end, text in new_chapter_entries:
|
||||
start_time = self._ass_time(start)
|
||||
end_time = self._ass_time(end)
|
||||
# Use karaoke effect for highlighting mode
|
||||
effect = (
|
||||
"karaoke"
|
||||
if self.subtitle_mode
|
||||
== "Sentence + Highlighting"
|
||||
else ""
|
||||
)
|
||||
chapter_subtitle_file.write(
|
||||
f"Dialogue: 0,{start_time},{end_time},Default,,{chapter_subtitle_margin},{chapter_subtitle_margin},0,{effect},{chapter_subtitle_alignment_tag}{text}\n"
|
||||
)
|
||||
else:
|
||||
for entry in new_chapter_entries:
|
||||
start, end, text = entry
|
||||
chapter_subtitle_file.write(
|
||||
f"{chapter_srt_index}\n{self._srt_time(start)} --> {self._srt_time(end)}\n{text}\n\n"
|
||||
)
|
||||
chapter_srt_index += 1
|
||||
if merge_chapters_at_end:
|
||||
current_time += chunk_dur
|
||||
if chapter_out_file or chapter_ffmpeg_proc:
|
||||
chapter_current_time += chunk_dur
|
||||
else:
|
||||
if chapter_out_file or chapter_ffmpeg_proc:
|
||||
chapter_current_time += chunk_dur
|
||||
# Calculate percentage based on characters processed
|
||||
percent = min(
|
||||
int(
|
||||
self.processed_char_count / self.total_char_count * 100
|
||||
),
|
||||
99,
|
||||
)
|
||||
|
||||
# Calculate ETR based on characters processed
|
||||
etr_str = "Processing..."
|
||||
chars_done = self.processed_char_count
|
||||
elapsed = time.time() - self.etr_start_time
|
||||
|
||||
# Calculate ETR if enough data is available
|
||||
if (
|
||||
chars_done > 0 and elapsed > 0.5
|
||||
): # Check elapsed > 0.5 to avoid instability
|
||||
avg_time_per_char = elapsed / chars_done
|
||||
remaining = (
|
||||
self.total_char_count - self.processed_char_count
|
||||
current_time += chunk_dur
|
||||
if chapter_out_file or chapter_ffmpeg_proc:
|
||||
chapter_current_time += chunk_dur
|
||||
else:
|
||||
if chapter_out_file or chapter_ffmpeg_proc:
|
||||
chapter_current_time += chunk_dur
|
||||
# Calculate percentage based on characters processed
|
||||
percent = min(
|
||||
int(
|
||||
self.processed_char_count / self.total_char_count * 100
|
||||
),
|
||||
99,
|
||||
)
|
||||
if remaining > 0:
|
||||
secs = avg_time_per_char * remaining
|
||||
h = int(secs // 3600)
|
||||
m = int((secs % 3600) // 60)
|
||||
s = int(secs % 60)
|
||||
etr_str = f"{h:02d}:{m:02d}:{s:02d}"
|
||||
|
||||
# Update progress more frequently (after each result)
|
||||
self.progress_updated.emit(percent, etr_str)
|
||||
# Calculate ETR based on characters processed
|
||||
etr_str = "Processing..."
|
||||
chars_done = self.processed_char_count
|
||||
elapsed = time.time() - self.etr_start_time
|
||||
|
||||
# Calculate ETR if enough data is available
|
||||
if (
|
||||
chars_done > 0 and elapsed > 0.5
|
||||
): # Check elapsed > 0.5 to avoid instability
|
||||
avg_time_per_char = elapsed / chars_done
|
||||
remaining = (
|
||||
self.total_char_count - self.processed_char_count
|
||||
)
|
||||
if remaining > 0:
|
||||
secs = avg_time_per_char * remaining
|
||||
h = int(secs // 3600)
|
||||
m = int((secs % 3600) // 60)
|
||||
s = int(secs % 60)
|
||||
etr_str = f"{h:02d}:{m:02d}:{s:02d}"
|
||||
|
||||
# Update progress more frequently (after each result)
|
||||
self.progress_updated.emit(percent, etr_str)
|
||||
|
||||
# Add silence between chapters for merged output (except after the last chapter)
|
||||
if merge_chapters_at_end and chapter_idx < total_chapters:
|
||||
|
||||
Reference in New Issue
Block a user