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Add spaCy support for improved sentence segmentation, possible fix for #91
This commit is contained in:
Deniz Şafak
@@ -1,7 +1,14 @@
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# 1.2.4 (Pre-release)
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- **Subtitle generation is now available for all languages!** Abogen now supports subtitle generation for non-English languages using audio duration-based timing. Available modes include `Line`, `Sentence`, and `Sentence + Comma`. (Note: Word-level subtitle modes remain English-only due to Kokoro's timestamp token limitations.)
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- New option: **"Use spaCy for sentence segmentation"** You can now use [spaCy](https://spacy.io/) to automatically detect sentence boundaries and produce cleaner, more readable subtitles. Quick summary:
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- **What it does:** Splits text into natural sentences so subtitle entries read better and align more naturally with speech.
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- **Why this helps:** The previous punctuation-based splitting could break sentences incorrectly at common abbreviations (e.g. "Mr.", "Dr.", "Prof.") or initials, producing wrong subtitle breaks. spaCy avoids those false splits by using linguistic rules to detect real sentence boundaries.
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- **For Non-English:** spaCy runs **before** audio generation to create better sentence chunks for TTS.
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- **For English:** spaCy runs **during** subtitle generation to find accurate sentence breaks after TTS.
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- **Note:** spaCy segmentation is only applied when subtitle mode is `Sentence` or `Sentence + Comma`. When turned off, it falls back to simple punctuation-based splitting.
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- Added support for `.` separator in timestamps (e.g. `HH:MM:SS.ms`) for timestamp-based text files.
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- Optimized regex compilation and eliminated busy-wait loops.
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- Possibly fixed `Silent truncation of long paragraphs` issue mentioned in [#91](https://github.com/denizsafak/abogen/issues/91) by [@xklzlxr](https://github.com/xklzlxr)
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- Fixed unused regex patterns and variable naming conventions.
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- Improvements in code and documentation.
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@@ -140,6 +140,7 @@ Here’s Abogen in action: in this demo, it processes ∼3,000 characters of tex
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| **Voice mixer** | Create custom voices by mixing different voice models with a profile system. See [Voice Mixer](#voice-mixer) for more details. |
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| **Voice preview** | Listen to the selected voice before processing. |
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| **Generate subtitles** | `Disabled`, `Line`, `Sentence`, `Sentence + Comma`, `Sentence + Highlighting`, `1 word`, `2 words`, `3 words`, etc. (Represents the number of words in each subtitle entry) |
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| **Use spaCy for sentence segmentation** | When this option is enabled, Abogen uses [spaCy](https://spacy.io/) to detect sentence boundaries more accurately. Previously, it only used punctuation marks (like periods, question marks, etc.) to split sentences, which could incorrectly cut off phrases like "Mr." or "Dr.". With spaCy, sentences are divided more accurately. For non-English text, spaCy runs **before** audio generation to create sentence chunks. For English text, spaCy runs **during** subtitle generation to improve timing and readability. spaCy is only used when subtitle mode is `Sentence` or `Sentence + Comma`. If you prefer the old punctuation splitting method, you can turn this option off. |
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| **Output voice format** | `.WAV`, `.FLAC`, `.MP3`, `.OPUS (best compression)` and `M4B (with chapters)` |
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| **Output subtitle format** | Configures the subtitle format as `SRT (standard)`, `ASS (wide)`, `ASS (narrow)`, `ASS (centered wide)`, or `ASS (centered narrow)`. |
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| **Replace single newlines with spaces** | Replaces single newlines with spaces in the text. This is useful for texts that have imaginary line breaks. |
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@@ -476,6 +477,7 @@ Feel free to explore the code and make any changes you like.
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## `Credits`
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- Abogen uses [Kokoro](https://github.com/hexgrad/kokoro) for its high-quality, natural-sounding text-to-speech synthesis. Huge thanks to the Kokoro team for making this possible.
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- Thanks to the [spaCy](https://spacy.io/) project for its sentence-segmentation tools, which help Abogen produce cleaner, more natural sentence segmentation.
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- Thanks to [@wojiushixiaobai](https://github.com/wojiushixiaobai) for [Embedded Python](https://github.com/wojiushixiaobai/Python-Embed-Win64) packages. These modified packages include pip pre-installed, enabling Abogen to function as a standalone application without requiring users to separately install Python in Windows.
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- Thanks to creators of [EbookLib](https://github.com/aerkalov/ebooklib), a Python library for reading and writing ePub files, which is used for extracting text from ePub files.
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- Special thanks to the [PyQt](https://www.riverbankcomputing.com/software/pyqt/) team for providing the cross-platform GUI toolkit that powers Abogen's interface.
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+361
-240
@@ -610,24 +610,42 @@ class ConversionThread(QThread):
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log_updated = pyqtSignal(object) # Updated signal for log updates
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chapters_detected = pyqtSignal(int) # Signal for chapter detection
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# Default split pattern for TTS processing
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DEFAULT_SPLIT_PATTERN = r"\n+"
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# Punctuation constants for unified handling across languages
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PUNCTUATION_SENTENCE = ".!?।。！？"
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PUNCTUATION_SENTENCE_COMMA = ".!?,।。！？、，"
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PUNCTUATION_COMMAS = ",，、"
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# Languages that should not use split pattern (better handled by Kokoro internally)
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# These languages have different text segmentation rules (no spaces, character-based, etc.)
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NO_SPLIT_LANGUAGES = {"z", "j"} # Chinese, Japanese
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def _get_split_pattern(self, lang_code, subtitle_mode):
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"""
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Get the appropriate split pattern based on language and subtitle mode.
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# Language-specific punctuation patterns for subtitle splitting
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LANGUAGE_PUNCTUATION = {
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"z": {
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"sentence": r"[。！？]", # Chinese: period, exclamation, question
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"comma": r"[。！？、，]", # Chinese: includes enumeration comma and comma
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},
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"j": {
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"sentence": r"[。！？]", # Japanese: period, exclamation, question
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"comma": r"[。！？、，]", # Japanese: includes enumeration comma and comma
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},
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}
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Args:
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lang_code: Language code (a, b, e, f, etc.)
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subtitle_mode: Subtitle mode ("Sentence", "Sentence + Comma", "Line", etc.)
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Returns:
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Split pattern string
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"""
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# For English, always use newline splitting only
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if lang_code in ["a", "b"]:
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return "\n"
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# Determine spacing pattern based on language
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spacing_pattern = r"\s*" if lang_code in ["z", "j"] else r"\s+"
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# For Chinese/Japanese, when subtitle mode is Disabled or Line, prefer
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# punctuation-based splitting instead of plain newline splitting.
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if subtitle_mode in ("Disabled", "Line") and lang_code in ["z", "j"]:
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return r"(?<=[{}]){}|\n+".format(self.PUNCTUATION_SENTENCE, spacing_pattern)
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if subtitle_mode == "Line":
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return "\n"
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elif subtitle_mode == "Sentence":
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return r"(?<=[{}]){}|\n+".format(self.PUNCTUATION_SENTENCE, spacing_pattern)
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elif subtitle_mode == "Sentence + Comma":
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return r"(?<=[{}]){}|\n+".format(self.PUNCTUATION_SENTENCE_COMMA, spacing_pattern)
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else:
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return r"\n+" # Default to line breaks
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def __init__(
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self,
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@@ -677,31 +695,9 @@ class ConversionThread(QThread):
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self.use_gpu = use_gpu # Store the GPU setting
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self.max_subtitle_words = 50 # Default value, will be overridden from GUI
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self.silence_duration = 2.0 # Default value, will be overridden from GUI
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# Set split pattern based on language - some languages handle splitting better internally
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self.split_pattern = (
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None if lang_code in self.NO_SPLIT_LANGUAGES else self.DEFAULT_SPLIT_PATTERN
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)
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# Override split pattern for non-English languages when sentence-based subtitles are requested
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# This ensures we get sentence-level audio chunks since we don't have word-level timestamps
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if (
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self.subtitle_mode in ["Sentence", "Sentence + Comma"]
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and lang_code not in ["a", "b"]
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):
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if lang_code in self.NO_SPLIT_LANGUAGES:
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# Split by CJK punctuation (keeping it)
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if self.subtitle_mode == "Sentence + Comma":
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# Use comma pattern if available
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self.split_pattern = r"(?<=[。！？、，])"
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else:
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self.split_pattern = r"(?<=[。！？])"
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else:
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# Split by sentence endings (keeping punctuation) or newlines
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if self.subtitle_mode == "Sentence + Comma":
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# Include commas in split pattern
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self.split_pattern = r"(?<=[.!?,])\s+|\n+"
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else:
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self.split_pattern = r"(?<=[.!?])\s+|\n+"
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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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def _stream_audio_in_chunks(
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self, segments, process_func, progress_prefix="Processing"
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@@ -721,7 +717,7 @@ class ConversionThread(QThread):
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total_samples = sum(len(segment) for segment in segments)
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samples_processed = 0
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self.log_updated.emit(f"\n{progress_prefix} segments...")
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self.log_updated.emit((f"\n{progress_prefix} segments...", "grey"))
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# Stream each segment individually
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for i, segment in enumerate(segments):
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@@ -749,7 +745,7 @@ class ConversionThread(QThread):
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# Clear segment bytes from memory
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del segment_bytes
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except Exception as e:
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self.log_updated.emit(f"Error processing segment {i}: {str(e)}")
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self.log_updated.emit((f"Error processing segment {i}: {str(e)}", "red"))
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raise
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return samples_processed
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@@ -807,6 +803,7 @@ class ConversionThread(QThread):
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self.log_updated.emit(
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f"- Subtitle format: {next((label for value, label in SUBTITLE_FORMATS if value == getattr(self, 'subtitle_format', 'srt')), getattr(self, 'subtitle_format', 'srt'))}"
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)
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self.log_updated.emit(f"- Use spaCy for sentence segmentation: {'Yes' if getattr(self, 'use_spacy_segmentation', False) else 'No'}")
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self.log_updated.emit(f"- Save option: {self.save_option}")
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if self.replace_single_newlines:
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self.log_updated.emit(f"- Replace single newlines: Yes")
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@@ -860,7 +857,7 @@ class ConversionThread(QThread):
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f"- Output folder: {self.output_folder or os.getcwd()}"
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)
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self.log_updated.emit("\nInitializing TTS pipeline...")
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self.log_updated.emit(("\nInitializing TTS pipeline...", "grey"))
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# Set device based on use_gpu setting and platform
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if self.use_gpu:
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@@ -887,7 +884,7 @@ class ConversionThread(QThread):
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)
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elif file_ext == ".txt" and detect_timestamps_in_text(self.file_name):
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is_timestamp_text = True
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self.log_updated.emit("\nDetected timestamps in text file")
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self.log_updated.emit(("\nDetected timestamps in text file", "grey"))
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# Signal to ask user (-1 indicates timestamp detection)
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self.chapters_detected.emit(-1)
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# Wait for user response using event with timeout for responsive cancellation
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@@ -983,7 +980,7 @@ class ConversionThread(QThread):
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(f"\nDetected chapters ({total_chapters}):\n" + chapter_list)
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)
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else:
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self.log_updated.emit((f"\nProcessing {chapters[0][0]}..."))
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self.log_updated.emit((f"\nProcessing {chapters[0][0]}...", "grey"))
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# If save_chapters_separately is enabled, find a unique suffix ONCE and use for both folder and merged file
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save_chapters_separately = getattr(self, "save_chapters_separately", False)
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@@ -1383,194 +1380,251 @@ class ConversionThread(QThread):
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else:
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chapter_subtitle_path = None
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chapter_subtitle_file = None
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for result in tts(
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chapter_text,
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voice=loaded_voice,
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speed=self.speed,
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split_pattern=self.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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# 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() 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 use_spacy and self.lang_code in ["a", "b"] and self.subtitle_mode in ["Sentence", "Sentence + Comma"]:
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from abogen.spacy_utils import get_spacy_model
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nlp = get_spacy_model(self.lang_code, log_callback=lambda msg: self.log_updated.emit(msg))
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if nlp:
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self.log_updated.emit(("\nUsing spaCy for sentence segmentation (only for subtitles)...", "grey"))
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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(("\nUsing spaCy for sentence segmentation (pre-TTS)...", "grey"))
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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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)
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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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if spacy_sentences:
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self.log_updated.emit((f"\nspaCy: Text segmented into {len(spacy_sentences)} sentences...", "grey"))
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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(self.PUNCTUATION_COMMAS, spacing_pattern)
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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":
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tokens_list = getattr(result, "tokens", [])
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active_split_pattern = "\n" # Use newline splitting for Sentence mode
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else:
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self.log_updated.emit(("\nspaCy: Fallback to default segmentation...", "grey"))
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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 [chapter_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 = [FakeToken(result.graphemes, 0, chunk_dur)]
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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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|
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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
|
||||
# Write audio directly to merged file ONLY if merging
|
||||
if merge_chapters_at_end and merged_out_file:
|
||||
merged_out_file.write(result.audio)
|
||||
elif merge_chapters_at_end and ffmpeg_proc:
|
||||
if hasattr(result.audio, "numpy"):
|
||||
audio_bytes = (
|
||||
result.audio.numpy().astype("float32").tobytes()
|
||||
)
|
||||
else:
|
||||
audio_bytes = result.audio.astype("float32").tobytes()
|
||||
ffmpeg_proc.stdin.write(audio_bytes)
|
||||
if chapter_out_file:
|
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chapter_out_file.write(result.audio)
|
||||
elif chapter_ffmpeg_proc:
|
||||
if hasattr(result.audio, "numpy"):
|
||||
audio_bytes = (
|
||||
result.audio.numpy().astype("float32").tobytes()
|
||||
)
|
||||
else:
|
||||
audio_bytes = result.audio.astype("float32").tobytes()
|
||||
chapter_ffmpeg_proc.stdin.write(audio_bytes)
|
||||
# Subtitle logic
|
||||
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
|
||||
)
|
||||
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 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}"
|
||||
# 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)
|
||||
# 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:
|
||||
@@ -1764,7 +1818,7 @@ class ConversionThread(QThread):
|
||||
)
|
||||
return
|
||||
|
||||
self.log_updated.emit(f"\nFound {len(subtitles)} subtitle entries")
|
||||
self.log_updated.emit((f"\nFound {len(subtitles)} subtitle entries", "grey"))
|
||||
|
||||
# Setup output paths
|
||||
base_name = os.path.splitext(os.path.basename(base_path))[0]
|
||||
@@ -2350,16 +2404,19 @@ class ConversionThread(QThread):
|
||||
|
||||
processed_tokens = tokens_with_timestamps # Use tokens directly
|
||||
|
||||
# For English with spaCy enabled and sentence-based modes, use spaCy for sentence boundaries
|
||||
# spaCy is disabled when subtitle mode is "Disabled" or "Line"
|
||||
use_spacy_for_english = (
|
||||
getattr(self, "use_spacy_segmentation", False)
|
||||
and self.subtitle_mode not in ["Disabled", "Line"]
|
||||
and self.lang_code in ["a", "b"]
|
||||
and self.subtitle_mode in ["Sentence", "Sentence + Comma"]
|
||||
)
|
||||
# Use processed_tokens instead of tokens_with_timestamps for the rest of the method
|
||||
if self.subtitle_mode == "Sentence + Highlighting":
|
||||
# Sentence-based processing with karaoke highlighting
|
||||
# Use language-specific punctuation for CJK languages (without comma)
|
||||
lang_punct = self.LANGUAGE_PUNCTUATION.get(self.lang_code, {})
|
||||
separator = (
|
||||
lang_punct.get("sentence", r"[.!?]")
|
||||
if isinstance(lang_punct, dict)
|
||||
else r"[.!?]"
|
||||
)
|
||||
# Use punctuation without comma
|
||||
separator = r"[{}]".format(self.PUNCTUATION_SENTENCE)
|
||||
current_sentence = []
|
||||
word_count = 0
|
||||
|
||||
@@ -2416,25 +2473,89 @@ class ConversionThread(QThread):
|
||||
subtitle_entries[-1] = (start, fallback_end_time, text)
|
||||
|
||||
elif self.subtitle_mode in ["Sentence", "Sentence + Comma", "Line"]:
|
||||
# Check if we should use spaCy for English sentence boundaries
|
||||
if use_spacy_for_english and self.subtitle_mode != "Line":
|
||||
# Use spaCy for English sentence boundary detection (model already loaded)
|
||||
from abogen.spacy_utils import get_spacy_model
|
||||
nlp = get_spacy_model(self.lang_code) # No log_callback since model is already loaded
|
||||
if nlp:
|
||||
# Build full text and track character positions to token indices
|
||||
full_text = ""
|
||||
char_to_token = [] # Maps character index to token index
|
||||
for idx, token in enumerate(processed_tokens):
|
||||
start_char = len(full_text)
|
||||
text_part = token["text"] + (token.get("whitespace", "") or "")
|
||||
full_text += text_part
|
||||
char_to_token.extend([idx] * len(text_part))
|
||||
|
||||
# Get sentence boundaries from spaCy
|
||||
doc = nlp(full_text)
|
||||
sentence_boundaries = [sent.end_char for sent in doc.sents]
|
||||
|
||||
# For "Sentence + Comma" mode, also split on commas
|
||||
if self.subtitle_mode == "Sentence + Comma":
|
||||
comma_positions = [i + 1 for i, c in enumerate(full_text) if c == ',']
|
||||
sentence_boundaries = sorted(set(sentence_boundaries + comma_positions))
|
||||
|
||||
# Group tokens by sentence boundaries
|
||||
current_sentence = []
|
||||
word_count = 0
|
||||
current_char_pos = 0
|
||||
boundary_idx = 0
|
||||
|
||||
for idx, token in enumerate(processed_tokens):
|
||||
current_sentence.append(token)
|
||||
word_count += 1
|
||||
text_len = len(token["text"]) + len(token.get("whitespace", "") or "")
|
||||
current_char_pos += text_len
|
||||
|
||||
# Check if we've hit a sentence boundary or max words
|
||||
at_boundary = (
|
||||
boundary_idx < len(sentence_boundaries)
|
||||
and current_char_pos >= sentence_boundaries[boundary_idx]
|
||||
)
|
||||
if at_boundary or word_count >= max_subtitle_words:
|
||||
if current_sentence:
|
||||
start_time = current_sentence[0]["start"]
|
||||
end_time = current_sentence[-1]["end"]
|
||||
sentence_text = "".join(
|
||||
t["text"] + (t.get("whitespace", "") or "")
|
||||
for t in current_sentence
|
||||
)
|
||||
subtitle_entries.append((start_time, end_time, sentence_text.strip()))
|
||||
current_sentence = []
|
||||
word_count = 0
|
||||
if at_boundary:
|
||||
boundary_idx += 1
|
||||
|
||||
# Add remaining tokens
|
||||
if current_sentence:
|
||||
start_time = current_sentence[0]["start"]
|
||||
end_time = current_sentence[-1]["end"]
|
||||
sentence_text = "".join(
|
||||
t["text"] + (t.get("whitespace", "") or "")
|
||||
for t in current_sentence
|
||||
)
|
||||
subtitle_entries.append((start_time, end_time, sentence_text.strip()))
|
||||
|
||||
# Fallback for last entry
|
||||
if subtitle_entries and fallback_end_time is not None:
|
||||
last_entry = subtitle_entries[-1]
|
||||
start, end, text = last_entry
|
||||
if end is None or end <= start or end <= 0:
|
||||
subtitle_entries[-1] = (start, fallback_end_time, text)
|
||||
return # Exit early, spaCy processing complete
|
||||
|
||||
# Default regex-based processing (non-English or spaCy unavailable)
|
||||
# Define separator pattern based on mode
|
||||
if self.subtitle_mode == "Line":
|
||||
separator = r"\n"
|
||||
elif self.subtitle_mode == "Sentence":
|
||||
# Use language-specific punctuation for CJK languages (without comma)
|
||||
lang_punct = self.LANGUAGE_PUNCTUATION.get(self.lang_code, {})
|
||||
separator = (
|
||||
lang_punct.get("sentence", r"[.!?]")
|
||||
if isinstance(lang_punct, dict)
|
||||
else r"[.!?]"
|
||||
)
|
||||
# Use punctuation without comma
|
||||
separator = r"[{}]".format(self.PUNCTUATION_SENTENCE)
|
||||
else: # Sentence + Comma
|
||||
# Use language-specific punctuation for CJK languages (with comma)
|
||||
lang_punct = self.LANGUAGE_PUNCTUATION.get(self.lang_code, {})
|
||||
separator = (
|
||||
lang_punct.get("comma", r"[.!?,]")
|
||||
if isinstance(lang_punct, dict)
|
||||
else r"[.!?,]"
|
||||
)
|
||||
# Use punctuation with comma
|
||||
separator = r"[{}]".format(self.PUNCTUATION_SENTENCE_COMMA)
|
||||
current_sentence = []
|
||||
word_count = 0
|
||||
|
||||
|
||||
+21
-2
@@ -828,6 +828,7 @@ class abogen(QWidget):
|
||||
self.replace_single_newlines = self.config.get("replace_single_newlines", True)
|
||||
self.use_silent_gaps = self.config.get("use_silent_gaps", True)
|
||||
self.subtitle_speed_method = self.config.get("subtitle_speed_method", "tts")
|
||||
self.use_spacy_segmentation = self.config.get("use_spacy_segmentation", True)
|
||||
self._pending_close_event = None
|
||||
self.gpu_ok = False # Initialize GPU availability status
|
||||
|
||||
@@ -2081,7 +2082,7 @@ class abogen(QWidget):
|
||||
# pipeline_loaded_callback remains unchanged
|
||||
def pipeline_loaded_callback(np_module, kpipeline_class, error):
|
||||
if error:
|
||||
self.update_log((f"Error loading numpy or KPipeline: {error}", False))
|
||||
self.update_log((f"Error loading numpy or KPipeline: {error}", "red"))
|
||||
prevent_sleep_end()
|
||||
return
|
||||
|
||||
@@ -2128,6 +2129,8 @@ class abogen(QWidget):
|
||||
self.conversion_thread.use_silent_gaps = self.use_silent_gaps
|
||||
# Pass subtitle_speed_method setting
|
||||
self.conversion_thread.subtitle_speed_method = self.subtitle_speed_method
|
||||
# Pass use_spacy_segmentation setting
|
||||
self.conversion_thread.use_spacy_segmentation = self.use_spacy_segmentation
|
||||
# Pass separate_chapters_format setting
|
||||
self.conversion_thread.separate_chapters_format = (
|
||||
self.separate_chapters_format
|
||||
@@ -2264,7 +2267,7 @@ class abogen(QWidget):
|
||||
self.is_converting = False
|
||||
elapsed = int(time.time() - self.start_time)
|
||||
h, m, s = elapsed // 3600, (elapsed % 3600) // 60, elapsed % 60
|
||||
self.update_log(f"\nTime elapsed: {h:02d}:{m:02d}:{s:02d}")
|
||||
self.update_log((f"\nTime elapsed: {h:02d}:{m:02d}:{s:02d}", "grey"))
|
||||
|
||||
# Default to showing the button
|
||||
show_open_file_button = True
|
||||
@@ -3206,6 +3209,18 @@ class abogen(QWidget):
|
||||
# Add separator
|
||||
menu.addSeparator()
|
||||
|
||||
# Add spaCy sentence segmentation option
|
||||
spacy_action = QAction("Use spaCy for sentence segmentation", self)
|
||||
spacy_action.setCheckable(True)
|
||||
spacy_action.setChecked(self.use_spacy_segmentation)
|
||||
spacy_action.triggered.connect(
|
||||
lambda checked: self.toggle_spacy_segmentation(checked)
|
||||
)
|
||||
menu.addAction(spacy_action)
|
||||
|
||||
# Add separator
|
||||
menu.addSeparator()
|
||||
|
||||
# Add "Disable Kokoro's internet access" option
|
||||
disable_kokoro_action = QAction("Disable Kokoro's internet access", self)
|
||||
disable_kokoro_action.setCheckable(True)
|
||||
@@ -3270,6 +3285,10 @@ class abogen(QWidget):
|
||||
self.config["subtitle_speed_method"] = method
|
||||
save_config(self.config)
|
||||
|
||||
def toggle_spacy_segmentation(self, enabled):
|
||||
self.use_spacy_segmentation = enabled
|
||||
self.config["use_spacy_segmentation"] = enabled
|
||||
save_config(self.config)
|
||||
def restart_app(self):
|
||||
|
||||
import sys
|
||||
|
||||
@@ -0,0 +1,143 @@
|
||||
"""
|
||||
Lazy-loaded spaCy utilities for sentence segmentation.
|
||||
"""
|
||||
|
||||
# Cached spaCy module and models (lazy loaded)
|
||||
_spacy = None
|
||||
_nlp_cache = {}
|
||||
|
||||
# Language code to spaCy model mapping
|
||||
SPACY_MODELS = {
|
||||
"a": "en_core_web_sm", # American English
|
||||
"b": "en_core_web_sm", # British English
|
||||
"e": "es_core_news_sm", # Spanish
|
||||
"f": "fr_core_news_sm", # French
|
||||
"i": "it_core_news_sm", # Italian
|
||||
"p": "pt_core_news_sm", # Brazilian Portuguese
|
||||
"z": "zh_core_web_sm", # Mandarin Chinese
|
||||
"j": "ja_core_news_sm", # Japanese
|
||||
"h": "xx_sent_ud_sm", # Hindi (multi-language model)
|
||||
}
|
||||
|
||||
|
||||
def _load_spacy():
|
||||
"""Lazy load spaCy module."""
|
||||
global _spacy
|
||||
if _spacy is None:
|
||||
try:
|
||||
import spacy
|
||||
_spacy = spacy
|
||||
except ImportError:
|
||||
return None
|
||||
return _spacy
|
||||
|
||||
|
||||
def get_spacy_model(lang_code, log_callback=None):
|
||||
"""
|
||||
Get or load a spaCy model for the given language code.
|
||||
Downloads the model automatically if not available.
|
||||
|
||||
Args:
|
||||
lang_code: Language code (a, b, e, f, etc.)
|
||||
log_callback: Optional function to log messages
|
||||
|
||||
Returns:
|
||||
Loaded spaCy model or None if unavailable
|
||||
"""
|
||||
def log(msg, is_error=False):
|
||||
# Prefer GUI log callback when provided to avoid spamming stdout.
|
||||
if log_callback:
|
||||
color = "red" if is_error else "grey"
|
||||
try:
|
||||
log_callback((msg, color))
|
||||
except Exception:
|
||||
# Fallback to printing if callback misbehaves
|
||||
print(msg)
|
||||
else:
|
||||
print(msg)
|
||||
|
||||
# Check if model is cached
|
||||
if lang_code in _nlp_cache:
|
||||
return _nlp_cache[lang_code]
|
||||
|
||||
# Check if language is supported
|
||||
model_name = SPACY_MODELS.get(lang_code)
|
||||
if not model_name:
|
||||
log(f"\nspaCy: No model mapping for language '{lang_code}'...")
|
||||
return None
|
||||
|
||||
# Lazy load spaCy
|
||||
spacy = _load_spacy()
|
||||
if spacy is None:
|
||||
log("\nspaCy: Module not installed, falling back to default segmentation...")
|
||||
return None
|
||||
|
||||
# Try to load the model
|
||||
try:
|
||||
log(f"\nLoading spaCy model '{model_name}'...")
|
||||
nlp = spacy.load(model_name, disable=["ner", "parser", "tagger", "lemmatizer", "attribute_ruler"])
|
||||
# Enable sentence segmentation only
|
||||
if "sentencizer" not in nlp.pipe_names:
|
||||
nlp.add_pipe("sentencizer")
|
||||
_nlp_cache[lang_code] = nlp
|
||||
return nlp
|
||||
except OSError:
|
||||
# Model not found, attempt download
|
||||
log(f"\nspaCy: Downloading model '{model_name}'...")
|
||||
try:
|
||||
from spacy.cli import download
|
||||
download(model_name)
|
||||
# Retry loading
|
||||
nlp = spacy.load(model_name, disable=["ner", "parser", "tagger", "lemmatizer", "attribute_ruler"])
|
||||
if "sentencizer" not in nlp.pipe_names:
|
||||
nlp.add_pipe("sentencizer")
|
||||
_nlp_cache[lang_code] = nlp
|
||||
log(f"spaCy model '{model_name}' downloaded and loaded")
|
||||
return nlp
|
||||
except Exception as e:
|
||||
log(f"\nspaCy: Failed to download model '{model_name}': {e}...", is_error=True)
|
||||
return None
|
||||
except Exception as e:
|
||||
log(f"\nspaCy: Error loading model '{model_name}': {e}...", is_error=True)
|
||||
return None
|
||||
|
||||
|
||||
def segment_sentences(text, lang_code, log_callback=None):
|
||||
"""
|
||||
Segment text into sentences using spaCy.
|
||||
|
||||
Args:
|
||||
text: Text to segment
|
||||
lang_code: Language code
|
||||
log_callback: Optional function to log messages
|
||||
|
||||
Returns:
|
||||
List of sentence strings, or None if spaCy unavailable
|
||||
"""
|
||||
nlp = get_spacy_model(lang_code, log_callback)
|
||||
if nlp is None:
|
||||
return None
|
||||
|
||||
# Ensure spaCy can handle large texts by adjusting max_length if necessary
|
||||
try:
|
||||
text_len = len(text or "")
|
||||
if text_len and hasattr(nlp, "max_length") and text_len > nlp.max_length:
|
||||
# increase a bit beyond the text length to be safe
|
||||
nlp.max_length = text_len + 1000
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Process text and extract sentences
|
||||
doc = nlp(text)
|
||||
return [sent.text.strip() for sent in doc.sents if sent.text.strip()]
|
||||
|
||||
|
||||
def is_spacy_available():
|
||||
"""Check if spaCy can be imported."""
|
||||
return _load_spacy() is not None
|
||||
|
||||
|
||||
def clear_cache():
|
||||
"""Clear the model cache to free memory."""
|
||||
global _nlp_cache
|
||||
_nlp_cache.clear()
|
||||
+2
-1
@@ -25,7 +25,8 @@ dependencies = [
|
||||
"charset_normalizer>=3.4.1",
|
||||
"chardet>=5.2.0",
|
||||
"static_ffmpeg>=2.13",
|
||||
"Markdown>=3.9"
|
||||
"Markdown>=3.9",
|
||||
"spacy>=3.8.7"
|
||||
]
|
||||
|
||||
classifiers = [
|
||||
|
||||
Reference in New Issue
Block a user