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https://github.com/denizsafak/abogen.git
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refactor: both UIs use shared tts_segments() from domain/conversion_pipeline.py
- domain/conversion_pipeline.py: add tts_segments() for pre-normalized text; emit_text_segments() now delegates to tts_segments() internally - pyqt/conversion.py: inner TTS loop replaced with tts_segments() iterator; removed FakeToken import (handled by domain) - webui/conversion_runner.py: emit_text() inner loop replaced with tts_segments() iterator; removed FakeToken import - Both UIs now share the same TTS emission logic — normalization + backend invocation + segment iteration + token extraction - +3 tests for tts_segments (no-normalization, chunk_start, basic) - 1188 tests pass
This commit is contained in:
Artem Akymenko
+32
-70
@@ -36,7 +36,7 @@ from abogen.domain.output_paths import (
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)
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from abogen.domain.audio_helpers import build_ffmpeg_command, to_float32
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from abogen.domain.audio_sink import AudioSink, open_audio_sink
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from abogen.domain.tokens import FakeToken
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from abogen.domain.conversion_pipeline import tts_segments
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from abogen.domain.audio_buffer import (
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create_silence,
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mix_audio,
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@@ -936,8 +936,7 @@ class ConversionThread(QThread):
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print("Using split pattern: (unprintable)")
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for text_segment in text_segments:
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# Normalize text through the shared pipeline
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# (heteronym + pronunciation + apostrophe normalization)
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# Normalize text before TTS
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try:
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text_segment = prepare_text_for_tts(
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text_segment,
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@@ -951,118 +950,81 @@ class ConversionThread(QThread):
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(f"Warning: Text normalization failed: {exc}", "orange")
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)
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for result in self.backend(
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for seg in tts_segments(
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text_segment,
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backend=self.backend,
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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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current_time=current_time,
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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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sink_stack.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.processed_char_count += len(seg.graphemes)
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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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f"\n{self.processed_char_count:,}/{self.total_char_count:,}: {seg.graphemes}"
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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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audio_np = to_float32(result.audio)
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# Write audio directly to merged file ONLY if merging
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# Write audio
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if merge_chapters_at_end and merged_sink:
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merged_sink.write(audio_np)
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merged_sink.write(seg.audio)
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if chapter_sink:
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chapter_sink.write(audio_np)
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chapter_sink.write(seg.audio)
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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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# 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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tokens_list = [
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FakeToken(result.graphemes, 0, chunk_dur)
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]
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tokens_with_timestamps = []
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chapter_tokens_with_timestamps = []
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# Process every token, regardless of text or timestamps
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for tok in tokens_list:
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tokens_with_timestamps.append(
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{
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"start": chunk_start + (tok.start_ts or 0),
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"end": chunk_start + (tok.end_ts or 0),
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"text": tok.text,
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"whitespace": tok.whitespace,
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}
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)
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if chapter_sink:
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chapter_tokens_with_timestamps.append(
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{
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"start": chapter_current_time
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+ (tok.start_ts or 0),
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"end": chapter_current_time
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+ (tok.end_ts or 0),
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"text": tok.text,
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"whitespace": tok.whitespace,
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}
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)
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# Process tokens according to subtitle mode
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# Global subtitle processing ONLY if merging
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if self.subtitle_mode != "Disabled" and seg.tokens:
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tokens_with_timestamps = list(seg.tokens)
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chapter_tokens_with_timestamps = [
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{
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"start": chapter_current_time + (t["start"] - seg.chunk_start),
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"end": chapter_current_time + (t["end"] - seg.chunk_start),
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"text": t["text"],
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"whitespace": t["whitespace"],
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}
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for t in seg.tokens
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]
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if merge_chapters_at_end:
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# Incremental subtitle writing for merged output
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new_entries = []
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self._process_subtitle_tokens(
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tokens_with_timestamps,
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new_entries,
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self.max_subtitle_words,
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fallback_end_time=chunk_start + chunk_dur,
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fallback_end_time=seg.chunk_start + seg.duration,
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)
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if merged_subtitle_writer:
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for start, end, text in new_entries:
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merged_subtitle_writer.write_entry(start, end, text)
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# Per-chapter subtitle processing for both file and sink
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if chapter_sink:
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new_chapter_entries = []
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self._process_subtitle_tokens(
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chapter_tokens_with_timestamps,
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new_chapter_entries,
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self.max_subtitle_words,
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fallback_end_time=chapter_current_time + chunk_dur,
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fallback_end_time=chapter_current_time + seg.duration,
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)
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if chapter_subtitle_writer:
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for start, end, text in new_chapter_entries:
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chapter_subtitle_writer.write_entry(start, end, text)
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# Update timing
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if merge_chapters_at_end:
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current_time += chunk_dur
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if chapter_sink:
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chapter_current_time += chunk_dur
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else:
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if chapter_sink:
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chapter_current_time += chunk_dur
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# Calculate percentage based on characters processed
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current_time += seg.duration
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if chapter_sink:
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chapter_current_time += seg.duration
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# Progress
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percent = min(
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int(
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self.processed_char_count / self.total_char_count * 100
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),
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int(self.processed_char_count / self.total_char_count * 100),
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99,
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)
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# Calculate ETR based on characters processed
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etr_str = calc_etr_str(
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time.time() - self.etr_start_time,
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self.processed_char_count,
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self.total_char_count,
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)
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# Update progress more frequently (after each result)
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self.progress_updated.emit(percent, etr_str)
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# Add silence between chapters for merged output (except after the last chapter)
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