mirror of
https://github.com/denizsafak/abogen.git
synced 2026-07-22 07:10:28 +02:00
Artem Akymenko
1193185833
- domain/conversion_pipeline.py: emit_text_segments() — generator yielding SegmentResult for each TTS segment; emit_text_to_sinks() — convenience wrapper handling audio writing + token accumulation + subtitle flushing - Both WebUI and PyQt can call these instead of reimplementing the TTS loop - Caller provides backend, voice, speed, split_pattern; domain handles normalization, TTS invocation, token extraction - +7 tests (segment yielding, empty audio skip, chunk_start, tokens, fallback) - 1185 tests pass
138 lines
4.1 KiB
Python
138 lines
4.1 KiB
Python
"""Tests for domain/conversion_pipeline.py — emit_text_segments."""
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from dataclasses import dataclass, field
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from typing import Any, List, Optional
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from unittest.mock import MagicMock
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import numpy as np
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import pytest
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from abogen.domain.conversion_pipeline import emit_text_segments, SegmentResult
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@dataclass
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class FakeSegment:
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graphemes: str
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audio: Any
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tokens: list = field(default_factory=list)
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@dataclass
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class FakeTokenObj:
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text: str
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start_ts: float
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end_ts: float
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whitespace: str = ""
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def make_backend(segments):
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"""Create a mock TTS backend that yields FakeSegments."""
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def backend(text, voice=None, speed=1.0, split_pattern=None):
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for seg in segments:
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yield seg
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return backend
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class TestEmitTextSegments:
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def test_yields_segments(self):
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audio = np.ones(24000, dtype="float32")
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segments = [FakeSegment("Hello", audio)]
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results = list(emit_text_segments(
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"Hello world",
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backend=make_backend(segments),
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voice="A",
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speed=1.0,
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split_pattern=r"\s+",
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))
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assert len(results) == 1
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assert results[0].graphemes == "Hello"
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assert results[0].duration == 1.0
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def test_skips_empty_audio(self):
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segments = [
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FakeSegment("Hello", np.ones(24000, dtype="float32")),
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FakeSegment("", np.array([], dtype="float32")),
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FakeSegment("World", np.ones(12000, dtype="float32")),
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]
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results = list(emit_text_segments(
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"test",
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backend=make_backend(segments),
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voice="A",
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speed=1.0,
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split_pattern=r"\s+",
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))
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assert len(results) == 2
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assert results[0].graphemes == "Hello"
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assert results[1].graphemes == "World"
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def test_chunk_start_increments(self):
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audio = np.ones(24000, dtype="float32")
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segments = [FakeSegment("A", audio), FakeSegment("B", audio)]
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results = list(emit_text_segments(
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"test",
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backend=make_backend(segments),
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voice="A",
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speed=1.0,
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split_pattern=r"\s+",
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current_time=5.0,
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))
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assert results[0].chunk_start == 5.0
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assert results[1].chunk_start == 6.0
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def test_tokens_extracted(self):
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token = FakeTokenObj("Hello", 0.0, 0.5, " ")
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audio = np.ones(24000, dtype="float32")
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segments = [FakeSegment("Hello", audio, [token])]
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results = list(emit_text_segments(
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"test",
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backend=make_backend(segments),
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voice="A",
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speed=1.0,
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split_pattern=r"\s+",
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current_time=2.0,
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))
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assert len(results[0].tokens) == 1
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assert results[0].tokens[0]["start"] == 2.0
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assert results[0].tokens[0]["end"] == 2.5
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assert results[0].tokens[0]["text"] == "Hello"
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def test_fake_token_fallback(self):
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"""When no tokens provided, creates a single FakeToken for the segment."""
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audio = np.ones(24000, dtype="float32")
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segments = [FakeSegment("Hello", audio)] # No tokens
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results = list(emit_text_segments(
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"test",
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backend=make_backend(segments),
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voice="A",
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speed=1.0,
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split_pattern=r"\s+",
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))
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assert len(results[0].tokens) == 1
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assert results[0].tokens[0]["text"] == "Hello"
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def test_empty_text(self):
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results = list(emit_text_segments(
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"",
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backend=make_backend([]),
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voice="A",
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speed=1.0,
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split_pattern=r"\s+",
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))
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assert len(results) == 0
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def test_segment_result_fields(self):
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audio = np.ones(48000, dtype="float32")
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segments = [FakeSegment("Test", audio)]
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results = list(emit_text_segments(
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"test",
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backend=make_backend(segments),
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voice="A",
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speed=1.0,
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split_pattern=r"\s+",
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))
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seg = results[0]
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assert isinstance(seg, SegmentResult)
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assert seg.graphemes == "Test"
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assert seg.duration == 2.0
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assert len(seg.audio) == 48000
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