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voice-to-notes/python/tests/test_transcribe.py

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"""Tests for transcription service."""
from voice_to_notes.services.transcribe import (
SegmentResult,
TranscriptionResult,
WordResult,
result_to_payload,
)
def test_result_to_payload():
"""Test converting TranscriptionResult to IPC payload."""
result = TranscriptionResult(
segments=[
SegmentResult(
text="hello world",
start_ms=0,
end_ms=2000,
words=[
WordResult(word="hello", start_ms=0, end_ms=500, confidence=0.95),
WordResult(word="world", start_ms=600, end_ms=2000, confidence=0.92),
],
),
],
language="en",
language_probability=0.98,
duration_ms=2000,
)
payload = result_to_payload(result)
assert payload["language"] == "en"
assert payload["duration_ms"] == 2000
assert len(payload["segments"]) == 1
seg = payload["segments"][0]
assert seg["text"] == "hello world"
assert seg["start_ms"] == 0
assert seg["end_ms"] == 2000
assert len(seg["words"]) == 2
assert seg["words"][0]["word"] == "hello"
assert seg["words"][0]["confidence"] == 0.95
def test_result_to_payload_empty():
"""Test empty transcription result."""
result = TranscriptionResult()
payload = result_to_payload(result)
assert payload["segments"] == []
assert payload["language"] == ""
assert payload["duration_ms"] == 0
def test_chunk_report_size_progress():
"""Test CHUNK_REPORT_SIZE progress emission."""
from voice_to_notes.services.transcribe import CHUNK_REPORT_SIZE
assert CHUNK_REPORT_SIZE == 10
def test_transcribe_chunked_with_mocked_ffmpeg(monkeypatch):
"""Test transcribe_chunked with mocked ffmpeg/ffprobe and mocked WhisperModel."""
from unittest.mock import MagicMock, patch
from voice_to_notes.services.transcribe import TranscribeService, SegmentResult, WordResult
# Mock subprocess.run for ffprobe (returns duration of 700s = ~2 chunks at 300s each)
original_run = __import__("subprocess").run
def mock_subprocess_run(cmd, **kwargs):
if "ffprobe" in cmd:
result = MagicMock()
result.stdout = "700.0\n"
result.returncode = 0
return result
elif "ffmpeg" in cmd:
# Create an empty temp file (simulate chunk extraction)
# The output file is the last argument
import pathlib
output_file = cmd[-1]
pathlib.Path(output_file).touch()
result = MagicMock()
result.returncode = 0
return result
return original_run(cmd, **kwargs)
# Mock WhisperModel
mock_model = MagicMock()
def mock_transcribe_call(file_path, **kwargs):
mock_segments = []
for i in range(3):
seg = MagicMock()
seg.start = i * 1.0
seg.end = (i + 1) * 1.0
seg.text = f"Segment {i}"
seg.words = []
mock_segments.append(seg)
mock_info = MagicMock()
mock_info.language = "en"
mock_info.language_probability = 0.99
mock_info.duration = 300.0
return iter(mock_segments), mock_info
mock_model.transcribe = mock_transcribe_call
service = TranscribeService()
service._model = mock_model
service._current_model_name = "base"
service._current_device = "cpu"
service._current_compute_type = "int8"
written_messages = []
def mock_write(msg):
written_messages.append(msg)
with patch("subprocess.run", mock_subprocess_run), \
patch("voice_to_notes.services.transcribe.write_message", mock_write):
result = service.transcribe_chunked("req-1", "/fake/long_audio.wav")
# Should have segments from multiple chunks
assert len(result.segments) > 0
# Verify timestamp offsets — segments from chunk 1 should start at 0,
# segments from chunk 2 should be offset by 300000ms
if len(result.segments) > 3:
# Chunk 2 segments should have offset timestamps
assert result.segments[3].start_ms >= 300000
assert result.duration_ms == 700000
assert result.language == "en"