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https://github.com/Alishahryar1/free-claude-code.git
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## Summary Added NVIDIA NIM as a second transcription option ( alongside local Whisper). This lets you transcribe voice notes using NVIDIA's cloud API instead of running Whisper locally. ## What changed - **Transcription**: Now supports the two backends - Local Whisper: Free, runs on your GPU/CPU (existing) - NVIDIA NIM: Cloud API via Riva gRPC (new) - **Supported models**: 8 NVIDIA NIM models added (Parakeet variants for different languages, Whisper Large V3) --------- Co-authored-by: Alishahryar1 <alishahryar2@gmail.com>
125 lines
4.3 KiB
Python
125 lines
4.3 KiB
Python
"""Tests for voice note transcription."""
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import tempfile
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from pathlib import Path
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from unittest.mock import MagicMock, patch
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import pytest
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from messaging.transcription import (
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MAX_AUDIO_SIZE_BYTES,
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transcribe_audio,
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)
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def test_transcribe_file_not_found_raises():
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"""Non-existent file raises FileNotFoundError."""
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with pytest.raises(FileNotFoundError, match="not found"):
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transcribe_audio(Path("/nonexistent/file.ogg"), "audio/ogg")
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def test_transcribe_file_too_large_raises():
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"""File exceeding max size raises ValueError."""
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with tempfile.NamedTemporaryFile(suffix=".ogg", delete=False) as f:
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f.write(b"x" * (MAX_AUDIO_SIZE_BYTES + 1))
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path = Path(f.name)
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try:
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with pytest.raises(ValueError, match="too large"):
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transcribe_audio(path, "audio/ogg", whisper_device="auto")
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finally:
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path.unlink(missing_ok=True)
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def test_transcribe_local_success():
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"""Local backend returns transcribed text."""
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with tempfile.NamedTemporaryFile(suffix=".ogg", delete=False) as f:
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f.write(b"fake ogg content")
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path = Path(f.name)
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try:
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mock_pipe = MagicMock()
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mock_pipe.return_value = {"text": "Hello world"}
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fake_audio = {"array": [0.0], "sampling_rate": 16000}
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with (
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patch("messaging.transcription._load_audio", return_value=fake_audio),
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patch(
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"messaging.transcription._get_pipeline",
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return_value=mock_pipe,
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),
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):
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result = transcribe_audio(path, "audio/ogg", whisper_model="base")
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assert result == "Hello world"
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mock_pipe.assert_called_once_with(
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fake_audio, generate_kwargs={"language": "en", "task": "transcribe"}
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)
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finally:
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path.unlink(missing_ok=True)
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def test_transcribe_local_empty_segments_returns_no_speech():
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"""Local backend with no speech returns placeholder."""
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with tempfile.NamedTemporaryFile(suffix=".ogg", delete=False) as f:
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f.write(b"fake ogg")
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path = Path(f.name)
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try:
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mock_pipe = MagicMock()
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mock_pipe.return_value = {"text": ""}
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fake_audio = {"array": [0.0], "sampling_rate": 16000}
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with (
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patch("messaging.transcription._load_audio", return_value=fake_audio),
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patch(
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"messaging.transcription._get_pipeline",
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return_value=mock_pipe,
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),
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):
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result = transcribe_audio(path, "audio/ogg", whisper_model="base")
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assert result == "(no speech detected)"
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finally:
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path.unlink(missing_ok=True)
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def test_transcribe_invalid_device_raises():
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"""Invalid whisper_device raises ValueError."""
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with tempfile.NamedTemporaryFile(suffix=".ogg", delete=False) as f:
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f.write(b"fake ogg")
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path = Path(f.name)
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try:
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# Mock settings to return invalid device "auto"
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mock_settings = MagicMock()
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mock_settings.whisper_device = "auto"
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mock_settings.whisper_model = "base"
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# Patch _load_audio to avoid ImportError from missing librosa
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# Device validation happens in _get_pipeline before torch import
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with (
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patch("messaging.transcription.get_settings", return_value=mock_settings),
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patch("messaging.transcription._load_audio"),
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pytest.raises(ValueError, match="whisper_device must be 'cpu' or 'cuda'"),
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):
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transcribe_audio(path, "audio/ogg", whisper_device="auto")
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finally:
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path.unlink(missing_ok=True)
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def test_transcribe_local_import_error_raises():
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"""Local backend when voice_local extra not installed raises ImportError."""
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with tempfile.NamedTemporaryFile(suffix=".ogg", delete=False) as f:
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f.write(b"fake ogg")
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path = Path(f.name)
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try:
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with (
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patch(
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"messaging.transcription._get_pipeline",
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side_effect=ImportError(
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"Local Whisper requires the voice_local extra. "
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"Install with: uv sync --extra voice_local"
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),
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),
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pytest.raises(ImportError, match="voice_local extra"),
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):
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transcribe_audio(path, "audio/ogg", whisper_device="auto")
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finally:
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path.unlink(missing_ok=True)
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