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Consolidates the incremental refactor work into a single change set: modular web tools (api/web_tools), native Anthropic request building and SSE block policy, OpenAI conversion and error handling, provider transports and rate limiting, messaging handler and tree queue, safe logging, smoke tests, and broad test coverage.
53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
from __future__ import annotations
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import math
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import os
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import wave
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from pathlib import Path
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import pytest
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from messaging.transcription import transcribe_audio
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from smoke.lib.config import SmokeConfig
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pytestmark = [pytest.mark.live, pytest.mark.smoke_target("voice")]
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def test_voice_transcription_backend_when_explicitly_enabled(
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smoke_config: SmokeConfig, tmp_path: Path
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) -> None:
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if not smoke_config.settings.voice_note_enabled:
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pytest.skip("VOICE_NOTE_ENABLED is false")
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if os.getenv("FCC_SMOKE_RUN_VOICE") != "1":
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pytest.skip("set FCC_SMOKE_RUN_VOICE=1 to run transcription smoke")
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wav_path = tmp_path / "smoke-tone.wav"
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_write_tone_wav(wav_path)
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try:
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t_kw: dict[str, str] = {
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"whisper_model": smoke_config.settings.whisper_model,
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"whisper_device": smoke_config.settings.whisper_device,
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}
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if smoke_config.settings.whisper_device == "nvidia_nim":
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t_kw["nvidia_nim_api_key"] = smoke_config.settings.nvidia_nim_api_key
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text = transcribe_audio(wav_path, "audio/wav", **t_kw)
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except ImportError as exc:
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pytest.skip(str(exc))
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assert isinstance(text, str)
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assert text.strip()
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def _write_tone_wav(path: Path) -> None:
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sample_rate = 16000
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duration_s = 0.25
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amplitude = 8000
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frames = bytearray()
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for i in range(int(sample_rate * duration_s)):
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sample = int(amplitude * math.sin(2 * math.pi * 440 * i / sample_rate))
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frames.extend(sample.to_bytes(2, byteorder="little", signed=True))
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with wave.open(str(path), "wb") as wav:
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wav.setnchannels(1)
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wav.setsampwidth(2)
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wav.setframerate(sample_rate)
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wav.writeframes(bytes(frames))
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