wangyu
ac5fbed6c0
Signed-off-by: wangyu <410167048@qq.com> Signed-off-by: [Your Name] <your.email@example.com>
613 lines
24 KiB
Python
613 lines
24 KiB
Python
"""Assertion and response validation helpers for tests."""
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import io
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import tempfile
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import threading
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import wave
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from io import BytesIO
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from pathlib import Path
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from typing import TYPE_CHECKING, Any
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if TYPE_CHECKING:
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from tests.helpers.runtime import DiffusionResponse
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import numpy as np
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import soundfile as sf
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from PIL import Image
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from tests.helpers.media import (
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cosine_similarity_text,
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)
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_GENDER_PIPELINE = None
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_GENDER_PIPELINE_LOCK = threading.Lock()
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_PCM_SPEECH_SAMPLE_RATE_HZ = 24_000
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_MIN_PCM_SPEECH_HNR_DB = 1.0
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_PRESET_VOICE_GENDER_MAP: dict[str, str] = {
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"serena": "female",
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"uncle_fu": "male",
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"chelsie": "female",
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"clone": "female",
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"ethan": "male",
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}
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def assert_image_diffusion_response(
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response: "DiffusionResponse",
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request_config: dict[str, Any],
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run_level: str = None,
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) -> None:
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"""
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Validate image diffusion response.
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Expected request_config schema:
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{
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"request_type": "image",
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"extra_body": {
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"num_outputs_per_prompt": 1,
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"width": ...,
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"height": ...,
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...
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}
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}
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"""
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assert response.images is not None, "Image response is None"
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assert len(response.images) > 0, "No images in response"
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extra_body = request_config.get("extra_body") or {}
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num_outputs_per_prompt = extra_body.get("num_outputs_per_prompt")
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if num_outputs_per_prompt is not None:
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assert len(response.images) == num_outputs_per_prompt, (
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f"Expected {num_outputs_per_prompt} images, got {len(response.images)}"
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)
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if run_level in {"advanced_model", "full_model"}:
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width = extra_body.get("width")
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height = extra_body.get("height")
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if width is not None or height is not None:
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if isinstance(width, (list, tuple)) and isinstance(height, (list, tuple)):
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assert len(response.images) == len(width) == len(height), (
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f"Per-output size lists require one image per entry; got {len(response.images)} images, "
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f"len(width)={len(width)}, len(height)={len(height)}"
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)
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for img, w, h in zip(response.images, width, height, strict=True):
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assert_image_valid(img, width=int(w), height=int(h))
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else:
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for img in response.images:
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assert_image_valid(
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img,
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width=_maybe_int(width) if width is not None else None,
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height=_maybe_int(height) if height is not None else None,
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)
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def assert_video_diffusion_response(
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response: "DiffusionResponse",
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request_config: dict[str, Any],
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run_level: str = None,
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) -> None:
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"""
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Validate video diffusion response.
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Expected request_config schema:
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{
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"request_type": "video",
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"form_data": {
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"prompt": "...",
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"num_frames": ...,
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"width": ...,
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"height": ...,
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"fps": ...,
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...
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}
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}
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"""
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form_data = request_config.get("form_data", {})
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assert response.videos is not None, "Video response is None"
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assert len(response.videos) > 0, "No videos in response"
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expected_frames = _maybe_int(form_data.get("num_frames"))
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expected_width = _maybe_int(form_data.get("width"))
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expected_height = _maybe_int(form_data.get("height"))
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expected_fps = _maybe_int(form_data.get("fps"))
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for vid_bytes in response.videos:
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assert_video_valid(
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vid_bytes,
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num_frames=expected_frames,
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width=expected_width,
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height=expected_height,
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fps=expected_fps,
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)
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def assert_audio_diffusion_response(
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response: "DiffusionResponse",
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request_config: dict[str, Any],
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run_level: str = None,
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) -> None:
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"""
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Validate audio diffusion response.
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`response.audios` carries one entry per choice, each a `dict` with raw WAV
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bytes (`wav_bytes`) and the OpenAI audio metadata (`id`, `expires_at`).
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"""
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assert response.audios, "Audio response is empty"
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for audio in response.audios:
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wav_bytes = audio.get("wav_bytes")
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assert wav_bytes, "Audio entry missing decoded WAV bytes"
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with wave.open(io.BytesIO(wav_bytes), "rb") as wav_file:
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assert wav_file.getnframes() > 0, "Decoded WAV has zero frames"
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assert wav_file.getframerate() > 0, "Decoded WAV has invalid sample rate"
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def _maybe_int(value: Any) -> int | None:
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if value is None:
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return None
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return int(value)
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def assert_image_valid(image: Path | Image.Image, *, width: int | None = None, height: int | None = None):
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"""Assert the file is a loadable image with optional exact dimensions."""
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if isinstance(image, Path):
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assert image.exists(), f"Image not found: {image}"
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image = Image.open(image)
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image.load()
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assert image.width > 0 and image.height > 0
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if width is not None:
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assert image.width == width, f"Expected width={width}, got {image.width}"
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if height is not None:
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assert image.height == height, f"Expected height={height}, got {image.height}"
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return image
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def assert_video_valid(
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video: Path | bytes | BytesIO,
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*,
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num_frames: int | None = None,
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width: int | None = None,
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height: int | None = None,
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fps: float | None = None,
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) -> dict[str, int | float]:
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"""Assert the MP4 has the expected resolution and frame count.
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For several diffusion backends, encoded MP4 frame count follows a codec-aligned
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convention (e.g. request `num_frames=8` can produce 9 encoded frames). Keep
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this compatibility behavior to avoid false negatives in online-serving tests.
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"""
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temp_path = None
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cap = None
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try:
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import cv2
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if isinstance(video, Path):
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if not video.exists():
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raise AssertionError(f"Video file not found: {video}")
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video_path = str(video)
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else:
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suffix = ".mp4"
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with tempfile.NamedTemporaryFile(delete=False, suffix=suffix, mode="wb") as tmp:
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if isinstance(video, bytes):
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tmp.write(video)
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elif isinstance(video, BytesIO):
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tmp.write(video.getvalue())
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else:
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raise TypeError(f"Unsupported video type: {type(video)}")
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temp_path = Path(tmp.name)
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video_path = str(temp_path)
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cap = cv2.VideoCapture(video_path)
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if not cap.isOpened():
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raise AssertionError("Failed to open video capture")
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actual_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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actual_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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actual_fps = float(cap.get(cv2.CAP_PROP_FPS))
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actual_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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if width is not None:
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assert actual_width == width, f"Expected width={width}, got {actual_width}"
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if height is not None:
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assert actual_height == height, f"Expected height={height}, got {actual_height}"
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if fps is not None and actual_fps:
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assert abs(actual_fps - float(fps)) < 1.0, f"Expected fps~={fps}, got {actual_fps}"
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if num_frames is not None:
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expected_frames = (int(num_frames) // 4) * 4 + 1
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assert actual_frames == expected_frames, f"Expected frames={expected_frames}, got {actual_frames}"
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return {
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"width": actual_width,
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"height": actual_height,
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"fps": actual_fps,
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"num_frames": actual_frames,
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}
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except Exception as e:
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print(f"ERROR: {type(e).__name__}: {e}", flush=True)
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raise
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finally:
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if cap is not None:
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cap.release()
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if temp_path and temp_path.exists():
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try:
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temp_path.unlink()
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except OSError:
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pass
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def assert_audio_valid(
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audio_or_path: Path | np.ndarray,
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*,
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sample_rate: int,
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channels: int,
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duration_s: float,
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) -> None:
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"""Assert WAV file or (batch, channels, samples) ndarray matches expected audio format."""
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expected_samples = int(duration_s * sample_rate)
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if isinstance(audio_or_path, np.ndarray):
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audio = audio_or_path
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assert audio.ndim == 3, f"Expected audio ndim=3 (batch, channels, samples), got shape {audio.shape}"
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assert audio.shape[0] == 1, f"Expected batch size 1, got {audio.shape[0]}"
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assert audio.shape[1] == channels, f"Expected {channels} channels, got {audio.shape[1]}"
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assert audio.shape[2] == expected_samples, (
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f"Expected {expected_samples} samples ({duration_s}s @ {sample_rate} Hz), got {audio.shape[2]}"
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)
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return
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path = audio_or_path
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assert path.exists(), f"Audio not found: {path}"
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info = sf.info(str(path))
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assert info.samplerate == sample_rate, f"Expected sample_rate={sample_rate}, got {info.samplerate}"
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assert info.channels == channels, f"Expected {channels} channel(s), got {info.channels}"
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assert info.frames == expected_samples, (
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f"Expected {expected_samples} frames ({duration_s}s @ {sample_rate} Hz), got {info.frames}"
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)
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def _load_gender_pipeline():
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global _GENDER_PIPELINE
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if _GENDER_PIPELINE is not None:
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return _GENDER_PIPELINE
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model_name = "7wolf/wav2vec2-base-gender-classification"
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try:
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from transformers import pipeline
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_GENDER_PIPELINE = pipeline(task="audio-classification", model=model_name, device=-1)
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return _GENDER_PIPELINE
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except Exception as exc: # pragma: no cover
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print(f"Warning: failed to create gender pipeline '{model_name}': {exc}")
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_GENDER_PIPELINE = None
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return None
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def _median_pitch_hz_from_autocorr(mono: np.ndarray, sr: int) -> float | None:
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x = np.asarray(mono, dtype=np.float64)
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x = x - np.mean(x)
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if x.size < int(0.15 * sr):
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return None
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frame_len = int(0.04 * sr)
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hop = max(frame_len // 2, 1)
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f0_min_hz, f0_max_hz = 70.0, 400.0
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lag_min = max(1, int(sr / f0_max_hz))
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lag_max = min(frame_len - 2, int(sr / f0_min_hz))
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if lag_max <= lag_min:
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return None
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win = np.hamming(frame_len)
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pitches: list[float] = []
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for start in range(0, int(x.shape[0]) - frame_len, hop):
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frame = x[start : start + frame_len] * win
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frame = frame - np.mean(frame)
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if float(np.sqrt(np.mean(frame**2))) < 1e-4:
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continue
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ac = np.correlate(frame, frame, mode="full")[frame_len - 1 :]
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ac = ac / (float(ac[0]) + 1e-12)
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region = ac[lag_min : lag_max + 1]
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peak_rel = int(np.argmax(region))
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peak_lag = peak_rel + lag_min
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if peak_lag <= 0:
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continue
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f0 = float(sr) / float(peak_lag)
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if f0_min_hz <= f0 <= f0_max_hz:
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pitches.append(f0)
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if len(pitches) < 4:
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return None
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return float(np.median(np.asarray(pitches, dtype=np.float64)))
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def _estimate_voice_gender_from_audio(audio_bytes: bytes) -> str:
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data, sr = sf.read(io.BytesIO(audio_bytes), dtype="float32", always_2d=True)
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if data.size == 0:
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raise ValueError("Empty audio")
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mono = np.mean(data, axis=1)
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try:
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target_sr = 16000
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if int(sr) != target_sr and mono.size > 1:
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src_len = int(mono.shape[0])
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dst_len = max(1, int(round(src_len * float(target_sr) / float(sr))))
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src_idx = np.arange(src_len, dtype=np.float32)
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dst_idx = np.linspace(0, src_len - 1, dst_len, dtype=np.float32)
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mono = np.interp(dst_idx, src_idx, mono.astype(np.float32, copy=False)).astype(np.float32)
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sr = target_sr
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median_f0 = _median_pitch_hz_from_autocorr(mono, sr)
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clf = _load_gender_pipeline()
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if clf is None:
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print("gender model not available, returning 'unknown'")
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return "unknown"
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with _GENDER_PIPELINE_LOCK:
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outputs = clf(mono, sampling_rate=sr)
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if not outputs:
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return "unknown"
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top = outputs[0]
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label = str(top.get("label", "")).lower()
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conf = float(top.get("score", 0.0))
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if conf < 0.6:
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gender = "unknown"
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elif ("female" in label) or ("жен" in label):
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gender = "female"
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elif ("male" in label) or ("муж" in label):
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gender = "male"
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else:
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gender = "unknown"
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if gender == "female" and median_f0 is not None and median_f0 < 165.0 and conf < 0.88:
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print(f"gender pitch assist: reclassifying female->male (median_f0={median_f0:.1f} Hz, conf={conf:.3f})")
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gender = "male"
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elif gender == "male" and median_f0 is not None and median_f0 > 230.0 and conf < 0.88:
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print(f"gender pitch assist: reclassifying male->female (median_f0={median_f0:.1f} Hz, conf={conf:.3f})")
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gender = "female"
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print(
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f"gender classifier: label={label}, conf={conf:.3f}, gender={gender}"
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+ (f", median_f0={median_f0:.1f}Hz" if median_f0 is not None else "")
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)
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return gender
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except Exception as exc: # pragma: no cover
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print(f"Warning: gender classification failed, returning 'unknown': {exc}")
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return "unknown"
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def _assert_preset_voice_gender_from_audio(audio_bytes: bytes | None, voice_name: str | None) -> None:
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"""If ``voice_name`` matches a known preset, assert classifier gender matches (skip when unknown)."""
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if not voice_name or not audio_bytes:
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return
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key = str(voice_name).lower()
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expected_gender = _PRESET_VOICE_GENDER_MAP.get(key)
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if expected_gender is None:
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return
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estimated_gender = _estimate_voice_gender_from_audio(audio_bytes)
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print(f"Preset voice gender check: preset={key!r}, estimated={estimated_gender!r}, expected={expected_gender!r}")
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if estimated_gender != "unknown":
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assert estimated_gender == expected_gender, (
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f"{voice_name!r} is expected {expected_gender}, but estimated gender is {estimated_gender!r}"
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)
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def _compute_pcm_hnr_db(pcm_samples: np.ndarray, sr: int = _PCM_SPEECH_SAMPLE_RATE_HZ) -> float:
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frame_len = int(0.03 * sr)
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hop = frame_len // 2
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hnr_values: list[float] = []
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for start in range(0, len(pcm_samples) - frame_len, hop):
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frame = pcm_samples[start : start + frame_len].astype(np.float32, copy=False)
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frame = frame - np.mean(frame)
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if np.max(np.abs(frame)) < 0.01:
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continue
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ac = np.correlate(frame, frame, mode="full")[len(frame) - 1 :]
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ac = ac / (ac[0] + 1e-10)
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min_lag = int(sr / 400)
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max_lag = min(int(sr / 80), len(ac))
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if min_lag >= max_lag:
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continue
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peak = float(np.max(ac[min_lag:max_lag]))
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if 0 < peak < 1:
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hnr_values.append(10 * np.log10(peak / (1 - peak + 1e-10)))
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return float(np.mean(hnr_values)) if hnr_values else 0.0
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def _assert_pcm_int16_speech_hnr(audio_bytes: bytes, min_hnr_db: float = _MIN_PCM_SPEECH_HNR_DB) -> None:
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"""Validate harmonic-to-noise ratio on raw int16 PCM from /v1/audio/speech.
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min_hnr_db defaults to the global _MIN_PCM_SPEECH_HNR_DB (1.0 dB),
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which matches the cleaner TTS models the helper was originally calibrated
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for. Quieter codecs (e.g. MOSS-TTS-Nano, whose voice_clone output is
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intrinsically around -2 dB) can pass a lower per-test threshold via
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request_config["min_hnr_db"] to keep the catastrophic-failure check
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while not gating CI on a model-intrinsic property.
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"""
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assert audio_bytes is not None and len(audio_bytes) >= 2, "missing PCM bytes"
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assert len(audio_bytes) % 2 == 0, "PCM byte length must be aligned to int16"
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pcm_samples = np.frombuffer(audio_bytes, dtype=np.int16).astype(np.float32) / 32768.0
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hnr = _compute_pcm_hnr_db(pcm_samples)
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print(f"PCM speech HNR: {hnr:.2f} dB (threshold: {min_hnr_db} dB)")
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assert hnr >= min_hnr_db, (
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f"Audio distortion detected: HNR={hnr:.2f} dB < {min_hnr_db} dB. "
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"Voice clone decoder may be losing ref_code speaker context on later chunks."
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)
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def assert_omni_response(response: Any, request_config: dict[str, Any], run_level):
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"""
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Validate response results.
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Args:
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response: OmniResponse object
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Raises:
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AssertionError: When the response does not meet validation criteria
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"""
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assert response.success, "The request failed."
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modalities = request_config.get("modalities", ["text", "audio"])
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if run_level in {"advanced_model", "full_model"}:
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# Verify output success
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if "audio" in modalities:
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assert response.audio_content is not None, "No audio output is generated"
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print(f"audio content is: {response.audio_content}")
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speaker = request_config.get("speaker")
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if speaker:
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_assert_preset_voice_gender_from_audio(
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response.audio_bytes,
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speaker,
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)
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if "text" in modalities:
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assert response.text_content is not None, "No text output is generated"
|
|
print(f"text content is: {response.text_content}")
|
|
|
|
# Verify keywords in output
|
|
word_types = ["text", "image", "audio", "video"]
|
|
keywords_dict = request_config.get("key_words", {})
|
|
for word_type in word_types:
|
|
keywords = keywords_dict.get(word_type)
|
|
if "text" in modalities:
|
|
if keywords:
|
|
text_lower = response.text_content.lower()
|
|
assert any(str(kw).lower() in text_lower for kw in keywords), (
|
|
"The output does not contain any of the keywords."
|
|
)
|
|
else:
|
|
if keywords:
|
|
audio_lower = response.audio_content.lower()
|
|
assert any(str(kw).lower() in audio_lower for kw in keywords), (
|
|
"The output does not contain any of the keywords."
|
|
)
|
|
|
|
# Verify similarity (Whisper transcript vs streamed/detokenized text)
|
|
if "audio" in modalities:
|
|
audio_ref_text = request_config.get("audio_ref_text")
|
|
if "text" in modalities:
|
|
transcript = (response.audio_content or "").strip()
|
|
text_output = (response.text_content or "").strip()
|
|
similarity = cosine_similarity_text(
|
|
transcript.lower(),
|
|
text_output.lower(),
|
|
)
|
|
assert similarity > 0.9, "The audio content is not same as the text"
|
|
print(f"similarity is: {similarity}")
|
|
if audio_ref_text:
|
|
audio_similarity = cosine_similarity_text(
|
|
response.audio_content.lower(),
|
|
str(audio_ref_text).lower(),
|
|
)
|
|
assert audio_similarity > 0.9, (
|
|
f"The audio content does not match reference text: similarity={audio_similarity:.3f}"
|
|
)
|
|
|
|
|
|
def _speech_combined_error_text_for_assert(response: Any) -> str:
|
|
"""Join ``error_message`` and UTF-8 body (e.g. HTTP 200 + JSON) for substring checks."""
|
|
parts: list[str] = []
|
|
em = getattr(response, "error_message", None)
|
|
if em:
|
|
parts.append(str(em))
|
|
ab = getattr(response, "audio_bytes", None)
|
|
if ab:
|
|
parts.append(ab.decode("utf-8", errors="replace"))
|
|
return "\n".join(parts)
|
|
|
|
|
|
def _assert_speech_error_substrings(haystack: str, err_expected: Any) -> None:
|
|
if isinstance(err_expected, (list, tuple)):
|
|
assert any(str(s) in haystack for s in err_expected), (
|
|
f"Expected error text to contain one of {err_expected!r}, got: {haystack!r}"
|
|
)
|
|
else:
|
|
assert str(err_expected) in haystack, f"Expected error text to contain {str(err_expected)!r}, got: {haystack!r}"
|
|
|
|
|
|
def assert_audio_speech_response(response: Any, request_config: dict[str, Any], run_level: str) -> None:
|
|
"""Validate speech API results.
|
|
|
|
If ``status_code`` is set: assert HTTP status, then optional ``err_message`` (4xx: ``error_message`` only;
|
|
HTTP 200: ``error_message`` and/or body, e.g. JSON with HTTP 200).
|
|
|
|
If only ``err_message`` is set: assert substring(s) on combined error text. Otherwise success/min_audio/etc.
|
|
"""
|
|
# When present, only compare HTTP status and skip all other checks (e.g. expected 4xx).
|
|
# ``status_code`` may be a single int or a collection of allowed codes (e.g. ``(400, 422)``).
|
|
expected_status = request_config.get("status_code")
|
|
err_expected = request_config.get("err_message")
|
|
if expected_status is not None:
|
|
actual = getattr(response, "status_code", None)
|
|
assert actual is not None, (
|
|
"request_config has status_code but response has no .status_code "
|
|
"(set OmniResponse.status_code or pass a response object with status_code)"
|
|
)
|
|
if isinstance(expected_status, (list, tuple, set, frozenset)):
|
|
assert actual in expected_status, f"Expected HTTP status in {expected_status!r}, got {actual}"
|
|
else:
|
|
assert actual == int(expected_status), f"Expected HTTP status {int(expected_status)}, got {actual}"
|
|
|
|
if err_expected is not None:
|
|
if actual is not None and actual != 200:
|
|
msg = (getattr(response, "error_message", None) or "") + ""
|
|
_assert_speech_error_substrings(msg, err_expected)
|
|
else:
|
|
_assert_speech_error_substrings(_speech_combined_error_text_for_assert(response), err_expected)
|
|
return
|
|
|
|
if err_expected is not None:
|
|
_assert_speech_error_substrings(_speech_combined_error_text_for_assert(response), err_expected)
|
|
return
|
|
|
|
assert response.success, "The request failed."
|
|
|
|
# Optional floor on decoded audio size (models with very short clips may use a lower value).
|
|
min_audio = request_config.get("min_audio_bytes")
|
|
if min_audio is not None:
|
|
n = int(min_audio)
|
|
if n > 0:
|
|
ab = response.audio_bytes
|
|
assert ab is not None, "Expected audio bytes when min_audio_bytes is set"
|
|
assert len(ab) > n, f"Audio payload too small: {len(ab)} bytes, expected more than {n} (min_audio_bytes)"
|
|
|
|
req_fmt = request_config.get("response_format")
|
|
if req_fmt == "pcm" and response.audio_bytes:
|
|
min_hnr_db = float(request_config.get("min_hnr_db", _MIN_PCM_SPEECH_HNR_DB))
|
|
_assert_pcm_int16_speech_hnr(response.audio_bytes, min_hnr_db=min_hnr_db)
|
|
if response.audio_format:
|
|
assert "pcm" in response.audio_format.lower(), (
|
|
f"Expected audio/pcm content-type, got {response.audio_format!r}"
|
|
)
|
|
elif req_fmt == "wav" and response.audio_format:
|
|
assert req_fmt in response.audio_format
|
|
|
|
if run_level in {"advanced_model", "full_model"} and req_fmt != "pcm":
|
|
expected_text = request_config.get("input")
|
|
if expected_text:
|
|
transcript = (response.audio_content or "").strip()
|
|
print(f"audio content is: {transcript}")
|
|
print(f"input text is: {expected_text}")
|
|
similarity = cosine_similarity_text(transcript.lower(), expected_text.lower())
|
|
print(f"Cosine similarity: {similarity:.3f}")
|
|
assert similarity > 0.9, (
|
|
f"Transcript doesn't match input: similarity={similarity:.2f}, transcript='{transcript}'"
|
|
)
|
|
_assert_preset_voice_gender_from_audio(response.audio_bytes, request_config.get("voice"))
|
|
|
|
|
|
def assert_diffusion_response(response: "DiffusionResponse", request_config: dict[str, Any], run_level: str = None):
|
|
assert response.success, "The request failed."
|
|
has_any_content = any(content is not None for content in (response.images, response.videos, response.audios))
|
|
assert has_any_content, "Response contains no images, videos, or audios"
|
|
if response.images is not None:
|
|
assert_image_diffusion_response(response=response, request_config=request_config, run_level=run_level)
|
|
if response.videos is not None:
|
|
assert_video_diffusion_response(response=response, request_config=request_config, run_level=run_level)
|
|
if response.audios is not None:
|
|
assert_audio_diffusion_response(response=response, request_config=request_config, run_level=run_level)
|
|
|
|
|
|
__all__ = [
|
|
"assert_audio_diffusion_response",
|
|
"assert_audio_speech_response",
|
|
"assert_diffusion_response",
|
|
"assert_image_diffusion_response",
|
|
"assert_image_valid",
|
|
"assert_omni_response",
|
|
"assert_video_diffusion_response",
|
|
"assert_video_valid",
|
|
"assert_audio_valid",
|
|
]
|