File size: 2,704 Bytes
35d483e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | """Serialization helpers for the stable runtime model contract."""
from __future__ import annotations
from typing import Any
from .features import LogMelConfig
def build_runtime_metadata(
feature_config: LogMelConfig,
*,
max_seconds: float,
threshold: float,
model_name: str,
architecture: str,
model_version: str = "1",
development_only: bool = True,
training_status: str = "development",
data_scope: str | None = None,
data_revision: str | None = None,
parameter_count: int | None = None,
) -> dict[str, Any]:
"""Build JSON accepted by :class:`runtime.predictor.ModelMetadata`.
The dependency-light runtime currently implements the HTK filterbank. A
Slaney-mel Whisper teacher therefore must be distilled before deployment.
"""
if feature_config.mel_scale != "htk":
raise ValueError("the runtime frontend currently supports HTK mel filters only")
if feature_config.normalize:
raise ValueError("per-utterance standardization is not represented by runtime metadata")
if feature_config.log_scale not in {"whisper", "standard"}:
raise ValueError(f"unsupported runtime log scale: {feature_config.log_scale}")
if not 0.0 <= threshold <= 1.0:
raise ValueError("threshold must be in [0, 1]")
return {
"model_name": model_name,
"architecture": architecture,
"frontend": {
"sample_rate": feature_config.sample_rate,
"max_seconds": max_seconds,
"n_fft": feature_config.n_fft,
"win_length": feature_config.win_length,
"hop_length": feature_config.hop_length,
"n_mels": feature_config.n_mels,
"f_min": feature_config.f_min,
"f_max": feature_config.f_max,
"normalization": ("whisper" if feature_config.log_scale == "whisper" else "log10"),
"pad_side": feature_config.pad_side,
},
"threshold": threshold,
"controller": {
"endpoint_threshold": threshold,
"long_pause_threshold": max(0.0, threshold - 0.18),
"min_silence_ms": 200.0,
"relax_after_ms": 800.0,
"max_silence_ms": 1800.0,
"required_confirmations": 1,
},
"input_features_name": "log_mel",
"frame_mask_name": "frame_mask",
"endpoint_output_name": "endpoint_probability",
"output_type": "probability",
"model_version": model_version,
"development_only": development_only,
"training_status": training_status,
"data_scope": data_scope,
"data_revision": data_revision,
"parameter_count": parameter_count,
}
|