| """TRCS Hugging Face Dataset Loader Utilities.""" |
|
|
| from __future__ import annotations |
|
|
| import json |
| from pathlib import Path |
| from typing import Any, Dict, List, Optional |
|
|
| import datasets |
|
|
| PACKAGE_ROOT = Path(__file__).resolve().parent |
|
|
|
|
| def get_manifest_path(split: str = "telephone_grid") -> Path: |
| """Return absolute path to dataset split manifest.""" |
| split_norm = split.lower().strip() |
| if split_norm in ("telephone_grid", "e2"): |
| p = PACKAGE_ROOT / "manifests" / "e2_telephone_grid.jsonl" |
| if not p.exists(): |
| p = PACKAGE_ROOT / "data" / "telephone_grid" / "test.jsonl" |
| return p |
| elif split_norm in ("hallucination", "e1"): |
| p = PACKAGE_ROOT / "manifests" / "e1_hallucination.jsonl" |
| if not p.exists(): |
| p = PACKAGE_ROOT / "data" / "hallucination" / "test.jsonl" |
| return p |
| elif split_norm == "default": |
| p = PACKAGE_ROOT / "data" / "default" / "test.jsonl" |
| return p |
| else: |
| raise ValueError(f"Unknown split: {split}. Choose from ['telephone_grid', 'hallucination', 'default']") |
|
|
|
|
| def load_trcs_manifest(split: str = "telephone_grid") -> List[Dict[str, Any]]: |
| """Load TRCS manifest records as a list of Python dictionaries.""" |
| m_path = get_manifest_path(split) |
| if not m_path.exists(): |
| raise FileNotFoundError(f"Manifest not found: {m_path}") |
|
|
| records = [] |
| with open(m_path, "r", encoding="utf-8") as f: |
| for line in f: |
| line = line.strip() |
| if line: |
| rec = json.loads(line) |
| |
| if "audio_path" in rec: |
| p = Path(rec["audio_path"]) |
| if not p.is_absolute(): |
| rec["audio_path_abs"] = str(PACKAGE_ROOT / p) |
| else: |
| rec["audio_path_abs"] = str(p) |
| records.append(rec) |
| return records |
|
|
|
|
| def load_trcs_dataset( |
| split: str = "telephone_grid", |
| resolve_audio_paths: bool = True, |
| as_audio: bool = False, |
| ) -> datasets.Dataset: |
| """Load TRCS dataset as a Hugging Face `datasets.Dataset` object. |
| |
| Args: |
| split: Split name ('telephone_grid'/'e2', 'hallucination'/'e1', or 'default'). |
| resolve_audio_paths: If True, adds 'audio_path_abs' with absolute path to audio files. |
| as_audio: If True, adds an 'audio' column cast to `datasets.Audio(sampling_rate=16000)`. |
| """ |
| split_norm = split.lower().strip() |
| config_name = "telephone_grid" if split_norm in ("telephone_grid", "e2") else ("hallucination" if split_norm in ("hallucination", "e1") else "default") |
| ds = datasets.load_dataset(str(PACKAGE_ROOT), config_name, split="test", verification_mode="no_checks") |
|
|
| if resolve_audio_paths or as_audio: |
| def _add_abs(ex: Dict[str, Any]) -> Dict[str, Any]: |
| rel_p = Path(ex["audio_path"]) |
| abs_p = str(rel_p if rel_p.is_absolute() else (PACKAGE_ROOT / rel_p)) |
| out = {"audio_path_abs": abs_p} |
| if as_audio: |
| out["audio"] = abs_p |
| return out |
|
|
| ds = ds.map(_add_abs) |
| if as_audio: |
| ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16000)) |
|
|
| return ds |
|
|
