Datasets:
Formats:
csv
Size:
< 1K
Tags:
speech-recognition
code-switching
nigerian-languages
asr-benchmark
sahara-codeswitch-challenge
License:
File size: 3,130 Bytes
dc1d281 | 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 74 75 76 77 78 79 80 81 82 83 84 | """
_common.py — shared plumbing for the transcription scripts.
Every transcribe_*.py does the same three things:
1. read the list of clips (clip_id + wav path) for a language,
2. run each clip through one engine,
3. write results/<engine>.csv in the shared schema: clip_id,engine,transcript
This module holds steps 1 and 3 so each engine script only has to implement
"given a wav path, return a transcript string". That keeps the engines honest:
they all read the exact same audio and write the exact same shape, which is the
whole point of a fair benchmark.
"""
import csv
import os
REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
AUDIO_DIR = os.path.join(REPO_ROOT, "audio")
RESULTS_DIR = os.path.join(REPO_ROOT, "results")
GROUND_TRUTH_CSV = os.path.join(AUDIO_DIR, "ground_truth.csv")
def load_clips(language):
"""
Return [(clip_id, wav_path), ...] for one language, read from
ground_truth.csv. Verifies each wav actually exists on disk so a missing
file fails loudly here instead of deep inside an engine call.
"""
if not os.path.exists(GROUND_TRUTH_CSV):
raise SystemExit(
f"[STOP] {GROUND_TRUTH_CSV} not found. Run prepare_data.py first "
f"(Step 3) to generate the clips and ground truth."
)
clips = []
with open(GROUND_TRUTH_CSV, newline="", encoding="utf-8") as f:
reader = csv.DictReader(f)
for row in reader:
if row.get("language") != language:
continue
clip_id = row["clip_id"]
wav_path = os.path.join(AUDIO_DIR, language, f"{clip_id}.wav")
if not os.path.exists(wav_path):
raise SystemExit(
f"[STOP] Ground truth lists '{clip_id}' but {wav_path} is "
f"missing. Re-run prepare_data.py for '{language}'."
)
clips.append((clip_id, wav_path))
if not clips:
raise SystemExit(
f"[STOP] No clips found for language '{language}' in "
f"{GROUND_TRUTH_CSV}. Did prepare_data.py run for this language?"
)
return clips
def write_results(engine, rows):
"""
Write results/<engine>.csv with the shared schema. `rows` is a list of
(clip_id, transcript) — the engine name is filled in here so it's spelled
identically for the scorer to join on.
"""
os.makedirs(RESULTS_DIR, exist_ok=True)
out_path = os.path.join(RESULTS_DIR, f"{engine}.csv")
with open(out_path, "w", newline="", encoding="utf-8") as f:
writer = csv.writer(f)
writer.writerow(["clip_id", "engine", "transcript"])
for clip_id, transcript in rows:
writer.writerow([clip_id, engine, transcript])
print(f"\n[done] Wrote {len(rows)} rows to {out_path}")
return out_path
def require_env(name, how_to_fix):
"""Fetch an API key from the environment or stop with a clear message."""
value = os.environ.get(name)
if not value:
raise SystemExit(
f"[STOP] Environment variable {name} is not set.\n{how_to_fix}"
)
return value
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