| """Batch-transcribe the first N utterances (sorted by utterance ID) of LibriSpeech test-clean with Whisper on CPU.""" |
| import sys, glob, os, json, time, platform, torch, soundfile as sf |
| from transformers import WhisperProcessor, WhisperForConditionalGeneration |
| MODEL, N, OUT = sys.argv[1], int(sys.argv[2]), sys.argv[3] |
| torch.set_num_threads(8) |
| refs = {} |
| for f in glob.glob("LibriSpeech/test-clean/*/*/*.trans.txt"): |
| for line in open(f): |
| uid, txt = line.strip().split(" ", 1); refs[uid] = txt |
| ids = sorted(refs)[:N] |
| proc = WhisperProcessor.from_pretrained(MODEL) |
| model = WhisperForConditionalGeneration.from_pretrained(MODEL).eval() |
| def path(uid): |
| s, c, _ = uid.split("-"); return f"LibriSpeech/test-clean/{s}/{c}/{uid}.flac" |
| rows, failed, retried, BS = [], [], 0, 8 |
| t0 = time.time() |
| done = set() |
| if os.path.exists(OUT + ".partial.jsonl"): |
| for l in open(OUT + ".partial.jsonl"): r = json.loads(l); rows.append(r); done.add(r["id"]) |
| todo = [u for u in ids if u not in done] |
| pf = open(OUT + ".partial.jsonl", "a") |
| def run(batch): |
| audio = [sf.read(path(u))[0] for u in batch] |
| feats = proc(audio, sampling_rate=16000, return_tensors="pt") |
| with torch.no_grad(): g = model.generate(feats.input_features, max_new_tokens=200) |
| return audio, proc.batch_decode(g, skip_special_tokens=True) |
| for i in range(0, len(todo), BS): |
| batch = todo[i:i+BS] |
| for attempt in range(2): |
| try: audio, hyps = run(batch); break |
| except Exception as e: |
| print("batch error", batch[0], e, flush=True) |
| if attempt == 0: retried += 1 |
| else: failed += batch; audio = None |
| if audio is None: continue |
| for u, a, h in zip(batch, audio, hyps): |
| r = {"id": u, "speaker_id": u.split("-")[0], "chapter_id": u.split("-")[1], "duration_s": round(len(a)/16000, 3), "reference": refs[u], "transcript": h.strip()} |
| rows.append(r); pf.write(json.dumps(r) + "\n") |
| pf.flush(); print(f"{len(rows)}/{len(ids)} {time.time()-t0:.0f}s", flush=True) |
| wall = time.time() - t0 |
| json.dump({"model": MODEL, "n_requested": N, "n_processed": len(rows), "failed": failed, "retried_batches": retried, |
| "audio_seconds": round(sum(r["duration_s"] for r in rows), 1), "transcribe_wall_seconds_this_run": round(wall, 1), |
| "batch_size": BS, "threads": 8, "cpu": "8-core Intel Xeon VM, 15 GB RAM, no GPU"}, open(OUT + ".stats.json", "w"), indent=1) |
| json.dump(rows, open(OUT + ".rows.json", "w")) |
|
|
