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a51a477 64ba922 a51a477 | 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 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | # /// script
# requires-python = ">=3.10"
# dependencies = [
# "torch",
# "transformers>=4.56",
# "mistral-common[audio]>=1.8.1",
# "datasets",
# "accelerate",
# "jiwer",
# "librosa",
# "soundfile",
# "requests",
# "numpy",
# ]
# ///
"""
Voxtral baseline WER eval — HuggingFace Job script (uv run --script).
Transcribes the user's held-out labeled recordings (same seed-42/10% split as
training and the local eval_voxtral.py) with an open-weights Voxtral model and
reports WER. No personalization — this is the cold-model baseline.
Env: MODEL_ID (default mistralai/Voxtral-Mini-3B-2507), N_CLIPS (default 30),
USER_ID; secrets: HF_TOKEN, SUPABASE_SERVICE_ROLE_KEY.
"""
import os, tempfile, time
import requests
import torch
import jiwer
from datasets import Dataset
MODEL_ID = os.environ.get("MODEL_ID", "mistralai/Voxtral-Mini-3B-2507")
N_CLIPS = int(os.environ.get("N_CLIPS", "30"))
USER_ID = os.environ["USER_ID"]
SUPABASE_URL = os.environ.get("SUPABASE_URL", "https://hehlulmegluxmtlupwgp.supabase.co")
SB_KEY = os.environ["SUPABASE_SERVICE_ROLE_KEY"]
hdrs = {"apikey": SB_KEY, "Authorization": f"Bearer {SB_KEY}"}
recs = requests.get(f"{SUPABASE_URL}/rest/v1/training_recordings", headers=hdrs,
params={"select": "audio_url,phrase_id", "user_id": f"eq.{USER_ID}"}).json()
phrases = requests.get(f"{SUPABASE_URL}/rest/v1/training_phrases", headers=hdrs,
params={"select": "id,text"}).json()
phrase_map = {p["id"]: p["text"] for p in phrases}
dataset_all = [{"audio_url": r["audio_url"], "text": phrase_map[r["phrase_id"]]}
for r in recs if r["phrase_id"] in phrase_map]
print(f"{len(dataset_all)} labeled recordings")
# Same held-out split as training / local eval (seed 42, 10% of the FULL list).
test_size = max(1, int(len(dataset_all) * 0.1))
full = Dataset.from_list([{"idx": i} for i in range(len(dataset_all))])
eval_indices = [r["idx"] for r in full.train_test_split(test_size=test_size, seed=42)["test"]]
dataset = [dataset_all[i] for i in eval_indices]
# WAV clips only (no ffmpeg on the job image; the modern pipeline records WAV).
wav_ds, skipped = [], 0
WAV_DIR = tempfile.mkdtemp()
for i, item in enumerate(dataset):
url = item["audio_url"]
if not url.split("?")[0].lower().endswith(".wav"):
skipped += 1
continue
r = requests.get(url.replace("/object/public/", "/object/"), headers=hdrs)
if not r.ok:
skipped += 1
continue
path = os.path.join(WAV_DIR, f"{i}.wav")
open(path, "wb").write(r.content)
wav_ds.append({"path": path, "text": item["text"]})
if len(wav_ds) >= N_CLIPS:
break
print(f"eval clips: {len(wav_ds)} wav ({skipped} non-wav/failed skipped)")
from transformers import AutoProcessor, VoxtralForConditionalGeneration
print(f"loading {MODEL_ID}…", flush=True)
processor = AutoProcessor.from_pretrained(MODEL_ID)
model = VoxtralForConditionalGeneration.from_pretrained(
MODEL_ID, torch_dtype=torch.bfloat16, device_map="cuda")
model.eval()
# Method name had a typo in early transformers releases — support both.
_req = getattr(processor, "apply_transcription_request",
getattr(processor, "apply_transcrition_request", None))
norm = jiwer.Compose([jiwer.ToLowerCase(), jiwer.RemovePunctuation(),
jiwer.RemoveMultipleSpaces(), jiwer.Strip(),
jiwer.ReduceToListOfListOfWords()])
rows = []
t0 = time.time()
for i, item in enumerate(wav_ds):
inputs = _req(language="en", audio=item["path"], model_id=MODEL_ID)
inputs = inputs.to("cuda", dtype=torch.bfloat16)
with torch.no_grad():
out = model.generate(**inputs, max_new_tokens=200)
hyp = processor.batch_decode(out[:, inputs.input_ids.shape[1]:],
skip_special_tokens=True)[0].strip()
wer = jiwer.wer(item["text"], hyp or "",
reference_transform=norm, hypothesis_transform=norm)
rows.append((wer, item["text"], hyp))
print(f"[{i}] wer={wer:.2f} ref={item['text']!r} hyp={hyp!r}", flush=True)
mean_wer = sum(w for w, _, _ in rows) / max(1, len(rows))
print("\n================ RESULTS ================")
print(f"model: {MODEL_ID}")
print(f"mean WER: {mean_wer:.3f} over {len(rows)} clips "
f"({(time.time()-t0)/max(1,len(rows)):.1f}s/clip)")
print("worst 5:")
for w, ref, hyp in sorted(rows, reverse=True)[:5]:
print(f" {w:.2f} ref: {ref!r}")
print(f" hyp: {hyp!r}")
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