Upload voxtral_eval_job.py with huggingface_hub

voxtral_eval_job.py

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+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#   "torch",
+#   "transformers>=4.56",
+#   "mistral-common[audio]>=1.8.1",
+#   "datasets",
+#   "accelerate",
+#   "jiwer",
+#   "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}")