fix(offline-guard): make every cache check cover the files the forced-offline load reads

The load body now runs with HF_HUB_OFFLINE forced when _is_model_cached()
reports True, so a snapshot that holds the weights but not the small files
the loader also opens would fail hard instead of fetching them. Verified
against chatterbox-tts 0.1.7 (mtl_tts.py allow_patterns, tts_turbo.py
from_local) and the TADA loader's unsloth/Llama-3.2-1B tokenizer download;
list those files in the required_files checks. Also note in the changelog
why the 0.4.5 removal of this guard no longer applies.

CHANGELOG.md

@@ -13,7 +13,16 @@
   model now forces offline mode for the duration of the load, so it skips the network HEAD
   request (and its 5-retry backoff) for every config file — `config.json`,
   `generation_config.json`, and the rest — instead of retrying each one in sequence before the
-  app becomes ready.
+  app becomes ready. This reinstates the load-time `force_offline_if_cached` guard that 0.4.5
+  ([#530](https://github.com/jamiepine/voicebox/pull/530)) removed: that removal was a hotfix
+  for the `_patch_mistral_regex` crash ([#526](https://github.com/jamiepine/voicebox/issues/526)),
+  which the wrapper installed in the same release now catches at the source, so the guard no
+  longer trips it. The per-file HEAD retries from
+  [#434](https://github.com/jamiepine/voicebox/issues/434) were never covered by that wrapper.
+  Because a load now fails hard offline when any file is missing, every backend's cache check
+  lists the full set of files its load reads (Chatterbox tokenizer/conds, TADA's Llama
+  tokenizer mirror), so a partially downloaded snapshot reports "not cached" and downloads
+  online instead.
 
 ### Linux
 

backend/backends/chatterbox_backend.py

@@ -29,11 +29,16 @@ logger = logging.getLogger(__name__)
 
 CHATTERBOX_HF_REPO = "ResembleAI/chatterbox"
 
-# Files that must be present for the multilingual model
+# Every file ChatterboxMultilingualTTS.from_pretrained() downloads (chatterbox-tts
+# 0.1.7, mtl_tts.py allow_patterns). The load runs with HF offline mode forced
+# when this reports cached, so a partial snapshot must not count as cached.
 _MTL_WEIGHT_FILES = [
     "t3_mtl23ls_v2.safetensors",
     "s3gen.pt",
     "ve.pt",
+    "grapheme_mtl_merged_expanded_v1.json",
+    "conds.pt",
+    "Cangjie5_TC.json",
 ]
 
 

backend/backends/chatterbox_turbo_backend.py

@@ -29,11 +29,18 @@ logger = logging.getLogger(__name__)
 
 CHATTERBOX_TURBO_HF_REPO = "ResembleAI/chatterbox-turbo"
 
-# Files that must be present for the turbo model
+# Every file ChatterboxTurboTTS.from_local() reads (chatterbox-tts 0.1.7): the
+# three weight files, the GPT-2 tokenizer files AutoTokenizer needs, and the
+# built-in voice. The load runs with HF offline mode forced when this reports
+# cached, so a partial snapshot must not count as cached.
 _TURBO_WEIGHT_FILES = [
     "t3_turbo_v1.safetensors",
     "s3gen_meanflow.safetensors",
     "ve.safetensors",
+    "tokenizer_config.json",
+    "vocab.json",
+    "merges.txt",
+    "conds.pt",
 ]
 
 

backend/backends/hume_backend.py

@@ -35,6 +35,9 @@ logger = logging.getLogger(__name__)
 
 # HuggingFace repos
 TADA_CODEC_REPO = "HumeAI/tada-codec"
+# TADA hardcodes the gated meta-llama/Llama-3.2-1B tokenizer; we load it from
+# this ungated mirror instead (see load_model).
+TADA_TOKENIZER_REPO = "unsloth/Llama-3.2-1B"
 TADA_1B_REPO = "HumeAI/tada-1b"
 TADA_3B_ML_REPO = "HumeAI/tada-3b-ml"
 
@@ -52,6 +55,12 @@ _TADA_CODEC_WEIGHT_FILES = [
     "encoder/model.safetensors",
 ]
 
+_TADA_TOKENIZER_FILES = [
+    "tokenizer.json",
+    "tokenizer_config.json",
+    "special_tokens_map.json",
+]
+
 
 class HumeTadaBackend:
     """HumeAI TADA TTS backend for high-quality voice cloning."""
@@ -80,7 +89,8 @@ class HumeTadaBackend:
         repo = TADA_MODEL_REPOS.get(model_size, TADA_1B_REPO)
         model_cached = is_model_cached(repo, required_files=_TADA_MODEL_WEIGHT_FILES)
         codec_cached = is_model_cached(TADA_CODEC_REPO, required_files=_TADA_CODEC_WEIGHT_FILES)
-        return model_cached and codec_cached
+        tokenizer_cached = is_model_cached(TADA_TOKENIZER_REPO, required_files=_TADA_TOKENIZER_FILES)
+        return model_cached and codec_cached and tokenizer_cached
 
     async def load_model(self, model_size: str = "1B") -> None:
         """Load the TADA model and encoder."""
@@ -140,7 +150,7 @@ class HumeTadaBackend:
             # local cache path so we can point TADA at it directly.
             logger.info("Downloading Llama tokenizer (ungated mirror)...")
             tokenizer_path = snapshot_download(
-                repo_id="unsloth/Llama-3.2-1B",
+                repo_id=TADA_TOKENIZER_REPO,
                 token=None,
                 allow_patterns=["tokenizer*", "special_tokens*"],
             )