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fix(offline): remove inference-path HF_HUB_OFFLINE guards (#524)

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0.4.3 wrapped every inference body (`generate`, `transcribe`,
`create_voice_clone_prompt`) with `force_offline_if_cached(True, …)` to
prevent lazy HF lookups from hanging when the network drops
mid-inference (#462). That trade broke online users: the guard flips
`huggingface_hub.constants.HF_HUB_OFFLINE` globally, so any legitimate
metadata call the library makes during generation (e.g. revision
resolution via `HfApi().model_info`) now raises:

    Cannot reach https://huggingface.co/api/models/Qwen/Qwen3-TTS-…:
    offline mode is enabled.

Hit by multiple users on 0.4.3 within hours of release. The offline
blast radius is much larger than the original hang it fixed.

This reverts the inference-path guards. Load-path guards stay — those
worked fine in 0.4.2 and aren't the source of the regression. The
`force_offline_if_cached` helper itself is unchanged; tests still pass.

The #462 hang (network dropping mid-inference) remains unaddressed by
this commit and will need a targeted fix that doesn't flip a global
flag — most likely per-call timeouts or library-specific
`local_files_only` arguments, not a process-wide env mutation.
This commit is contained in:
Jamie Pine
2026-04-21 04:25:28 -07:00
committed by GitHub
parent 7e7feeac54
commit 0047352df1
3 changed files with 80 additions and 96 deletions
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backend/backends/pytorch_backend.py
+47 -55
View File
@@ -172,19 +172,17 @@ class PyTorchTTSBackend:
# This shouldn't happen in practice, but handle it
return {"prompt": cached_prompt}, True
model_name = f"qwen-tts-{self._current_model_size}"
def _create_prompt_sync():
"""Run synchronous voice prompt creation in thread pool."""
# Model is loaded → weights are on disk. Force offline so
# lazy tokenizer/config lookups inside qwen_tts don't hang
# when the user is disconnected (issue #462).
with force_offline_if_cached(True, model_name):
return self.model.create_voice_clone_prompt(
ref_audio=str(audio_path),
ref_text=reference_text,
x_vector_only_mode=False,
)
# Inference runs with the process's default HF_HUB_OFFLINE
# state. Forcing offline here (issue #462) regressed online
# users whose libraries issue legitimate metadata lookups
# during voice-prompt creation.
return self.model.create_voice_clone_prompt(
ref_audio=str(audio_path),
ref_text=reference_text,
x_vector_only_mode=False,
)
# Run blocking operation in thread pool
voice_prompt_items = await asyncio.to_thread(_create_prompt_sync)
@@ -227,24 +225,20 @@ class PyTorchTTSBackend:
# Load model
await self.load_model_async(None)
model_name = f"qwen-tts-{self._current_model_size}"
def _generate_sync():
"""Run synchronous generation in thread pool."""
# Set seed if provided
if seed is not None:
manual_seed(seed, self.device)
# Model is loaded → weights are on disk. Force offline so
# lazy tokenizer/config lookups inside qwen_tts don't hang
# when the user is disconnected (issue #462).
with force_offline_if_cached(True, model_name):
wavs, sample_rate = self.model.generate_voice_clone(
text=text,
voice_clone_prompt=voice_prompt,
language=LANGUAGE_CODE_TO_NAME.get(language, "auto"),
instruct=instruct,
)
# See _create_prompt_sync comment — inference runs with the
# process's default HF_HUB_OFFLINE state (issue #462).
wavs, sample_rate = self.model.generate_voice_clone(
text=text,
voice_clone_prompt=voice_prompt,
language=LANGUAGE_CODE_TO_NAME.get(language, "auto"),
instruct=instruct,
)
return wavs[0], sample_rate
# Run blocking inference in thread pool to avoid blocking event loop
@@ -342,46 +336,44 @@ class PyTorchSTTBackend:
"""
await self.load_model_async(model_size)
progress_model_name = f"whisper-{self.model_size}"
def _transcribe_sync():
"""Run synchronous transcription in thread pool."""
# Load audio
audio, _sr = load_audio(audio_path, sample_rate=16000)
# Model is loaded → weights are on disk. Force offline so
# `get_decoder_prompt_ids` and any lazy tokenizer lookups
# don't hang when the user is disconnected (issue #462).
with force_offline_if_cached(True, progress_model_name):
# Process audio
inputs = self.processor(
audio,
sampling_rate=16000,
return_tensors="pt",
# Inference runs with the process's default HF_HUB_OFFLINE
# state — forcing offline here (issue #462) broke online users
# whose `get_decoder_prompt_ids` / tokenizer calls issue
# legitimate metadata lookups.
# Process audio
inputs = self.processor(
audio,
sampling_rate=16000,
return_tensors="pt",
)
inputs = inputs.to(self.device)
# Generate transcription
# If language is provided, force it; otherwise let Whisper auto-detect
generate_kwargs = {}
if language:
forced_decoder_ids = self.processor.get_decoder_prompt_ids(
language=language,
task="transcribe",
)
inputs = inputs.to(self.device)
generate_kwargs["forced_decoder_ids"] = forced_decoder_ids
# Generate transcription
# If language is provided, force it; otherwise let Whisper auto-detect
generate_kwargs = {}
if language:
forced_decoder_ids = self.processor.get_decoder_prompt_ids(
language=language,
task="transcribe",
)
generate_kwargs["forced_decoder_ids"] = forced_decoder_ids
with torch.no_grad():
predicted_ids = self.model.generate(
inputs["input_features"],
**generate_kwargs,
)
with torch.no_grad():
predicted_ids = self.model.generate(
inputs["input_features"],
**generate_kwargs,
)
# Decode
transcription = self.processor.batch_decode(
predicted_ids,
skip_special_tokens=True,
)[0]
# Decode
transcription = self.processor.batch_decode(
predicted_ids,
skip_special_tokens=True,
)[0]
return transcription.strip()