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fix(offline): remove inference-path HF_HUB_OFFLINE guards

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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 04:18:53 -07:00
parent 7e7feeac54
commit bcab47f25a
3 changed files with 80 additions and 96 deletions
Download Patch File Download Diff File Expand all files Collapse all files
backend/backends/mlx_backend.py
+28 -35
View File
@@ -193,8 +193,6 @@ class MLXTTSBackend:
logger.info("Generating audio for text: %s", text)
model_name = f"qwen-tts-{self._current_model_size}"
def _generate_sync():
"""Run synchronous generation in thread pool."""
# MLX generate() returns a generator yielding GenerationResult objects
@@ -220,40 +218,38 @@ class MLXTTSBackend:
logger.warning("Regenerating without voice prompt.")
ref_audio = None
# Model is loaded → weights are on disk. Force offline so
# lazy tokenizer/config lookups inside mlx_audio don't hang
# when the user is disconnected (issue #462).
with force_offline_if_cached(True, model_name):
# Check if model supports voice cloning via generate method
# MLX API may support ref_audio parameter directly
try:
# Try with voice cloning parameters if supported
if ref_audio:
# Check if generate accepts ref_audio parameter
import inspect
# Inference runs with the process's default HF_HUB_OFFLINE
# state. Forcing offline here (previously used to avoid lazy
# mlx_audio lookups hanging when the network drops mid-inference,
# issue #462) regressed online users because libraries make
# legitimate metadata calls during generation.
try:
if ref_audio:
# Check if generate accepts ref_audio parameter
import inspect
sig = inspect.signature(self.model.generate)
if "ref_audio" in sig.parameters:
# Generate with voice cloning
for result in self.model.generate(text, ref_audio=ref_audio, ref_text=ref_text, lang_code=lang):
audio_chunks.append(np.array(result.audio))
sample_rate = result.sample_rate
else:
# Fallback: generate without voice cloning
for result in self.model.generate(text, lang_code=lang):
audio_chunks.append(np.array(result.audio))
sample_rate = result.sample_rate
sig = inspect.signature(self.model.generate)
if "ref_audio" in sig.parameters:
# Generate with voice cloning
for result in self.model.generate(text, ref_audio=ref_audio, ref_text=ref_text, lang_code=lang):
audio_chunks.append(np.array(result.audio))
sample_rate = result.sample_rate
else:
# No voice prompt, generate normally
# Fallback: generate without voice cloning
for result in self.model.generate(text, lang_code=lang):
audio_chunks.append(np.array(result.audio))
sample_rate = result.sample_rate
except Exception as e:
# If voice cloning fails, try without it
logger.warning("Voice cloning failed, generating without voice prompt: %s", e)
else:
# No voice prompt, generate normally
for result in self.model.generate(text, lang_code=lang):
audio_chunks.append(np.array(result.audio))
sample_rate = result.sample_rate
except Exception as e:
# If voice cloning fails, try without it
logger.warning("Voice cloning failed, generating without voice prompt: %s", e)
for result in self.model.generate(text, lang_code=lang):
audio_chunks.append(np.array(result.audio))
sample_rate = result.sample_rate
# Concatenate all chunks
if audio_chunks:
@@ -347,8 +343,6 @@ class MLXSTTBackend:
"""
await self.load_model_async(model_size)
progress_model_name = f"whisper-{self.model_size}"
def _transcribe_sync():
"""Run synchronous transcription in thread pool."""
# MLX Whisper transcription using generate method
@@ -357,11 +351,10 @@ class MLXSTTBackend:
if language:
decode_options["language"] = language
# Model is loaded → weights are on disk. Force offline so
# lazy tokenizer/config lookups don't hang when the user is
# disconnected (issue #462).
with force_offline_if_cached(True, progress_model_name):
result = self.model.generate(str(audio_path), **decode_options)
# Inference runs with the process's default HF_HUB_OFFLINE
# state — see the comment in MLXTTSBackend.generate for the
# regression this revert fixes (issue #462).
result = self.model.generate(str(audio_path), **decode_options)
# Extract text from result
if isinstance(result, str):
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()
backend/backends/qwen_custom_voice_backend.py
+5 -6
View File
@@ -186,7 +186,6 @@ class QwenCustomVoiceBackend:
await self.load_model_async(None)
speaker = voice_prompt.get("preset_voice_id") or QWEN_CV_DEFAULT_SPEAKER
model_name = f"qwen-custom-voice-{self._current_model_size}"
def _generate_sync():
if seed is not None:
@@ -206,11 +205,11 @@ class QwenCustomVoiceBackend:
if instruct:
kwargs["instruct"] = instruct
# 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_custom_voice(**kwargs)
# 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 generation.
wavs, sample_rate = self.model.generate_custom_voice(**kwargs)
return wavs[0], sample_rate
audio, sample_rate = await asyncio.to_thread(_generate_sync)