Synchronize the bilingual current-public fixtures, evidence assertion, and valid English fixture with the GPT-SoVITS #2824 contract. Source commit was signed and carried a DCO trailer; focused and related contract tests passed.
Prefer matching discrete GPU devices while retaining a matching integrated GPU as fallback. Tested with the pinned llama.cpp revision in CPU and Vulkan builds; this addresses the iGPU selector bug without claiming to fix the separate RX 9070 XT backend crash.
* Remove hard torchaudio dependency for inference; add kaldi-native-fbank fbank backend
Make torchaudio an optional dependency for the inference path so FunASR
can run on Ascend NPU (aarch64) servers, where no matching torchaudio
wheel exists (Huawei's Ascend torch is version-decoupled from the PyTorch
upstream release line).
- Add funasr/utils/fbank.py: an fbank shim that delegates to
torchaudio.compliance.kaldi when torchaudio is installed, otherwise
falls back to kaldi-native-fbank (a torch-free C++ library with aarch64
wheels). Options and frame-shift API are mapped to torchaudio semantics
so outputs are equivalent.
- Switch funasr/frontends/wav_frontend.py, funasr/utils/speaker_utils.py
and funasr/models/campplus/utils.py to the shim.
- Make the torchaudio import optional in funasr/utils/load_utils.py
(resampler falls back to librosa).
No device-specific branching is introduced — a pure backend fallback that
also benefits CPU-only and other accelerator platforms.
Verified end-to-end on Ascend 910B (npu:0) with torchaudio absent:
paraformer-large loads and infers correctly, RTF ~0.016 (~60x realtime).
* Make torchaudio import optional in paraformer_v2, fun_asr_nano and dataset preprocessors
Follow-up to the inference-path change: make the remaining top-level
torchaudio imports optional so these modules import cleanly when
torchaudio is not installed (e.g. on Ascend NPU servers).
- funasr/models/paraformer_v2_community/model.py
- funasr/models/fun_asr_nano/tools/utils.py
- funasr/datasets/audio_datasets/preprocessor.py
- funasr/datasets/llm_datasets/preprocessor.py
Functions that still require torchaudio (forced_align, Resample) degrade
to a clear error only when called, rather than a hard import failure.
* Address review: declare knf extra, harden fbank fallback and torchaudio guards
Respond to maintainer review feedback.
- Declare kaldi-native-fbank as an optional 'knf' extra in setup.py; the
fbank module now raises an actionable ImportError only when fbank() is
actually called with neither torchaudio nor kaldi-native-fbank installed
(previously it failed at import time with a bare ModuleNotFoundError).
- Harden the fbank fallback contract: handle the channel argument explicitly
and reject torchaudio-only options (subtract_mean, min_duration, VTLN
warping, non-default blackman_coeff) with NotImplementedError instead of
silently dropping them via **kwargs.
- Add a shared guard funasr/utils/torchaudio_compat.py and use it for
resampling, forced alignment and speed perturbation so a missing torchaudio
fails loudly instead of returning a silently wrong result (empty alignment).
- Add regression tests (tests/test_torchaudio_optional.py) that import the
affected modules with torchaudio absent.
Use the non-deprecated torch.amp APIs when available while preserving the torch.cuda.amp fallback and legacy autocast argument semantics on older PyTorch releases.
Replace invalid string raises with real chained exceptions so runtime users keep the underlying import or download failure.
Local verification covered all seven changed files with `py_compile`, `git diff --check`, and an AST audit of all 30 changed exception paths.
Signed-off-by: LauraGPT <[email protected]>
`raise "some message"` is invalid in Python 3 — a str is not an exception — so
the interpreter discards the message and raises
TypeError: exceptions must derive from BaseException
instead. Paired with a bare `except:`, the original cause is lost too, and the
user is told to install something they may already have.
Concretely: funasr was installed, but importing it failed on a missing
torchaudio. The message said 'please install funasr', the traceback said
TypeError, and the actual ModuleNotFoundError was nowhere to be seen.
This converts all 30 occurrences under runtime/python to real exceptions:
- guarded imports -> except ImportError as e: raise ImportError(...) from e
- everything else -> except Exception as e: raise RuntimeError(...) from e
`from e` keeps the original traceback, so a missing transitive dependency stays
visible. Narrowing the bare `except:` also stops unrelated failures from being
reported as a missing package — the model_dir branch in particular fired for any
failure, so a mistyped path also surfaced as TypeError.
Messages are unchanged. No behaviour changes beyond the exception type.
LauraGPT