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.
* 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.
Resolve distributed trainer settings once with top-level precedence and backwards-compatible train_conf fallbacks. Remove consumed mode keys before expanding trainer_conf, reject simultaneous FSDP and DeepSpeed, and cover the behavior with focused tests.
The tests merged in #3456 were flaky: the state-dict round-trip test draws from
the unseeded global RNG, and the merged vs unmerged fp32 forwards can differ by
~4e-5 (different op orderings), above torch.allclose's default 1e-5 rtol. The
test failed on roughly one in three full-file runs.
- Seed the RNG per test so every test is reproducible.
- Compare merge/unmerge at a fp32-appropriate rtol=1e-3. A genuinely broken
merge differs by the full adapter term (O(1-10)), not ~1e-5, so the tolerance
is still discriminating (verified: a 'mark merged but don't fold' bug yields
max_abs_diff ~12).
- Add test_mark_only_lora_as_trainable_keeps_adapters_trainable, which exercises
the lora_only=true flow (mark_only_lora_as_trainable in train.py/train_ds.py)
that the previous reply described but did not actually test.
Verified: 8/8 pass, stable across repeated runs; the full nano set is 35 passed.
Co-authored-by: Claude <[email protected]>
LauraGPT