modelscope
python fastapi进行离线文件识别报错choose a window size 400 that is [2, 0]
🐛 Bug
使用FunASR/runtime/python/http/server.py文件进行离线文件识别， 服务端使用两个进程 uvicorn.run( app="fun_test:app", host=args.host, port=args.port, ssl_keyfile=args.keyfile, ssl_certfile=args.certfile, workers=2 ) 当两个用户同时访问，报如下错误 choose a window size 400 that is [2, 0]
Environment
- OS : Linux
- FunASR Version : 1.1.5
- PyTorch Version : 2.3.1
同样遇到这个错误，assert 2 <= window_size <= len(waveform), "choose a window size {} that is [2, {}]".format( AssertionError: choose a window size 400 that is [2, 160] Environment OS : Linux FunASR Version : 1.1.6 PyTorch Version : 2.3.1
fix bug： 方法一 升级funasr到1.1.6版本
-
找到/funasr/fronteds/wav_fronted.py/WavFonted 类的forward 方法
-
转到第137行，在调用kaldi.fbank方法中，修改代码为：
mat = kaldi.fbank( waveform, num_mel_bins=self.n_mels, frame_length=min(self.frame_length,waveform_length/self.fs*1000), frame_shift=self.frame_shift, dither=self.dither, energy_floor=0.0, window_type=self.window, sample_frequency=self.fs, snip_edges=self.snip_edges, )
在我的场景中此法有效
方法二： MyWavFrontend 或者，如果不想直接修改funasr包代码，可以新建一个MYWavFrontend类 ` @tables.register("frontend_classes", "my_wav_frontend") @tables.register("frontend_classes", "MyWavFrontend") class MYWavFrontend(WavFrontend): """Conventional frontend structure for ASR."""
def forward(
self,
input: torch.Tensor,
input_lengths,
**kwargs,
) -> Tuple[torch.Tensor, torch.Tensor]:
batch_size = input.size(0)
feats = []
feats_lens = []
for i in range(batch_size):
waveform_length = input_lengths[i]
waveform = input[i][:waveform_length]
if self.upsacle_samples:
waveform = waveform * (1 << 15)
waveform = waveform.unsqueeze(0)
mat = kaldi.fbank(
waveform,
num_mel_bins=self.n_mels,
# frame_length=self.frame_length,
frame_length=min(self.frame_length,waveform_length/self.fs*1000),
frame_shift=self.frame_shift,
dither=self.dither,
energy_floor=0.0,
window_type=self.window,
sample_frequency=self.fs,
snip_edges=self.snip_edges,
)
if self.lfr_m != 1 or self.lfr_n != 1:
mat = apply_lfr(mat, self.lfr_m, self.lfr_n)
if self.cmvn is not None:
mat = apply_cmvn(mat, self.cmvn)
feat_length = mat.size(0)
feats.append(mat)
feats_lens.append(feat_length)
feats_lens = torch.as_tensor(feats_lens)
if batch_size == 1:
feats_pad = feats[0][None, :, :]
else:
feats_pad = pad_sequence(feats, batch_first=True, padding_value=0.0)
return feats_pad, feats_lens`
并在模型配置文件speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/config.yaml 中修改WavFrontend为MyWavFrontend
fix bug： 方法一 升级funasr到1.1.6版本
- 找到/funasr/fronteds/wav_fronted.py/WavFonted 类的forward 方法
- 转到第137行，在调用kaldi.fbank方法中，修改代码为：
mat = kaldi.fbank( waveform, num_mel_bins=self.n_mels, frame_length=min(self.frame_length,waveform_length/self.fs*1000), frame_shift=self.frame_shift, dither=self.dither, energy_floor=0.0, window_type=self.window, sample_frequency=self.fs, snip_edges=self.snip_edges, )在我的场景中此法有效
方法二： MyWavFrontend 或者，如果不想直接修改funasr包代码，可以新建一个MYWavFrontend类 ` @tables.register("frontend_classes", "my_wav_frontend") @tables.register("frontend_classes", "MyWavFrontend") class MYWavFrontend(WavFrontend): """Conventional frontend structure for ASR."""
def forward( self, input: torch.Tensor, input_lengths, **kwargs, ) -> Tuple[torch.Tensor, torch.Tensor]: batch_size = input.size(0) feats = [] feats_lens = [] for i in range(batch_size): waveform_length = input_lengths[i] waveform = input[i][:waveform_length] if self.upsacle_samples: waveform = waveform * (1 << 15) waveform = waveform.unsqueeze(0) mat = kaldi.fbank( waveform, num_mel_bins=self.n_mels, # frame_length=self.frame_length, frame_length=min(self.frame_length,waveform_length/self.fs*1000), frame_shift=self.frame_shift, dither=self.dither, energy_floor=0.0, window_type=self.window, sample_frequency=self.fs, snip_edges=self.snip_edges, ) if self.lfr_m != 1 or self.lfr_n != 1: mat = apply_lfr(mat, self.lfr_m, self.lfr_n) if self.cmvn is not None: mat = apply_cmvn(mat, self.cmvn) feat_length = mat.size(0) feats.append(mat) feats_lens.append(feat_length) feats_lens = torch.as_tensor(feats_lens) if batch_size == 1: feats_pad = feats[0][None, :, :] else: feats_pad = pad_sequence(feats, batch_first=True, padding_value=0.0) return feats_pad, feats_lens`并在模型配置文件speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/config.yaml 中修改WavFrontend为MyWavFrontend
试了您的两种方法，我这边还是报同样错误，我想请问一下，您的torch版本是多少？
PyTorch Version : 2.3.1，应该跟torch版本关系不大， 你报的还是这个错误吗？
除了报这个错外：choose a window size 400 that is [2,0]，还会报list index out of range
list index out of range 这个你可以尝试一下更新到最新版本funasr， 我前一阵子pull requests修了两个与这个相关的bug，你看是否可以解决你的问题。 choose a window size 400 that is [2,0]， 这个，你可以尝试一下用try except 在遇到错误的时候 reload model（重新对asr_model进行初始化）试试。
list index out of range
非常感谢您，list index out of range这个bug已经通过更新funasr版本得到解决，choose a window size 400 that is [2,0]这个bug，用try execpt还是无法解决并发问题
list index out of range 这个你可以尝试一下更新到最新版本funasr， 我前一阵子pull requests修了两个与这个相关的bug，你看是否可以解决你的问题。 choose a window size 400 that is [2,0]， 这个，你可以尝试一下用try except 在遇到错误的时候 reload model（重新对asr_model进行初始化）试试。
升级funasr版本之后，又出现另一个报错： File "/home/malimin/.conda/envs/offline_test/lib/python3.10/site-packages/funasr/auto/auto_model.py", line 343, in inference | res = model.inference(**batch, **kwargs) | File "/home/malimin/.conda/envs/offline_test/lib/python3.10/site-packages/funasr/models/fsmn_vad_streaming/model.py", line 717, in inference | "waveform": cache["frontend"]["waveforms"], | KeyError: 'waveforms'
list index out of range
非常感谢您，list index out of range这个bug已经通过更新funasr版本得到解决，choose a window size 400 that is [2,0]这个bug，用try execpt还是无法解决并发问题
请问解决了吗？咋解决的
