runtime error

Container logs:

[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data]     /home/user/nltk_data...
[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.
[nltk_data] Downloading package cmudict to /home/user/nltk_data...
[nltk_data]   Unzipping corpora/cmudict.zip.

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/home/user/.local/lib/python3.8/site-packages/espnet2/gan_tts/vits/monotonic_align/__init__.py:21: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace`
  warnings.warn(

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Running on local URL:  http://0.0.0.0:7860/

To create a public link, set `share=True` in `launch()`.
Downloading: "https://github.com/r9y9/open_jtalk/releases/download/v1.11.1/open_jtalk_dic_utf_8-1.11.tar.gz"
Extracting tar file /home/user/.local/lib/python3.8/site-packages/pyopenjtalk/dic.tar.gz

dic.tar.gz: 0.00B [00:00, ?B/s]
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dic.tar.gz: 100%|██████████| 22.6M/22.6M [00:00<00:00, 46.1MB/s]
WARNING: JPCommonLabel_make() in jcomon_label.c: No phoneme.
Traceback (most recent call last):
  File "/home/user/.local/lib/python3.8/site-packages/gradio/networking.py", line 195, in predict
    prediction, durations = app.interface.process(raw_input)
  File "/home/user/.local/lib/python3.8/site-packages/gradio/interface.py", line 371, in process
    predictions, durations = self.run_prediction(
  File "/home/user/.local/lib/python3.8/site-packages/gradio/interface.py", line 337, in run_prediction
    prediction = predict_fn(*processed_input)
  File "app.py", line 84, in inference
    wav = text2speechjp(text)["wav"]
  File "/home/user/.local/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 28, in decorate_context
    return func(*args, **kwargs)
  File "/home/user/.local/lib/python3.8/site-packages/espnet2/bin/tts_inference.py", line 204, in __call__
    output_dict = self.model.inference(**batch, **cfg)
  File "/home/user/.local/lib/python3.8/site-packages/espnet2/tts/espnet_model.py", line 299, in inference
    output_dict = self.tts.inference(**input_dict, **decode_config)
  File "/home/user/.local/lib/python3.8/site-packages/espnet2/gan_tts/vits/vits.py", line 599, in inference
    wav, att_w, dur = self.generator.inference(
  File "/home/user/.local/lib/python3.8/site-packages/espnet2/gan_tts/vits/generator.py", line 455, in inference
    x, m_p, logs_p, x_mask = self.text_encoder(text, text_lengths)
  File "/home/user/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/user/.local/lib/python3.8/site-packages/espnet2/gan_tts/vits/text_encoder.py", line 134, in forward
    x, _ = self.encoder(x, x_mask)
  File "/home/user/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/user/.local/lib/python3.8/site-packages/espnet/nets/pytorch_backend/conformer/encoder.py", line 250, in forward
    xs, masks = self.encoders(xs, masks)
  File "/home/user/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/user/.local/lib/python3.8/site-packages/espnet/nets/pytorch_backend/transformer/repeat.py", line 18, in forward
    args = m(*args)
  File "/home/user/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/user/.local/lib/python3.8/site-packages/espnet/nets/pytorch_backend/conformer/encoder_layer.py", line 121, in forward
    self.feed_forward_macaron(x)
  File "/home/user/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/user/.local/lib/python3.8/site-packages/espnet/nets/pytorch_backend/transformer/multi_layer_conv.py", line 62, in forward
    x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
  File "/home/user/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/user/.local/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 301, in forward
    return self._conv_forward(input, self.weight, self.bias)
  File "/home/user/.local/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 297, in _conv_forward
    return F.conv1d(input, weight, bias, self.stride,
RuntimeError: Calculated padded input size per channel: (2). Kernel size: (3). Kernel size can't be greater than actual input size