Tts_handel = UnetTTS(models_and_params, text2id_mapper, feats_yaml) 报错

[xiaocao666tzh]

环境：Google Colab 使用GPU，使用官方文档的notebook
运行到Tts_handel = UnetTTS(models_and_params, text2id_mapper, feats_yaml)报错

UnknownError                              Traceback (most recent call last)
[<ipython-input-16-2776df11a7fe>](https://localhost:8080/#) in <module>()
----> 1 Tts_handel = UnetTTS(models_and_params, text2id_mapper, feats_yaml)

23 frames
[/content/One-Shot-Voice-Cloning/UnetTTS_syn.py](https://localhost:8080/#) in __init__(self, models_and_params, text2id_mapper, feats_yaml)
     22         self.phone_dur_min          = 5
     23         self.phone_dur_max          = 20
---> 24         self.__init_models()
     25 
     26     def one_shot_TTS(self, text, src_audio, duration_stats=None, is_wrap_txt=True):

[/content/One-Shot-Voice-Cloning/UnetTTS_syn.py](https://localhost:8080/#) in __init_models(self)
     72         self.duration_model = TFAutoModel.from_pretrained(config=AutoConfig.from_pretrained(self.models_and_params["duration_param"]), 
     73                                       pretrained_path=self.models_and_params["duration_model"],
---> 74                                       name="Normalized_duration_predictor")
     75         print("duration model load finished.")
     76 

[/usr/local/lib/python3.7/dist-packages/tensorflow_tts/inference/auto_model.py](https://localhost:8080/#) in from_pretrained(cls, config, pretrained_path, **kwargs)
     59                 model = model_class(config=config, **kwargs)
     60                 if is_build:
---> 61                     model._build()
     62                 if pretrained_path is not None and ".h5" in pretrained_path:
     63                     model.load_weights(pretrained_path)

[/usr/local/lib/python3.7/dist-packages/tensorflow_tts/models/unetts.py](https://localhost:8080/#) in _build(self)
     55         char_ids = tf.convert_to_tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]], tf.int32)
     56         duration_stat = tf.convert_to_tensor([[1., 1., 1., 1.]], tf.float32)
---> 57         self(char_ids, duration_stat)
     58 
     59     def call(

[/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py](https://localhost:8080/#) in __call__(self, *args, **kwargs)
   1035         with autocast_variable.enable_auto_cast_variables(
   1036             self._compute_dtype_object):
-> 1037           outputs = call_fn(inputs, *args, **kwargs)
   1038 
   1039         if self._activity_regularizer:

[/usr/local/lib/python3.7/dist-packages/tensorflow_tts/models/unetts.py](https://localhost:8080/#) in call(self, char_ids, duration_stat, training, **kwargs)
     72         embedding_output = self.embeddings(char_ids)
     73 
---> 74         encoder_output             = self.encoder([embedding_output, attention_mask], training=training)
     75         last_encoder_hidden_states = encoder_output[0]
     76 

[/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py](https://localhost:8080/#) in __call__(self, *args, **kwargs)
   1035         with autocast_variable.enable_auto_cast_variables(
   1036             self._compute_dtype_object):
-> 1037           outputs = call_fn(inputs, *args, **kwargs)
   1038 
   1039         if self._activity_regularizer:

[/usr/local/lib/python3.7/dist-packages/tensorflow_tts/models/moduls/core.py](https://localhost:8080/#) in call(self, inputs, training)
    377 
    378             layer_outputs = layer_module(
--> 379                 [hidden_states, attention_mask], training=training
    380             )
    381             hidden_states = layer_outputs[0]

[/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py](https://localhost:8080/#) in __call__(self, *args, **kwargs)
   1035         with autocast_variable.enable_auto_cast_variables(
   1036             self._compute_dtype_object):
-> 1037           outputs = call_fn(inputs, *args, **kwargs)
   1038 
   1039         if self._activity_regularizer:

[/usr/local/lib/python3.7/dist-packages/tensorflow_tts/models/moduls/core.py](https://localhost:8080/#) in call(self, inputs, training)
    339         attention_output = attention_outputs[0]
    340         intermediate_output = self.intermediate(
--> 341             [attention_output, attention_mask], training=training
    342         )
    343         layer_output = self.bert_output(

[/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py](https://localhost:8080/#) in __call__(self, *args, **kwargs)
   1035         with autocast_variable.enable_auto_cast_variables(
   1036             self._compute_dtype_object):
-> 1037           outputs = call_fn(inputs, *args, **kwargs)
   1038 
   1039         if self._activity_regularizer:

[/usr/local/lib/python3.7/dist-packages/tensorflow_tts/models/moduls/core.py](https://localhost:8080/#) in call(self, inputs)
    290         hidden_states, attention_mask = inputs
    291 
--> 292         hidden_states = self.conv1d_1(hidden_states)
    293         hidden_states = self.intermediate_act_fn(hidden_states)
    294         hidden_states = self.conv1d_2(hidden_states)

[/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py](https://localhost:8080/#) in __call__(self, *args, **kwargs)
   1035         with autocast_variable.enable_auto_cast_variables(
   1036             self._compute_dtype_object):
-> 1037           outputs = call_fn(inputs, *args, **kwargs)
   1038 
   1039         if self._activity_regularizer:

[/usr/local/lib/python3.7/dist-packages/keras/layers/convolutional.py](https://localhost:8080/#) in call(self, inputs)
    247       inputs = tf.pad(inputs, self._compute_causal_padding(inputs))
    248 
--> 249     outputs = self._convolution_op(inputs, self.kernel)
    250 
    251     if self.use_bias:

[/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py](https://localhost:8080/#) in wrapper(*args, **kwargs)
    204     """Call target, and fall back on dispatchers if there is a TypeError."""
    205     try:
--> 206       return target(*args, **kwargs)
    207     except (TypeError, ValueError):
    208       # Note: convert_to_eager_tensor currently raises a ValueError, not a

[/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/nn_ops.py](https://localhost:8080/#) in convolution_v2(input, filters, strides, padding, data_format, dilations, name)
   1136       data_format=data_format,
   1137       dilations=dilations,
-> 1138       name=name)
   1139 
   1140 

[/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/nn_ops.py](https://localhost:8080/#) in convolution_internal(input, filters, strides, padding, data_format, dilations, name, call_from_convolution, num_spatial_dims)
   1266           data_format=data_format,
   1267           dilations=dilations,
-> 1268           name=name)
   1269     else:
   1270       if channel_index == 1:

[/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py](https://localhost:8080/#) in wrapper(*args, **kwargs)
    204     """Call target, and fall back on dispatchers if there is a TypeError."""
    205     try:
--> 206       return target(*args, **kwargs)
    207     except (TypeError, ValueError):
    208       # Note: convert_to_eager_tensor currently raises a ValueError, not a

[/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py](https://localhost:8080/#) in new_func(*args, **kwargs)
    615                   func.__module__, arg_name, arg_value, 'in a future version'
    616                   if date is None else ('after %s' % date), instructions)
--> 617       return func(*args, **kwargs)
    618 
    619     doc = _add_deprecated_arg_value_notice_to_docstring(

[/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py](https://localhost:8080/#) in new_func(*args, **kwargs)
    615                   func.__module__, arg_name, arg_value, 'in a future version'
    616                   if date is None else ('after %s' % date), instructions)
--> 617       return func(*args, **kwargs)
    618 
    619     doc = _add_deprecated_arg_value_notice_to_docstring(

[/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/nn_ops.py](https://localhost:8080/#) in conv1d(value, filters, stride, padding, use_cudnn_on_gpu, data_format, name, input, dilations)
   2009           data_format=data_format,
   2010           dilations=dilations,
-> 2011           name=name)
   2012     else:
   2013       result = squeeze_batch_dims(

[/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gen_nn_ops.py](https://localhost:8080/#) in conv2d(input, filter, strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, dilations, name)
    930       return _result
    931     except _core._NotOkStatusException as e:
--> 932       _ops.raise_from_not_ok_status(e, name)
    933     except _core._FallbackException:
    934       pass

[/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py](https://localhost:8080/#) in raise_from_not_ok_status(e, name)
   6939   message = e.message + (" name: " + name if name is not None else "")
   6940   # pylint: disable=protected-access
-> 6941   six.raise_from(core._status_to_exception(e.code, message), None)
   6942   # pylint: enable=protected-access
   6943 

/usr/local/lib/python3.7/dist-packages/six.py in raise_from(value, from_value)

UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [Op:Conv2D]

[CMsmartvoice]

这可能是tensorflow版本 和 cuda版本不对应的问题。你试下下面的colab，一步步执行，就没有问题
https://colab.research.google.com/drive/1sEDvKTJCY7uosb7TvTqwyUdwNPiv3pBW?usp=sharing

[xiaocao666tzh]

这可能是tensorflow版本 和 cuda版本不对应的问题。你试下下面的colab，一步步执行，就没有问题 https://colab.research.google.com/drive/1sEDvKTJCY7uosb7TvTqwyUdwNPiv3pBW?usp=sharing

谢谢大佬的notebook，这个可以正常运行。