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| | | import os |
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| | | import torch |
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| | | from torch.nn import functional as F |
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| | | import yaml |
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| | | import torch |
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| | | import numpy as np |
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| | | from torch.nn import functional as F |
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| | | |
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| | | def sequence_mask(lengths, maxlen=None, dtype=torch.float32, device=None): |
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| | | if maxlen is None: |
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| | | maxlen = lengths.max() |
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| | | row_vector = torch.arange(0, maxlen, 1).to(lengths.device) |
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| | | matrix = torch.unsqueeze(lengths, dim=-1) |
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| | | mask = row_vector < matrix |
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| | | mask = mask.detach() |
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| | | if maxlen is None: |
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| | | maxlen = lengths.max() |
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| | | row_vector = torch.arange(0, maxlen, 1).to(lengths.device) |
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| | | matrix = torch.unsqueeze(lengths, dim=-1) |
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| | | mask = row_vector < matrix |
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| | | mask = mask.detach() |
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| | | |
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| | | return mask.type(dtype).to(device) if device is not None else mask.type(dtype) |
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| | | return mask.type(dtype).to(device) if device is not None else mask.type(dtype) |
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| | | |
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| | | |
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| | | def apply_cmvn(inputs, mvn): |
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| | | device = inputs.device |
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| | | dtype = inputs.dtype |
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| | | frame, dim = inputs.shape |
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| | | meams = np.tile(mvn[0:1, :dim], (frame, 1)) |
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| | | vars = np.tile(mvn[1:2, :dim], (frame, 1)) |
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| | | inputs -= torch.from_numpy(meams).type(dtype).to(device) |
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| | | inputs *= torch.from_numpy(vars).type(dtype).to(device) |
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| | | device = inputs.device |
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| | | dtype = inputs.dtype |
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| | | frame, dim = inputs.shape |
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| | | meams = np.tile(mvn[0:1, :dim], (frame, 1)) |
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| | | vars = np.tile(mvn[1:2, :dim], (frame, 1)) |
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| | | inputs -= torch.from_numpy(meams).type(dtype).to(device) |
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| | | inputs *= torch.from_numpy(vars).type(dtype).to(device) |
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| | | |
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| | | return inputs.type(torch.float32) |
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| | | return inputs.type(torch.float32) |
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| | | |
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| | | |
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| | | def drop_and_add( |
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| | | inputs: torch.Tensor, |
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| | | outputs: torch.Tensor, |
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| | | training: bool, |
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| | | dropout_rate: float = 0.1, |
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| | | stoch_layer_coeff: float = 1.0, |
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| | | ): |
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| | | |
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| | | outputs = F.dropout(outputs, p=dropout_rate, training=training, inplace=True) |
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| | | outputs *= stoch_layer_coeff |
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| | | |
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| | | def drop_and_add(inputs: torch.Tensor, |
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| | | outputs: torch.Tensor, |
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| | | training: bool, |
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| | | dropout_rate: float = 0.1, |
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| | | stoch_layer_coeff: float = 1.0): |
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| | | input_dim = inputs.size(-1) |
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| | | output_dim = outputs.size(-1) |
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| | | |
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| | | |
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| | | |
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| | | outputs = F.dropout(outputs, p=dropout_rate, training=training, inplace=True) |
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| | | outputs *= stoch_layer_coeff |
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| | | |
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| | | input_dim = inputs.size(-1) |
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| | | output_dim = outputs.size(-1) |
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| | | |
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| | | if input_dim == output_dim: |
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| | | outputs += inputs |
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| | | return outputs |
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| | | if input_dim == output_dim: |
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| | | outputs += inputs |
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| | | return outputs |
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| | | |
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| | | |
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| | | def proc_tf_vocab(vocab_path): |
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| | | with open(vocab_path, encoding="utf-8") as f: |
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| | | token_list = [line.rstrip() for line in f] |
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| | | if '<unk>' not in token_list: |
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| | | token_list.append('<unk>') |
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| | | return token_list |
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| | | with open(vocab_path, encoding="utf-8") as f: |
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| | | token_list = [line.rstrip() for line in f] |
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| | | if "<unk>" not in token_list: |
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| | | token_list.append("<unk>") |
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| | | return token_list |
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| | | |
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| | | |
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| | | def gen_config_for_tfmodel(config_path, vocab_path, output_dir): |
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| | | token_list = proc_tf_vocab(vocab_path) |
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| | | with open(config_path, encoding="utf-8") as f: |
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| | | config = yaml.safe_load(f) |
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| | | |
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| | | config['token_list'] = token_list |
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| | | |
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| | | if not os.path.exists(output_dir): |
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| | | os.makedirs(output_dir) |
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| | | |
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| | | with open(os.path.join(output_dir, "config.yaml"), "w", encoding="utf-8") as f: |
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| | | yaml_no_alias_safe_dump(config, f, indent=4, sort_keys=False) |
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| | | token_list = proc_tf_vocab(vocab_path) |
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| | | with open(config_path, encoding="utf-8") as f: |
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| | | config = yaml.safe_load(f) |
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| | | |
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| | | config["token_list"] = token_list |
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| | | |
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| | | if not os.path.exists(output_dir): |
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| | | os.makedirs(output_dir) |
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| | | |
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| | | with open(os.path.join(output_dir, "config.yaml"), "w", encoding="utf-8") as f: |
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| | | yaml_no_alias_safe_dump(config, f, indent=4, sort_keys=False) |
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| | | |
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| | | |
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| | | class NoAliasSafeDumper(yaml.SafeDumper): |
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| | | # Disable anchor/alias in yaml because looks ugly |
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| | | def ignore_aliases(self, data): |
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| | | return True |
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| | | # Disable anchor/alias in yaml because looks ugly |
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| | | def ignore_aliases(self, data): |
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| | | return True |
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| | | |
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| | | |
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| | | def yaml_no_alias_safe_dump(data, stream=None, **kwargs): |
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| | | """Safe-dump in yaml with no anchor/alias""" |
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| | | return yaml.dump( |
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| | | data, stream, allow_unicode=True, Dumper=NoAliasSafeDumper, **kwargs |
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| | | ) |
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| | | """Safe-dump in yaml with no anchor/alias""" |
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| | | return yaml.dump(data, stream, allow_unicode=True, Dumper=NoAliasSafeDumper, **kwargs) |
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| | | |
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| | | |
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| | | if __name__ == '__main__': |
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| | | import sys |
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| | | |
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| | | config_path = sys.argv[1] |
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| | | vocab_path = sys.argv[2] |
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| | | output_dir = sys.argv[3] |
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| | | gen_config_for_tfmodel(config_path, vocab_path, output_dir) |
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| | | if __name__ == "__main__": |
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| | | import sys |
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| | | |
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| | | config_path = sys.argv[1] |
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| | | vocab_path = sys.argv[2] |
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| | | output_dir = sys.argv[3] |
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| | | gen_config_for_tfmodel(config_path, vocab_path, output_dir) |
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