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import os
from transformers import PretrainedConfig
class RebornUASRConfig(PretrainedConfig):
'''
We can use this class to define the configuration of the reborn model.
The reborn UASR is composed of a segmenter, a discriminator, and a generator.
We only include the required configurations for the discriminator and the generator from fairseq's wav2vec-U model configuration.
'''
model_type = "reborn_uasr"
def __init__(self,
segmenter_type: str = "cnn",
segmenter_input_dim: int = 512,
segmenter_hidden_dim: int = 512,
segmenter_dropout: float = 0.1,
segmenter_kernel_size: int = 7,
discriminator_input_dim: int = 512,
discriminator_kernel: int = 3,
discriminator_dilation: int = 1,
discriminator_dim: int = 256,
discriminator_causal: bool = True,
discriminator_linear_emb: bool = False,
discriminator_depth: int = 1,
discriminator_max_pool: bool = False,
discriminator_act_after_linear: bool = False,
discriminator_dropout: float = 0.0,
discriminator_spectral_norm: bool = False,
discriminator_weight_norm: bool = False,
generator_input_dim: int = 512,
generator_output_dim: int = 40,
generator_kernel: int = 4,
generator_dilation: int = 1,
generator_stride: int = 1,
generator_bias: bool = False,
generator_dropout: float = 0.0,
generator_bn_apply: bool = False,
generator_bn_init_weight: float = 30.0,
phones: list = [],
dict_fpath: str = "",
special_token_nums: int = 4, # [<s>, <pad>, </s>, <unk>]
**kwargs
):
super().__init__(**kwargs)
# read in all the configurations
self.segmenter_type = segmenter_type
self.segmenter_input_dim = segmenter_input_dim
self.segmenter_hidden_dim = segmenter_hidden_dim
self.segmenter_dropout = segmenter_dropout
self.segmenter_kernel_size = segmenter_kernel_size
self.discriminator_input_dim = discriminator_input_dim
self.discriminator_kernel = discriminator_kernel
self.discriminator_dilation = discriminator_dilation
self.discriminator_dim = discriminator_dim
self.discriminator_causal = discriminator_causal
self.discriminator_linear_emb = discriminator_linear_emb
self.discriminator_depth = discriminator_depth
self.discriminator_max_pool = discriminator_max_pool
self.discriminator_act_after_linear = discriminator_act_after_linear
self.discriminator_dropout = discriminator_dropout
self.discriminator_spectral_norm = discriminator_spectral_norm
self.discriminator_weight_norm = discriminator_weight_norm
self.generator_input_dim = generator_input_dim
self.generator_output_dim = generator_output_dim
self.generator_kernel = generator_kernel
self.generator_dilation = generator_dilation
self.generator_stride = generator_stride
self.generator_bias = generator_bias
self.generator_dropout = generator_dropout
self.generator_bn_apply = generator_bn_apply
self.generator_bn_init_weight = generator_bn_init_weight
self.special_token_nums = special_token_nums
if os.path.isfile(dict_fpath):
self.phones = self.read_phns_dict_from_fpath(dict_fpath)
else:
self.phones = phones
if len(self.phones) > 0:
self.generator_output_dim = len(self.phones) + self.special_token_nums
self.discriminator_input_dim = self.generator_output_dim
def read_phns_dict_from_fpath(self, fpath: str):
phns = []
with open(fpath, "r") as f:
for l in f:
phn = l.strip().split('\t')[0].split(' ')[0]
phns.append(phn)
return phns
def main():
config = RebornUASRConfig(dict_fpath="/home/andybi7676/Desktop/uasr-rl/data/ls_100h_new/text/prep/phones/dict.phn.txt")
print(config)
config.save_pretrained("reborn_uasr")
if __name__ == "__main__":
main() |