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WavCoch configuration for Hugging Face Transformers.
"""
from transformers import PretrainedConfig
class WavCochConfig(PretrainedConfig):
"""Configuration class for WavCoch checkpoints with optional vocoder."""
model_type = "wavcoch"
def __init__(
self,
window_size: int = 1001,
window_padding: int = 1000,
hop_length: int = 80,
out_channels: int = 211,
causal_convs: bool = True,
causal_pad_mode: str = "repeat",
encoder_layers: int = 8,
encoder_dim: int = 512,
encoder_kernel_size: int = 3,
decoder_layers: int = 8,
decoder_dim: int = 512,
decoder_kernel_size: int = 9,
quantizer: str = "FSQ",
channels=None,
vocab_size: int = None,
sample_rate: int = 16000,
has_vocoder: bool = False,
vocoder_upsample_rates=None,
vocoder_upsample_kernel_sizes=None,
vocoder_upsample_initial_channel: int = 512,
vocoder_resblock: str = "1",
vocoder_resblock_kernel_sizes=None,
vocoder_resblock_dilation_sizes=None,
**kwargs,
):
channels = list(channels or [8, 8, 8, 4, 4])
if vocab_size is None:
vocab_size = 1
for level in channels:
vocab_size *= int(level)
self.window_size = int(window_size)
self.window_padding = int(window_padding)
self.hop_length = int(hop_length)
self.out_channels = int(out_channels)
self.causal_convs = bool(causal_convs)
self.causal_pad_mode = str(causal_pad_mode)
self.encoder_layers = int(encoder_layers)
self.encoder_dim = int(encoder_dim)
self.encoder_kernel_size = int(encoder_kernel_size)
self.decoder_layers = int(decoder_layers)
self.decoder_dim = int(decoder_dim)
self.decoder_kernel_size = int(decoder_kernel_size)
self.quantizer = str(quantizer)
self.channels = channels
self.vocab_size = int(vocab_size)
self.sample_rate = int(sample_rate)
self.has_vocoder = bool(has_vocoder)
self.vocoder_upsample_rates = list(vocoder_upsample_rates or [5, 4, 2, 2])
self.vocoder_upsample_kernel_sizes = list(vocoder_upsample_kernel_sizes or [10, 8, 4, 4])
self.vocoder_upsample_initial_channel = int(vocoder_upsample_initial_channel)
self.vocoder_resblock = str(vocoder_resblock)
self.vocoder_resblock_kernel_sizes = list(vocoder_resblock_kernel_sizes or [11, 7, 3])
self.vocoder_resblock_dilation_sizes = [
list(d) for d in (vocoder_resblock_dilation_sizes or [[1, 3, 5], [1, 3, 5], [1, 3, 5]])
]
super().__init__(**kwargs)
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