File size: 5,542 Bytes
5e9bb10 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
# coding=utf-8
# Copyright 2023 Xiaomi Corporation and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" CED model configuration"""
from transformers import PretrainedConfig
from transformers.utils import logging
from transformers.utils.hub import cached_file
logger = logging.get_logger(__name__)
CED_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"mispeech/ced-tiny": "https://huggingface.co/mispeech/ced-tiny/resolve/main/config.json",
}
class CedConfig(PretrainedConfig):
model_type = "ced"
r"""
Configuration class for the CED model.
Args:
name (str, optional, *optional*):
Name of the pre-defined configuration. Can be "ced-tiny", "ced-mini", "ced-small" or "ced-base".
attn_drop_rate (float, *optional*, defaults to 0.0):
Dropout probability for attention weights. Default to 0.0.
depth (int, *optional*, defaults to 12): Number of transformer layers. Default to 12.
drop_path_rate (float, *optional*, defaults to 0.0): Drop path is taken from timm. Default to 0.0.
drop_rate (float, *optional*, defaults to 0.0):
Dropout probability for input embeddings. Default to 0.0.
embed_dim (int, *optional*, defaults to 768):
Dimensionality of the audio patch embeddings. Default to 768.
eval_avg (str, *optional*, defaults to `"mean"`):
Type of pooling to use for evaluation. Can be "mean", "token", "dm" or "logit". Default to "mean".
mlp_ratio (float, *optional*, defaults to 4.0):
Ratio of hidden size in the feedforward layer to the embedding size. Default to 4.0.
num_heads (int, *optional*, defaults to 12): Number of attention heads. Default to 12.
outputdim (int, *optional*, defaults to 527): Dimensionality of the output. Default to 527.
patch_size (int, *optional*, defaults to 16): Size of the patches. Default to 16.
patch_stride (int, *optional*, defaults to 16): Stride of the patches. Default to 16.
pooling (str, *optional*, defaults to `"mean"`):
Type of pooling to use for the output. Can be "mean", "token", "dm" or "logit". Default to "mean".
qkv_bias (bool, *optional*, defaults to `True`):
Whether to include bias terms in the query, key and value projections. Default to True.
target_length (int, *optional*, defaults to 1012): Frames of an audio chunk. Default to 1012.
"""
def __init__(
self,
name=None,
attn_drop_rate=0.0,
depth=12,
drop_path_rate=0.0,
drop_rate=0.0,
embed_dim=768,
eval_avg="mean",
mlp_ratio=4.0,
num_heads=12,
outputdim=527,
patch_size=16,
patch_stride=16,
pooling="mean",
qkv_bias=True,
target_length=1012,
**kwargs,
):
r"""
TODO: Add docstring
"""
super().__init__(**kwargs)
if name == "ced-tiny":
embed_dim = 192
num_heads = 3
elif name == "ced-mini":
embed_dim = 256
num_heads = 4
elif name == "ced-small":
embed_dim = 384
num_heads = 6
elif name == "ced-base":
embed_dim = 768
num_heads = 12
else:
logger.info("No model name specified for CedConfig, use default settings.")
assert pooling in ("mean", "token", "dm", "logit")
self.name = name
self.attn_drop_rate = attn_drop_rate
self.center = kwargs.get("center", True)
self.depth = depth
self.drop_path_rate = drop_path_rate
self.drop_rate = drop_rate
self.embed_dim = embed_dim
self.eval_avg = eval_avg
self.f_max = kwargs.get("f_max", 8000)
self.f_min = kwargs.get("f_min", 0)
self.hop_size = kwargs.get("hop_size", 160)
self.mlp_ratio = mlp_ratio
self.n_fft = kwargs.get("n_fft", 512)
self.n_mels = kwargs.get("n_mels", 64)
self.n_mels = kwargs.get("n_mels", 64)
self.num_heads = num_heads
self.outputdim = outputdim
self.pad_last = kwargs.get("pad_last", True)
self.patch_size = patch_size
self.patch_stride = patch_stride
self.pooling = pooling
self.qkv_bias = qkv_bias
self.target_length = target_length
self.win_size = kwargs.get("win_size", 512)
if self.outputdim == 527:
with open(
cached_file("topel/ConvNeXt-Tiny-AT", "class_labels_indices.csv"), "r"
) as f:
self.id2label = {
int(line.split(",", maxsplit=3)[0]): line.split(",", maxsplit=3)[2]
.replace('"', "")
.strip("\n")
for line in f.readlines()[1:]
}
self.label2id = {v: k for k, v in self.id2label.items()}
else:
self.id2label = None
self.label2id = None
|