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""" CED model configuration""" |
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from transformers import PretrainedConfig |
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from transformers.utils import logging |
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from transformers.utils.hub import cached_file |
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logger = logging.get_logger(__name__) |
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CED_PRETRAINED_CONFIG_ARCHIVE_MAP = { |
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"mispeech/ced-tiny": "https://huggingface.co/mispeech/ced-tiny/resolve/main/config.json", |
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} |
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class CedConfig(PretrainedConfig): |
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model_type = "ced" |
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r""" |
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Configuration class for the CED model. |
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Args: |
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name (str, optional, *optional*): |
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Name of the pre-defined configuration. Can be "ced-tiny", "ced-mini", "ced-small" or "ced-base". |
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attn_drop_rate (float, *optional*, defaults to 0.0): |
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Dropout probability for attention weights. Default to 0.0. |
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depth (int, *optional*, defaults to 12): Number of transformer layers. Default to 12. |
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drop_path_rate (float, *optional*, defaults to 0.0): Drop path is taken from timm. Default to 0.0. |
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drop_rate (float, *optional*, defaults to 0.0): |
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Dropout probability for input embeddings. Default to 0.0. |
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embed_dim (int, *optional*, defaults to 768): |
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Dimensionality of the audio patch embeddings. Default to 768. |
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eval_avg (str, *optional*, defaults to `"mean"`): |
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Type of pooling to use for evaluation. Can be "mean", "token", "dm" or "logit". Default to "mean". |
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mlp_ratio (float, *optional*, defaults to 4.0): |
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Ratio of hidden size in the feedforward layer to the embedding size. Default to 4.0. |
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num_heads (int, *optional*, defaults to 12): Number of attention heads. Default to 12. |
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outputdim (int, *optional*, defaults to 527): Dimensionality of the output. Default to 527. |
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patch_size (int, *optional*, defaults to 16): Size of the patches. Default to 16. |
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patch_stride (int, *optional*, defaults to 16): Stride of the patches. Default to 16. |
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pooling (str, *optional*, defaults to `"mean"`): |
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Type of pooling to use for the output. Can be "mean", "token", "dm" or "logit". Default to "mean". |
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qkv_bias (bool, *optional*, defaults to `True`): |
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Whether to include bias terms in the query, key and value projections. Default to True. |
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target_length (int, *optional*, defaults to 1012): Frames of an audio chunk. Default to 1012. |
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""" |
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def __init__( |
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self, |
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name=None, |
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attn_drop_rate=0.0, |
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depth=12, |
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drop_path_rate=0.0, |
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drop_rate=0.0, |
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embed_dim=768, |
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eval_avg="mean", |
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mlp_ratio=4.0, |
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num_heads=12, |
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outputdim=527, |
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patch_size=16, |
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patch_stride=16, |
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pooling="mean", |
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qkv_bias=True, |
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target_length=1012, |
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**kwargs, |
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): |
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r""" |
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TODO: Add docstring |
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""" |
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super().__init__(**kwargs) |
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if name == "ced-tiny": |
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embed_dim = 192 |
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num_heads = 3 |
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elif name == "ced-mini": |
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embed_dim = 256 |
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num_heads = 4 |
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elif name == "ced-small": |
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embed_dim = 384 |
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num_heads = 6 |
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elif name == "ced-base": |
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embed_dim = 768 |
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num_heads = 12 |
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else: |
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logger.info("No model name specified for CedConfig, use default settings.") |
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assert pooling in ("mean", "token", "dm", "logit") |
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self.name = name |
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self.attn_drop_rate = attn_drop_rate |
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self.center = kwargs.get("center", True) |
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self.depth = depth |
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self.drop_path_rate = drop_path_rate |
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self.drop_rate = drop_rate |
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self.embed_dim = embed_dim |
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self.eval_avg = eval_avg |
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self.f_max = kwargs.get("f_max", 8000) |
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self.f_min = kwargs.get("f_min", 0) |
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self.hop_size = kwargs.get("hop_size", 160) |
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self.mlp_ratio = mlp_ratio |
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self.n_fft = kwargs.get("n_fft", 512) |
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self.n_mels = kwargs.get("n_mels", 64) |
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self.n_mels = kwargs.get("n_mels", 64) |
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self.num_heads = num_heads |
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self.outputdim = outputdim |
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self.pad_last = kwargs.get("pad_last", True) |
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self.patch_size = patch_size |
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self.patch_stride = patch_stride |
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self.pooling = pooling |
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self.qkv_bias = qkv_bias |
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self.target_length = target_length |
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self.win_size = kwargs.get("win_size", 512) |
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if self.outputdim == 527: |
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with open( |
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cached_file("topel/ConvNeXt-Tiny-AT", "class_labels_indices.csv"), "r" |
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) as f: |
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self.id2label = { |
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int(line.split(",", maxsplit=3)[0]): line.split(",", maxsplit=3)[2] |
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.replace('"', "") |
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.strip("\n") |
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for line in f.readlines()[1:] |
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} |
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self.label2id = {v: k for k, v in self.id2label.items()} |
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else: |
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self.id2label = None |
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self.label2id = None |
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