Commit
·
34060cf
1
Parent(s):
0c446cc
Remove model codes
Browse files- ced_model/__init__.py +0 -0
- ced_model/configuration_ced.py +0 -137
- ced_model/feature_extraction_ced.py +0 -163
- ced_model/modeling_ced.py +0 -550
- requirements.txt +1 -3
ced_model/__init__.py
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ced_model/configuration_ced.py
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# coding=utf-8
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# Copyright 2023 Xiaomi Corporation and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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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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self.loss = "BCE"
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if self.outputdim == 527:
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with open(cached_file("topel/ConvNeXt-Tiny-AT", "class_labels_indices.csv"), "r") as f:
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self.id2label = {
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int(line.split(",", maxsplit=3)[0]): line.split(",", maxsplit=3)[2].replace('"', "").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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ced_model/feature_extraction_ced.py
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# coding=utf-8
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# Copyright 2023 Xiaomi Corporation and The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Feature extractor class for CED.
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"""
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from typing import List, Optional, Union
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import numpy as np
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import torch
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import torchaudio.transforms as audio_transforms
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from transformers.feature_extraction_sequence_utils import SequenceFeatureExtractor
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from transformers.feature_extraction_utils import BatchFeature
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class CedFeatureExtractor(SequenceFeatureExtractor):
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r"""
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CedFeatureExtractor extracts Mel spectrogram features from audio signals.
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Args:
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f_min (int, *optional*, defaults to 0): Minimum frequency for the Mel filterbank.
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sampling_rate (int, *optional*, defaults to 16000):
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Sampling rate of the input audio signal.
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win_size (int, *optional*, defaults to 512): Window size for the STFT.
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center (bool, *optional*, defaults to `True`):
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Whether to pad the signal on both sides to center it.
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n_fft (int, *optional*, defaults to 512): Number of FFT points for the STFT.
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f_max (int, optional, *optional*): Maximum frequency for the Mel filterbank.
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hop_size (int, *optional*, defaults to 160): Hop size for the STFT.
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feature_size (int, *optional*, defaults to 64): Number of Mel bands to generate.
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padding_value (float, *optional*, defaults to 0.0): Value for padding.
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Returns:
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BatchFeature: A BatchFeature object containing the extracted features.
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"""
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def __init__(
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self,
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f_min: int = 0,
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sampling_rate: int = 16000,
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win_size: int = 512,
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center: bool = True,
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n_fft: int = 512,
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f_max: Optional[int] = None,
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hop_size: int = 160,
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feature_size: int = 64,
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padding_value: float = 0.0,
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**kwargs,
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):
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super().__init__(
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feature_size=feature_size,
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sampling_rate=sampling_rate,
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padding_value=padding_value,
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**kwargs,
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)
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self.f_min = f_min
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self.win_size = win_size
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self.center = center
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self.n_fft = n_fft
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self.f_max = f_max
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self.hop_size = hop_size
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self.model_input_names = ["input_values"]
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def __call__(
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self,
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x: Union[np.ndarray, torch.Tensor, List[np.ndarray], List[torch.Tensor]],
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sampling_rate: Optional[int] = None,
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max_length: Optional[int] = 16000,
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truncation: bool = True,
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return_tensors="pt",
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) -> BatchFeature:
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r"""
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Extracts Mel spectrogram features from an audio signal tensor.
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Args:
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x: Input audio signal tensor.
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sampling_rate (int, *optional*, defaults to `None`):
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Sampling rate of the input audio signal.
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max_length (int, *optional*, defaults to 16000):
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Maximum length of the input audio signal.
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truncation (bool, *optional*, defaults to `True`):
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Whether to truncate the input signal to max_length.
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return_tensors (str, *optional*, defaults to "pt"):
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If set to "pt", the return type will be a PyTorch tensor.
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Returns:
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BatchFeature: A dictionary containing the extracted features.
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"""
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if sampling_rate is None:
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sampling_rate = self.sampling_rate
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if return_tensors != "pt":
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raise NotImplementedError("Only return_tensors='pt' is currently supported.")
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mel_spectrogram = audio_transforms.MelSpectrogram(
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f_min=self.f_min,
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sample_rate=sampling_rate,
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win_length=self.win_size,
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center=self.center,
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n_fft=self.n_fft,
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f_max=self.f_max,
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hop_length=self.hop_size,
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n_mels=self.feature_size,
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)
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amplitude_to_db = audio_transforms.AmplitudeToDB(top_db=120)
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if isinstance(x, np.ndarray):
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if x.ndim == 1:
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x = x[np.newaxis, :]
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if x.ndim != 2:
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raise ValueError("np.ndarray input must be a 1D or 2D.")
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x = torch.from_numpy(x)
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elif isinstance(x, torch.Tensor):
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if x.dim() == 1:
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x = x.unsqueeze(0)
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if x.dim() != 2:
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raise ValueError("torch.Tensor input must be a 1D or 2D.")
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elif isinstance(x, (list, tuple)):
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longest_length = max(x_.shape[0] for x_ in x)
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if not truncation and max_length < longest_length:
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max_length = longest_length
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if all(isinstance(x_, np.ndarray) for x_ in x):
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if not all(x_.ndim == 1 for x_ in x):
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raise ValueError("All np.ndarray in a list must be 1D.")
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x_trim = [x_[:max_length] for x_ in x]
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x_pad = [np.pad(x_, (0, max_length - x_.shape[0]), mode="constant", constant_values=0) for x_ in x_trim]
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x = torch.stack([torch.from_numpy(x_) for x_ in x_pad])
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elif all(isinstance(x_, torch.Tensor) for x_ in x):
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if not all(x_.dim() == 1 for x_ in x):
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raise ValueError("All torch.Tensor in a list must be 1D.")
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x_pad = [torch.nn.functional.pad(x_, (0, max_length - x_.shape[0]), value=0) for x_ in x]
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x = torch.stack(x_pad)
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else:
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raise ValueError("Input list must be numpy arrays or PyTorch tensors.")
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else:
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raise ValueError(
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"Input must be a numpy array, a list of numpy arrays, a PyTorch tensor, or a list of PyTorch tensor."
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)
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x = x.float()
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x = mel_spectrogram(x)
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x = amplitude_to_db(x)
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return BatchFeature({"input_values": x})
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ced_model/modeling_ced.py
DELETED
@@ -1,550 +0,0 @@
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# coding=utf-8
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# Copyright 2023 Xiaomi Corporation and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
|
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# limitations under the License.
|
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""" PyTorch CED (Ced) model."""
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-
|
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import collections
|
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import math
|
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from functools import partial
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from typing import Any, Callable, Optional, Tuple, Union
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-
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import torch
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import torch.utils.checkpoint
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from torch import nn
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-
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from transformers.modeling_outputs import SequenceClassifierOutput
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from transformers.modeling_utils import PreTrainedModel
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from transformers.utils import (
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add_code_sample_docstrings,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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logging,
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)
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from .configuration_ced import CedConfig
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logger = logging.get_logger(__name__)
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-
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_CONFIG_FOR_DOC = "CedConfig"
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_SEQ_CLASS_EXPECTED_OUTPUT = "'Speech synthesizer'"
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_SEQ_CLASS_EXPECTED_LOSS = 0.69
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# Audio classification docstring
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_SEQ_CLASS_CHECKPOINT = "mispeech/ced-tiny"
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-
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CED_PRETRAINED_MODEL_ARCHIVE_LIST = [
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"mispeech/ced-tiny",
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"mispeech/ced-mini",
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"mispeech/ced-small",
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"mispeech/ced-base",
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# See all CED models at https://huggingface.co/models?search=mispeech%2Fced
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]
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|
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class CedPreTrainedModel(PreTrainedModel):
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"""
|
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An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
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models.
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"""
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config_class = CedConfig
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base_model_prefix = "ced"
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main_input_name = "input_values"
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supports_gradient_checkpointing = True
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-
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def _init_weights(self, module):
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"""Initialize the weights"""
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if isinstance(module, nn.Linear):
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trunc_normal_(module.weight, std=0.02)
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if module.bias is not None:
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nn.init.zeros_(module.bias)
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elif isinstance(module, nn.LayerNorm):
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nn.init.constant_(module.bias, 0)
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nn.init.constant_(module.weight, 1.0)
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-
|
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-
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Conv_Kernel = Union[int, Tuple[int, int]]
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-
|
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-
|
81 |
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def to_2tuple(x: Any) -> Tuple[Any, Any]:
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82 |
-
if isinstance(x, collections.abc.Iterable):
|
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return x
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return (x, x)
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85 |
-
|
86 |
-
|
87 |
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class CedAudioPatchEmbed(nn.Module):
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88 |
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def __init__(
|
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self,
|
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input_size: Conv_Kernel = 224,
|
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-
patch_size: Conv_Kernel = 16,
|
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patch_stride: Conv_Kernel = 16,
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93 |
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in_chans: int = 1,
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embed_dim: int = 768,
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norm_layer: Optional[Callable] = None,
|
96 |
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flatten: bool = False,
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-
):
|
98 |
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super().__init__()
|
99 |
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self.input_size = to_2tuple(input_size)
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self.patch_size = to_2tuple(patch_size)
|
101 |
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self.patch_stride = to_2tuple(patch_stride)
|
102 |
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self.grid_size = (
|
103 |
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self.input_size[0] // self.patch_stride[0],
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self.input_size[1] // self.patch_stride[1],
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)
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self.num_patches = self.grid_size[0] * self.grid_size[1]
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self.flatten = flatten
|
108 |
-
|
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self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_stride)
|
110 |
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self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
|
111 |
-
|
112 |
-
def forward(self, x):
|
113 |
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x = self.proj(x)
|
114 |
-
if self.flatten:
|
115 |
-
x = torch.permute(torch.flatten(x, 2, 3), (0, 2, 1))
|
116 |
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x = self.norm(x)
|
117 |
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return x
|
118 |
-
|
119 |
-
|
120 |
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class CedAttention(nn.Module):
|
121 |
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def __init__(
|
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self,
|
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dim,
|
124 |
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num_heads=8,
|
125 |
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qkv_bias=False,
|
126 |
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attn_drop=0.0,
|
127 |
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proj_drop=0.0,
|
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causal: bool = False,
|
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):
|
130 |
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super().__init__()
|
131 |
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assert dim % num_heads == 0, "dim should be divisible by num_heads"
|
132 |
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self.num_heads = num_heads
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133 |
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head_dim = dim // num_heads
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134 |
-
self.scale = head_dim**-0.5
|
135 |
-
|
136 |
-
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
137 |
-
self.attn_drop = nn.Dropout(attn_drop)
|
138 |
-
self.proj = nn.Linear(dim, dim)
|
139 |
-
self.proj_drop = nn.Dropout(proj_drop)
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140 |
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self.causal = causal
|
141 |
-
|
142 |
-
def forward(self, x):
|
143 |
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B, N, C = x.shape
|
144 |
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qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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145 |
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q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
|
146 |
-
|
147 |
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attn = (q @ k.transpose(-2, -1)) * self.scale
|
148 |
-
# if mask is not None:
|
149 |
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# # Mask is a tensor of shape [B, T, T]
|
150 |
-
# # Different from self.causal == True, the mask might be something like:
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151 |
-
# # [False, False, True]
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152 |
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# # [False, False, True]
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153 |
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# # [True, True, True]
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154 |
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# # We use -inf to pad here, since if we would pad by any number, the entries at rows only containing
|
155 |
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# # [True, True, True] would lead to weights such as: [0.33,0.33,0.33], which is not correct
|
156 |
-
# mask_value = torch.as_tensor(-float('inf'))
|
157 |
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# print(mask.shape, attn.shape)
|
158 |
-
# attn = attn.masked_fill(mask, mask_value)
|
159 |
-
if self.causal:
|
160 |
-
mask_value = -torch.finfo(attn.dtype).max
|
161 |
-
i, j = attn.shape[-2:]
|
162 |
-
mask = torch.ones(i, j, device=q.device, dtype=torch.bool).triu(j - i + 1)
|
163 |
-
attn = attn.masked_fill(mask, mask_value)
|
164 |
-
attn = attn.softmax(dim=-1)
|
165 |
-
# Only for the case that a mask with all True entries on a row is passed.
|
166 |
-
# attn = torch.nan_to_num(attn)
|
167 |
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attn = self.attn_drop(attn)
|
168 |
-
|
169 |
-
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
170 |
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x = self.proj(x)
|
171 |
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x = self.proj_drop(x)
|
172 |
-
return x
|
173 |
-
|
174 |
-
|
175 |
-
class CedMlp(nn.Module):
|
176 |
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def __init__(
|
177 |
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self,
|
178 |
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in_features: int,
|
179 |
-
hidden_features: Optional[int] = None,
|
180 |
-
out_features: Optional[int] = None,
|
181 |
-
act_layer: Callable = nn.GELU,
|
182 |
-
drop: float = 0.0,
|
183 |
-
):
|
184 |
-
super().__init__()
|
185 |
-
out_features = out_features or in_features
|
186 |
-
hidden_features = hidden_features or in_features
|
187 |
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self.fc1 = nn.Linear(in_features, hidden_features)
|
188 |
-
self.act = act_layer()
|
189 |
-
self.fc2 = nn.Linear(hidden_features, out_features)
|
190 |
-
self.drop = nn.Dropout(drop)
|
191 |
-
|
192 |
-
def forward(self, x):
|
193 |
-
x = self.fc1(x)
|
194 |
-
x = self.act(x)
|
195 |
-
x = self.drop(x)
|
196 |
-
x = self.fc2(x)
|
197 |
-
x = self.drop(x)
|
198 |
-
return x
|
199 |
-
|
200 |
-
|
201 |
-
# Drop path is taken from Timm
|
202 |
-
# https://github.com/huggingface/pytorch-image-models/blob/7c67d6aca992f039eece0af5f7c29a43d48c00e4/timm/models/layers/drop.py#L155
|
203 |
-
class DropPath(nn.Module):
|
204 |
-
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
|
205 |
-
|
206 |
-
def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True):
|
207 |
-
super(DropPath, self).__init__()
|
208 |
-
self.drop_prob = drop_prob
|
209 |
-
self.scale_by_keep = scale_by_keep
|
210 |
-
|
211 |
-
def forward(self, x):
|
212 |
-
return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
|
213 |
-
|
214 |
-
def extra_repr(self):
|
215 |
-
return f"drop_prob={round(self.drop_prob,3):0.3f}"
|
216 |
-
|
217 |
-
|
218 |
-
def drop_path(x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True):
|
219 |
-
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
|
220 |
-
|
221 |
-
This is the same as the DropConnect impl I (https://github.com/rwightman) created for EfficientNet, etc networks,
|
222 |
-
however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
|
223 |
-
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
|
224 |
-
layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
|
225 |
-
argument.
|
226 |
-
|
227 |
-
"""
|
228 |
-
if drop_prob == 0.0 or not training:
|
229 |
-
return x
|
230 |
-
keep_prob = 1 - drop_prob
|
231 |
-
shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
|
232 |
-
random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
|
233 |
-
if keep_prob > 0.0 and scale_by_keep:
|
234 |
-
random_tensor.div_(keep_prob)
|
235 |
-
return x * random_tensor
|
236 |
-
|
237 |
-
|
238 |
-
class CedBlock(nn.Module):
|
239 |
-
def __init__(
|
240 |
-
self,
|
241 |
-
dim,
|
242 |
-
num_heads,
|
243 |
-
mlp_ratio=4.0,
|
244 |
-
qkv_bias=False,
|
245 |
-
drop=0.0,
|
246 |
-
attn_drop=0.0,
|
247 |
-
drop_path=0.0,
|
248 |
-
act_layer: Callable = nn.GELU,
|
249 |
-
norm_layer: Callable = nn.LayerNorm,
|
250 |
-
attention_type: Callable = CedAttention,
|
251 |
-
attention_kwargs={},
|
252 |
-
**kwargs,
|
253 |
-
):
|
254 |
-
super().__init__()
|
255 |
-
self.norm1 = norm_layer(dim)
|
256 |
-
self.attn = attention_type(
|
257 |
-
dim,
|
258 |
-
num_heads=num_heads,
|
259 |
-
qkv_bias=qkv_bias,
|
260 |
-
attn_drop=attn_drop,
|
261 |
-
proj_drop=drop,
|
262 |
-
**attention_kwargs,
|
263 |
-
)
|
264 |
-
self.ls1 = nn.Identity()
|
265 |
-
self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
|
266 |
-
|
267 |
-
self.norm2 = norm_layer(dim)
|
268 |
-
self.mlp = CedMlp(
|
269 |
-
in_features=dim,
|
270 |
-
hidden_features=int(dim * mlp_ratio),
|
271 |
-
act_layer=act_layer,
|
272 |
-
drop=drop,
|
273 |
-
)
|
274 |
-
self.ls2 = nn.Identity()
|
275 |
-
self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
|
276 |
-
|
277 |
-
def forward(self, x):
|
278 |
-
x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x))))
|
279 |
-
x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
|
280 |
-
return x
|
281 |
-
|
282 |
-
|
283 |
-
# Taken from timm
|
284 |
-
def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
|
285 |
-
return _no_grad_trunc_normal_(tensor, mean, std, a, b)
|
286 |
-
|
287 |
-
|
288 |
-
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
|
289 |
-
# Cut & paste from PyTorch official master until it's in a few official releases - RW
|
290 |
-
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
|
291 |
-
def norm_cdf(x):
|
292 |
-
# Computes standard normal cumulative distribution function
|
293 |
-
return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
|
294 |
-
|
295 |
-
with torch.no_grad():
|
296 |
-
# Values are generated by using a truncated uniform distribution and
|
297 |
-
# then using the inverse CDF for the normal distribution.
|
298 |
-
# Get upper and lower cdf values
|
299 |
-
l = norm_cdf((a - mean) / std)
|
300 |
-
u = norm_cdf((b - mean) / std)
|
301 |
-
|
302 |
-
# Uniformly fill tensor with values from [l, u], then translate to
|
303 |
-
# [2l-1, 2u-1].
|
304 |
-
tensor.uniform_(2 * l - 1, 2 * u - 1)
|
305 |
-
|
306 |
-
# Use inverse cdf transform for normal distribution to get truncated
|
307 |
-
# standard normal
|
308 |
-
tensor.erfinv_()
|
309 |
-
|
310 |
-
# Transform to proper mean, std
|
311 |
-
tensor.mul_(std * math.sqrt(2.0))
|
312 |
-
tensor.add_(mean)
|
313 |
-
|
314 |
-
# Clamp to ensure it's in the proper range
|
315 |
-
tensor.clamp_(min=a, max=b)
|
316 |
-
return tensor
|
317 |
-
|
318 |
-
|
319 |
-
CED_START_DOCSTRING = r"""
|
320 |
-
|
321 |
-
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
322 |
-
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
323 |
-
etc.)
|
324 |
-
|
325 |
-
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
326 |
-
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
327 |
-
and behavior.
|
328 |
-
|
329 |
-
Parameters:
|
330 |
-
config ([`CedConfig`]): Model configuration class with all the parameters of the model.
|
331 |
-
Initializing with a config file does not load the weights associated with the model, only the
|
332 |
-
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
333 |
-
"""
|
334 |
-
|
335 |
-
CED_INPUTS_DOCSTRING = r"""
|
336 |
-
Args:
|
337 |
-
input_values (`torch.FloatTensor` of shape `(batch_size, n_mels, sequence_length)`):
|
338 |
-
The sequence of audio features extracted from the audio signal. Can be obtained from a raw audio waveform
|
339 |
-
using `~transformers.CedFeatureExtractor.__call__`.
|
340 |
-
"""
|
341 |
-
|
342 |
-
|
343 |
-
@add_start_docstrings(
|
344 |
-
"The bare Ced Model transformer outputting raw hidden-states without any specific head on top.",
|
345 |
-
CED_START_DOCSTRING,
|
346 |
-
)
|
347 |
-
class CedModel(CedPreTrainedModel):
|
348 |
-
def __init__(self, config: CedConfig) -> None:
|
349 |
-
super().__init__(config)
|
350 |
-
self.config = config
|
351 |
-
self.name = config.name
|
352 |
-
|
353 |
-
# Allowed length in number of frames, otherwise the positional embedding will throw an error
|
354 |
-
self.maximal_allowed_length = self.config.target_length
|
355 |
-
|
356 |
-
self.init_bn = torch.nn.BatchNorm2d(config.n_mels, momentum=0.01)
|
357 |
-
|
358 |
-
self.patch_embed = CedAudioPatchEmbed(
|
359 |
-
input_size=(config.n_mels, config.target_length),
|
360 |
-
embed_dim=config.embed_dim,
|
361 |
-
patch_size=config.patch_size,
|
362 |
-
flatten=False,
|
363 |
-
patch_stride=config.patch_stride,
|
364 |
-
)
|
365 |
-
|
366 |
-
self.time_pos_embed = nn.Parameter(torch.randn(1, config.embed_dim, 1, self.patch_embed.grid_size[1]) * 0.02)
|
367 |
-
self.freq_pos_embed = nn.Parameter(torch.randn(1, config.embed_dim, self.patch_embed.grid_size[0], 1) * 0.02)
|
368 |
-
norm_layer = partial(nn.LayerNorm, eps=1e-6)
|
369 |
-
act_layer = nn.GELU
|
370 |
-
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.depth)] # stochastic depth decay rule
|
371 |
-
self.pos_drop = nn.Dropout(p=config.drop_rate)
|
372 |
-
self.blocks = nn.Sequential(
|
373 |
-
*[
|
374 |
-
CedBlock(
|
375 |
-
dim=config.embed_dim,
|
376 |
-
num_heads=config.num_heads,
|
377 |
-
mlp_ratio=config.mlp_ratio,
|
378 |
-
qkv_bias=config.qkv_bias,
|
379 |
-
drop=config.drop_rate,
|
380 |
-
attn_drop=config.attn_drop_rate,
|
381 |
-
drop_path=dpr[i],
|
382 |
-
norm_layer=norm_layer,
|
383 |
-
act_layer=act_layer,
|
384 |
-
attention_type=CedAttention,
|
385 |
-
)
|
386 |
-
for i in range(config.depth)
|
387 |
-
]
|
388 |
-
)
|
389 |
-
self.norm = norm_layer(config.embed_dim)
|
390 |
-
|
391 |
-
# Initialize weights and apply final processing
|
392 |
-
self.post_init()
|
393 |
-
|
394 |
-
def _freeze_parameters(self):
|
395 |
-
for param in self.parameters():
|
396 |
-
param.requires_grad = False
|
397 |
-
self._requires_grad = False
|
398 |
-
|
399 |
-
def forward_features(self, x: torch.Tensor) -> torch.Tensor:
|
400 |
-
x = self.patch_embed(x)
|
401 |
-
_, _, _, t = x.shape
|
402 |
-
x = x + self.time_pos_embed[:, :, :, :t]
|
403 |
-
x = x + self.freq_pos_embed[:, :, :, :] # Just to support __getitem__ in posembed
|
404 |
-
|
405 |
-
# x = rearrange(x, 'b c f t -> b (f t) c')
|
406 |
-
x = torch.permute(torch.flatten(x, 2, 3), (0, 2, 1))
|
407 |
-
|
408 |
-
if self.config.pooling == "token":
|
409 |
-
cls_token = self.cls_token.expand(x.shape[0], -1, -1)
|
410 |
-
cls_token = cls_token + self.token_pos_embed
|
411 |
-
x = torch.cat((cls_token, x), dim=1)
|
412 |
-
x = self.pos_drop(x)
|
413 |
-
x = self.blocks(x)
|
414 |
-
x = self.norm(x)
|
415 |
-
return x
|
416 |
-
|
417 |
-
def forward(self, input_values: torch.Tensor):
|
418 |
-
r"""
|
419 |
-
Runs a forward pass of the CED model as an audio encoder.
|
420 |
-
"""
|
421 |
-
x = torch.unsqueeze(input_values, 1)
|
422 |
-
|
423 |
-
x = torch.permute(x, (0, 2, 1, 3))
|
424 |
-
x = self.init_bn(x)
|
425 |
-
x = torch.permute(x, (0, 2, 1, 3))
|
426 |
-
|
427 |
-
if x.shape[-1] > self.maximal_allowed_length:
|
428 |
-
splits = x.split(self.maximal_allowed_length, -1)
|
429 |
-
|
430 |
-
if splits[-1].shape[-1] < self.maximal_allowed_length:
|
431 |
-
if self.config.pad_last:
|
432 |
-
pad = torch.zeros(*x.shape[:-1], self.maximal_allowed_length, device=x.device)
|
433 |
-
pad[..., : splits[-1].shape[-1]] = splits[-1]
|
434 |
-
splits = torch.stack((*splits[:-1], pad), dim=0)
|
435 |
-
else:
|
436 |
-
splits = torch.stack(splits[:-1], dim=0)
|
437 |
-
else:
|
438 |
-
splits = torch.stack(splits[:-1], dim=0)
|
439 |
-
n_splits = len(splits)
|
440 |
-
x = torch.flatten(splits, 0, 1) # spl b c f t-> (spl b) c f t
|
441 |
-
else:
|
442 |
-
n_splits = 1
|
443 |
-
|
444 |
-
x = self.forward_features(x)
|
445 |
-
x = torch.reshape(x, (x.shape[0] // n_splits, -1, x.shape[-1]))
|
446 |
-
|
447 |
-
return SequenceClassifierOutput(logits=x)
|
448 |
-
|
449 |
-
|
450 |
-
@add_start_docstrings(
|
451 |
-
"""
|
452 |
-
Ced model with an audio classification head on top (a linear layer on top of the pooled output).
|
453 |
-
""",
|
454 |
-
CED_START_DOCSTRING,
|
455 |
-
)
|
456 |
-
class CedForAudioClassification(CedPreTrainedModel):
|
457 |
-
def __init__(self, config: CedConfig) -> None:
|
458 |
-
super().__init__(config)
|
459 |
-
self.config = config
|
460 |
-
|
461 |
-
self.encoder = CedModel(config)
|
462 |
-
|
463 |
-
# Classifier head
|
464 |
-
self.outputlayer = nn.Sequential(
|
465 |
-
nn.LayerNorm(config.embed_dim),
|
466 |
-
nn.Linear(config.embed_dim, config.outputdim),
|
467 |
-
)
|
468 |
-
|
469 |
-
# Initialize weights and apply final processing
|
470 |
-
self.post_init()
|
471 |
-
|
472 |
-
def forward_head(self, x: torch.Tensor) -> torch.Tensor:
|
473 |
-
if self.config.pooling == "token":
|
474 |
-
x = x[:, 0]
|
475 |
-
return self.outputlayer(x).sigmoid()
|
476 |
-
elif self.config.pooling == "mean":
|
477 |
-
x = x.mean(1)
|
478 |
-
return self.outputlayer(x).sigmoid()
|
479 |
-
elif self.config.pooling == "logit":
|
480 |
-
x = x.mean(1)
|
481 |
-
return self.outputlayer(x)
|
482 |
-
elif self.config.pooling == "dm":
|
483 |
-
# Unpack using the frequency dimension, which is constant
|
484 |
-
# 'b (f t) d -> b f t d', f=self.patch_embed.grid_size[0])
|
485 |
-
x = torch.reshape(x, (x.shape[0], self.patch_embed.grid_size[0], -1, x.shape[3]))
|
486 |
-
|
487 |
-
# First poolin frequency, then sigmoid the (B T D) output
|
488 |
-
x = self.outputlayer(x.mean(1)).sigmoid()
|
489 |
-
return x.mean(1)
|
490 |
-
else:
|
491 |
-
return x.mean(1)
|
492 |
-
|
493 |
-
def freeze_encoder(self):
|
494 |
-
self.encoder._freeze_parameters()
|
495 |
-
|
496 |
-
@add_start_docstrings_to_model_forward(CED_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
497 |
-
@add_code_sample_docstrings(
|
498 |
-
checkpoint=_SEQ_CLASS_CHECKPOINT,
|
499 |
-
output_type=SequenceClassifierOutput,
|
500 |
-
config_class=_CONFIG_FOR_DOC,
|
501 |
-
modality="audio",
|
502 |
-
model_cls="CedForAudioClassification",
|
503 |
-
expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
|
504 |
-
expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
|
505 |
-
)
|
506 |
-
def forward(self, input_values: torch.Tensor, labels: Optional[torch.Tensor] = None):
|
507 |
-
"""
|
508 |
-
Runs a forward pass of the CED model for audio classification task.
|
509 |
-
|
510 |
-
Examples:
|
511 |
-
|
512 |
-
```python
|
513 |
-
>>> from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
|
514 |
-
>>> from datasets import load_dataset
|
515 |
-
>>> import torch
|
516 |
-
|
517 |
-
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
|
518 |
-
>>> dataset = dataset.sort("id")
|
519 |
-
>>> sampling_rate = dataset.features["audio"].sampling_rate
|
520 |
-
|
521 |
-
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("mispeech/ced-tiny")
|
522 |
-
>>> model = AutoModelForAudioClassification.from_pretrained("mispeech/ced-tiny")
|
523 |
-
|
524 |
-
>>> inputs = feature_extractor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
|
525 |
-
|
526 |
-
>>> with torch.no_grad():
|
527 |
-
... logits = model(**inputs).logits
|
528 |
-
|
529 |
-
>>> predicted_class_ids = torch.argmax(logits, dim=-1).item()
|
530 |
-
>>> predicted_label = model.config.id2label[predicted_class_ids]
|
531 |
-
>>> predicted_label
|
532 |
-
'Speech synthesizer'
|
533 |
-
```
|
534 |
-
"""
|
535 |
-
last_hidden_states = self.encoder(input_values).logits
|
536 |
-
logits = self.forward_head(last_hidden_states)
|
537 |
-
|
538 |
-
if labels is not None:
|
539 |
-
if self.config.loss == "CE":
|
540 |
-
loss_fct = nn.CrossEntropyLoss()
|
541 |
-
elif self.config.loss == "BCE":
|
542 |
-
loss_fct = nn.BCEWithLogitsLoss()
|
543 |
-
else:
|
544 |
-
raise NotImplementedError("Need to set 'CE' or 'BCE' as config.loss.")
|
545 |
-
labels = nn.functional.one_hot(labels, num_classes=self.config.outputdim).float()
|
546 |
-
loss = loss_fct(logits, labels)
|
547 |
-
else:
|
548 |
-
loss = None
|
549 |
-
|
550 |
-
return SequenceClassifierOutput(logits=logits, loss=loss, hidden_states=last_hidden_states)
|
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|
requirements.txt
CHANGED
@@ -1,3 +1 @@
|
|
1 |
-
|
2 |
-
torchaudio==2.1.1
|
3 |
-
transformers==4.35.2
|
|
|
1 |
+
git+https://github.com/jimbozhang/hf_transformers_custom_model_ced.git
|
|
|
|