344 lines
17 KiB
Python
344 lines
17 KiB
Python
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# coding=utf-8
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# Copyright 2022 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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""" Whisper model configuration"""
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from collections import OrderedDict
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from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
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from ...configuration_utils import PretrainedConfig
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from ...onnx import OnnxConfig, OnnxSeq2SeqConfigWithPast
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from ...utils import logging
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if TYPE_CHECKING:
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from ...feature_extraction_utils import FeatureExtractionMixin
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from ...tokenization_utils_base import PreTrainedTokenizerBase
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from ...utils import TensorType
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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# fmt: off
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NON_SPEECH_TOKENS = [
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1, 2, 7, 8, 9, 10, 14, 25,
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26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
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63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
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705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
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1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
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4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
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11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
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17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
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34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
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]
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NON_SPEECH_TOKENS_MULTI = [
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1, 2, 7, 8, 9, 10, 14, 25,
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26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
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63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
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893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
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3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
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7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
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14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
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22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
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42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
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]
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# fmt: on
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class WhisperConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`WhisperModel`]. It is used to instantiate a
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Whisper model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the Whisper
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[openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 51865):
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Vocabulary size of the Whisper model. Defines the number of different tokens that can be represented by the
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`decoder_input_ids` passed when calling [`WhisperModel`]
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num_mel_bins (`int`, *optional*, defaults to 80):
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Number of mel features used per input features. Should correspond to the value used in the
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`WhisperProcessor` class.
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encoder_layers (`int`, *optional*, defaults to 4):
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Number of encoder layers.
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decoder_layers (`int`, *optional*, defaults to 4):
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Number of decoder layers.
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encoder_attention_heads (`int`, *optional*, defaults to 6):
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Number of attention heads for each attention layer in the Transformer encoder.
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decoder_attention_heads (`int`, *optional*, defaults to 6):
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Number of attention heads for each attention layer in the Transformer decoder.
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encoder_ffn_dim (`int`, *optional*, defaults to 1536):
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Dimensionality of the "intermediate" (often named feed-forward) layer in encoder.
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decoder_ffn_dim (`int`, *optional*, defaults to 1536):
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Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
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encoder_layerdrop (`float`, *optional*, defaults to 0.0):
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The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
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for more details.
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decoder_layerdrop (`float`, *optional*, defaults to 0.0):
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The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
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for more details.
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decoder_start_token_id (`int`, *optional*, defaults to 50257):
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Corresponds to the "<|startoftranscript|>" token, which is automatically used when no `decoder_input_ids`
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are provided to the `generate` function. It is used to guide the model`s generation process depending on
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the task.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models).
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is_encoder_decoder (`bool`, *optional*, defaults to `True`):
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Whether the model is used as an encoder/decoder or not.
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activation_function (`str`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"silu"` and `"gelu_new"` are supported.
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d_model (`int`, *optional*, defaults to 384):
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Dimensionality of the layers.
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dropout (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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activation_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for activations inside the fully connected layer.
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init_std (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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scale_embedding (`bool`, *optional*, defaults to False):
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Scale embeddings by diving by sqrt(d_model).
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max_source_positions (`int`, *optional*, defaults to 1500):
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The maximum sequence length of log-mel filter-bank features that this model might ever be used with.
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max_target_positions (`int`, *optional*, defaults to 448):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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pad_token_id (`int`, *optional*, defaults to 50256):
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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 50256):
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Begin of stream token id.
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eos_token_id (`int`, *optional*, defaults to 50256):
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End of stream token id.
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suppress_tokens (`List[int]`, *optional*):
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A list containing the non-speech tokens that will be used by the logit processor in the `generate`
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function. NON_SPEECH_TOKENS and NON_SPEECH_TOKENS_MULTI each correspond to the `english-only` and the
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`multilingual` model.
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begin_suppress_tokens (`List[int]`, *optional*, defaults to `[220,50256]`):
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A list containing tokens that will be supressed at the beginning of the sampling process. Initialized as
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the token for `" "` (`blank_token_id`) and the `eos_token_id`
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use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
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Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
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instance of [`WhisperForAudioClassification`].
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classifier_proj_size (`int`, *optional*, defaults to 256):
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Dimensionality of the projection before token mean-pooling for classification. Only relevant when using an
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instance of [`WhisperForAudioClassification`].
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apply_spec_augment (`bool`, *optional*, defaults to `False`):
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Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
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[SpecAugment: A Simple Data Augmentation Method for Automatic Speech
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Recognition](https://arxiv.org/abs/1904.08779).
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mask_time_prob (`float`, *optional*, defaults to 0.05):
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Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
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procecure generates `mask_time_prob*len(time_axis)/mask_time_length` independent masks over the axis. If
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reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
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masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
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actual percentage of masked vectors. This is only relevant if `apply_spec_augment == True`.
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mask_time_length (`int`, *optional*, defaults to 10):
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Length of vector span along the time axis.
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mask_time_min_masks (`int`, *optional*, defaults to 2),:
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The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
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irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
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mask_time_min_masks''
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mask_feature_prob (`float`, *optional*, defaults to 0.0):
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Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
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masking procecure generates `mask_feature_prob*len(feature_axis)/mask_time_length` independent masks over
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the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
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span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
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may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
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True`.
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mask_feature_length (`int`, *optional*, defaults to 10):
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Length of vector span along the feature axis.
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mask_feature_min_masks (`int`, *optional*, defaults to 0),:
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The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
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step, irrespectively of `mask_feature_prob`. Only relevant if
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`mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks`.
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median_filter_width (`int`, *optional*, defaults to 7):
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Width of the median filter used to smoothen to cross-attention outputs when computing token timestamps.
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Should be an odd number.
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Example:
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```python
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>>> from transformers import WhisperConfig, WhisperModel
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>>> # Initializing a Whisper tiny style configuration
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>>> configuration = WhisperConfig()
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>>> # Initializing a model (with random weights) from the tiny style configuration
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>>> model = WhisperModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "whisper"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
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def __init__(
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self,
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vocab_size=51865,
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num_mel_bins=80,
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encoder_layers=4,
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encoder_attention_heads=6,
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decoder_layers=4,
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decoder_attention_heads=6,
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decoder_ffn_dim=1536,
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encoder_ffn_dim=1536,
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encoder_layerdrop=0.0,
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decoder_layerdrop=0.0,
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decoder_start_token_id=50257,
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use_cache=True,
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is_encoder_decoder=True,
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activation_function="gelu",
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d_model=384,
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dropout=0.0,
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attention_dropout=0.0,
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activation_dropout=0.0,
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init_std=0.02,
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scale_embedding=False,
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max_source_positions=1500,
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max_target_positions=448,
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pad_token_id=50256,
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bos_token_id=50256,
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eos_token_id=50256,
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suppress_tokens=None,
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begin_suppress_tokens=[220, 50256],
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use_weighted_layer_sum=False,
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classifier_proj_size=256,
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apply_spec_augment=False,
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mask_time_prob=0.05,
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mask_time_length=10,
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mask_time_min_masks=2,
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mask_feature_prob=0.0,
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mask_feature_length=10,
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mask_feature_min_masks=0,
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median_filter_width=7,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.num_mel_bins = num_mel_bins
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self.d_model = d_model
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self.encoder_layers = encoder_layers
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self.encoder_attention_heads = encoder_attention_heads
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self.decoder_layers = decoder_layers
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self.decoder_attention_heads = decoder_attention_heads
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self.decoder_ffn_dim = decoder_ffn_dim
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self.encoder_ffn_dim = encoder_ffn_dim
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.activation_dropout = activation_dropout
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self.activation_function = activation_function
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self.init_std = init_std
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self.encoder_layerdrop = encoder_layerdrop
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self.decoder_layerdrop = decoder_layerdrop
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self.use_cache = use_cache
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self.num_hidden_layers = encoder_layers
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self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
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self.max_source_positions = max_source_positions
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self.max_target_positions = max_target_positions
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# Audio Classification-specific parameters. Feel free to ignore for other classes.
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self.classifier_proj_size = classifier_proj_size
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self.use_weighted_layer_sum = use_weighted_layer_sum
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# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
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self.apply_spec_augment = apply_spec_augment
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self.mask_time_prob = mask_time_prob
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self.mask_time_length = mask_time_length
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self.mask_time_min_masks = mask_time_min_masks
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self.mask_feature_prob = mask_feature_prob
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self.mask_feature_length = mask_feature_length
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self.mask_feature_min_masks = mask_feature_min_masks
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self.median_filter_width = median_filter_width
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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is_encoder_decoder=is_encoder_decoder,
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decoder_start_token_id=decoder_start_token_id,
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suppress_tokens=suppress_tokens,
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begin_suppress_tokens=begin_suppress_tokens,
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**kwargs,
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)
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class WhisperOnnxConfig(OnnxSeq2SeqConfigWithPast):
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@property
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def inputs(self) -> Mapping[str, Mapping[int, str]]:
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common_inputs = OrderedDict(
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[
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("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
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]
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)
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if self.use_past:
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common_inputs["decoder_input_ids"] = {0: "batch"}
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else:
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common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
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if self.use_past:
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self.fill_with_past_key_values_(common_inputs, direction="inputs")
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return common_inputs
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def generate_dummy_inputs(
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self,
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preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],
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batch_size: int = -1,
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seq_length: int = -1,
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is_pair: bool = False,
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framework: Optional["TensorType"] = None,
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||
|
|
sampling_rate: int = 22050,
|
||
|
|
time_duration: float = 5.0,
|
||
|
|
frequency: int = 220,
|
||
|
|
) -> Mapping[str, Any]:
|
||
|
|
dummy_inputs = OrderedDict()
|
||
|
|
encoder_inputs = OnnxConfig.generate_dummy_inputs(
|
||
|
|
self,
|
||
|
|
preprocessor=preprocessor.feature_extractor,
|
||
|
|
batch_size=batch_size,
|
||
|
|
framework=framework,
|
||
|
|
sampling_rate=sampling_rate,
|
||
|
|
time_duration=time_duration,
|
||
|
|
frequency=frequency,
|
||
|
|
)
|
||
|
|
encoder_sequence_length = encoder_inputs["input_features"].shape[2]
|
||
|
|
seq_length = encoder_sequence_length // 2 if self.use_past else seq_length
|
||
|
|
|
||
|
|
decoder_inputs = super().generate_dummy_inputs(
|
||
|
|
preprocessor.tokenizer, batch_size, seq_length, is_pair, framework
|
||
|
|
)
|
||
|
|
|
||
|
|
dummy_inputs["input_features"] = encoder_inputs.pop("input_features")
|
||
|
|
dummy_inputs["decoder_input_ids"] = decoder_inputs.pop("decoder_input_ids")
|
||
|
|
|
||
|
|
if "past_key_values" in decoder_inputs:
|
||
|
|
dummy_inputs["past_key_values"] = decoder_inputs.pop("past_key_values")
|
||
|
|
|
||
|
|
return dummy_inputs
|
||
|
|
|
||
|
|
@property
|
||
|
|
def atol_for_validation(self) -> float:
|
||
|
|
return 1e-3
|