1643 lines
72 KiB
Python
1643 lines
72 KiB
Python
# coding=utf-8
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# Copyright 2022 The OpenAI Authors 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 Whisper model."""
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import math
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from typing import Callable, Optional, Union
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import numpy as np
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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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from torch.nn import CrossEntropyLoss
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from ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
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from ...generation import GenerationMixin
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from ...masking_utils import create_causal_mask
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from ...modeling_flash_attention_utils import (
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FlashAttentionKwargs,
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)
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import (
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BaseModelOutput,
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BaseModelOutputWithPastAndCrossAttentions,
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CausalLMOutputWithCrossAttentions,
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Seq2SeqLMOutput,
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Seq2SeqModelOutput,
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SequenceClassifierOutput,
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)
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import auto_docstring, logging
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from .configuration_whisper import WhisperConfig
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from .generation_whisper import WhisperGenerationMixin
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logger = logging.get_logger(__name__)
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_HIDDEN_STATES_START_POSITION = 1
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def sinusoids(length: int, channels: int, max_timescale: float = 10000) -> torch.Tensor:
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"""Returns sinusoids for positional embedding"""
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if channels % 2 != 0:
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raise ValueError(
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f"Number of channels has to be divisible by 2 for sinusoidal positional embeddings, got {channels} channels."
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)
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log_timescale_increment = math.log(max_timescale) / (channels // 2 - 1)
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inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
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scaled_time = torch.arange(length).view(-1, 1) * inv_timescales.view(1, -1)
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return torch.cat([scaled_time.sin(), scaled_time.cos()], dim=1)
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# Copied from transformers.models.bart.modeling_bart.shift_tokens_right
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def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
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"""
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Shift input ids one token to the right.
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"""
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shifted_input_ids = input_ids.new_zeros(input_ids.shape)
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shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
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shifted_input_ids[:, 0] = decoder_start_token_id
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if pad_token_id is None:
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raise ValueError("self.model.config.pad_token_id has to be defined.")
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# replace possible -100 values in labels by `pad_token_id`
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shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
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return shifted_input_ids
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# Copied from transformers.models.wav2vec2.modeling_wav2vec2._compute_mask_indices
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def _compute_mask_indices(
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shape: tuple[int, int],
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mask_prob: float,
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mask_length: int,
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attention_mask: Optional[torch.LongTensor] = None,
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min_masks: int = 0,
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) -> np.ndarray:
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"""
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Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for
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ASR](https://huggingface.co/papers/1904.08779). Note that this method is not optimized to run on TPU and should be run on
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CPU as part of the preprocessing during training.
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Args:
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shape: The shape for which to compute masks. This should be of a tuple of size 2 where
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the first element is the batch size and the second element is the length of the axis to span.
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mask_prob: The percentage of the whole axis (between 0 and 1) which will be masked. The number of
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independently generated mask spans of length `mask_length` is computed by
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`mask_prob*shape[1]/mask_length`. Note that due to overlaps, `mask_prob` is an upper bound and the
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actual percentage will be smaller.
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mask_length: size of the mask
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min_masks: minimum number of masked spans
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attention_mask: A (right-padded) attention mask which independently shortens the feature axis of
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each batch dimension.
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"""
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batch_size, sequence_length = shape
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if mask_length < 1:
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raise ValueError("`mask_length` has to be bigger than 0.")
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if mask_length > sequence_length:
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raise ValueError(
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f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length}"
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f" and `sequence_length`: {sequence_length}`"
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)
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# epsilon is used for probabilistic rounding
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epsilon = np.random.rand(1).item()
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def compute_num_masked_span(input_length):
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"""Given input length, compute how many spans should be masked"""
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num_masked_span = int(mask_prob * input_length / mask_length + epsilon)
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num_masked_span = max(num_masked_span, min_masks)
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# make sure num masked span <= sequence_length
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if num_masked_span * mask_length > sequence_length:
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num_masked_span = sequence_length // mask_length
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# make sure num_masked span is also <= input_length - (mask_length - 1)
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if input_length - (mask_length - 1) < num_masked_span:
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num_masked_span = max(input_length - (mask_length - 1), 0)
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return num_masked_span
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# compute number of masked spans in batch
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input_lengths = (
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attention_mask.detach().sum(-1).tolist()
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if attention_mask is not None
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else [sequence_length for _ in range(batch_size)]
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)
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# SpecAugment mask to fill
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spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool)
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spec_aug_mask_idxs = []
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max_num_masked_span = compute_num_masked_span(sequence_length)
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if max_num_masked_span == 0:
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return spec_aug_mask
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for input_length in input_lengths:
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# compute num of masked spans for this input
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num_masked_span = compute_num_masked_span(input_length)
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# get random indices to mask
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spec_aug_mask_idx = np.random.choice(
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np.arange(input_length - (mask_length - 1)), num_masked_span, replace=False
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)
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# pick first sampled index that will serve as a dummy index to pad vector
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# to ensure same dimension for all batches due to probabilistic rounding
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# Picking first sample just pads those vectors twice.
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if len(spec_aug_mask_idx) == 0:
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# this case can only happen if `input_length` is strictly smaller then
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# `sequence_length` in which case the last token has to be a padding
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# token which we can use as a dummy mask id
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dummy_mask_idx = sequence_length - 1
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else:
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dummy_mask_idx = spec_aug_mask_idx[0]
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spec_aug_mask_idx = np.concatenate(
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[spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx]
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)
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spec_aug_mask_idxs.append(spec_aug_mask_idx)
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spec_aug_mask_idxs = np.array(spec_aug_mask_idxs)
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# expand masked indices to masked spans
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spec_aug_mask_idxs = np.broadcast_to(
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spec_aug_mask_idxs[:, :, None], (batch_size, max_num_masked_span, mask_length)
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)
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spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, max_num_masked_span * mask_length)
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# add offset to the starting indexes so that indexes now create a span
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offsets = np.arange(mask_length)[None, None, :]
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offsets = np.broadcast_to(offsets, (batch_size, max_num_masked_span, mask_length)).reshape(
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batch_size, max_num_masked_span * mask_length
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)
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spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
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# ensure that we cannot have indices larger than sequence_length
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if spec_aug_mask_idxs.max() > sequence_length - 1:
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spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1
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# scatter indices to mask
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np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1)
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return spec_aug_mask
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class WhisperPositionalEmbedding(nn.Embedding):
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def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
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super().__init__(num_positions, embedding_dim)
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def forward(self, input_ids, past_key_values_length=0, position_ids=None):
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if position_ids is None:
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return self.weight[past_key_values_length : past_key_values_length + input_ids.shape[1]]
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else:
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return self.weight[position_ids]
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: Optional[torch.Tensor],
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scaling: Optional[float] = None,
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dropout: float = 0.0,
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head_mask: Optional[torch.Tensor] = None,
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**kwargs,
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):
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if scaling is None:
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scaling = query.size(-1) ** -0.5
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attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
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if attention_mask is not None and attention_mask.ndim == 4:
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attn_weights = attn_weights + attention_mask[:, :, :, : key.shape[-2]]
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attn_weights = nn.functional.softmax(attn_weights, dim=-1)
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if head_mask is not None:
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attn_weights = attn_weights * head_mask.view(1, -1, 1, 1)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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class WhisperAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(
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self,
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embed_dim: int,
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num_heads: int,
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dropout: float = 0.0,
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is_decoder: bool = False,
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bias: bool = True,
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is_causal: bool = False,
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layer_idx: Optional[int] = None,
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config: Optional[WhisperConfig] = None,
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):
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super().__init__()
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self.embed_dim = embed_dim
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self.num_heads = num_heads
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self.dropout = dropout
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self.head_dim = embed_dim // num_heads
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self.config = config
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if (self.head_dim * num_heads) != self.embed_dim:
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raise ValueError(
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f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
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f" and `num_heads`: {num_heads})."
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)
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self.scaling = self.head_dim**-0.5
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self.is_decoder = is_decoder
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self.is_causal = is_causal
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if layer_idx is None and is_decoder:
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logger.warning_once(
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f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
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"will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
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"when creating this class."
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)
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self.layer_idx = layer_idx
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self.k_proj = nn.Linear(embed_dim, embed_dim, bias=False)
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self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
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self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
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self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
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def forward(
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self,
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hidden_states: torch.Tensor,
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key_value_states: Optional[torch.Tensor] = None,
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past_key_value: Optional[Cache] = None,
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attention_mask: Optional[torch.Tensor] = None,
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layer_head_mask: Optional[torch.Tensor] = None,
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output_attentions: bool = False,
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cache_position: Optional[torch.Tensor] = None,
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# TODO: we need a refactor so that the different attention modules can get their specific kwargs
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# ATM, we have mixed things encoder, decoder, and encoder-decoder attn
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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"""Input shape: Batch x Time x Channel"""
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# if key_value_states are provided this layer is used as a cross-attention layer
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# for the decoder
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is_cross_attention = key_value_states is not None
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# determine input shapes
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bsz, tgt_len = hidden_states.shape[:-1]
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q_input_shape = (bsz, tgt_len, -1, self.head_dim)
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# Scaling is susceptible to floating point arithmetics' inprecisions
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# which can lead to different results (this is dependent from model
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# to model, e.g. whisper is one such case). We therefore keep the
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# original order of scaling to follow the original implementation
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# and enforce no scaling (1.0) in the attention call below.
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query_states = self.q_proj(hidden_states) * self.scaling
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query_states = query_states.view(*q_input_shape)
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query_states = query_states.transpose(1, 2).contiguous()
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# Check is encoder-decoder model is being used. Otherwise we'll get `DynamicCache`
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if past_key_value is not None and isinstance(past_key_value, EncoderDecoderCache):
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is_updated = past_key_value.is_updated.get(self.layer_idx)
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if is_cross_attention:
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# after the first generated id, we can subsequently re-use all key/value_states from cache
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past_key_value.is_updated[self.layer_idx] = True
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past_key_value = past_key_value.cross_attention_cache
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else:
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past_key_value = past_key_value.self_attention_cache
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# use key_value_states if cross attention
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current_states = key_value_states if key_value_states is not None else hidden_states
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if is_cross_attention and past_key_value and is_updated:
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# reuse k,v, cross_attentions
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key_states = past_key_value.layers[self.layer_idx].keys
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value_states = past_key_value.layers[self.layer_idx].values
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else:
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key_states = self.k_proj(current_states).view(bsz, -1, self.num_heads, self.head_dim)
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value_states = self.v_proj(current_states).view(bsz, -1, self.num_heads, self.head_dim)
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key_states = key_states.transpose(1, 2).contiguous()
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value_states = value_states.transpose(1, 2).contiguous()
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if past_key_value is not None:
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# save all key/value_states to cache to be re-used for fast auto-regressive generation
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cache_position = cache_position if not is_cross_attention else None
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key_states, value_states = past_key_value.update(
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key_states, value_states, self.layer_idx, {"cache_position": cache_position}
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)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0 if not self.training else self.dropout,
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scaling=1.0,
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output_attentions=output_attentions,
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head_mask=layer_head_mask,
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**kwargs,
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)
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attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
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attn_output = self.out_proj(attn_output)
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return attn_output, attn_weights
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# Copied from transformers.models.mbart.modeling_mbart.MBartEncoderLayer with MBart->Whisper, MBART->WHISPER
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class WhisperEncoderLayer(GradientCheckpointingLayer):
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def __init__(self, config: WhisperConfig):
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super().__init__()
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self.embed_dim = config.d_model
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self.self_attn = WhisperAttention(
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embed_dim=self.embed_dim,
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num_heads=config.encoder_attention_heads,
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dropout=config.attention_dropout,
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config=config,
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)
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self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
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self.dropout = config.dropout
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self.activation_fn = ACT2FN[config.activation_function]
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self.activation_dropout = config.activation_dropout
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self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
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self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
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self.final_layer_norm = nn.LayerNorm(self.embed_dim)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: torch.Tensor,
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layer_head_mask: torch.Tensor,
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output_attentions: bool = False,
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) -> torch.Tensor:
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"""
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Args:
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hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
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attention_mask (`torch.FloatTensor`): attention mask of size
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`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
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layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
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`(encoder_attention_heads,)`.
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output_attentions (`bool`, *optional*):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under
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returned tensors for more detail.
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"""
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residual = hidden_states
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hidden_states = self.self_attn_layer_norm(hidden_states)
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hidden_states, attn_weights = self.self_attn(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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layer_head_mask=layer_head_mask,
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output_attentions=output_attentions,
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)
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hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
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hidden_states = residual + hidden_states
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residual = hidden_states
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hidden_states = self.final_layer_norm(hidden_states)
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hidden_states = self.activation_fn(self.fc1(hidden_states))
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hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
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hidden_states = self.fc2(hidden_states)
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hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
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hidden_states = residual + hidden_states
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if hidden_states.dtype == torch.float16:
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clamp_value = torch.finfo(hidden_states.dtype).max - 1000
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hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
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return hidden_states, attn_weights
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class WhisperDecoderLayer(GradientCheckpointingLayer):
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def __init__(self, config: WhisperConfig, layer_idx: Optional[int] = None):
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super().__init__()
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self.embed_dim = config.d_model
|
|
|
|
self.self_attn = WhisperAttention(
|
|
embed_dim=self.embed_dim,
|
|
num_heads=config.decoder_attention_heads,
|
|
dropout=config.attention_dropout,
|
|
is_decoder=True,
|
|
is_causal=True,
|
|
layer_idx=layer_idx,
|
|
config=config,
|
|
)
|
|
self.dropout = config.dropout
|
|
self.activation_fn = ACT2FN[config.activation_function]
|
|
self.activation_dropout = config.activation_dropout
|
|
|
|
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
|
|
self.encoder_attn = WhisperAttention(
|
|
self.embed_dim,
|
|
config.decoder_attention_heads,
|
|
dropout=config.attention_dropout,
|
|
is_decoder=True,
|
|
layer_idx=layer_idx,
|
|
config=config,
|
|
)
|
|
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
|
|
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
|
|
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
|
|
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
layer_head_mask: Optional[torch.Tensor] = None,
|
|
cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
|
|
past_key_value: Optional[EncoderDecoderCache] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
use_cache: Optional[bool] = True,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
) -> torch.Tensor:
|
|
"""
|
|
Args:
|
|
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
|
attention_mask (`torch.FloatTensor`): attention mask of size
|
|
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
|
encoder_hidden_states (`torch.FloatTensor`):
|
|
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
|
|
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
|
|
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
|
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
|
|
`(encoder_attention_heads,)`.
|
|
cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
|
|
size `(decoder_attention_heads,)`.
|
|
past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
|
returned tensors for more detail.
|
|
"""
|
|
residual = hidden_states
|
|
hidden_states = self.self_attn_layer_norm(hidden_states)
|
|
|
|
# Self Attention
|
|
hidden_states, self_attn_weights = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
past_key_value=past_key_value,
|
|
attention_mask=attention_mask,
|
|
layer_head_mask=layer_head_mask,
|
|
output_attentions=output_attentions,
|
|
cache_position=cache_position,
|
|
)
|
|
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
|
hidden_states = residual + hidden_states
|
|
|
|
# Cross-Attention Block
|
|
cross_attn_weights = None
|
|
if encoder_hidden_states is not None:
|
|
residual = hidden_states
|
|
hidden_states = self.encoder_attn_layer_norm(hidden_states)
|
|
hidden_states, cross_attn_weights = self.encoder_attn(
|
|
hidden_states=hidden_states,
|
|
key_value_states=encoder_hidden_states,
|
|
attention_mask=encoder_attention_mask,
|
|
layer_head_mask=cross_attn_layer_head_mask,
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
)
|
|
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
|
hidden_states = residual + hidden_states
|
|
|
|
# Fully Connected
|
|
residual = hidden_states
|
|
hidden_states = self.final_layer_norm(hidden_states)
|
|
hidden_states = self.activation_fn(self.fc1(hidden_states))
|
|
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
|
|
hidden_states = self.fc2(hidden_states)
|
|
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
|
hidden_states = residual + hidden_states
|
|
|
|
outputs = (hidden_states,)
|
|
|
|
if output_attentions:
|
|
outputs += (self_attn_weights, cross_attn_weights)
|
|
|
|
return outputs
|
|
|
|
|
|
@auto_docstring
|
|
class WhisperPreTrainedModel(PreTrainedModel):
|
|
config: WhisperConfig
|
|
base_model_prefix = "model"
|
|
main_input_name = "input_features"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["WhisperEncoderLayer", "WhisperDecoderLayer"]
|
|
_supports_flash_attn = True
|
|
_supports_sdpa = True
|
|
_supports_flex_attn = True
|
|
|
|
_can_compile_fullgraph = True
|
|
|
|
def _init_weights(self, module):
|
|
std = self.config.init_std
|
|
if isinstance(module, (nn.Linear, nn.Conv1d)):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.weight.data.fill_(1.0)
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, WhisperEncoder):
|
|
module.embed_positions.weight.copy_(sinusoids(*module.embed_positions.weight.shape))
|
|
elif isinstance(module, WhisperForAudioClassification):
|
|
if self.config.use_weighted_layer_sum:
|
|
module.layer_weights.data.fill_(1.0 / (self.config.num_hidden_layers + 1))
|
|
|
|
def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor):
|
|
"""
|
|
Computes the output length of the convolutional layers
|
|
"""
|
|
input_lengths = (input_lengths - 1) // 2 + 1
|
|
|
|
return input_lengths
|
|
|
|
|
|
class WhisperEncoder(WhisperPreTrainedModel):
|
|
"""
|
|
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
|
|
[`WhisperEncoderLayer`].
|
|
|
|
Args:
|
|
config: WhisperConfig
|
|
"""
|
|
|
|
def __init__(self, config: WhisperConfig):
|
|
super().__init__(config)
|
|
self.dropout = config.dropout
|
|
self.layerdrop = config.encoder_layerdrop
|
|
|
|
embed_dim = config.d_model
|
|
self.num_mel_bins = config.num_mel_bins
|
|
self.padding_idx = config.pad_token_id
|
|
self.max_source_positions = config.max_source_positions
|
|
self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
|
|
|
|
self.conv1 = nn.Conv1d(self.num_mel_bins, embed_dim, kernel_size=3, padding=1)
|
|
self.conv2 = nn.Conv1d(embed_dim, embed_dim, kernel_size=3, stride=2, padding=1)
|
|
|
|
self.embed_positions = nn.Embedding(self.max_source_positions, embed_dim)
|
|
self.embed_positions.requires_grad_(False)
|
|
|
|
self.layers = nn.ModuleList([WhisperEncoderLayer(config) for _ in range(config.encoder_layers)])
|
|
self.layer_norm = nn.LayerNorm(config.d_model)
|
|
|
|
self.gradient_checkpointing = False
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def _freeze_parameters(self):
|
|
for param in self.parameters():
|
|
param.requires_grad = False
|
|
self._requires_grad = False
|
|
|
|
def get_input_embeddings(self) -> nn.Module:
|
|
return self.conv1
|
|
|
|
def set_input_embeddings(self, value: nn.Module):
|
|
self.conv1 = value
|
|
|
|
def forward(
|
|
self,
|
|
input_features,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
r"""
|
|
Args:
|
|
input_features (`torch.LongTensor` of shape `(batch_size, feature_size, sequence_length)`):
|
|
Float values of mel features extracted from the raw speech waveform. Raw speech waveform can be
|
|
obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]`, a
|
|
`numpy.ndarray` or a `torch.Tensor`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
|
|
the soundfile library (`pip install soundfile`). To prepare the array into
|
|
`input_features`, the [`AutoFeatureExtractor`] should be used for extracting the mel features, padding
|
|
and conversion into a tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
|
|
attention_mask (`torch.Tensor`)`, *optional*):
|
|
Whisper does not support masking of the `input_features`, this argument is preserved for compatibility,
|
|
but it is not used. By default the silence in the input log mel spectrogram are ignored.
|
|
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
|
|
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
|
returned tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
|
|
for more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
expected_seq_length = self.config.max_source_positions * self.conv1.stride[0] * self.conv2.stride[0]
|
|
if input_features.shape[-1] != expected_seq_length:
|
|
raise ValueError(
|
|
f"Whisper expects the mel input features to be of length {expected_seq_length}, but found {input_features.shape[-1]}. Make sure to pad the input mel features to {expected_seq_length}."
|
|
)
|
|
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
inputs_embeds = nn.functional.gelu(self.conv1(input_features))
|
|
inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds))
|
|
|
|
inputs_embeds = inputs_embeds.permute(0, 2, 1)
|
|
all_positions = torch.arange(self.embed_positions.num_embeddings, device=inputs_embeds.device)
|
|
|
|
hidden_states = inputs_embeds + self.embed_positions(all_positions)
|
|
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
|
|
|
encoder_states = () if output_hidden_states else None
|
|
all_attentions = () if output_attentions else None
|
|
|
|
# check if head_mask has a correct number of layers specified if desired
|
|
if head_mask is not None:
|
|
assert head_mask.size()[0] == (len(self.layers)), (
|
|
f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
|
|
)
|
|
|
|
for idx, encoder_layer in enumerate(self.layers):
|
|
if output_hidden_states:
|
|
encoder_states = encoder_states + (hidden_states,)
|
|
# add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
|
|
to_drop = False
|
|
if self.training:
|
|
dropout_probability = torch.rand([])
|
|
if dropout_probability < self.layerdrop: # skip the layer
|
|
to_drop = True
|
|
|
|
if to_drop:
|
|
layer_outputs = (None, None)
|
|
else:
|
|
layer_outputs = encoder_layer(
|
|
hidden_states,
|
|
None,
|
|
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
|
|
output_attentions=output_attentions,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
all_attentions = all_attentions + (layer_outputs[1],)
|
|
|
|
hidden_states = self.layer_norm(hidden_states)
|
|
if output_hidden_states:
|
|
encoder_states = encoder_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
|
|
)
|
|
|
|
|
|
class WhisperDecoder(WhisperPreTrainedModel):
|
|
"""
|
|
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`WhisperDecoderLayer`]
|
|
|
|
Args:
|
|
config: WhisperConfig
|
|
"""
|
|
|
|
main_input_name = "input_ids"
|
|
|
|
def __init__(self, config: WhisperConfig):
|
|
super().__init__(config)
|
|
self.dropout = config.dropout
|
|
self.layerdrop = config.decoder_layerdrop
|
|
self.padding_idx = config.pad_token_id
|
|
self.max_target_positions = config.max_target_positions
|
|
self.max_source_positions = config.max_source_positions
|
|
self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
|
|
|
|
self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
|
|
self.embed_positions = WhisperPositionalEmbedding(self.max_target_positions, config.d_model)
|
|
|
|
self.layers = nn.ModuleList(
|
|
[WhisperDecoderLayer(config, layer_idx) for layer_idx in range(config.decoder_layers)]
|
|
)
|
|
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
|
self._use_sdpa = config._attn_implementation == "sdpa"
|
|
|
|
self.layer_norm = nn.LayerNorm(config.d_model)
|
|
|
|
self.gradient_checkpointing = False
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
encoder_hidden_states=None,
|
|
head_mask=None,
|
|
cross_attn_head_mask=None,
|
|
past_key_values=None,
|
|
inputs_embeds=None,
|
|
position_ids=None,
|
|
use_cache=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
cache_position=None,
|
|
):
|
|
r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
|
|
provide it.
|
|
|
|
Indices can be obtained using [`WhisperTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
|
|
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
|
|
of the decoder.
|
|
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
|
|
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
|
|
Mask to nullify selected heads of the attention modules in encoder to avoid performing cross-attention
|
|
on hidden heads. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
past_key_values (`EncoderDecoderCache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
|
|
Pre-computed hidden-states that can be used to speed up auto-regressive (sequential) decoding. There are
|
|
four sets of pre-computed hidden-states: key and values states in the self-attention blocks (2) and
|
|
in the cross-attention blocks (2). The `past_key_values` are returned when `use_cache=True` is passed or
|
|
when `config.use_cache=True`
|
|
|
|
Two formats are allowed:
|
|
- An [`~cache_utils.EncoderDecoderCache`] instance;
|
|
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
|
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
|
|
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
|
|
|
|
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
|
|
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
|
|
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
|
inputs_embeds (`torch.FloatTensor` of
|
|
shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing
|
|
`input_ids` you can choose to directly pass an embedded representation. This is useful if you want more
|
|
control over how to convert `input_ids` indices into associated vectors than the model's internal
|
|
embedding lookup matrix.
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
|
returned tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
|
|
for more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
|
Indices depicting the position of the input sequence tokens in the sequence. It is used to update the
|
|
cache in the correct position and to infer the complete sequence length.
|
|
"""
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# retrieve input_ids and inputs_embeds
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
input_shape = input_ids.size()
|
|
input_ids = input_ids.view(-1, input_shape[-1])
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
if use_cache and past_key_values is None:
|
|
if self.config.is_encoder_decoder:
|
|
past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache())
|
|
else:
|
|
past_key_values = DynamicCache()
|
|
|
|
past_key_values_length = 0
|
|
if cache_position is not None:
|
|
past_key_values_length = cache_position[0]
|
|
elif past_key_values is not None:
|
|
past_key_values_length = past_key_values.get_seq_length()
|
|
|
|
if cache_position is None:
|
|
cache_position = torch.arange(
|
|
past_key_values_length, past_key_values_length + input_shape[1], device=inputs_embeds.device
|
|
)
|
|
|
|
if position_ids is None:
|
|
position_ids = cache_position.unsqueeze(0).repeat(input_shape[0], 1)
|
|
|
|
# embed positions
|
|
if input_ids is not None:
|
|
positions = self.embed_positions(
|
|
input_ids, past_key_values_length=past_key_values_length, position_ids=position_ids
|
|
)
|
|
else:
|
|
positions = self.embed_positions(
|
|
inputs_embeds, past_key_values_length=past_key_values_length, position_ids=position_ids
|
|
)
|
|
|
|
hidden_states = inputs_embeds + positions.to(inputs_embeds.device)
|
|
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
|
|
|
causal_mask = create_causal_mask(
|
|
config=self.config,
|
|
input_embeds=inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
cache_position=cache_position,
|
|
past_key_values=past_key_values,
|
|
position_ids=position_ids,
|
|
)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
logger.warning_once(
|
|
"`use_cache = True` is incompatible with gradient checkpointing. Setting `use_cache = False`..."
|
|
)
|
|
use_cache = False
|
|
# decoder layers
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attns = () if output_attentions else None
|
|
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
|
|
|
|
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
|
|
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
|
|
if attn_mask is not None:
|
|
assert attn_mask.size()[0] == (len(self.layers)), (
|
|
f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
|
|
f" {head_mask.size()[0]}."
|
|
)
|
|
for idx, decoder_layer in enumerate(self.layers):
|
|
# add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
if self.training:
|
|
dropout_probability = torch.rand([])
|
|
if dropout_probability < self.layerdrop:
|
|
continue
|
|
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=causal_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
|
|
cross_attn_layer_head_mask=(cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None),
|
|
past_key_value=past_key_values if use_cache else None,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
)
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
all_self_attns += (layer_outputs[1],)
|
|
|
|
if encoder_hidden_states is not None:
|
|
all_cross_attentions += (layer_outputs[2],)
|
|
|
|
hidden_states = self.layer_norm(hidden_states)
|
|
# add hidden states from the last decoder layer
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
next_cache = past_key_values if use_cache else None
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
|
|
if v is not None
|
|
)
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
cross_attentions=all_cross_attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class WhisperModel(WhisperPreTrainedModel):
|
|
def __init__(self, config: WhisperConfig):
|
|
super().__init__(config)
|
|
|
|
self.encoder = WhisperEncoder(config)
|
|
self.decoder = WhisperDecoder(config)
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.decoder.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.decoder.embed_tokens = value
|
|
|
|
def get_encoder(self):
|
|
return self.encoder
|
|
|
|
def get_decoder(self):
|
|
return self.decoder
|
|
|
|
def freeze_encoder(self):
|
|
"""
|
|
Calling this function will disable the gradient computation for the Whisper encoder so that its parameters will
|
|
not be updated during training.
|
|
"""
|
|
self.encoder._freeze_parameters()
|
|
|
|
def _mask_input_features(
|
|
self,
|
|
input_features: torch.FloatTensor,
|
|
attention_mask: Optional[torch.LongTensor] = None,
|
|
):
|
|
"""
|
|
Masks extracted features along time axis and/or along feature axis according to
|
|
[SpecAugment](https://huggingface.co/papers/1904.08779).
|
|
"""
|
|
|
|
# `config.apply_spec_augment` can set masking to False
|
|
if not getattr(self.config, "apply_spec_augment", True):
|
|
return input_features
|
|
|
|
# generate indices & apply SpecAugment along time axis
|
|
batch_size, hidden_size, sequence_length = input_features.size()
|
|
|
|
if self.config.mask_time_prob > 0 and self.training:
|
|
# generate indices & apply SpecAugment along time axis
|
|
mask_time_indices = _compute_mask_indices(
|
|
(batch_size, sequence_length),
|
|
mask_prob=self.config.mask_time_prob,
|
|
mask_length=self.config.mask_time_length,
|
|
attention_mask=attention_mask,
|
|
min_masks=self.config.mask_time_min_masks,
|
|
)
|
|
mask_time_indices = torch.tensor(mask_time_indices, device=input_features.device, dtype=torch.bool)
|
|
mask_time_indices = mask_time_indices[:, None].expand(-1, hidden_size, -1)
|
|
input_features[mask_time_indices] = 0
|
|
|
|
if self.config.mask_feature_prob > 0 and self.training:
|
|
# generate indices & apply SpecAugment along feature axis
|
|
mask_feature_indices = _compute_mask_indices(
|
|
(batch_size, hidden_size),
|
|
mask_prob=self.config.mask_feature_prob,
|
|
mask_length=self.config.mask_feature_length,
|
|
min_masks=self.config.mask_feature_min_masks,
|
|
)
|
|
mask_feature_indices = torch.tensor(mask_feature_indices, device=input_features.device, dtype=torch.bool)
|
|
input_features[mask_feature_indices] = 0
|
|
|
|
return input_features
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_features: Optional[torch.FloatTensor] = None,
|
|
attention_mask: Optional[torch.LongTensor] = None,
|
|
decoder_input_ids: Optional[torch.LongTensor] = None,
|
|
decoder_attention_mask: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
decoder_head_mask: Optional[torch.Tensor] = None,
|
|
cross_attn_head_mask: Optional[torch.Tensor] = None,
|
|
encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
past_key_values: Optional[Union[Cache]] = None,
|
|
decoder_inputs_embeds: Optional[tuple[torch.FloatTensor]] = None,
|
|
decoder_position_ids: Optional[tuple[torch.LongTensor]] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
) -> Union[tuple[torch.Tensor], Seq2SeqModelOutput]:
|
|
r"""
|
|
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
|
|
Indices of decoder input sequence tokens in the vocabulary.
|
|
|
|
Indices can be obtained using [`WhisperTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are decoder input IDs?](../glossary#decoder-input-ids)
|
|
|
|
Whisper uses the `decoder_start_token_id` as the starting token for `decoder_input_ids` generation. If
|
|
`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
|
|
`past_key_values`).
|
|
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
|
|
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
|
|
be used by default.
|
|
|
|
If you want to change padding behavior, you should read
|
|
[`modeling_whisper._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the BART
|
|
paper](https://huggingface.co/papers/1910.13461) for more information on the default strategy.
|
|
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
|
|
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
decoder_position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.n_positions - 1]`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
|
|
Example:
|
|
```python
|
|
>>> import torch
|
|
>>> from transformers import AutoFeatureExtractor, WhisperModel
|
|
>>> from datasets import load_dataset
|
|
|
|
>>> model = WhisperModel.from_pretrained("openai/whisper-base")
|
|
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base")
|
|
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
|
>>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt")
|
|
>>> input_features = inputs.input_features
|
|
>>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
|
|
>>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
|
|
>>> list(last_hidden_state.shape)
|
|
[1, 2, 512]
|
|
```"""
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if encoder_outputs is None:
|
|
input_features = self._mask_input_features(input_features, attention_mask=attention_mask)
|
|
|
|
encoder_outputs = self.encoder(
|
|
input_features,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
|
|
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
|
|
encoder_outputs = BaseModelOutput(
|
|
last_hidden_state=encoder_outputs[0],
|
|
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
|
|
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
|
|
)
|
|
|
|
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
|
|
decoder_outputs = self.decoder(
|
|
input_ids=decoder_input_ids,
|
|
attention_mask=decoder_attention_mask,
|
|
encoder_hidden_states=encoder_outputs[0],
|
|
head_mask=decoder_head_mask,
|
|
cross_attn_head_mask=cross_attn_head_mask,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=decoder_inputs_embeds,
|
|
position_ids=decoder_position_ids,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
cache_position=cache_position,
|
|
)
|
|
|
|
if not return_dict:
|
|
return decoder_outputs + encoder_outputs
|
|
|
|
return Seq2SeqModelOutput(
|
|
last_hidden_state=decoder_outputs.last_hidden_state,
|
|
past_key_values=decoder_outputs.past_key_values,
|
|
decoder_hidden_states=decoder_outputs.hidden_states,
|
|
decoder_attentions=decoder_outputs.attentions,
|
|
cross_attentions=decoder_outputs.cross_attentions,
|
|
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
|
|
encoder_hidden_states=encoder_outputs.hidden_states,
|
|
encoder_attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The Whisper Model with a language modeling head. Can be used for automatic speech recognition.
|
|
"""
|
|
)
|
|
class WhisperForConditionalGeneration(WhisperGenerationMixin, WhisperPreTrainedModel):
|
|
base_model_prefix = "model"
|
|
_tied_weights_keys = ["proj_out.weight"]
|
|
|
|
def __init__(self, config: WhisperConfig):
|
|
super().__init__(config)
|
|
self.model = WhisperModel(config)
|
|
self.proj_out = nn.Linear(config.d_model, config.vocab_size, bias=False)
|
|
self.max_target_positions = config.max_target_positions
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_encoder(self):
|
|
return self.model.get_encoder()
|
|
|
|
def get_decoder(self):
|
|
return self.model.get_decoder()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.proj_out
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.proj_out = new_embeddings
|
|
|
|
def get_input_embeddings(self) -> nn.Module:
|
|
return self.model.get_input_embeddings()
|
|
|
|
def freeze_encoder(self):
|
|
"""
|
|
Calling this function will disable the gradient computation for the Whisper encoder so that its parameters will
|
|
not be updated during training.
|
|
"""
|
|
self.model.encoder._freeze_parameters()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_features: Optional[torch.FloatTensor] = None,
|
|
attention_mask: Optional[torch.LongTensor] = None,
|
|
decoder_input_ids: Optional[torch.LongTensor] = None,
|
|
decoder_attention_mask: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
decoder_head_mask: Optional[torch.Tensor] = None,
|
|
cross_attn_head_mask: Optional[torch.Tensor] = None,
|
|
encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
past_key_values: Optional[Union[Cache]] = None,
|
|
decoder_inputs_embeds: Optional[tuple[torch.FloatTensor]] = None,
|
|
decoder_position_ids: Optional[tuple[torch.LongTensor]] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
) -> Union[tuple[torch.Tensor], Seq2SeqLMOutput]:
|
|
r"""
|
|
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
|
|
Indices of decoder input sequence tokens in the vocabulary.
|
|
|
|
Indices can be obtained using [`WhisperTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are decoder input IDs?](../glossary#decoder-input-ids)
|
|
|
|
Whisper uses the `decoder_start_token_id` as the starting token for `decoder_input_ids` generation. If
|
|
`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
|
|
`past_key_values`).
|
|
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
|
|
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
|
|
be used by default.
|
|
|
|
If you want to change padding behavior, you should read
|
|
[`modeling_whisper._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the BART
|
|
paper](https://huggingface.co/papers/1910.13461) for more information on the default strategy.
|
|
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
|
|
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
decoder_position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.n_positions - 1]`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the language modeling loss. Indices should either be in `[0, ..., config.vocab_size]`
|
|
or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is
|
|
only computed for the tokens with labels in `[0, ..., config.vocab_size]`. `sequence_length` should be smaller than or equal to `config.max_target_positions`.
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> import torch
|
|
>>> from transformers import AutoProcessor, WhisperForConditionalGeneration
|
|
>>> from datasets import load_dataset
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en")
|
|
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
|
|
|
|
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
|
|
|
>>> inputs = processor(ds[0]["audio"]["array"], return_tensors="pt")
|
|
>>> input_features = inputs.input_features
|
|
|
|
>>> generated_ids = model.generate(inputs=input_features)
|
|
|
|
>>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
|
>>> transcription
|
|
' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if labels is not None:
|
|
if labels.shape[1] > self.max_target_positions:
|
|
raise ValueError(
|
|
f"Labels' sequence length {labels.shape[1]} cannot exceed the maximum allowed length of {self.max_target_positions} tokens."
|
|
)
|
|
if decoder_input_ids is None and decoder_inputs_embeds is None:
|
|
decoder_input_ids = shift_tokens_right(
|
|
labels, self.config.pad_token_id, self.config.decoder_start_token_id
|
|
)
|
|
|
|
outputs = self.model(
|
|
input_features,
|
|
attention_mask=attention_mask,
|
|
decoder_input_ids=decoder_input_ids,
|
|
encoder_outputs=encoder_outputs,
|
|
decoder_attention_mask=decoder_attention_mask,
|
|
head_mask=head_mask,
|
|
decoder_head_mask=decoder_head_mask,
|
|
cross_attn_head_mask=cross_attn_head_mask,
|
|
past_key_values=past_key_values,
|
|
decoder_inputs_embeds=decoder_inputs_embeds,
|
|
decoder_position_ids=decoder_position_ids,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
cache_position=cache_position,
|
|
)
|
|
lm_logits = self.proj_out(outputs[0])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
# move labels to correct device to enable PP
|
|
labels = labels.to(lm_logits.device)
|
|
loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.reshape(-1))
|
|
|
|
if not return_dict:
|
|
output = (lm_logits,) + outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return Seq2SeqLMOutput(
|
|
loss=loss,
|
|
logits=lm_logits,
|
|
past_key_values=outputs.past_key_values,
|
|
decoder_hidden_states=outputs.decoder_hidden_states,
|
|
decoder_attentions=outputs.decoder_attentions,
|
|
cross_attentions=outputs.cross_attentions,
|
|
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
|
|
encoder_hidden_states=outputs.encoder_hidden_states,
|
|
encoder_attentions=outputs.encoder_attentions,
|
|
)
|
|
|
|
|
|
class WhisperDecoderWrapper(WhisperPreTrainedModel):
|
|
"""
|
|
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
|
|
used in combination with the [`EncoderDecoderModel`] framework.
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
config.is_encoder_decoder = False
|
|
self.decoder = WhisperDecoder(config)
|
|
|
|
def get_input_embeddings(self):
|
|
return self.decoder.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.decoder.embed_tokens = value
|
|
|
|
def get_decoder(self):
|
|
return self.decoder
|
|
|
|
def forward(self, *args, **kwargs):
|
|
return self.decoder(*args, **kwargs)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Whisper decoder with a language modeling head on top (linear layer with weights tied to the input embeddings).
|
|
"""
|
|
)
|
|
class WhisperForCausalLM(WhisperPreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = ["proj_out.weight"]
|
|
main_input_name = "input_ids"
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
config.is_encoder_decoder = False
|
|
self.model = WhisperDecoderWrapper(config)
|
|
|
|
self.proj_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.proj_out
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.proj_out = new_embeddings
|
|
|
|
def get_input_embeddings(self) -> nn.Module:
|
|
return self.model.get_input_embeddings()
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.set_input_embeddings(value)
|
|
|
|
def set_decoder(self, decoder):
|
|
self.model.decoder = decoder
|
|
|
|
def get_decoder(self):
|
|
return self.model.decoder
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
encoder_outputs: Optional[tuple[torch.FloatTensor]] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
cross_attn_head_mask: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[Cache] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
) -> Union[tuple, CausalLMOutputWithCrossAttentions]:
|
|
r"""
|
|
encoder_outputs (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
|
|
if the model is configured as a decoder.
|
|
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
|
|
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
|
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
|
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import WhisperForCausalLM, WhisperForConditionalGeneration, WhisperProcessor
|
|
>>> import torch
|
|
>>> from datasets import load_dataset
|
|
|
|
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2")
|
|
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2")
|
|
|
|
>>> assistant_model = WhisperForCausalLM.from_pretrained("distil-whisper/distil-large-v2")
|
|
|
|
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
|
>>> sample = ds[0]["audio"]
|
|
>>> input_features = processor(
|
|
... sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt"
|
|
... ).input_features
|
|
|
|
>>> predicted_ids = model.generate(input_features, assistant_model=assistant_model)
|
|
|
|
>>> # decode token ids to text
|
|
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
|
|
>>> transcription
|
|
' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.'
|
|
```"""
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# If the user passed a tuple or `BaseModelOutput` for encoder_outputs, we extract only the hidden states
|
|
if isinstance(encoder_outputs, (BaseModelOutput, tuple, list)):
|
|
encoder_outputs = encoder_outputs[0]
|
|
|
|
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
|
outputs = self.model.decoder(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
encoder_hidden_states=encoder_outputs,
|
|
head_mask=head_mask,
|
|
cross_attn_head_mask=cross_attn_head_mask,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
cache_position=cache_position,
|
|
)
|
|
|
|
logits = self.proj_out(outputs[0])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
labels = labels.to(logits.device)
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return (loss,) + output if loss is not None else output
|
|
|
|
return CausalLMOutputWithCrossAttentions(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
cross_attentions=outputs.cross_attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Whisper Encoder Model with a sequence classification head on top (a linear layer over the pooled output) for tasks
|
|
like SUPERB Keyword Spotting.
|
|
"""
|
|
)
|
|
class WhisperForAudioClassification(WhisperPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.encoder = WhisperEncoder(config)
|
|
num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings
|
|
if config.use_weighted_layer_sum:
|
|
self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
|
|
self.projector = nn.Linear(config.hidden_size, config.classifier_proj_size)
|
|
self.classifier = nn.Linear(config.classifier_proj_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def freeze_encoder(self):
|
|
"""
|
|
Calling this function will disable the gradient computation for the Whisper encoder so that its parameters will
|
|
not be updated during training. Only the projection layers and classification head will be updated.
|
|
"""
|
|
self.encoder._freeze_parameters()
|
|
|
|
def get_input_embeddings(self) -> nn.Module:
|
|
return self.encoder.get_input_embeddings()
|
|
|
|
def set_input_embeddings(self, value: nn.Module):
|
|
self.encoder.set_input_embeddings(value)
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_features: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.Tensor], SequenceClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> import torch
|
|
>>> from transformers import AutoFeatureExtractor, WhisperForAudioClassification
|
|
>>> from datasets import load_dataset
|
|
|
|
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("sanchit-gandhi/whisper-medium-fleurs-lang-id")
|
|
>>> model = WhisperForAudioClassification.from_pretrained("sanchit-gandhi/whisper-medium-fleurs-lang-id")
|
|
|
|
>>> ds = load_dataset("google/fleurs", "all", split="validation", streaming=True)
|
|
>>> sample = next(iter(ds))
|
|
|
|
>>> inputs = feature_extractor(
|
|
... sample["audio"]["array"], sampling_rate=sample["audio"]["sampling_rate"], return_tensors="pt"
|
|
... )
|
|
>>> input_features = inputs.input_features
|
|
|
|
>>> with torch.no_grad():
|
|
... logits = model(input_features).logits
|
|
|
|
>>> predicted_class_ids = torch.argmax(logits).item()
|
|
>>> predicted_label = model.config.id2label[predicted_class_ids]
|
|
>>> predicted_label
|
|
'Afrikaans'
|
|
```"""
|
|
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
if self.config.use_weighted_layer_sum:
|
|
output_hidden_states = True
|
|
elif output_hidden_states is None:
|
|
output_hidden_states = self.config.output_hidden_states
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if encoder_outputs is None:
|
|
encoder_outputs = self.encoder(
|
|
input_features,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
if self.config.use_weighted_layer_sum:
|
|
hidden_states = encoder_outputs[_HIDDEN_STATES_START_POSITION]
|
|
hidden_states = torch.stack(hidden_states, dim=1)
|
|
norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
|
|
hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
|
|
else:
|
|
hidden_states = encoder_outputs[0]
|
|
|
|
hidden_states = self.projector(hidden_states)
|
|
pooled_output = hidden_states.mean(dim=1)
|
|
|
|
logits = self.classifier(pooled_output)
|
|
|
|
loss = None
|
|
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
# move labels to correct device to enable PP
|
|
labels = labels.to(logits.device)
|
|
loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + encoder_outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"WhisperForCausalLM",
|
|
"WhisperForConditionalGeneration",
|
|
"WhisperModel",
|
|
"WhisperPreTrainedModel",
|
|
"WhisperForAudioClassification",
|
|
]
|