/
modeling_speecht5.py
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modeling_speecht5.py
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# coding=utf-8
# Copyright 2023 The Fairseq Authors, Microsoft Research, and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch SpeechT5 model."""
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, L1Loss
from ...activations import ACT2FN
from ...integrations.deepspeed import is_deepspeed_zero3_enabled
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
Seq2SeqLMOutput,
Seq2SeqModelOutput,
Seq2SeqSpectrogramOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_speecht5 import SpeechT5Config, SpeechT5HifiGanConfig
logger = logging.get_logger(__name__)
_HIDDEN_STATES_START_POSITION = 1
# General docstring
_CONFIG_FOR_DOC = "SpeechT5Config"
SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST = [
"microsoft/speecht5_asr",
"microsoft/speecht5_tts",
"microsoft/speecht5_vc",
# See all SpeechT5 models at https://huggingface.co/models?filter=speecht5
]
# Copied from transformers.models.bart.modeling_bart.shift_tokens_right
def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
"""
Shift input ids one token to the right.
"""
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
shifted_input_ids[:, 0] = decoder_start_token_id
if pad_token_id is None:
raise ValueError("self.model.config.pad_token_id has to be defined.")
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
return shifted_input_ids
def shift_spectrograms_right(input_values: torch.Tensor, reduction_factor: int = 1):
"""
Shift input spectrograms one timestep to the right. Also applies the reduction factor to the sequence length.
"""
# thin out frames for reduction factor
if reduction_factor > 1:
input_values = input_values[:, reduction_factor - 1 :: reduction_factor]
shifted_input_values = input_values.new_zeros(input_values.shape)
shifted_input_values[:, 1:] = input_values[:, :-1].clone()
# replace possible -100 values in labels by zeros
shifted_input_values.masked_fill_(shifted_input_values == -100.0, 0.0)
return shifted_input_values
# Copied from transformers.models.wav2vec2.modeling_wav2vec2._compute_mask_indices
def _compute_mask_indices(
shape: Tuple[int, int],
mask_prob: float,
mask_length: int,
attention_mask: Optional[torch.LongTensor] = None,
min_masks: int = 0,
) -> np.ndarray:
"""
Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for
ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on
CPU as part of the preprocessing during training.
Args:
shape: The shape for which to compute masks. This should be of a tuple of size 2 where
the first element is the batch size and the second element is the length of the axis to span.
mask_prob: The percentage of the whole axis (between 0 and 1) which will be masked. The number of
independently generated mask spans of length `mask_length` is computed by
`mask_prob*shape[1]/mask_length`. Note that due to overlaps, `mask_prob` is an upper bound and the
actual percentage will be smaller.
mask_length: size of the mask
min_masks: minimum number of masked spans
attention_mask: A (right-padded) attention mask which independently shortens the feature axis of
each batch dimension.
"""
batch_size, sequence_length = shape
if mask_length < 1:
raise ValueError("`mask_length` has to be bigger than 0.")
if mask_length > sequence_length:
raise ValueError(
f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length}"
f" and `sequence_length`: {sequence_length}`"
)
# epsilon is used for probabilistic rounding
epsilon = np.random.rand(1).item()
def compute_num_masked_span(input_length):
"""Given input length, compute how many spans should be masked"""
num_masked_span = int(mask_prob * input_length / mask_length + epsilon)
num_masked_span = max(num_masked_span, min_masks)
# make sure num masked span <= sequence_length
if num_masked_span * mask_length > sequence_length:
num_masked_span = sequence_length // mask_length
# make sure num_masked span is also <= input_length - (mask_length - 1)
if input_length - (mask_length - 1) < num_masked_span:
num_masked_span = max(input_length - (mask_length - 1), 0)
return num_masked_span
# compute number of masked spans in batch
input_lengths = (
attention_mask.sum(-1).detach().tolist()
if attention_mask is not None
else [sequence_length for _ in range(batch_size)]
)
# SpecAugment mask to fill
spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool)
spec_aug_mask_idxs = []
max_num_masked_span = compute_num_masked_span(sequence_length)
if max_num_masked_span == 0:
return spec_aug_mask
for input_length in input_lengths:
# compute num of masked spans for this input
num_masked_span = compute_num_masked_span(input_length)
# get random indices to mask
spec_aug_mask_idx = np.random.choice(
np.arange(input_length - (mask_length - 1)), num_masked_span, replace=False
)
# pick first sampled index that will serve as a dummy index to pad vector
# to ensure same dimension for all batches due to probabilistic rounding
# Picking first sample just pads those vectors twice.
if len(spec_aug_mask_idx) == 0:
# this case can only happen if `input_length` is strictly smaller then
# `sequence_length` in which case the last token has to be a padding
# token which we can use as a dummy mask id
dummy_mask_idx = sequence_length - 1
else:
dummy_mask_idx = spec_aug_mask_idx[0]
spec_aug_mask_idx = np.concatenate(
[spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx]
)
spec_aug_mask_idxs.append(spec_aug_mask_idx)
spec_aug_mask_idxs = np.array(spec_aug_mask_idxs)
# expand masked indices to masked spans
spec_aug_mask_idxs = np.broadcast_to(
spec_aug_mask_idxs[:, :, None], (batch_size, max_num_masked_span, mask_length)
)
spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, max_num_masked_span * mask_length)
# add offset to the starting indexes so that indexes now create a span
offsets = np.arange(mask_length)[None, None, :]
offsets = np.broadcast_to(offsets, (batch_size, max_num_masked_span, mask_length)).reshape(
batch_size, max_num_masked_span * mask_length
)
spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
# ensure that we cannot have indices larger than sequence_length
if spec_aug_mask_idxs.max() > sequence_length - 1:
spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1
# scatter indices to mask
np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1)
return spec_aug_mask
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2NoLayerNormConvLayer with Wav2Vec2->SpeechT5
class SpeechT5NoLayerNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
self.out_conv_dim,
kernel_size=config.conv_kernel[layer_id],
stride=config.conv_stride[layer_id],
bias=config.conv_bias,
)
self.activation = ACT2FN[config.feat_extract_activation]
def forward(self, hidden_states):
hidden_states = self.conv(hidden_states)
hidden_states = self.activation(hidden_states)
return hidden_states
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2LayerNormConvLayer with Wav2Vec2->SpeechT5
class SpeechT5LayerNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
self.out_conv_dim,
kernel_size=config.conv_kernel[layer_id],
stride=config.conv_stride[layer_id],
bias=config.conv_bias,
)
self.layer_norm = nn.LayerNorm(self.out_conv_dim, elementwise_affine=True)
self.activation = ACT2FN[config.feat_extract_activation]
def forward(self, hidden_states):
hidden_states = self.conv(hidden_states)
hidden_states = hidden_states.transpose(-2, -1)
hidden_states = self.layer_norm(hidden_states)
hidden_states = hidden_states.transpose(-2, -1)
hidden_states = self.activation(hidden_states)
return hidden_states
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2GroupNormConvLayer with Wav2Vec2->SpeechT5
class SpeechT5GroupNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
self.out_conv_dim,
kernel_size=config.conv_kernel[layer_id],
stride=config.conv_stride[layer_id],
bias=config.conv_bias,
)
self.activation = ACT2FN[config.feat_extract_activation]
self.layer_norm = nn.GroupNorm(num_groups=self.out_conv_dim, num_channels=self.out_conv_dim, affine=True)
def forward(self, hidden_states):
hidden_states = self.conv(hidden_states)
hidden_states = self.layer_norm(hidden_states)
hidden_states = self.activation(hidden_states)
return hidden_states
# Copied from transformers.models.speech_to_text.modeling_speech_to_text.Speech2TextSinusoidalPositionalEmbedding with Speech2Text->SpeechT5
class SpeechT5SinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
super().__init__()
self.offset = 2
self.embedding_dim = embedding_dim
self.padding_idx = padding_idx
self.make_weights(num_positions + self.offset, embedding_dim, padding_idx)
def make_weights(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
emb_weights = self.get_embedding(num_embeddings, embedding_dim, padding_idx)
if hasattr(self, "weights"):
# in forward put the weights on the correct dtype and device of the param
emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device)
self.weights = nn.Parameter(emb_weights)
self.weights.requires_grad = False
self.weights.detach_()
@staticmethod
def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
"""
Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the
description in Section 3.5 of "Attention Is All You Need".
"""
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb)
emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0)
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
if embedding_dim % 2 == 1:
# zero pad
emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
if padding_idx is not None:
emb[padding_idx, :] = 0
return emb.to(torch.get_default_dtype())
@torch.no_grad()
def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0):
bsz, seq_len = input_ids.size()
# Create the position ids from the input token ids. Any padded tokens remain padded.
position_ids = self.create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length).to(
input_ids.device
)
# expand embeddings if needed
max_pos = self.padding_idx + 1 + seq_len
if max_pos > self.weights.size(0):
self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx)
return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, -1).detach()
def create_position_ids_from_input_ids(
self, input_ids: torch.Tensor, padding_idx: int, past_key_values_length: Optional[int] = 0
):
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding
symbols are ignored. This is modified from fairseq's `utils.make_positions`.
Args:
x: torch.Tensor x:
Returns: torch.Tensor
"""
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
mask = input_ids.ne(padding_idx).int()
incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
return incremental_indices.long() + padding_idx
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2PositionalConvEmbedding with Wav2Vec2->SpeechT5
class SpeechT5PositionalConvEmbedding(nn.Module):
def __init__(self, config):
super().__init__()
self.conv = nn.Conv1d(
config.hidden_size,
config.hidden_size,
kernel_size=config.num_conv_pos_embeddings,
padding=config.num_conv_pos_embeddings // 2,
groups=config.num_conv_pos_embedding_groups,
)
weight_norm = nn.utils.weight_norm
if hasattr(nn.utils.parametrizations, "weight_norm"):
weight_norm = nn.utils.parametrizations.weight_norm
if is_deepspeed_zero3_enabled():
import deepspeed
with deepspeed.zero.GatheredParameters(self.conv.weight, modifier_rank=0):
self.conv = weight_norm(self.conv, name="weight", dim=2)
deepspeed.zero.register_external_parameter(self, self.conv.weight_v)
deepspeed.zero.register_external_parameter(self, self.conv.weight_g)
else:
self.conv = weight_norm(self.conv, name="weight", dim=2)
self.padding = SpeechT5SamePadLayer(config.num_conv_pos_embeddings)
self.activation = ACT2FN[config.feat_extract_activation]
def forward(self, hidden_states):
hidden_states = hidden_states.transpose(1, 2)
hidden_states = self.conv(hidden_states)
hidden_states = self.padding(hidden_states)
hidden_states = self.activation(hidden_states)
hidden_states = hidden_states.transpose(1, 2)
return hidden_states
class SpeechT5ScaledPositionalEncoding(nn.Module):
"""
Scaled positional encoding, see 搂3.2 in https://arxiv.org/abs/1809.08895
"""
def __init__(self, dropout, dim, max_len=5000):
pe = torch.zeros(max_len, dim)
position = torch.arange(0, max_len).unsqueeze(1)
div_term = torch.exp((torch.arange(0, dim, 2, dtype=torch.int64).float() * -(math.log(10000.0) / dim)))
pe[:, 0::2] = torch.sin(position.float() * div_term)
pe[:, 1::2] = torch.cos(position.float() * div_term)
pe = pe.unsqueeze(0)
super().__init__()
self.register_buffer("pe", pe, persistent=False)
self.dropout = nn.Dropout(p=dropout)
self.dim = dim
self.alpha = torch.nn.Parameter(torch.tensor(1.0))
def forward(self, emb):
emb = emb + self.alpha * self.pe[:, : emb.size(1)]
emb = self.dropout(emb)
return emb
class SpeechT5RelativePositionalEncoding(torch.nn.Module):
def __init__(self, dim, max_length=1000):
super().__init__()
self.dim = dim
self.max_length = max_length
self.pe_k = torch.nn.Embedding(2 * max_length, dim)
def forward(self, hidden_states):
seq_len = hidden_states.shape[1]
pos_seq = torch.arange(0, seq_len).long().to(hidden_states.device)
pos_seq = pos_seq[:, None] - pos_seq[None, :]
pos_seq[pos_seq < -self.max_length] = -self.max_length
pos_seq[pos_seq >= self.max_length] = self.max_length - 1
pos_seq = pos_seq + self.max_length
return self.pe_k(pos_seq)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2SamePadLayer with Wav2Vec2->SpeechT5
class SpeechT5SamePadLayer(nn.Module):
def __init__(self, num_conv_pos_embeddings):
super().__init__()
self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0
def forward(self, hidden_states):
if self.num_pad_remove > 0:
hidden_states = hidden_states[:, :, : -self.num_pad_remove]
return hidden_states
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeatureEncoder with Wav2Vec2->SpeechT5
class SpeechT5FeatureEncoder(nn.Module):
"""Construct the features from raw audio waveform"""
def __init__(self, config):
super().__init__()
if config.feat_extract_norm == "group":
conv_layers = [SpeechT5GroupNormConvLayer(config, layer_id=0)] + [
SpeechT5NoLayerNormConvLayer(config, layer_id=i + 1) for i in range(config.num_feat_extract_layers - 1)
]
elif config.feat_extract_norm == "layer":
conv_layers = [
SpeechT5LayerNormConvLayer(config, layer_id=i) for i in range(config.num_feat_extract_layers)
]
else:
raise ValueError(
f"`config.feat_extract_norm` is {config.feat_extract_norm}, but has to be one of ['group', 'layer']"
)
self.conv_layers = nn.ModuleList(conv_layers)
self.gradient_checkpointing = False
self._requires_grad = True
def _freeze_parameters(self):
for param in self.parameters():
param.requires_grad = False
self._requires_grad = False
def forward(self, input_values):
hidden_states = input_values[:, None]
# make sure hidden_states require grad for gradient_checkpointing
if self._requires_grad and self.training:
hidden_states.requires_grad = True
for conv_layer in self.conv_layers:
if self._requires_grad and self.gradient_checkpointing and self.training:
hidden_states = self._gradient_checkpointing_func(
conv_layer.__call__,
hidden_states,
)
else:
hidden_states = conv_layer(hidden_states)
return hidden_states
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeatureProjection with Wav2Vec2->SpeechT5
class SpeechT5FeatureProjection(nn.Module):
def __init__(self, config):
super().__init__()
self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.layer_norm_eps)
self.projection = nn.Linear(config.conv_dim[-1], config.hidden_size)
self.dropout = nn.Dropout(config.feat_proj_dropout)
def forward(self, hidden_states):
# non-projected hidden states are needed for quantization
norm_hidden_states = self.layer_norm(hidden_states)
hidden_states = self.projection(norm_hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states, norm_hidden_states
class SpeechT5SpeechEncoderPrenet(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.feature_encoder = SpeechT5FeatureEncoder(config)
self.feature_projection = SpeechT5FeatureProjection(config)
# model only needs masking vector if mask prob is > 0.0
if config.mask_time_prob > 0.0 or config.mask_feature_prob > 0.0:
self.masked_spec_embed = nn.Parameter(torch.FloatTensor(config.hidden_size).uniform_())
self.pos_conv_embed = SpeechT5PositionalConvEmbedding(config)
self.pos_sinusoidal_embed = SpeechT5SinusoidalPositionalEmbedding(
config.max_speech_positions + config.pad_token_id + 1,
config.hidden_size,
config.pad_token_id,
)
def freeze_feature_encoder(self):
self.feature_encoder._freeze_parameters()
def forward(
self,
input_values: torch.Tensor,
attention_mask: Optional[torch.LongTensor] = None,
mask_time_indices: Optional[torch.FloatTensor] = None,
):
extract_features = self.feature_encoder(input_values)
extract_features = extract_features.transpose(1, 2)
if attention_mask is not None:
# compute reduced attention_mask corresponding to feature vectors
attention_mask = self._get_feature_vector_attention_mask(
extract_features.shape[1],
attention_mask,
)
hidden_states, extract_features = self.feature_projection(extract_features)
hidden_states = self._mask_hidden_states(
hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask
)
positional_conv_embedding = self.pos_conv_embed(hidden_states)
hidden_states = hidden_states + positional_conv_embedding
if attention_mask is not None:
padding_mask = attention_mask.ne(1).long()
else:
padding_mask = torch.zeros(hidden_states.shape[:2], dtype=torch.long, device=hidden_states.device)
positional_sinusoidal_embeddings = self.pos_sinusoidal_embed(padding_mask)
hidden_states = hidden_states + positional_sinusoidal_embeddings
return hidden_states, attention_mask
# Copied from transformers.models.unispeech.modeling_unispeech.UniSpeechPreTrainedModel._get_feature_vector_attention_mask
def _get_feature_vector_attention_mask(self, feature_vector_length: int, attention_mask: torch.LongTensor):
# Effectively attention_mask.sum(-1), but not inplace to be able to run
# on inference mode.
non_padded_lengths = attention_mask.cumsum(dim=-1)[:, -1]
output_lengths = self._get_feat_extract_output_lengths(non_padded_lengths).to(torch.long)
batch_size = attention_mask.shape[0]
attention_mask = torch.zeros(
(batch_size, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device
)
# these two operations makes sure that all values before the output lengths idxs are attended to
attention_mask[(torch.arange(attention_mask.shape[0], device=attention_mask.device), output_lengths - 1)] = 1
attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool()
return attention_mask
# Copied from transformers.models.unispeech.modeling_unispeech.UniSpeechPreTrainedModel._get_feat_extract_output_lengths
def _get_feat_extract_output_lengths(self, input_lengths: Union[torch.LongTensor, int]):
"""
Computes the output length of the convolutional layers
"""
def _conv_out_length(input_length, kernel_size, stride):
# 1D convolutional layer output length formula taken
# from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
return torch.div(input_length - kernel_size, stride, rounding_mode="floor") + 1
for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
input_lengths = _conv_out_length(input_lengths, kernel_size, stride)
return input_lengths
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Model._mask_hidden_states
def _mask_hidden_states(
self,
hidden_states: torch.FloatTensor,
mask_time_indices: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
):
"""
Masks extracted features along time axis and/or along feature axis according to
[SpecAugment](https://arxiv.org/abs/1904.08779).
"""
# `config.apply_spec_augment` can set masking to False
if not getattr(self.config, "apply_spec_augment", True):
return hidden_states
# generate indices & apply SpecAugment along time axis
batch_size, sequence_length, hidden_size = hidden_states.size()
if mask_time_indices is not None:
# apply SpecAugment along time axis with given mask_time_indices
hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype)
elif self.config.mask_time_prob > 0 and self.training:
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=hidden_states.device, dtype=torch.bool)
hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype)
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=hidden_states.device, dtype=torch.bool)
mask_feature_indices = mask_feature_indices[:, None].expand(-1, sequence_length, -1)
hidden_states[mask_feature_indices] = 0
return hidden_states
class SpeechT5SpeechDecoderPrenet(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layers = nn.ModuleList(
[
nn.Linear(
config.num_mel_bins if i == 0 else config.speech_decoder_prenet_units,
config.speech_decoder_prenet_units,
)
for i in range(config.speech_decoder_prenet_layers)
]
)
self.final_layer = nn.Linear(config.speech_decoder_prenet_units, config.hidden_size)
self.encode_positions = SpeechT5ScaledPositionalEncoding(
config.positional_dropout,
config.hidden_size,
config.max_speech_positions,
)
self.speaker_embeds_layer = nn.Linear(config.speaker_embedding_dim + config.hidden_size, config.hidden_size)
def _consistent_dropout(self, inputs_embeds, p):
mask = torch.bernoulli(inputs_embeds[0], p=p)
all_masks = mask.unsqueeze(0).repeat(inputs_embeds.size(0), 1, 1)
return torch.where(all_masks == 1, inputs_embeds, 0) * 1 / (1 - p)
def forward(
self,
input_values: torch.Tensor,
speaker_embeddings: Optional[torch.Tensor] = None,
):
# Dropout is always applied, even when evaluating. See 搂2.2 in https://arxiv.org/abs/1712.05884.
inputs_embeds = input_values
for layer in self.layers:
inputs_embeds = nn.functional.relu(layer(inputs_embeds))
inputs_embeds = self._consistent_dropout(inputs_embeds, self.config.speech_decoder_prenet_dropout)
inputs_embeds = self.final_layer(inputs_embeds)
inputs_embeds = self.encode_positions(inputs_embeds)
if speaker_embeddings is not None:
speaker_embeddings = nn.functional.normalize(speaker_embeddings)
speaker_embeddings = speaker_embeddings.unsqueeze(1).expand(-1, inputs_embeds.size(1), -1)
inputs_embeds = torch.cat([inputs_embeds, speaker_embeddings], dim=-1)
inputs_embeds = nn.functional.relu(self.speaker_embeds_layer(inputs_embeds))
return inputs_embeds
class SpeechT5BatchNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
if layer_id == 0:
in_conv_dim = config.num_mel_bins
else:
in_conv_dim = config.speech_decoder_postnet_units
if layer_id == config.speech_decoder_postnet_layers - 1:
out_conv_dim = config.num_mel_bins
else:
out_conv_dim = config.speech_decoder_postnet_units
self.conv = nn.Conv1d(
in_conv_dim,
out_conv_dim,
kernel_size=config.speech_decoder_postnet_kernel,
stride=1,
padding=(config.speech_decoder_postnet_kernel - 1) // 2,
bias=False,
)
self.batch_norm = nn.BatchNorm1d(out_conv_dim)
if layer_id < config.speech_decoder_postnet_layers - 1:
self.activation = nn.Tanh()
else:
self.activation = None
self.dropout = nn.Dropout(config.speech_decoder_postnet_dropout)
def forward(self, hidden_states):
hidden_states = self.conv(hidden_states)
hidden_states = self.batch_norm(hidden_states)
if self.activation is not None:
hidden_states = self.activation(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class SpeechT5SpeechDecoderPostnet(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.feat_out = nn.Linear(config.hidden_size, config.num_mel_bins * config.reduction_factor)
self.prob_out = nn.Linear(config.hidden_size, config.reduction_factor)
self.layers = nn.ModuleList(
[SpeechT5BatchNormConvLayer(config, i) for i in range(config.speech_decoder_postnet_layers)]
)
def forward(self, hidden_states: torch.Tensor):
outputs_before_postnet = self.feat_out(hidden_states).view(hidden_states.size(0), -1, self.config.num_mel_bins)
outputs_after_postnet = self.postnet(outputs_before_postnet)
logits = self.prob_out(hidden_states).view(hidden_states.size(0), -1)
return outputs_before_postnet, outputs_after_postnet, logits
def postnet(self, hidden_states: torch.Tensor):
layer_output = hidden_states.transpose(1, 2)
for layer in self.layers:
layer_output = layer(layer_output)
return hidden_states + layer_output.transpose(1, 2)
class SpeechT5TextEncoderPrenet(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
self.encode_positions = SpeechT5ScaledPositionalEncoding(
config.positional_dropout,
config.hidden_size,
config.max_text_positions,
)
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
def forward(self, input_ids: torch.Tensor):
inputs_embeds = self.embed_tokens(input_ids)
inputs_embeds = self.encode_positions(inputs_embeds)
return inputs_embeds
class SpeechT5TextDecoderPrenet(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.dropout = nn.Dropout(config.positional_dropout)
self.embed_scale = math.sqrt(config.hidden_size) if config.scale_embedding else 1.0
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
self.embed_positions = SpeechT5SinusoidalPositionalEmbedding(
config.max_text_positions + config.pad_token_id + 1,
config.hidden_size,
config.pad_token_id,
)
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
def forward(
self,
input_ids: torch.Tensor,
attention_mask: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
):
if input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
else:
raise ValueError("You have to specify `decoder_input_ids`")
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
positions = self.embed_positions(input_ids, past_key_values_length)
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
inputs_embeds += positions
inputs_embeds = self.dropout(inputs_embeds)
return inputs_embeds, attention_mask
class SpeechT5TextDecoderPostnet(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
def forward(self, hidden_states: torch.Tensor):
return self.lm_head(hidden_states)
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
class SpeechT5Attention(nn.Module):
"""
Multi-headed attention from 'Attention Is All You Need' paper with relative position bias (see
https://aclanthology.org/N18-2074.pdf)
"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
position_bias: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
# relative attention bias
if position_bias is not None:
reshape_q = query_states.contiguous().view(bsz * self.num_heads, -1, self.head_dim).transpose(0, 1)
rel_pos_bias = torch.matmul(reshape_q, position_bias.transpose(-2, -1))
rel_pos_bias = rel_pos_bias.transpose(0, 1).view(
bsz * self.num_heads, position_bias.size(0), position_bias.size(1)
)
attn_weights += rel_pos_bias
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)