475 lines
21 KiB
Python
475 lines
21 KiB
Python
# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu)
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# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn)
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# 2024 Alibaba Inc (Xiang Lyu)
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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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# Modified from ESPnet(https://github.com/espnet/espnet)
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"""Encoder definition."""
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from typing import Tuple
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import torch
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import torch.utils.checkpoint as ckpt
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from cosyvoice.transformer.convolution import ConvolutionModule
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from cosyvoice.transformer.encoder_layer import TransformerEncoderLayer
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from cosyvoice.transformer.encoder_layer import ConformerEncoderLayer
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from cosyvoice.transformer.positionwise_feed_forward import PositionwiseFeedForward
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from cosyvoice.utils.class_utils import (
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COSYVOICE_EMB_CLASSES,
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COSYVOICE_SUBSAMPLE_CLASSES,
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COSYVOICE_ATTENTION_CLASSES,
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COSYVOICE_ACTIVATION_CLASSES,
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)
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from cosyvoice.utils.mask import make_pad_mask
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from cosyvoice.utils.mask import add_optional_chunk_mask
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class BaseEncoder(torch.nn.Module):
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def __init__(
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self,
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input_size: int,
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output_size: int = 256,
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attention_heads: int = 4,
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linear_units: int = 2048,
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num_blocks: int = 6,
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dropout_rate: float = 0.1,
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positional_dropout_rate: float = 0.1,
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attention_dropout_rate: float = 0.0,
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input_layer: str = "conv2d",
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pos_enc_layer_type: str = "abs_pos",
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normalize_before: bool = True,
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static_chunk_size: int = 0,
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use_dynamic_chunk: bool = False,
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global_cmvn: torch.nn.Module = None,
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use_dynamic_left_chunk: bool = False,
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gradient_checkpointing: bool = False,
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):
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"""
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Args:
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input_size (int): input dim
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output_size (int): dimension of attention
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attention_heads (int): the number of heads of multi head attention
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linear_units (int): the hidden units number of position-wise feed
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forward
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num_blocks (int): the number of decoder blocks
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dropout_rate (float): dropout rate
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attention_dropout_rate (float): dropout rate in attention
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positional_dropout_rate (float): dropout rate after adding
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positional encoding
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input_layer (str): input layer type.
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optional [linear, conv2d, conv2d6, conv2d8]
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pos_enc_layer_type (str): Encoder positional encoding layer type.
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opitonal [abs_pos, scaled_abs_pos, rel_pos, no_pos]
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normalize_before (bool):
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True: use layer_norm before each sub-block of a layer.
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False: use layer_norm after each sub-block of a layer.
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static_chunk_size (int): chunk size for static chunk training and
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decoding
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use_dynamic_chunk (bool): whether use dynamic chunk size for
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training or not, You can only use fixed chunk(chunk_size > 0)
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or dyanmic chunk size(use_dynamic_chunk = True)
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global_cmvn (Optional[torch.nn.Module]): Optional GlobalCMVN module
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use_dynamic_left_chunk (bool): whether use dynamic left chunk in
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dynamic chunk training
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key_bias: whether use bias in attention.linear_k, False for whisper models.
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gradient_checkpointing: rerunning a forward-pass segment for each
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checkpointed segment during backward.
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"""
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super().__init__()
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self._output_size = output_size
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self.global_cmvn = global_cmvn
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self.embed = COSYVOICE_SUBSAMPLE_CLASSES[input_layer](
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input_size,
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output_size,
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dropout_rate,
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COSYVOICE_EMB_CLASSES[pos_enc_layer_type](output_size,
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positional_dropout_rate),
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)
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self.normalize_before = normalize_before
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self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5)
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self.static_chunk_size = static_chunk_size
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self.use_dynamic_chunk = use_dynamic_chunk
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self.use_dynamic_left_chunk = use_dynamic_left_chunk
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self.gradient_checkpointing = gradient_checkpointing
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def output_size(self) -> int:
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return self._output_size
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def forward(
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self,
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xs: torch.Tensor,
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xs_lens: torch.Tensor,
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decoding_chunk_size: int = 0,
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num_decoding_left_chunks: int = -1,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Embed positions in tensor.
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Args:
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xs: padded input tensor (B, T, D)
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xs_lens: input length (B)
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decoding_chunk_size: decoding chunk size for dynamic chunk
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0: default for training, use random dynamic chunk.
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<0: for decoding, use full chunk.
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>0: for decoding, use fixed chunk size as set.
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num_decoding_left_chunks: number of left chunks, this is for decoding,
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the chunk size is decoding_chunk_size.
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>=0: use num_decoding_left_chunks
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<0: use all left chunks
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Returns:
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encoder output tensor xs, and subsampled masks
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xs: padded output tensor (B, T' ~= T/subsample_rate, D)
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masks: torch.Tensor batch padding mask after subsample
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(B, 1, T' ~= T/subsample_rate)
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NOTE(xcsong):
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We pass the `__call__` method of the modules instead of `forward` to the
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checkpointing API because `__call__` attaches all the hooks of the module.
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https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2
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"""
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T = xs.size(1)
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masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T)
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if self.global_cmvn is not None:
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xs = self.global_cmvn(xs)
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xs, pos_emb, masks = self.embed(xs, masks)
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mask_pad = masks # (B, 1, T/subsample_rate)
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chunk_masks = add_optional_chunk_mask(xs, masks,
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self.use_dynamic_chunk,
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self.use_dynamic_left_chunk,
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decoding_chunk_size,
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self.static_chunk_size,
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num_decoding_left_chunks)
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if self.gradient_checkpointing and self.training:
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xs = self.forward_layers_checkpointed(xs, chunk_masks, pos_emb,
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mask_pad)
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else:
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xs = self.forward_layers(xs, chunk_masks, pos_emb, mask_pad)
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if self.normalize_before:
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xs = self.after_norm(xs)
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# Here we assume the mask is not changed in encoder layers, so just
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# return the masks before encoder layers, and the masks will be used
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# for cross attention with decoder later
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return xs, masks
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def forward_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor,
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pos_emb: torch.Tensor,
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mask_pad: torch.Tensor) -> torch.Tensor:
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for layer in self.encoders:
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xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
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return xs
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@torch.jit.unused
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def forward_layers_checkpointed(self, xs: torch.Tensor,
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chunk_masks: torch.Tensor,
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pos_emb: torch.Tensor,
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mask_pad: torch.Tensor) -> torch.Tensor:
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for layer in self.encoders:
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xs, chunk_masks, _, _ = ckpt.checkpoint(layer.__call__, xs,
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chunk_masks, pos_emb,
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mask_pad)
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return xs
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@torch.jit.export
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def forward_chunk(
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self,
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xs: torch.Tensor,
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offset: int,
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required_cache_size: int,
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att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
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cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
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att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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""" Forward just one chunk
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Args:
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xs (torch.Tensor): chunk input, with shape (b=1, time, mel-dim),
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where `time == (chunk_size - 1) * subsample_rate + \
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subsample.right_context + 1`
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offset (int): current offset in encoder output time stamp
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required_cache_size (int): cache size required for next chunk
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compuation
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>=0: actual cache size
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<0: means all history cache is required
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att_cache (torch.Tensor): cache tensor for KEY & VALUE in
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transformer/conformer attention, with shape
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(elayers, head, cache_t1, d_k * 2), where
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`head * d_k == hidden-dim` and
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`cache_t1 == chunk_size * num_decoding_left_chunks`.
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cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer,
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(elayers, b=1, hidden-dim, cache_t2), where
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`cache_t2 == cnn.lorder - 1`
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Returns:
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torch.Tensor: output of current input xs,
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with shape (b=1, chunk_size, hidden-dim).
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torch.Tensor: new attention cache required for next chunk, with
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dynamic shape (elayers, head, ?, d_k * 2)
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depending on required_cache_size.
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torch.Tensor: new conformer cnn cache required for next chunk, with
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same shape as the original cnn_cache.
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"""
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assert xs.size(0) == 1
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# tmp_masks is just for interface compatibility
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tmp_masks = torch.ones(1,
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xs.size(1),
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device=xs.device,
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dtype=torch.bool)
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tmp_masks = tmp_masks.unsqueeze(1)
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if self.global_cmvn is not None:
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xs = self.global_cmvn(xs)
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# NOTE(xcsong): Before embed, shape(xs) is (b=1, time, mel-dim)
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xs, pos_emb, _ = self.embed(xs, tmp_masks, offset)
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# NOTE(xcsong): After embed, shape(xs) is (b=1, chunk_size, hidden-dim)
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elayers, cache_t1 = att_cache.size(0), att_cache.size(2)
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chunk_size = xs.size(1)
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attention_key_size = cache_t1 + chunk_size
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pos_emb = self.embed.position_encoding(offset=offset - cache_t1,
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size=attention_key_size)
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if required_cache_size < 0:
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next_cache_start = 0
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elif required_cache_size == 0:
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next_cache_start = attention_key_size
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else:
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next_cache_start = max(attention_key_size - required_cache_size, 0)
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r_att_cache = []
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r_cnn_cache = []
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for i, layer in enumerate(self.encoders):
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# NOTE(xcsong): Before layer.forward
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# shape(att_cache[i:i + 1]) is (1, head, cache_t1, d_k * 2),
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# shape(cnn_cache[i]) is (b=1, hidden-dim, cache_t2)
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xs, _, new_att_cache, new_cnn_cache = layer(
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xs,
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att_mask,
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pos_emb,
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att_cache=att_cache[i:i + 1] if elayers > 0 else att_cache,
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cnn_cache=cnn_cache[i] if cnn_cache.size(0) > 0 else cnn_cache)
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# NOTE(xcsong): After layer.forward
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# shape(new_att_cache) is (1, head, attention_key_size, d_k * 2),
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# shape(new_cnn_cache) is (b=1, hidden-dim, cache_t2)
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r_att_cache.append(new_att_cache[:, :, next_cache_start:, :])
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r_cnn_cache.append(new_cnn_cache.unsqueeze(0))
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if self.normalize_before:
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xs = self.after_norm(xs)
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# NOTE(xcsong): shape(r_att_cache) is (elayers, head, ?, d_k * 2),
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# ? may be larger than cache_t1, it depends on required_cache_size
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r_att_cache = torch.cat(r_att_cache, dim=0)
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# NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2)
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r_cnn_cache = torch.cat(r_cnn_cache, dim=0)
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return (xs, r_att_cache, r_cnn_cache)
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@torch.jit.unused
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def forward_chunk_by_chunk(
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self,
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xs: torch.Tensor,
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decoding_chunk_size: int,
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num_decoding_left_chunks: int = -1,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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""" Forward input chunk by chunk with chunk_size like a streaming
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fashion
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Here we should pay special attention to computation cache in the
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streaming style forward chunk by chunk. Three things should be taken
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into account for computation in the current network:
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1. transformer/conformer encoder layers output cache
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2. convolution in conformer
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3. convolution in subsampling
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However, we don't implement subsampling cache for:
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1. We can control subsampling module to output the right result by
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overlapping input instead of cache left context, even though it
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wastes some computation, but subsampling only takes a very
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small fraction of computation in the whole model.
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2. Typically, there are several covolution layers with subsampling
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in subsampling module, it is tricky and complicated to do cache
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with different convolution layers with different subsampling
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rate.
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3. Currently, nn.Sequential is used to stack all the convolution
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layers in subsampling, we need to rewrite it to make it work
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with cache, which is not preferred.
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Args:
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xs (torch.Tensor): (1, max_len, dim)
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chunk_size (int): decoding chunk size
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"""
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assert decoding_chunk_size > 0
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# The model is trained by static or dynamic chunk
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assert self.static_chunk_size > 0 or self.use_dynamic_chunk
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subsampling = self.embed.subsampling_rate
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context = self.embed.right_context + 1 # Add current frame
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stride = subsampling * decoding_chunk_size
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decoding_window = (decoding_chunk_size - 1) * subsampling + context
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num_frames = xs.size(1)
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att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)
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cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)
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outputs = []
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offset = 0
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required_cache_size = decoding_chunk_size * num_decoding_left_chunks
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# Feed forward overlap input step by step
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for cur in range(0, num_frames - context + 1, stride):
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end = min(cur + decoding_window, num_frames)
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chunk_xs = xs[:, cur:end, :]
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(y, att_cache,
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cnn_cache) = self.forward_chunk(chunk_xs, offset,
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required_cache_size, att_cache,
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cnn_cache)
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outputs.append(y)
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offset += y.size(1)
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ys = torch.cat(outputs, 1)
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masks = torch.ones((1, 1, ys.size(1)),
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device=ys.device,
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dtype=torch.bool)
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return ys, masks
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class TransformerEncoder(BaseEncoder):
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"""Transformer encoder module."""
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def __init__(
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self,
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input_size: int,
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output_size: int = 256,
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attention_heads: int = 4,
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linear_units: int = 2048,
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num_blocks: int = 6,
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dropout_rate: float = 0.1,
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positional_dropout_rate: float = 0.1,
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attention_dropout_rate: float = 0.0,
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input_layer: str = "conv2d",
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pos_enc_layer_type: str = "abs_pos",
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normalize_before: bool = True,
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static_chunk_size: int = 0,
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use_dynamic_chunk: bool = False,
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global_cmvn: torch.nn.Module = None,
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use_dynamic_left_chunk: bool = False,
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key_bias: bool = True,
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selfattention_layer_type: str = "selfattn",
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activation_type: str = "relu",
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gradient_checkpointing: bool = False,
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):
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""" Construct TransformerEncoder
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See Encoder for the meaning of each parameter.
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"""
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super().__init__(input_size, output_size, attention_heads,
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linear_units, num_blocks, dropout_rate,
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positional_dropout_rate, attention_dropout_rate,
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input_layer, pos_enc_layer_type, normalize_before,
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static_chunk_size, use_dynamic_chunk, global_cmvn,
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use_dynamic_left_chunk, gradient_checkpointing)
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activation = COSYVOICE_ACTIVATION_CLASSES[activation_type]()
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self.encoders = torch.nn.ModuleList([
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TransformerEncoderLayer(
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output_size,
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COSYVOICE_ATTENTION_CLASSES[selfattention_layer_type](attention_heads,
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output_size,
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attention_dropout_rate,
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key_bias),
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PositionwiseFeedForward(output_size, linear_units,
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dropout_rate, activation),
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dropout_rate, normalize_before) for _ in range(num_blocks)
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])
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class ConformerEncoder(BaseEncoder):
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"""Conformer encoder module."""
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def __init__(
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self,
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input_size: int,
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output_size: int = 256,
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attention_heads: int = 4,
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linear_units: int = 2048,
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num_blocks: int = 6,
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dropout_rate: float = 0.1,
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positional_dropout_rate: float = 0.1,
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attention_dropout_rate: float = 0.0,
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input_layer: str = "conv2d",
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pos_enc_layer_type: str = "rel_pos",
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normalize_before: bool = True,
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static_chunk_size: int = 0,
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use_dynamic_chunk: bool = False,
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global_cmvn: torch.nn.Module = None,
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use_dynamic_left_chunk: bool = False,
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positionwise_conv_kernel_size: int = 1,
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macaron_style: bool = True,
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selfattention_layer_type: str = "rel_selfattn",
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activation_type: str = "swish",
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use_cnn_module: bool = True,
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cnn_module_kernel: int = 15,
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causal: bool = False,
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cnn_module_norm: str = "batch_norm",
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key_bias: bool = True,
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gradient_checkpointing: bool = False,
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):
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"""Construct ConformerEncoder
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Args:
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input_size to use_dynamic_chunk, see in BaseEncoder
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positionwise_conv_kernel_size (int): Kernel size of positionwise
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conv1d layer.
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macaron_style (bool): Whether to use macaron style for
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positionwise layer.
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selfattention_layer_type (str): Encoder attention layer type,
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the parameter has no effect now, it's just for configure
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compatibility.
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activation_type (str): Encoder activation function type.
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use_cnn_module (bool): Whether to use convolution module.
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cnn_module_kernel (int): Kernel size of convolution module.
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causal (bool): whether to use causal convolution or not.
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key_bias: whether use bias in attention.linear_k, False for whisper models.
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"""
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super().__init__(input_size, output_size, attention_heads,
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linear_units, num_blocks, dropout_rate,
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positional_dropout_rate, attention_dropout_rate,
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input_layer, pos_enc_layer_type, normalize_before,
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static_chunk_size, use_dynamic_chunk, global_cmvn,
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use_dynamic_left_chunk, gradient_checkpointing)
|
|
activation = COSYVOICE_ACTIVATION_CLASSES[activation_type]()
|
|
|
|
# self-attention module definition
|
|
encoder_selfattn_layer_args = (
|
|
attention_heads,
|
|
output_size,
|
|
attention_dropout_rate,
|
|
key_bias,
|
|
)
|
|
# feed-forward module definition
|
|
positionwise_layer_args = (
|
|
output_size,
|
|
linear_units,
|
|
dropout_rate,
|
|
activation,
|
|
)
|
|
# convolution module definition
|
|
convolution_layer_args = (output_size, cnn_module_kernel, activation,
|
|
cnn_module_norm, causal)
|
|
|
|
self.encoders = torch.nn.ModuleList([
|
|
ConformerEncoderLayer(
|
|
output_size,
|
|
COSYVOICE_ATTENTION_CLASSES[selfattention_layer_type](
|
|
*encoder_selfattn_layer_args),
|
|
PositionwiseFeedForward(*positionwise_layer_args),
|
|
PositionwiseFeedForward(
|
|
*positionwise_layer_args) if macaron_style else None,
|
|
ConvolutionModule(
|
|
*convolution_layer_args) if use_cnn_module else None,
|
|
dropout_rate,
|
|
normalize_before,
|
|
) for _ in range(num_blocks)
|
|
])
|