Delete put_contain_in_site_packages_folder directory
This commit is contained in:
Shmuel Ronen
@@ -1,2 +0,0 @@
|
||||
from .tts import ChatterboxTTS
|
||||
from .vc import ChatterboxVC
|
||||
@@ -1,2 +0,0 @@
|
||||
from .s3gen import S3Token2Wav as S3Gen
|
||||
from .const import S3GEN_SR
|
||||
@@ -1 +0,0 @@
|
||||
S3GEN_SR = 24000
|
||||
@@ -1,317 +0,0 @@
|
||||
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
|
||||
#
|
||||
# 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.
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from einops import pack, rearrange, repeat
|
||||
|
||||
from .utils.mask import add_optional_chunk_mask
|
||||
from .matcha.decoder import SinusoidalPosEmb, Block1D, ResnetBlock1D, Downsample1D, \
|
||||
TimestepEmbedding, Upsample1D
|
||||
from .matcha.transformer import BasicTransformerBlock
|
||||
|
||||
|
||||
def mask_to_bias(mask: torch.Tensor, dtype: torch.dtype) -> torch.Tensor:
|
||||
assert mask.dtype == torch.bool
|
||||
assert dtype in [torch.float32, torch.bfloat16, torch.float16]
|
||||
mask = mask.to(dtype)
|
||||
# attention mask bias
|
||||
# NOTE(Mddct): torch.finfo jit issues
|
||||
# chunk_masks = (1.0 - chunk_masks) * torch.finfo(dtype).min
|
||||
mask = (1.0 - mask) * -1.0e+10
|
||||
return mask
|
||||
|
||||
|
||||
|
||||
class Transpose(torch.nn.Module):
|
||||
def __init__(self, dim0: int, dim1: int):
|
||||
super().__init__()
|
||||
self.dim0 = dim0
|
||||
self.dim1 = dim1
|
||||
|
||||
def forward(self, x: torch.Tensor):
|
||||
x = torch.transpose(x, self.dim0, self.dim1)
|
||||
return x
|
||||
|
||||
|
||||
class CausalBlock1D(Block1D):
|
||||
def __init__(self, dim: int, dim_out: int):
|
||||
super(CausalBlock1D, self).__init__(dim, dim_out)
|
||||
self.block = torch.nn.Sequential(
|
||||
CausalConv1d(dim, dim_out, 3),
|
||||
Transpose(1, 2),
|
||||
nn.LayerNorm(dim_out),
|
||||
Transpose(1, 2),
|
||||
nn.Mish(),
|
||||
)
|
||||
|
||||
def forward(self, x: torch.Tensor, mask: torch.Tensor):
|
||||
output = self.block(x * mask)
|
||||
return output * mask
|
||||
|
||||
|
||||
class CausalResnetBlock1D(ResnetBlock1D):
|
||||
def __init__(self, dim: int, dim_out: int, time_emb_dim: int, groups: int = 8):
|
||||
super(CausalResnetBlock1D, self).__init__(dim, dim_out, time_emb_dim, groups)
|
||||
self.block1 = CausalBlock1D(dim, dim_out)
|
||||
self.block2 = CausalBlock1D(dim_out, dim_out)
|
||||
|
||||
|
||||
class CausalConv1d(torch.nn.Conv1d):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels: int,
|
||||
out_channels: int,
|
||||
kernel_size: int,
|
||||
stride: int = 1,
|
||||
dilation: int = 1,
|
||||
groups: int = 1,
|
||||
bias: bool = True,
|
||||
padding_mode: str = 'zeros',
|
||||
device=None,
|
||||
dtype=None
|
||||
) -> None:
|
||||
super(CausalConv1d, self).__init__(in_channels, out_channels,
|
||||
kernel_size, stride,
|
||||
padding=0, dilation=dilation,
|
||||
groups=groups, bias=bias,
|
||||
padding_mode=padding_mode,
|
||||
device=device, dtype=dtype)
|
||||
assert stride == 1
|
||||
self.causal_padding = (kernel_size - 1, 0)
|
||||
|
||||
def forward(self, x: torch.Tensor):
|
||||
x = F.pad(x, self.causal_padding)
|
||||
x = super(CausalConv1d, self).forward(x)
|
||||
return x
|
||||
|
||||
|
||||
class ConditionalDecoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels=320,
|
||||
out_channels=80,
|
||||
causal=True,
|
||||
channels=[256],
|
||||
dropout=0.0,
|
||||
attention_head_dim=64,
|
||||
n_blocks=4,
|
||||
num_mid_blocks=12,
|
||||
num_heads=8,
|
||||
act_fn="gelu",
|
||||
):
|
||||
"""
|
||||
This decoder requires an input with the same shape of the target. So, if your text content
|
||||
is shorter or longer than the outputs, please re-sampling it before feeding to the decoder.
|
||||
"""
|
||||
super().__init__()
|
||||
channels = tuple(channels)
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
self.causal = causal
|
||||
self.time_embeddings = SinusoidalPosEmb(in_channels)
|
||||
time_embed_dim = channels[0] * 4
|
||||
self.time_mlp = TimestepEmbedding(
|
||||
in_channels=in_channels,
|
||||
time_embed_dim=time_embed_dim,
|
||||
act_fn="silu",
|
||||
)
|
||||
self.down_blocks = nn.ModuleList([])
|
||||
self.mid_blocks = nn.ModuleList([])
|
||||
self.up_blocks = nn.ModuleList([])
|
||||
|
||||
# NOTE jrm: `static_chunk_size` is missing?
|
||||
self.static_chunk_size = 0
|
||||
|
||||
output_channel = in_channels
|
||||
for i in range(len(channels)): # pylint: disable=consider-using-enumerate
|
||||
input_channel = output_channel
|
||||
output_channel = channels[i]
|
||||
is_last = i == len(channels) - 1
|
||||
resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) if self.causal else \
|
||||
ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
|
||||
transformer_blocks = nn.ModuleList(
|
||||
[
|
||||
BasicTransformerBlock(
|
||||
dim=output_channel,
|
||||
num_attention_heads=num_heads,
|
||||
attention_head_dim=attention_head_dim,
|
||||
dropout=dropout,
|
||||
activation_fn=act_fn,
|
||||
)
|
||||
for _ in range(n_blocks)
|
||||
]
|
||||
)
|
||||
downsample = (
|
||||
Downsample1D(output_channel) if not is_last else
|
||||
CausalConv1d(output_channel, output_channel, 3) if self.causal else nn.Conv1d(output_channel, output_channel, 3, padding=1)
|
||||
)
|
||||
self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample]))
|
||||
|
||||
for _ in range(num_mid_blocks):
|
||||
input_channel = channels[-1]
|
||||
out_channels = channels[-1]
|
||||
resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) if self.causal else \
|
||||
ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
|
||||
|
||||
transformer_blocks = nn.ModuleList(
|
||||
[
|
||||
BasicTransformerBlock(
|
||||
dim=output_channel,
|
||||
num_attention_heads=num_heads,
|
||||
attention_head_dim=attention_head_dim,
|
||||
dropout=dropout,
|
||||
activation_fn=act_fn,
|
||||
)
|
||||
for _ in range(n_blocks)
|
||||
]
|
||||
)
|
||||
|
||||
self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks]))
|
||||
|
||||
channels = channels[::-1] + (channels[0],)
|
||||
for i in range(len(channels) - 1):
|
||||
input_channel = channels[i] * 2
|
||||
output_channel = channels[i + 1]
|
||||
is_last = i == len(channels) - 2
|
||||
resnet = CausalResnetBlock1D(
|
||||
dim=input_channel,
|
||||
dim_out=output_channel,
|
||||
time_emb_dim=time_embed_dim,
|
||||
) if self.causal else ResnetBlock1D(
|
||||
dim=input_channel,
|
||||
dim_out=output_channel,
|
||||
time_emb_dim=time_embed_dim,
|
||||
)
|
||||
transformer_blocks = nn.ModuleList(
|
||||
[
|
||||
BasicTransformerBlock(
|
||||
dim=output_channel,
|
||||
num_attention_heads=num_heads,
|
||||
attention_head_dim=attention_head_dim,
|
||||
dropout=dropout,
|
||||
activation_fn=act_fn,
|
||||
)
|
||||
for _ in range(n_blocks)
|
||||
]
|
||||
)
|
||||
upsample = (
|
||||
Upsample1D(output_channel, use_conv_transpose=True)
|
||||
if not is_last
|
||||
else CausalConv1d(output_channel, output_channel, 3) if self.causal else nn.Conv1d(output_channel, output_channel, 3, padding=1)
|
||||
)
|
||||
self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample]))
|
||||
self.final_block = CausalBlock1D(channels[-1], channels[-1]) if self.causal else Block1D(channels[-1], channels[-1])
|
||||
self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1)
|
||||
self.initialize_weights()
|
||||
|
||||
def initialize_weights(self):
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.Conv1d):
|
||||
nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
|
||||
if m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.GroupNorm):
|
||||
nn.init.constant_(m.weight, 1)
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.Linear):
|
||||
nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
|
||||
if m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
|
||||
def forward(self, x, mask, mu, t, spks=None, cond=None):
|
||||
"""Forward pass of the UNet1DConditional model.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): shape (batch_size, in_channels, time)
|
||||
mask (_type_): shape (batch_size, 1, time)
|
||||
t (_type_): shape (batch_size)
|
||||
spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None.
|
||||
cond (_type_, optional): placeholder for future use. Defaults to None.
|
||||
|
||||
Raises:
|
||||
ValueError: _description_
|
||||
ValueError: _description_
|
||||
|
||||
Returns:
|
||||
_type_: _description_
|
||||
"""
|
||||
|
||||
t = self.time_embeddings(t).to(t.dtype)
|
||||
t = self.time_mlp(t)
|
||||
|
||||
x = pack([x, mu], "b * t")[0]
|
||||
|
||||
if spks is not None:
|
||||
spks = repeat(spks, "b c -> b c t", t=x.shape[-1])
|
||||
x = pack([x, spks], "b * t")[0]
|
||||
if cond is not None:
|
||||
x = pack([x, cond], "b * t")[0]
|
||||
|
||||
hiddens = []
|
||||
masks = [mask]
|
||||
for resnet, transformer_blocks, downsample in self.down_blocks:
|
||||
mask_down = masks[-1]
|
||||
x = resnet(x, mask_down, t)
|
||||
x = rearrange(x, "b c t -> b t c").contiguous()
|
||||
# attn_mask = torch.matmul(mask_down.transpose(1, 2).contiguous(), mask_down)
|
||||
attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, self.static_chunk_size, -1)
|
||||
attn_mask = mask_to_bias(attn_mask == 1, x.dtype)
|
||||
for transformer_block in transformer_blocks:
|
||||
x = transformer_block(
|
||||
hidden_states=x,
|
||||
attention_mask=attn_mask,
|
||||
timestep=t,
|
||||
)
|
||||
x = rearrange(x, "b t c -> b c t").contiguous()
|
||||
hiddens.append(x) # Save hidden states for skip connections
|
||||
x = downsample(x * mask_down)
|
||||
masks.append(mask_down[:, :, ::2])
|
||||
masks = masks[:-1]
|
||||
mask_mid = masks[-1]
|
||||
|
||||
for resnet, transformer_blocks in self.mid_blocks:
|
||||
x = resnet(x, mask_mid, t)
|
||||
x = rearrange(x, "b c t -> b t c").contiguous()
|
||||
# attn_mask = torch.matmul(mask_mid.transpose(1, 2).contiguous(), mask_mid)
|
||||
attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, self.static_chunk_size, -1)
|
||||
attn_mask = mask_to_bias(attn_mask == 1, x.dtype)
|
||||
for transformer_block in transformer_blocks:
|
||||
x = transformer_block(
|
||||
hidden_states=x,
|
||||
attention_mask=attn_mask,
|
||||
timestep=t,
|
||||
)
|
||||
x = rearrange(x, "b t c -> b c t").contiguous()
|
||||
|
||||
for resnet, transformer_blocks, upsample in self.up_blocks:
|
||||
mask_up = masks.pop()
|
||||
skip = hiddens.pop()
|
||||
x = pack([x[:, :, :skip.shape[-1]], skip], "b * t")[0]
|
||||
x = resnet(x, mask_up, t)
|
||||
x = rearrange(x, "b c t -> b t c").contiguous()
|
||||
# attn_mask = torch.matmul(mask_up.transpose(1, 2).contiguous(), mask_up)
|
||||
attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, self.static_chunk_size, -1)
|
||||
attn_mask = mask_to_bias(attn_mask == 1, x.dtype)
|
||||
for transformer_block in transformer_blocks:
|
||||
x = transformer_block(
|
||||
hidden_states=x,
|
||||
attention_mask=attn_mask,
|
||||
timestep=t,
|
||||
)
|
||||
x = rearrange(x, "b t c -> b c t").contiguous()
|
||||
x = upsample(x * mask_up)
|
||||
x = self.final_block(x, mask_up)
|
||||
output = self.final_proj(x * mask_up)
|
||||
return output * mask
|
||||
@@ -1,55 +0,0 @@
|
||||
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Kai Hu)
|
||||
#
|
||||
# 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.
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.nn.utils.parametrizations import weight_norm
|
||||
|
||||
|
||||
class ConvRNNF0Predictor(nn.Module):
|
||||
def __init__(self,
|
||||
num_class: int = 1,
|
||||
in_channels: int = 80,
|
||||
cond_channels: int = 512
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.num_class = num_class
|
||||
self.condnet = nn.Sequential(
|
||||
weight_norm(
|
||||
nn.Conv1d(in_channels, cond_channels, kernel_size=3, padding=1)
|
||||
),
|
||||
nn.ELU(),
|
||||
weight_norm(
|
||||
nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
|
||||
),
|
||||
nn.ELU(),
|
||||
weight_norm(
|
||||
nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
|
||||
),
|
||||
nn.ELU(),
|
||||
weight_norm(
|
||||
nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
|
||||
),
|
||||
nn.ELU(),
|
||||
weight_norm(
|
||||
nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
|
||||
),
|
||||
nn.ELU(),
|
||||
)
|
||||
self.classifier = nn.Linear(in_features=cond_channels, out_features=self.num_class)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = self.condnet(x)
|
||||
x = x.transpose(1, 2)
|
||||
return torch.abs(self.classifier(x).squeeze(-1))
|
||||
@@ -1,242 +0,0 @@
|
||||
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
|
||||
#
|
||||
# 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.
|
||||
import logging
|
||||
import random
|
||||
from typing import Dict, Optional
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.nn import functional as F
|
||||
from omegaconf import DictConfig
|
||||
from .utils.mask import make_pad_mask
|
||||
|
||||
|
||||
class MaskedDiffWithXvec(torch.nn.Module):
|
||||
def __init__(self,
|
||||
input_size: int = 512,
|
||||
output_size: int = 80,
|
||||
spk_embed_dim: int = 192,
|
||||
output_type: str = "mel",
|
||||
vocab_size: int = 4096,
|
||||
input_frame_rate: int = 50,
|
||||
only_mask_loss: bool = True,
|
||||
encoder: torch.nn.Module = None,
|
||||
length_regulator: torch.nn.Module = None,
|
||||
decoder: torch.nn.Module = None,
|
||||
decoder_conf: Dict = {'in_channels': 240, 'out_channel': 80, 'spk_emb_dim': 80, 'n_spks': 1,
|
||||
'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine',
|
||||
'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}),
|
||||
'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64,
|
||||
'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}},
|
||||
mel_feat_conf: Dict = {'n_fft': 1024, 'num_mels': 80, 'sampling_rate': 22050,
|
||||
'hop_size': 256, 'win_size': 1024, 'fmin': 0, 'fmax': 8000}):
|
||||
super().__init__()
|
||||
self.input_size = input_size
|
||||
self.output_size = output_size
|
||||
self.decoder_conf = decoder_conf
|
||||
self.mel_feat_conf = mel_feat_conf
|
||||
self.vocab_size = vocab_size
|
||||
self.output_type = output_type
|
||||
self.input_frame_rate = input_frame_rate
|
||||
logging.info(f"input frame rate={self.input_frame_rate}")
|
||||
self.input_embedding = nn.Embedding(vocab_size, input_size)
|
||||
self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size)
|
||||
self.encoder = encoder
|
||||
self.encoder_proj = torch.nn.Linear(self.encoder.output_size(), output_size)
|
||||
self.decoder = decoder
|
||||
self.length_regulator = length_regulator
|
||||
self.only_mask_loss = only_mask_loss
|
||||
|
||||
def forward(
|
||||
self,
|
||||
batch: dict,
|
||||
device: torch.device,
|
||||
) -> Dict[str, Optional[torch.Tensor]]:
|
||||
token = batch['speech_token'].to(device)
|
||||
token_len = batch['speech_token_len'].to(device)
|
||||
feat = batch['speech_feat'].to(device)
|
||||
feat_len = batch['speech_feat_len'].to(device)
|
||||
embedding = batch['embedding'].to(device)
|
||||
|
||||
# xvec projection
|
||||
embedding = F.normalize(embedding, dim=1)
|
||||
embedding = self.spk_embed_affine_layer(embedding)
|
||||
|
||||
# concat text and prompt_text
|
||||
mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device)
|
||||
token = self.input_embedding(torch.clamp(token, min=0)) * mask
|
||||
|
||||
# text encode
|
||||
h, h_lengths = self.encoder(token, token_len)
|
||||
h = self.encoder_proj(h)
|
||||
h, h_lengths = self.length_regulator(h, feat_len)
|
||||
|
||||
# get conditions
|
||||
conds = torch.zeros(feat.shape, device=token.device)
|
||||
for i, j in enumerate(feat_len):
|
||||
if random.random() < 0.5:
|
||||
continue
|
||||
index = random.randint(0, int(0.3 * j))
|
||||
conds[i, :index] = feat[i, :index]
|
||||
conds = conds.transpose(1, 2)
|
||||
|
||||
mask = (~make_pad_mask(feat_len)).to(h)
|
||||
feat = F.interpolate(feat.unsqueeze(dim=1), size=h.shape[1:], mode="nearest").squeeze(dim=1)
|
||||
loss, _ = self.decoder.compute_loss(
|
||||
feat.transpose(1, 2).contiguous(),
|
||||
mask.unsqueeze(1),
|
||||
h.transpose(1, 2).contiguous(),
|
||||
embedding,
|
||||
cond=conds
|
||||
)
|
||||
return {'loss': loss}
|
||||
|
||||
@torch.inference_mode()
|
||||
def inference(self,
|
||||
token,
|
||||
token_len,
|
||||
prompt_token,
|
||||
prompt_token_len,
|
||||
prompt_feat,
|
||||
prompt_feat_len,
|
||||
embedding,
|
||||
flow_cache):
|
||||
if self.fp16 is True:
|
||||
prompt_feat = prompt_feat.half()
|
||||
embedding = embedding.half()
|
||||
|
||||
assert token.shape[0] == 1
|
||||
# xvec projection
|
||||
embedding = F.normalize(embedding, dim=1)
|
||||
embedding = self.spk_embed_affine_layer(embedding)
|
||||
|
||||
# concat text and prompt_text
|
||||
token_len1, token_len2 = prompt_token.shape[1], token.shape[1]
|
||||
token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len
|
||||
mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding)
|
||||
token = self.input_embedding(torch.clamp(token, min=0)) * mask
|
||||
|
||||
# text encode
|
||||
h, h_lengths = self.encoder(token, token_len)
|
||||
h = self.encoder_proj(h)
|
||||
mel_len1, mel_len2 = prompt_feat.shape[1], int(token_len2 / self.input_frame_rate * 22050 / 256)
|
||||
h, h_lengths = self.length_regulator.inference(h[:, :token_len1], h[:, token_len1:], mel_len1, mel_len2, self.input_frame_rate)
|
||||
|
||||
# get conditions
|
||||
conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype)
|
||||
conds[:, :mel_len1] = prompt_feat
|
||||
conds = conds.transpose(1, 2)
|
||||
|
||||
mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h)
|
||||
feat, flow_cache = self.decoder(
|
||||
mu=h.transpose(1, 2).contiguous(),
|
||||
mask=mask.unsqueeze(1),
|
||||
spks=embedding,
|
||||
cond=conds,
|
||||
n_timesteps=10,
|
||||
prompt_len=mel_len1,
|
||||
flow_cache=flow_cache
|
||||
)
|
||||
feat = feat[:, :, mel_len1:]
|
||||
assert feat.shape[2] == mel_len2
|
||||
return feat.float(), flow_cache
|
||||
|
||||
|
||||
class CausalMaskedDiffWithXvec(torch.nn.Module):
|
||||
def __init__(self,
|
||||
input_size: int = 512,
|
||||
output_size: int = 80,
|
||||
spk_embed_dim: int = 192,
|
||||
output_type: str = "mel",
|
||||
vocab_size: int = 6561,
|
||||
input_frame_rate: int = 25,
|
||||
only_mask_loss: bool = True,
|
||||
token_mel_ratio: int = 2,
|
||||
pre_lookahead_len: int = 3,
|
||||
encoder: torch.nn.Module = None,
|
||||
decoder: torch.nn.Module = None,
|
||||
decoder_conf: Dict = {'in_channels': 240, 'out_channel': 80, 'spk_emb_dim': 80, 'n_spks': 1,
|
||||
'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine',
|
||||
'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}),
|
||||
'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64,
|
||||
'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}},
|
||||
mel_feat_conf: Dict = {'n_fft': 1024, 'num_mels': 80, 'sampling_rate': 22050,
|
||||
'hop_size': 256, 'win_size': 1024, 'fmin': 0, 'fmax': 8000}):
|
||||
super().__init__()
|
||||
self.input_size = input_size
|
||||
self.output_size = output_size
|
||||
self.decoder_conf = decoder_conf
|
||||
self.mel_feat_conf = mel_feat_conf
|
||||
self.vocab_size = vocab_size
|
||||
self.output_type = output_type
|
||||
self.input_frame_rate = input_frame_rate
|
||||
logging.info(f"input frame rate={self.input_frame_rate}")
|
||||
self.input_embedding = nn.Embedding(vocab_size, input_size)
|
||||
self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size)
|
||||
self.encoder = encoder
|
||||
self.encoder_proj = torch.nn.Linear(self.encoder.output_size(), output_size)
|
||||
self.decoder = decoder
|
||||
self.only_mask_loss = only_mask_loss
|
||||
self.token_mel_ratio = token_mel_ratio
|
||||
self.pre_lookahead_len = pre_lookahead_len
|
||||
|
||||
# FIXME: this was missing - just putting it in as false
|
||||
self.fp16 = False
|
||||
|
||||
@torch.inference_mode()
|
||||
def inference(self,
|
||||
token,
|
||||
token_len,
|
||||
prompt_token,
|
||||
prompt_token_len,
|
||||
prompt_feat,
|
||||
prompt_feat_len,
|
||||
embedding,
|
||||
finalize):
|
||||
if self.fp16 is True:
|
||||
prompt_feat = prompt_feat.half()
|
||||
embedding = embedding.half()
|
||||
|
||||
assert token.shape[0] == 1
|
||||
# xvec projection
|
||||
embedding = F.normalize(embedding, dim=1)
|
||||
embedding = self.spk_embed_affine_layer(embedding)
|
||||
|
||||
# concat text and prompt_text
|
||||
token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len
|
||||
mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding)
|
||||
token = self.input_embedding(torch.clamp(token, min=0)) * mask
|
||||
|
||||
# text encode
|
||||
h, h_lengths = self.encoder(token, token_len)
|
||||
if finalize is False:
|
||||
h = h[:, :-self.pre_lookahead_len * self.token_mel_ratio]
|
||||
mel_len1, mel_len2 = prompt_feat.shape[1], h.shape[1] - prompt_feat.shape[1]
|
||||
h = self.encoder_proj(h)
|
||||
|
||||
# get conditions
|
||||
conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype)
|
||||
conds[:, :mel_len1] = prompt_feat
|
||||
conds = conds.transpose(1, 2)
|
||||
|
||||
mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h)
|
||||
feat, _ = self.decoder(
|
||||
mu=h.transpose(1, 2).contiguous(),
|
||||
mask=mask.unsqueeze(1),
|
||||
spks=embedding,
|
||||
cond=conds,
|
||||
n_timesteps=10
|
||||
)
|
||||
feat = feat[:, :, mel_len1:]
|
||||
assert feat.shape[2] == mel_len2
|
||||
return feat.float(), None # NOTE jrm: why are they returning None here?
|
||||
@@ -1,228 +0,0 @@
|
||||
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
|
||||
#
|
||||
# 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.
|
||||
import threading
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from .matcha.flow_matching import BASECFM
|
||||
from omegaconf import OmegaConf
|
||||
|
||||
|
||||
CFM_PARAMS = OmegaConf.create({
|
||||
"sigma_min": 1e-06,
|
||||
"solver": "euler",
|
||||
"t_scheduler": "cosine",
|
||||
"training_cfg_rate": 0.2,
|
||||
"inference_cfg_rate": 0.7,
|
||||
"reg_loss_type": "l1"
|
||||
})
|
||||
|
||||
|
||||
class ConditionalCFM(BASECFM):
|
||||
def __init__(self, in_channels, cfm_params, n_spks=1, spk_emb_dim=64, estimator: torch.nn.Module = None):
|
||||
super().__init__(
|
||||
n_feats=in_channels,
|
||||
cfm_params=cfm_params,
|
||||
n_spks=n_spks,
|
||||
spk_emb_dim=spk_emb_dim,
|
||||
)
|
||||
self.t_scheduler = cfm_params.t_scheduler
|
||||
self.training_cfg_rate = cfm_params.training_cfg_rate
|
||||
self.inference_cfg_rate = cfm_params.inference_cfg_rate
|
||||
in_channels = in_channels + (spk_emb_dim if n_spks > 0 else 0)
|
||||
# Just change the architecture of the estimator here
|
||||
self.estimator = estimator
|
||||
self.lock = threading.Lock()
|
||||
|
||||
@torch.inference_mode()
|
||||
def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, prompt_len=0, flow_cache=torch.zeros(1, 80, 0, 2)):
|
||||
"""Forward diffusion
|
||||
|
||||
Args:
|
||||
mu (torch.Tensor): output of encoder
|
||||
shape: (batch_size, n_feats, mel_timesteps)
|
||||
mask (torch.Tensor): output_mask
|
||||
shape: (batch_size, 1, mel_timesteps)
|
||||
n_timesteps (int): number of diffusion steps
|
||||
temperature (float, optional): temperature for scaling noise. Defaults to 1.0.
|
||||
spks (torch.Tensor, optional): speaker ids. Defaults to None.
|
||||
shape: (batch_size, spk_emb_dim)
|
||||
cond: Not used but kept for future purposes
|
||||
|
||||
Returns:
|
||||
sample: generated mel-spectrogram
|
||||
shape: (batch_size, n_feats, mel_timesteps)
|
||||
"""
|
||||
|
||||
z = torch.randn_like(mu).to(mu.device).to(mu.dtype) * temperature
|
||||
cache_size = flow_cache.shape[2]
|
||||
# fix prompt and overlap part mu and z
|
||||
if cache_size != 0:
|
||||
z[:, :, :cache_size] = flow_cache[:, :, :, 0]
|
||||
mu[:, :, :cache_size] = flow_cache[:, :, :, 1]
|
||||
z_cache = torch.concat([z[:, :, :prompt_len], z[:, :, -34:]], dim=2)
|
||||
mu_cache = torch.concat([mu[:, :, :prompt_len], mu[:, :, -34:]], dim=2)
|
||||
flow_cache = torch.stack([z_cache, mu_cache], dim=-1)
|
||||
|
||||
t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype)
|
||||
if self.t_scheduler == 'cosine':
|
||||
t_span = 1 - torch.cos(t_span * 0.5 * torch.pi)
|
||||
return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond), flow_cache
|
||||
|
||||
def solve_euler(self, x, t_span, mu, mask, spks, cond):
|
||||
"""
|
||||
Fixed euler solver for ODEs.
|
||||
Args:
|
||||
x (torch.Tensor): random noise
|
||||
t_span (torch.Tensor): n_timesteps interpolated
|
||||
shape: (n_timesteps + 1,)
|
||||
mu (torch.Tensor): output of encoder
|
||||
shape: (batch_size, n_feats, mel_timesteps)
|
||||
mask (torch.Tensor): output_mask
|
||||
shape: (batch_size, 1, mel_timesteps)
|
||||
spks (torch.Tensor, optional): speaker ids. Defaults to None.
|
||||
shape: (batch_size, spk_emb_dim)
|
||||
cond: Not used but kept for future purposes
|
||||
"""
|
||||
t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0]
|
||||
t = t.unsqueeze(dim=0)
|
||||
|
||||
# I am storing this because I can later plot it by putting a debugger here and saving it to a file
|
||||
# Or in future might add like a return_all_steps flag
|
||||
sol = []
|
||||
|
||||
# Do not use concat, it may cause memory format changed and trt infer with wrong results!
|
||||
x_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=x.dtype)
|
||||
mask_in = torch.zeros([2, 1, x.size(2)], device=x.device, dtype=x.dtype)
|
||||
mu_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=x.dtype)
|
||||
t_in = torch.zeros([2], device=x.device, dtype=x.dtype)
|
||||
spks_in = torch.zeros([2, 80], device=x.device, dtype=x.dtype)
|
||||
cond_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=x.dtype)
|
||||
for step in range(1, len(t_span)):
|
||||
# Classifier-Free Guidance inference introduced in VoiceBox
|
||||
x_in[:] = x
|
||||
mask_in[:] = mask
|
||||
mu_in[0] = mu
|
||||
t_in[:] = t.unsqueeze(0)
|
||||
spks_in[0] = spks
|
||||
cond_in[0] = cond
|
||||
dphi_dt = self.forward_estimator(
|
||||
x_in, mask_in,
|
||||
mu_in, t_in,
|
||||
spks_in,
|
||||
cond_in
|
||||
)
|
||||
dphi_dt, cfg_dphi_dt = torch.split(dphi_dt, [x.size(0), x.size(0)], dim=0)
|
||||
dphi_dt = ((1.0 + self.inference_cfg_rate) * dphi_dt - self.inference_cfg_rate * cfg_dphi_dt)
|
||||
x = x + dt * dphi_dt
|
||||
t = t + dt
|
||||
sol.append(x)
|
||||
if step < len(t_span) - 1:
|
||||
dt = t_span[step + 1] - t
|
||||
|
||||
return sol[-1].float()
|
||||
|
||||
def forward_estimator(self, x, mask, mu, t, spks, cond):
|
||||
if isinstance(self.estimator, torch.nn.Module):
|
||||
return self.estimator.forward(x, mask, mu, t, spks, cond)
|
||||
else:
|
||||
with self.lock:
|
||||
self.estimator.set_input_shape('x', (2, 80, x.size(2)))
|
||||
self.estimator.set_input_shape('mask', (2, 1, x.size(2)))
|
||||
self.estimator.set_input_shape('mu', (2, 80, x.size(2)))
|
||||
self.estimator.set_input_shape('t', (2,))
|
||||
self.estimator.set_input_shape('spks', (2, 80))
|
||||
self.estimator.set_input_shape('cond', (2, 80, x.size(2)))
|
||||
# run trt engine
|
||||
self.estimator.execute_v2([x.contiguous().data_ptr(),
|
||||
mask.contiguous().data_ptr(),
|
||||
mu.contiguous().data_ptr(),
|
||||
t.contiguous().data_ptr(),
|
||||
spks.contiguous().data_ptr(),
|
||||
cond.contiguous().data_ptr(),
|
||||
x.data_ptr()])
|
||||
return x
|
||||
|
||||
def compute_loss(self, x1, mask, mu, spks=None, cond=None):
|
||||
"""Computes diffusion loss
|
||||
|
||||
Args:
|
||||
x1 (torch.Tensor): Target
|
||||
shape: (batch_size, n_feats, mel_timesteps)
|
||||
mask (torch.Tensor): target mask
|
||||
shape: (batch_size, 1, mel_timesteps)
|
||||
mu (torch.Tensor): output of encoder
|
||||
shape: (batch_size, n_feats, mel_timesteps)
|
||||
spks (torch.Tensor, optional): speaker embedding. Defaults to None.
|
||||
shape: (batch_size, spk_emb_dim)
|
||||
|
||||
Returns:
|
||||
loss: conditional flow matching loss
|
||||
y: conditional flow
|
||||
shape: (batch_size, n_feats, mel_timesteps)
|
||||
"""
|
||||
b, _, t = mu.shape
|
||||
|
||||
# random timestep
|
||||
t = torch.rand([b, 1, 1], device=mu.device, dtype=mu.dtype)
|
||||
if self.t_scheduler == 'cosine':
|
||||
t = 1 - torch.cos(t * 0.5 * torch.pi)
|
||||
# sample noise p(x_0)
|
||||
z = torch.randn_like(x1)
|
||||
|
||||
y = (1 - (1 - self.sigma_min) * t) * z + t * x1
|
||||
u = x1 - (1 - self.sigma_min) * z
|
||||
|
||||
# during training, we randomly drop condition to trade off mode coverage and sample fidelity
|
||||
if self.training_cfg_rate > 0:
|
||||
cfg_mask = torch.rand(b, device=x1.device) > self.training_cfg_rate
|
||||
mu = mu * cfg_mask.view(-1, 1, 1)
|
||||
spks = spks * cfg_mask.view(-1, 1)
|
||||
cond = cond * cfg_mask.view(-1, 1, 1)
|
||||
|
||||
pred = self.estimator(y, mask, mu, t.squeeze(), spks, cond)
|
||||
loss = F.mse_loss(pred * mask, u * mask, reduction="sum") / (torch.sum(mask) * u.shape[1])
|
||||
return loss, y
|
||||
|
||||
|
||||
class CausalConditionalCFM(ConditionalCFM):
|
||||
def __init__(self, in_channels=240, cfm_params=CFM_PARAMS, n_spks=1, spk_emb_dim=80, estimator=None):
|
||||
super().__init__(in_channels, cfm_params, n_spks, spk_emb_dim, estimator)
|
||||
self.rand_noise = torch.randn([1, 80, 50 * 300])
|
||||
|
||||
@torch.inference_mode()
|
||||
def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None):
|
||||
"""Forward diffusion
|
||||
|
||||
Args:
|
||||
mu (torch.Tensor): output of encoder
|
||||
shape: (batch_size, n_feats, mel_timesteps)
|
||||
mask (torch.Tensor): output_mask
|
||||
shape: (batch_size, 1, mel_timesteps)
|
||||
n_timesteps (int): number of diffusion steps
|
||||
temperature (float, optional): temperature for scaling noise. Defaults to 1.0.
|
||||
spks (torch.Tensor, optional): speaker ids. Defaults to None.
|
||||
shape: (batch_size, spk_emb_dim)
|
||||
cond: Not used but kept for future purposes
|
||||
|
||||
Returns:
|
||||
sample: generated mel-spectrogram
|
||||
shape: (batch_size, n_feats, mel_timesteps)
|
||||
"""
|
||||
|
||||
z = self.rand_noise[:, :, :mu.size(2)].to(mu.device).to(mu.dtype) * temperature
|
||||
# fix prompt and overlap part mu and z
|
||||
t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype)
|
||||
if self.t_scheduler == 'cosine':
|
||||
t_span = 1 - torch.cos(t_span * 0.5 * torch.pi)
|
||||
return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond), None
|
||||
@@ -1,474 +0,0 @@
|
||||
# jrm: adapted from CosyVoice/cosyvoice/hifigan/generator.py
|
||||
# most modules should be reusable, but I found their SineGen changed a git.
|
||||
|
||||
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Kai Hu)
|
||||
#
|
||||
# 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.
|
||||
|
||||
"""HIFI-GAN"""
|
||||
|
||||
from typing import Dict, Optional, List
|
||||
import numpy as np
|
||||
from scipy.signal import get_window
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch.nn import Conv1d
|
||||
from torch.nn import ConvTranspose1d
|
||||
from torch.nn.utils import remove_weight_norm
|
||||
from torch.nn.utils.parametrizations import weight_norm
|
||||
from torch.distributions.uniform import Uniform
|
||||
from torch import nn, sin, pow
|
||||
from torch.nn import Parameter
|
||||
|
||||
|
||||
class Snake(nn.Module):
|
||||
'''
|
||||
Implementation of a sine-based periodic activation function
|
||||
Shape:
|
||||
- Input: (B, C, T)
|
||||
- Output: (B, C, T), same shape as the input
|
||||
Parameters:
|
||||
- alpha - trainable parameter
|
||||
References:
|
||||
- This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
|
||||
https://arxiv.org/abs/2006.08195
|
||||
Examples:
|
||||
>>> a1 = snake(256)
|
||||
>>> x = torch.randn(256)
|
||||
>>> x = a1(x)
|
||||
'''
|
||||
def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
|
||||
'''
|
||||
Initialization.
|
||||
INPUT:
|
||||
- in_features: shape of the input
|
||||
- alpha: trainable parameter
|
||||
alpha is initialized to 1 by default, higher values = higher-frequency.
|
||||
alpha will be trained along with the rest of your model.
|
||||
'''
|
||||
super(Snake, self).__init__()
|
||||
self.in_features = in_features
|
||||
|
||||
# initialize alpha
|
||||
self.alpha_logscale = alpha_logscale
|
||||
if self.alpha_logscale: # log scale alphas initialized to zeros
|
||||
self.alpha = Parameter(torch.zeros(in_features) * alpha)
|
||||
else: # linear scale alphas initialized to ones
|
||||
self.alpha = Parameter(torch.ones(in_features) * alpha)
|
||||
|
||||
self.alpha.requires_grad = alpha_trainable
|
||||
|
||||
self.no_div_by_zero = 0.000000001
|
||||
|
||||
def forward(self, x):
|
||||
'''
|
||||
Forward pass of the function.
|
||||
Applies the function to the input elementwise.
|
||||
Snake ∶= x + 1/a * sin^2 (xa)
|
||||
'''
|
||||
alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
|
||||
if self.alpha_logscale:
|
||||
alpha = torch.exp(alpha)
|
||||
x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
|
||||
def get_padding(kernel_size, dilation=1):
|
||||
return int((kernel_size * dilation - dilation) / 2)
|
||||
|
||||
def init_weights(m, mean=0.0, std=0.01):
|
||||
classname = m.__class__.__name__
|
||||
if classname.find("Conv") != -1:
|
||||
m.weight.data.normal_(mean, std)
|
||||
|
||||
|
||||
"""hifigan based generator implementation.
|
||||
|
||||
This code is modified from https://github.com/jik876/hifi-gan
|
||||
,https://github.com/kan-bayashi/ParallelWaveGAN and
|
||||
https://github.com/NVIDIA/BigVGAN
|
||||
|
||||
"""
|
||||
|
||||
|
||||
class ResBlock(torch.nn.Module):
|
||||
"""Residual block module in HiFiGAN/BigVGAN."""
|
||||
def __init__(
|
||||
self,
|
||||
channels: int = 512,
|
||||
kernel_size: int = 3,
|
||||
dilations: List[int] = [1, 3, 5],
|
||||
):
|
||||
super(ResBlock, self).__init__()
|
||||
self.convs1 = nn.ModuleList()
|
||||
self.convs2 = nn.ModuleList()
|
||||
|
||||
for dilation in dilations:
|
||||
self.convs1.append(
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=dilation,
|
||||
padding=get_padding(kernel_size, dilation)
|
||||
)
|
||||
)
|
||||
)
|
||||
self.convs2.append(
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=1,
|
||||
padding=get_padding(kernel_size, 1)
|
||||
)
|
||||
)
|
||||
)
|
||||
self.convs1.apply(init_weights)
|
||||
self.convs2.apply(init_weights)
|
||||
self.activations1 = nn.ModuleList([
|
||||
Snake(channels, alpha_logscale=False)
|
||||
for _ in range(len(self.convs1))
|
||||
])
|
||||
self.activations2 = nn.ModuleList([
|
||||
Snake(channels, alpha_logscale=False)
|
||||
for _ in range(len(self.convs2))
|
||||
])
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
for idx in range(len(self.convs1)):
|
||||
xt = self.activations1[idx](x)
|
||||
xt = self.convs1[idx](xt)
|
||||
xt = self.activations2[idx](xt)
|
||||
xt = self.convs2[idx](xt)
|
||||
x = xt + x
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for idx in range(len(self.convs1)):
|
||||
remove_weight_norm(self.convs1[idx])
|
||||
remove_weight_norm(self.convs2[idx])
|
||||
|
||||
|
||||
class SineGen(torch.nn.Module):
|
||||
""" Definition of sine generator
|
||||
SineGen(samp_rate, harmonic_num = 0,
|
||||
sine_amp = 0.1, noise_std = 0.003,
|
||||
voiced_threshold = 0,
|
||||
flag_for_pulse=False)
|
||||
samp_rate: sampling rate in Hz
|
||||
harmonic_num: number of harmonic overtones (default 0)
|
||||
sine_amp: amplitude of sine-wavefrom (default 0.1)
|
||||
noise_std: std of Gaussian noise (default 0.003)
|
||||
voiced_thoreshold: F0 threshold for U/V classification (default 0)
|
||||
flag_for_pulse: this SinGen is used inside PulseGen (default False)
|
||||
Note: when flag_for_pulse is True, the first time step of a voiced
|
||||
segment is always sin(np.pi) or cos(0)
|
||||
"""
|
||||
|
||||
def __init__(self, samp_rate, harmonic_num=0,
|
||||
sine_amp=0.1, noise_std=0.003,
|
||||
voiced_threshold=0):
|
||||
super(SineGen, self).__init__()
|
||||
self.sine_amp = sine_amp
|
||||
self.noise_std = noise_std
|
||||
self.harmonic_num = harmonic_num
|
||||
self.sampling_rate = samp_rate
|
||||
self.voiced_threshold = voiced_threshold
|
||||
|
||||
def _f02uv(self, f0):
|
||||
# generate uv signal
|
||||
uv = (f0 > self.voiced_threshold).type(torch.float32)
|
||||
return uv
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(self, f0):
|
||||
"""
|
||||
:param f0: [B, 1, sample_len], Hz
|
||||
:return: [B, 1, sample_len]
|
||||
"""
|
||||
|
||||
F_mat = torch.zeros((f0.size(0), self.harmonic_num + 1, f0.size(-1))).to(f0.device)
|
||||
for i in range(self.harmonic_num + 1):
|
||||
F_mat[:, i: i + 1, :] = f0 * (i + 1) / self.sampling_rate
|
||||
|
||||
theta_mat = 2 * np.pi * (torch.cumsum(F_mat, dim=-1) % 1)
|
||||
u_dist = Uniform(low=-np.pi, high=np.pi)
|
||||
phase_vec = u_dist.sample(sample_shape=(f0.size(0), self.harmonic_num + 1, 1)).to(F_mat.device)
|
||||
phase_vec[:, 0, :] = 0
|
||||
|
||||
# generate sine waveforms
|
||||
sine_waves = self.sine_amp * torch.sin(theta_mat + phase_vec)
|
||||
|
||||
# generate uv signal
|
||||
uv = self._f02uv(f0)
|
||||
|
||||
# noise: for unvoiced should be similar to sine_amp
|
||||
# std = self.sine_amp/3 -> max value ~ self.sine_amp
|
||||
# . for voiced regions is self.noise_std
|
||||
noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
|
||||
noise = noise_amp * torch.randn_like(sine_waves)
|
||||
|
||||
# first: set the unvoiced part to 0 by uv
|
||||
# then: additive noise
|
||||
sine_waves = sine_waves * uv + noise
|
||||
return sine_waves, uv, noise
|
||||
|
||||
|
||||
class SourceModuleHnNSF(torch.nn.Module):
|
||||
""" SourceModule for hn-nsf
|
||||
SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
|
||||
add_noise_std=0.003, voiced_threshod=0)
|
||||
sampling_rate: sampling_rate in Hz
|
||||
harmonic_num: number of harmonic above F0 (default: 0)
|
||||
sine_amp: amplitude of sine source signal (default: 0.1)
|
||||
add_noise_std: std of additive Gaussian noise (default: 0.003)
|
||||
note that amplitude of noise in unvoiced is decided
|
||||
by sine_amp
|
||||
voiced_threshold: threhold to set U/V given F0 (default: 0)
|
||||
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
|
||||
F0_sampled (batchsize, length, 1)
|
||||
Sine_source (batchsize, length, 1)
|
||||
noise_source (batchsize, length 1)
|
||||
uv (batchsize, length, 1)
|
||||
"""
|
||||
|
||||
def __init__(self, sampling_rate, upsample_scale, harmonic_num=0, sine_amp=0.1,
|
||||
add_noise_std=0.003, voiced_threshod=0):
|
||||
super(SourceModuleHnNSF, self).__init__()
|
||||
|
||||
self.sine_amp = sine_amp
|
||||
self.noise_std = add_noise_std
|
||||
|
||||
# to produce sine waveforms
|
||||
self.l_sin_gen = SineGen(sampling_rate, harmonic_num,
|
||||
sine_amp, add_noise_std, voiced_threshod)
|
||||
|
||||
# to merge source harmonics into a single excitation
|
||||
self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
|
||||
self.l_tanh = torch.nn.Tanh()
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
|
||||
F0_sampled (batchsize, length, 1)
|
||||
Sine_source (batchsize, length, 1)
|
||||
noise_source (batchsize, length 1)
|
||||
"""
|
||||
# source for harmonic branch
|
||||
with torch.no_grad():
|
||||
sine_wavs, uv, _ = self.l_sin_gen(x.transpose(1, 2))
|
||||
sine_wavs = sine_wavs.transpose(1, 2)
|
||||
uv = uv.transpose(1, 2)
|
||||
sine_merge = self.l_tanh(self.l_linear(sine_wavs))
|
||||
|
||||
# source for noise branch, in the same shape as uv
|
||||
noise = torch.randn_like(uv) * self.sine_amp / 3
|
||||
return sine_merge, noise, uv
|
||||
|
||||
|
||||
class HiFTGenerator(nn.Module):
|
||||
"""
|
||||
HiFTNet Generator: Neural Source Filter + ISTFTNet
|
||||
https://arxiv.org/abs/2309.09493
|
||||
"""
|
||||
def __init__(
|
||||
self,
|
||||
in_channels: int = 80,
|
||||
base_channels: int = 512,
|
||||
nb_harmonics: int = 8,
|
||||
sampling_rate: int = 22050,
|
||||
nsf_alpha: float = 0.1,
|
||||
nsf_sigma: float = 0.003,
|
||||
nsf_voiced_threshold: float = 10,
|
||||
upsample_rates: List[int] = [8, 8],
|
||||
upsample_kernel_sizes: List[int] = [16, 16],
|
||||
istft_params: Dict[str, int] = {"n_fft": 16, "hop_len": 4},
|
||||
resblock_kernel_sizes: List[int] = [3, 7, 11],
|
||||
resblock_dilation_sizes: List[List[int]] = [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
|
||||
source_resblock_kernel_sizes: List[int] = [7, 11],
|
||||
source_resblock_dilation_sizes: List[List[int]] = [[1, 3, 5], [1, 3, 5]],
|
||||
lrelu_slope: float = 0.1,
|
||||
audio_limit: float = 0.99,
|
||||
f0_predictor: torch.nn.Module = None,
|
||||
):
|
||||
super(HiFTGenerator, self).__init__()
|
||||
|
||||
self.out_channels = 1
|
||||
self.nb_harmonics = nb_harmonics
|
||||
self.sampling_rate = sampling_rate
|
||||
self.istft_params = istft_params
|
||||
self.lrelu_slope = lrelu_slope
|
||||
self.audio_limit = audio_limit
|
||||
|
||||
self.num_kernels = len(resblock_kernel_sizes)
|
||||
self.num_upsamples = len(upsample_rates)
|
||||
self.m_source = SourceModuleHnNSF(
|
||||
sampling_rate=sampling_rate,
|
||||
upsample_scale=np.prod(upsample_rates) * istft_params["hop_len"],
|
||||
harmonic_num=nb_harmonics,
|
||||
sine_amp=nsf_alpha,
|
||||
add_noise_std=nsf_sigma,
|
||||
voiced_threshod=nsf_voiced_threshold)
|
||||
self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates) * istft_params["hop_len"])
|
||||
|
||||
self.conv_pre = weight_norm(
|
||||
Conv1d(in_channels, base_channels, 7, 1, padding=3)
|
||||
)
|
||||
|
||||
# Up
|
||||
self.ups = nn.ModuleList()
|
||||
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
||||
self.ups.append(
|
||||
weight_norm(
|
||||
ConvTranspose1d(
|
||||
base_channels // (2**i),
|
||||
base_channels // (2**(i + 1)),
|
||||
k,
|
||||
u,
|
||||
padding=(k - u) // 2,
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
# Down
|
||||
self.source_downs = nn.ModuleList()
|
||||
self.source_resblocks = nn.ModuleList()
|
||||
downsample_rates = [1] + upsample_rates[::-1][:-1]
|
||||
downsample_cum_rates = np.cumprod(downsample_rates)
|
||||
for i, (u, k, d) in enumerate(zip(downsample_cum_rates[::-1], source_resblock_kernel_sizes, source_resblock_dilation_sizes)):
|
||||
if u == 1:
|
||||
self.source_downs.append(
|
||||
Conv1d(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), 1, 1)
|
||||
)
|
||||
else:
|
||||
self.source_downs.append(
|
||||
Conv1d(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), u * 2, u, padding=(u // 2))
|
||||
)
|
||||
|
||||
self.source_resblocks.append(
|
||||
ResBlock(base_channels // (2 ** (i + 1)), k, d)
|
||||
)
|
||||
|
||||
self.resblocks = nn.ModuleList()
|
||||
for i in range(len(self.ups)):
|
||||
ch = base_channels // (2**(i + 1))
|
||||
for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
|
||||
self.resblocks.append(ResBlock(ch, k, d))
|
||||
|
||||
self.conv_post = weight_norm(Conv1d(ch, istft_params["n_fft"] + 2, 7, 1, padding=3))
|
||||
self.ups.apply(init_weights)
|
||||
self.conv_post.apply(init_weights)
|
||||
self.reflection_pad = nn.ReflectionPad1d((1, 0))
|
||||
self.stft_window = torch.from_numpy(get_window("hann", istft_params["n_fft"], fftbins=True).astype(np.float32))
|
||||
self.f0_predictor = f0_predictor
|
||||
|
||||
def remove_weight_norm(self):
|
||||
print('Removing weight norm...')
|
||||
for l in self.ups:
|
||||
remove_weight_norm(l)
|
||||
for l in self.resblocks:
|
||||
l.remove_weight_norm()
|
||||
remove_weight_norm(self.conv_pre)
|
||||
remove_weight_norm(self.conv_post)
|
||||
self.m_source.remove_weight_norm()
|
||||
for l in self.source_downs:
|
||||
remove_weight_norm(l)
|
||||
for l in self.source_resblocks:
|
||||
l.remove_weight_norm()
|
||||
|
||||
def _stft(self, x):
|
||||
spec = torch.stft(
|
||||
x,
|
||||
self.istft_params["n_fft"], self.istft_params["hop_len"], self.istft_params["n_fft"], window=self.stft_window.to(x.device),
|
||||
return_complex=True)
|
||||
spec = torch.view_as_real(spec) # [B, F, TT, 2]
|
||||
return spec[..., 0], spec[..., 1]
|
||||
|
||||
def _istft(self, magnitude, phase):
|
||||
magnitude = torch.clip(magnitude, max=1e2)
|
||||
real = magnitude * torch.cos(phase)
|
||||
img = magnitude * torch.sin(phase)
|
||||
inverse_transform = torch.istft(torch.complex(real, img), self.istft_params["n_fft"], self.istft_params["hop_len"],
|
||||
self.istft_params["n_fft"], window=self.stft_window.to(magnitude.device))
|
||||
return inverse_transform
|
||||
|
||||
def decode(self, x: torch.Tensor, s: torch.Tensor = torch.zeros(1, 1, 0)) -> torch.Tensor:
|
||||
s_stft_real, s_stft_imag = self._stft(s.squeeze(1))
|
||||
s_stft = torch.cat([s_stft_real, s_stft_imag], dim=1)
|
||||
|
||||
x = self.conv_pre(x)
|
||||
for i in range(self.num_upsamples):
|
||||
x = F.leaky_relu(x, self.lrelu_slope)
|
||||
x = self.ups[i](x)
|
||||
|
||||
if i == self.num_upsamples - 1:
|
||||
x = self.reflection_pad(x)
|
||||
|
||||
# fusion
|
||||
si = self.source_downs[i](s_stft)
|
||||
si = self.source_resblocks[i](si)
|
||||
x = x + si
|
||||
|
||||
xs = None
|
||||
for j in range(self.num_kernels):
|
||||
if xs is None:
|
||||
xs = self.resblocks[i * self.num_kernels + j](x)
|
||||
else:
|
||||
xs += self.resblocks[i * self.num_kernels + j](x)
|
||||
x = xs / self.num_kernels
|
||||
|
||||
x = F.leaky_relu(x)
|
||||
x = self.conv_post(x)
|
||||
magnitude = torch.exp(x[:, :self.istft_params["n_fft"] // 2 + 1, :])
|
||||
phase = torch.sin(x[:, self.istft_params["n_fft"] // 2 + 1:, :]) # actually, sin is redundancy
|
||||
|
||||
x = self._istft(magnitude, phase)
|
||||
x = torch.clamp(x, -self.audio_limit, self.audio_limit)
|
||||
return x
|
||||
|
||||
def forward(
|
||||
self,
|
||||
batch: dict,
|
||||
device: torch.device,
|
||||
) -> Dict[str, Optional[torch.Tensor]]:
|
||||
speech_feat = batch['speech_feat'].transpose(1, 2).to(device)
|
||||
# mel->f0
|
||||
f0 = self.f0_predictor(speech_feat)
|
||||
# f0->source
|
||||
s = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t
|
||||
s, _, _ = self.m_source(s)
|
||||
s = s.transpose(1, 2)
|
||||
# mel+source->speech
|
||||
generated_speech = self.decode(x=speech_feat, s=s)
|
||||
return generated_speech, f0
|
||||
|
||||
@torch.inference_mode()
|
||||
def inference(self, speech_feat: torch.Tensor, cache_source: torch.Tensor = torch.zeros(1, 1, 0)) -> torch.Tensor:
|
||||
# mel->f0
|
||||
f0 = self.f0_predictor(speech_feat)
|
||||
# f0->source
|
||||
s = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t
|
||||
s, _, _ = self.m_source(s)
|
||||
s = s.transpose(1, 2)
|
||||
# use cache_source to avoid glitch
|
||||
if cache_source.shape[2] != 0:
|
||||
s[:, :, :cache_source.shape[2]] = cache_source
|
||||
generated_speech = self.decode(x=speech_feat, s=s)
|
||||
return generated_speech, s
|
||||
@@ -1,443 +0,0 @@
|
||||
import math
|
||||
from typing import Optional
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from conformer import ConformerBlock
|
||||
from diffusers.models.activations import get_activation
|
||||
from einops import pack, rearrange, repeat
|
||||
|
||||
from .transformer import BasicTransformerBlock
|
||||
|
||||
|
||||
class SinusoidalPosEmb(torch.nn.Module):
|
||||
def __init__(self, dim):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
assert self.dim % 2 == 0, "SinusoidalPosEmb requires dim to be even"
|
||||
|
||||
def forward(self, x, scale=1000):
|
||||
if x.ndim < 1:
|
||||
x = x.unsqueeze(0)
|
||||
device = x.device
|
||||
half_dim = self.dim // 2
|
||||
emb = math.log(10000) / (half_dim - 1)
|
||||
emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
|
||||
emb = scale * x.unsqueeze(1) * emb.unsqueeze(0)
|
||||
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
|
||||
return emb
|
||||
|
||||
|
||||
class Block1D(torch.nn.Module):
|
||||
def __init__(self, dim, dim_out, groups=8):
|
||||
super().__init__()
|
||||
self.block = torch.nn.Sequential(
|
||||
torch.nn.Conv1d(dim, dim_out, 3, padding=1),
|
||||
torch.nn.GroupNorm(groups, dim_out),
|
||||
nn.Mish(),
|
||||
)
|
||||
|
||||
def forward(self, x, mask):
|
||||
output = self.block(x * mask)
|
||||
return output * mask
|
||||
|
||||
|
||||
class ResnetBlock1D(torch.nn.Module):
|
||||
def __init__(self, dim, dim_out, time_emb_dim, groups=8):
|
||||
super().__init__()
|
||||
self.mlp = torch.nn.Sequential(nn.Mish(), torch.nn.Linear(time_emb_dim, dim_out))
|
||||
|
||||
self.block1 = Block1D(dim, dim_out, groups=groups)
|
||||
self.block2 = Block1D(dim_out, dim_out, groups=groups)
|
||||
|
||||
self.res_conv = torch.nn.Conv1d(dim, dim_out, 1)
|
||||
|
||||
def forward(self, x, mask, time_emb):
|
||||
h = self.block1(x, mask)
|
||||
h += self.mlp(time_emb).unsqueeze(-1)
|
||||
h = self.block2(h, mask)
|
||||
output = h + self.res_conv(x * mask)
|
||||
return output
|
||||
|
||||
|
||||
class Downsample1D(nn.Module):
|
||||
def __init__(self, dim):
|
||||
super().__init__()
|
||||
self.conv = torch.nn.Conv1d(dim, dim, 3, 2, 1)
|
||||
|
||||
def forward(self, x):
|
||||
return self.conv(x)
|
||||
|
||||
|
||||
class TimestepEmbedding(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels: int,
|
||||
time_embed_dim: int,
|
||||
act_fn: str = "silu",
|
||||
out_dim: int = None,
|
||||
post_act_fn: Optional[str] = None,
|
||||
cond_proj_dim=None,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.linear_1 = nn.Linear(in_channels, time_embed_dim)
|
||||
|
||||
if cond_proj_dim is not None:
|
||||
self.cond_proj = nn.Linear(cond_proj_dim, in_channels, bias=False)
|
||||
else:
|
||||
self.cond_proj = None
|
||||
|
||||
self.act = get_activation(act_fn)
|
||||
|
||||
if out_dim is not None:
|
||||
time_embed_dim_out = out_dim
|
||||
else:
|
||||
time_embed_dim_out = time_embed_dim
|
||||
self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim_out)
|
||||
|
||||
if post_act_fn is None:
|
||||
self.post_act = None
|
||||
else:
|
||||
self.post_act = get_activation(post_act_fn)
|
||||
|
||||
def forward(self, sample, condition=None):
|
||||
if condition is not None:
|
||||
sample = sample + self.cond_proj(condition)
|
||||
sample = self.linear_1(sample)
|
||||
|
||||
if self.act is not None:
|
||||
sample = self.act(sample)
|
||||
|
||||
sample = self.linear_2(sample)
|
||||
|
||||
if self.post_act is not None:
|
||||
sample = self.post_act(sample)
|
||||
return sample
|
||||
|
||||
|
||||
class Upsample1D(nn.Module):
|
||||
"""A 1D upsampling layer with an optional convolution.
|
||||
|
||||
Parameters:
|
||||
channels (`int`):
|
||||
number of channels in the inputs and outputs.
|
||||
use_conv (`bool`, default `False`):
|
||||
option to use a convolution.
|
||||
use_conv_transpose (`bool`, default `False`):
|
||||
option to use a convolution transpose.
|
||||
out_channels (`int`, optional):
|
||||
number of output channels. Defaults to `channels`.
|
||||
"""
|
||||
|
||||
def __init__(self, channels, use_conv=False, use_conv_transpose=True, out_channels=None, name="conv"):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.out_channels = out_channels or channels
|
||||
self.use_conv = use_conv
|
||||
self.use_conv_transpose = use_conv_transpose
|
||||
self.name = name
|
||||
|
||||
self.conv = None
|
||||
if use_conv_transpose:
|
||||
self.conv = nn.ConvTranspose1d(channels, self.out_channels, 4, 2, 1)
|
||||
elif use_conv:
|
||||
self.conv = nn.Conv1d(self.channels, self.out_channels, 3, padding=1)
|
||||
|
||||
def forward(self, inputs):
|
||||
assert inputs.shape[1] == self.channels
|
||||
if self.use_conv_transpose:
|
||||
return self.conv(inputs)
|
||||
|
||||
outputs = F.interpolate(inputs, scale_factor=2.0, mode="nearest")
|
||||
|
||||
if self.use_conv:
|
||||
outputs = self.conv(outputs)
|
||||
|
||||
return outputs
|
||||
|
||||
|
||||
class ConformerWrapper(ConformerBlock):
|
||||
def __init__( # pylint: disable=useless-super-delegation
|
||||
self,
|
||||
*,
|
||||
dim,
|
||||
dim_head=64,
|
||||
heads=8,
|
||||
ff_mult=4,
|
||||
conv_expansion_factor=2,
|
||||
conv_kernel_size=31,
|
||||
attn_dropout=0,
|
||||
ff_dropout=0,
|
||||
conv_dropout=0,
|
||||
conv_causal=False,
|
||||
):
|
||||
super().__init__(
|
||||
dim=dim,
|
||||
dim_head=dim_head,
|
||||
heads=heads,
|
||||
ff_mult=ff_mult,
|
||||
conv_expansion_factor=conv_expansion_factor,
|
||||
conv_kernel_size=conv_kernel_size,
|
||||
attn_dropout=attn_dropout,
|
||||
ff_dropout=ff_dropout,
|
||||
conv_dropout=conv_dropout,
|
||||
conv_causal=conv_causal,
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
encoder_hidden_states=None,
|
||||
encoder_attention_mask=None,
|
||||
timestep=None,
|
||||
):
|
||||
return super().forward(x=hidden_states, mask=attention_mask.bool())
|
||||
|
||||
|
||||
class Decoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
channels=(256, 256),
|
||||
dropout=0.05,
|
||||
attention_head_dim=64,
|
||||
n_blocks=1,
|
||||
num_mid_blocks=2,
|
||||
num_heads=4,
|
||||
act_fn="snake",
|
||||
down_block_type="transformer",
|
||||
mid_block_type="transformer",
|
||||
up_block_type="transformer",
|
||||
):
|
||||
super().__init__()
|
||||
channels = tuple(channels)
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
|
||||
self.time_embeddings = SinusoidalPosEmb(in_channels)
|
||||
time_embed_dim = channels[0] * 4
|
||||
self.time_mlp = TimestepEmbedding(
|
||||
in_channels=in_channels,
|
||||
time_embed_dim=time_embed_dim,
|
||||
act_fn="silu",
|
||||
)
|
||||
|
||||
self.down_blocks = nn.ModuleList([])
|
||||
self.mid_blocks = nn.ModuleList([])
|
||||
self.up_blocks = nn.ModuleList([])
|
||||
|
||||
output_channel = in_channels
|
||||
for i in range(len(channels)): # pylint: disable=consider-using-enumerate
|
||||
input_channel = output_channel
|
||||
output_channel = channels[i]
|
||||
is_last = i == len(channels) - 1
|
||||
resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
|
||||
transformer_blocks = nn.ModuleList(
|
||||
[
|
||||
self.get_block(
|
||||
down_block_type,
|
||||
output_channel,
|
||||
attention_head_dim,
|
||||
num_heads,
|
||||
dropout,
|
||||
act_fn,
|
||||
)
|
||||
for _ in range(n_blocks)
|
||||
]
|
||||
)
|
||||
downsample = (
|
||||
Downsample1D(output_channel) if not is_last else nn.Conv1d(output_channel, output_channel, 3, padding=1)
|
||||
)
|
||||
|
||||
self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample]))
|
||||
|
||||
for i in range(num_mid_blocks):
|
||||
input_channel = channels[-1]
|
||||
out_channels = channels[-1]
|
||||
|
||||
resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
|
||||
|
||||
transformer_blocks = nn.ModuleList(
|
||||
[
|
||||
self.get_block(
|
||||
mid_block_type,
|
||||
output_channel,
|
||||
attention_head_dim,
|
||||
num_heads,
|
||||
dropout,
|
||||
act_fn,
|
||||
)
|
||||
for _ in range(n_blocks)
|
||||
]
|
||||
)
|
||||
|
||||
self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks]))
|
||||
|
||||
channels = channels[::-1] + (channels[0],)
|
||||
for i in range(len(channels) - 1):
|
||||
input_channel = channels[i]
|
||||
output_channel = channels[i + 1]
|
||||
is_last = i == len(channels) - 2
|
||||
|
||||
resnet = ResnetBlock1D(
|
||||
dim=2 * input_channel,
|
||||
dim_out=output_channel,
|
||||
time_emb_dim=time_embed_dim,
|
||||
)
|
||||
transformer_blocks = nn.ModuleList(
|
||||
[
|
||||
self.get_block(
|
||||
up_block_type,
|
||||
output_channel,
|
||||
attention_head_dim,
|
||||
num_heads,
|
||||
dropout,
|
||||
act_fn,
|
||||
)
|
||||
for _ in range(n_blocks)
|
||||
]
|
||||
)
|
||||
upsample = (
|
||||
Upsample1D(output_channel, use_conv_transpose=True)
|
||||
if not is_last
|
||||
else nn.Conv1d(output_channel, output_channel, 3, padding=1)
|
||||
)
|
||||
|
||||
self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample]))
|
||||
|
||||
self.final_block = Block1D(channels[-1], channels[-1])
|
||||
self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1)
|
||||
|
||||
self.initialize_weights()
|
||||
# nn.init.normal_(self.final_proj.weight)
|
||||
|
||||
@staticmethod
|
||||
def get_block(block_type, dim, attention_head_dim, num_heads, dropout, act_fn):
|
||||
if block_type == "conformer":
|
||||
block = ConformerWrapper(
|
||||
dim=dim,
|
||||
dim_head=attention_head_dim,
|
||||
heads=num_heads,
|
||||
ff_mult=1,
|
||||
conv_expansion_factor=2,
|
||||
ff_dropout=dropout,
|
||||
attn_dropout=dropout,
|
||||
conv_dropout=dropout,
|
||||
conv_kernel_size=31,
|
||||
)
|
||||
elif block_type == "transformer":
|
||||
block = BasicTransformerBlock(
|
||||
dim=dim,
|
||||
num_attention_heads=num_heads,
|
||||
attention_head_dim=attention_head_dim,
|
||||
dropout=dropout,
|
||||
activation_fn=act_fn,
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Unknown block type {block_type}")
|
||||
|
||||
return block
|
||||
|
||||
def initialize_weights(self):
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.Conv1d):
|
||||
nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
|
||||
|
||||
if m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
|
||||
elif isinstance(m, nn.GroupNorm):
|
||||
nn.init.constant_(m.weight, 1)
|
||||
nn.init.constant_(m.bias, 0)
|
||||
|
||||
elif isinstance(m, nn.Linear):
|
||||
nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
|
||||
|
||||
if m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
|
||||
def forward(self, x, mask, mu, t, spks=None, cond=None):
|
||||
"""Forward pass of the UNet1DConditional model.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): shape (batch_size, in_channels, time)
|
||||
mask (_type_): shape (batch_size, 1, time)
|
||||
t (_type_): shape (batch_size)
|
||||
spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None.
|
||||
cond (_type_, optional): placeholder for future use. Defaults to None.
|
||||
|
||||
Raises:
|
||||
ValueError: _description_
|
||||
ValueError: _description_
|
||||
|
||||
Returns:
|
||||
_type_: _description_
|
||||
"""
|
||||
|
||||
t = self.time_embeddings(t)
|
||||
t = self.time_mlp(t)
|
||||
|
||||
x = pack([x, mu], "b * t")[0]
|
||||
|
||||
if spks is not None:
|
||||
spks = repeat(spks, "b c -> b c t", t=x.shape[-1])
|
||||
x = pack([x, spks], "b * t")[0]
|
||||
|
||||
hiddens = []
|
||||
masks = [mask]
|
||||
for resnet, transformer_blocks, downsample in self.down_blocks:
|
||||
mask_down = masks[-1]
|
||||
x = resnet(x, mask_down, t)
|
||||
x = rearrange(x, "b c t -> b t c")
|
||||
mask_down = rearrange(mask_down, "b 1 t -> b t")
|
||||
for transformer_block in transformer_blocks:
|
||||
x = transformer_block(
|
||||
hidden_states=x,
|
||||
attention_mask=mask_down,
|
||||
timestep=t,
|
||||
)
|
||||
x = rearrange(x, "b t c -> b c t")
|
||||
mask_down = rearrange(mask_down, "b t -> b 1 t")
|
||||
hiddens.append(x) # Save hidden states for skip connections
|
||||
x = downsample(x * mask_down)
|
||||
masks.append(mask_down[:, :, ::2])
|
||||
|
||||
masks = masks[:-1]
|
||||
mask_mid = masks[-1]
|
||||
|
||||
for resnet, transformer_blocks in self.mid_blocks:
|
||||
x = resnet(x, mask_mid, t)
|
||||
x = rearrange(x, "b c t -> b t c")
|
||||
mask_mid = rearrange(mask_mid, "b 1 t -> b t")
|
||||
for transformer_block in transformer_blocks:
|
||||
x = transformer_block(
|
||||
hidden_states=x,
|
||||
attention_mask=mask_mid,
|
||||
timestep=t,
|
||||
)
|
||||
x = rearrange(x, "b t c -> b c t")
|
||||
mask_mid = rearrange(mask_mid, "b t -> b 1 t")
|
||||
|
||||
for resnet, transformer_blocks, upsample in self.up_blocks:
|
||||
mask_up = masks.pop()
|
||||
x = resnet(pack([x, hiddens.pop()], "b * t")[0], mask_up, t)
|
||||
x = rearrange(x, "b c t -> b t c")
|
||||
mask_up = rearrange(mask_up, "b 1 t -> b t")
|
||||
for transformer_block in transformer_blocks:
|
||||
x = transformer_block(
|
||||
hidden_states=x,
|
||||
attention_mask=mask_up,
|
||||
timestep=t,
|
||||
)
|
||||
x = rearrange(x, "b t c -> b c t")
|
||||
mask_up = rearrange(mask_up, "b t -> b 1 t")
|
||||
x = upsample(x * mask_up)
|
||||
|
||||
x = self.final_block(x, mask_up)
|
||||
output = self.final_proj(x * mask_up)
|
||||
|
||||
return output * mask
|
||||
@@ -1,129 +0,0 @@
|
||||
from abc import ABC
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
from .decoder import Decoder
|
||||
|
||||
|
||||
class BASECFM(torch.nn.Module, ABC):
|
||||
def __init__(
|
||||
self,
|
||||
n_feats,
|
||||
cfm_params,
|
||||
n_spks=1,
|
||||
spk_emb_dim=128,
|
||||
):
|
||||
super().__init__()
|
||||
self.n_feats = n_feats
|
||||
self.n_spks = n_spks
|
||||
self.spk_emb_dim = spk_emb_dim
|
||||
self.solver = cfm_params.solver
|
||||
if hasattr(cfm_params, "sigma_min"):
|
||||
self.sigma_min = cfm_params.sigma_min
|
||||
else:
|
||||
self.sigma_min = 1e-4
|
||||
|
||||
self.estimator = None
|
||||
|
||||
@torch.inference_mode()
|
||||
def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None):
|
||||
"""Forward diffusion
|
||||
|
||||
Args:
|
||||
mu (torch.Tensor): output of encoder
|
||||
shape: (batch_size, n_feats, mel_timesteps)
|
||||
mask (torch.Tensor): output_mask
|
||||
shape: (batch_size, 1, mel_timesteps)
|
||||
n_timesteps (int): number of diffusion steps
|
||||
temperature (float, optional): temperature for scaling noise. Defaults to 1.0.
|
||||
spks (torch.Tensor, optional): speaker ids. Defaults to None.
|
||||
shape: (batch_size, spk_emb_dim)
|
||||
cond: Not used but kept for future purposes
|
||||
|
||||
Returns:
|
||||
sample: generated mel-spectrogram
|
||||
shape: (batch_size, n_feats, mel_timesteps)
|
||||
"""
|
||||
z = torch.randn_like(mu) * temperature
|
||||
t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device)
|
||||
return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond)
|
||||
|
||||
def solve_euler(self, x, t_span, mu, mask, spks, cond):
|
||||
"""
|
||||
Fixed euler solver for ODEs.
|
||||
Args:
|
||||
x (torch.Tensor): random noise
|
||||
t_span (torch.Tensor): n_timesteps interpolated
|
||||
shape: (n_timesteps + 1,)
|
||||
mu (torch.Tensor): output of encoder
|
||||
shape: (batch_size, n_feats, mel_timesteps)
|
||||
mask (torch.Tensor): output_mask
|
||||
shape: (batch_size, 1, mel_timesteps)
|
||||
spks (torch.Tensor, optional): speaker ids. Defaults to None.
|
||||
shape: (batch_size, spk_emb_dim)
|
||||
cond: Not used but kept for future purposes
|
||||
"""
|
||||
t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0]
|
||||
|
||||
# I am storing this because I can later plot it by putting a debugger here and saving it to a file
|
||||
# Or in future might add like a return_all_steps flag
|
||||
sol = []
|
||||
|
||||
for step in range(1, len(t_span)):
|
||||
dphi_dt = self.estimator(x, mask, mu, t, spks, cond)
|
||||
|
||||
x = x + dt * dphi_dt
|
||||
t = t + dt
|
||||
sol.append(x)
|
||||
if step < len(t_span) - 1:
|
||||
dt = t_span[step + 1] - t
|
||||
|
||||
return sol[-1]
|
||||
|
||||
def compute_loss(self, x1, mask, mu, spks=None, cond=None):
|
||||
"""Computes diffusion loss
|
||||
|
||||
Args:
|
||||
x1 (torch.Tensor): Target
|
||||
shape: (batch_size, n_feats, mel_timesteps)
|
||||
mask (torch.Tensor): target mask
|
||||
shape: (batch_size, 1, mel_timesteps)
|
||||
mu (torch.Tensor): output of encoder
|
||||
shape: (batch_size, n_feats, mel_timesteps)
|
||||
spks (torch.Tensor, optional): speaker embedding. Defaults to None.
|
||||
shape: (batch_size, spk_emb_dim)
|
||||
|
||||
Returns:
|
||||
loss: conditional flow matching loss
|
||||
y: conditional flow
|
||||
shape: (batch_size, n_feats, mel_timesteps)
|
||||
"""
|
||||
b, _, t = mu.shape
|
||||
|
||||
# random timestep
|
||||
t = torch.rand([b, 1, 1], device=mu.device, dtype=mu.dtype)
|
||||
# sample noise p(x_0)
|
||||
z = torch.randn_like(x1)
|
||||
|
||||
y = (1 - (1 - self.sigma_min) * t) * z + t * x1
|
||||
u = x1 - (1 - self.sigma_min) * z
|
||||
|
||||
loss = F.mse_loss(self.estimator(y, mask, mu, t.squeeze(), spks), u, reduction="sum") / (
|
||||
torch.sum(mask) * u.shape[1]
|
||||
)
|
||||
return loss, y
|
||||
|
||||
|
||||
class CFM(BASECFM):
|
||||
def __init__(self, in_channels, out_channel, cfm_params, decoder_params, n_spks=1, spk_emb_dim=64):
|
||||
super().__init__(
|
||||
n_feats=in_channels,
|
||||
cfm_params=cfm_params,
|
||||
n_spks=n_spks,
|
||||
spk_emb_dim=spk_emb_dim,
|
||||
)
|
||||
|
||||
in_channels = in_channels + (spk_emb_dim if n_spks > 1 else 0)
|
||||
# Just change the architecture of the estimator here
|
||||
self.estimator = Decoder(in_channels=in_channels, out_channels=out_channel, **decoder_params)
|
||||
@@ -1,413 +0,0 @@
|
||||
""" from https://github.com/jaywalnut310/glow-tts """
|
||||
|
||||
import math
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from einops import rearrange
|
||||
|
||||
|
||||
def sequence_mask(length, max_length=None):
|
||||
if max_length is None:
|
||||
max_length = length.max()
|
||||
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
|
||||
return x.unsqueeze(0) < length.unsqueeze(1)
|
||||
|
||||
|
||||
|
||||
class LayerNorm(nn.Module):
|
||||
def __init__(self, channels, eps=1e-4):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.eps = eps
|
||||
|
||||
self.gamma = torch.nn.Parameter(torch.ones(channels))
|
||||
self.beta = torch.nn.Parameter(torch.zeros(channels))
|
||||
|
||||
def forward(self, x):
|
||||
n_dims = len(x.shape)
|
||||
mean = torch.mean(x, 1, keepdim=True)
|
||||
variance = torch.mean((x - mean) ** 2, 1, keepdim=True)
|
||||
|
||||
x = (x - mean) * torch.rsqrt(variance + self.eps)
|
||||
|
||||
shape = [1, -1] + [1] * (n_dims - 2)
|
||||
x = x * self.gamma.view(*shape) + self.beta.view(*shape)
|
||||
return x
|
||||
|
||||
|
||||
class ConvReluNorm(nn.Module):
|
||||
def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.out_channels = out_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.n_layers = n_layers
|
||||
self.p_dropout = p_dropout
|
||||
|
||||
self.conv_layers = torch.nn.ModuleList()
|
||||
self.norm_layers = torch.nn.ModuleList()
|
||||
self.conv_layers.append(torch.nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
|
||||
self.norm_layers.append(LayerNorm(hidden_channels))
|
||||
self.relu_drop = torch.nn.Sequential(torch.nn.ReLU(), torch.nn.Dropout(p_dropout))
|
||||
for _ in range(n_layers - 1):
|
||||
self.conv_layers.append(
|
||||
torch.nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2)
|
||||
)
|
||||
self.norm_layers.append(LayerNorm(hidden_channels))
|
||||
self.proj = torch.nn.Conv1d(hidden_channels, out_channels, 1)
|
||||
self.proj.weight.data.zero_()
|
||||
self.proj.bias.data.zero_()
|
||||
|
||||
def forward(self, x, x_mask):
|
||||
x_org = x
|
||||
for i in range(self.n_layers):
|
||||
x = self.conv_layers[i](x * x_mask)
|
||||
x = self.norm_layers[i](x)
|
||||
x = self.relu_drop(x)
|
||||
x = x_org + self.proj(x)
|
||||
return x * x_mask
|
||||
|
||||
|
||||
class DurationPredictor(nn.Module):
|
||||
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.p_dropout = p_dropout
|
||||
|
||||
self.drop = torch.nn.Dropout(p_dropout)
|
||||
self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
|
||||
self.norm_1 = LayerNorm(filter_channels)
|
||||
self.conv_2 = torch.nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
|
||||
self.norm_2 = LayerNorm(filter_channels)
|
||||
self.proj = torch.nn.Conv1d(filter_channels, 1, 1)
|
||||
|
||||
def forward(self, x, x_mask):
|
||||
x = self.conv_1(x * x_mask)
|
||||
x = torch.relu(x)
|
||||
x = self.norm_1(x)
|
||||
x = self.drop(x)
|
||||
x = self.conv_2(x * x_mask)
|
||||
x = torch.relu(x)
|
||||
x = self.norm_2(x)
|
||||
x = self.drop(x)
|
||||
x = self.proj(x * x_mask)
|
||||
return x * x_mask
|
||||
|
||||
|
||||
class RotaryPositionalEmbeddings(nn.Module):
|
||||
"""
|
||||
## RoPE module
|
||||
|
||||
Rotary encoding transforms pairs of features by rotating in the 2D plane.
|
||||
That is, it organizes the $d$ features as $\frac{d}{2}$ pairs.
|
||||
Each pair can be considered a coordinate in a 2D plane, and the encoding will rotate it
|
||||
by an angle depending on the position of the token.
|
||||
"""
|
||||
|
||||
def __init__(self, d: int, base: int = 10_000):
|
||||
r"""
|
||||
* `d` is the number of features $d$
|
||||
* `base` is the constant used for calculating $\Theta$
|
||||
"""
|
||||
super().__init__()
|
||||
|
||||
self.base = base
|
||||
self.d = int(d)
|
||||
self.cos_cached = None
|
||||
self.sin_cached = None
|
||||
|
||||
def _build_cache(self, x: torch.Tensor):
|
||||
r"""
|
||||
Cache $\cos$ and $\sin$ values
|
||||
"""
|
||||
# Return if cache is already built
|
||||
if self.cos_cached is not None and x.shape[0] <= self.cos_cached.shape[0]:
|
||||
return
|
||||
|
||||
# Get sequence length
|
||||
seq_len = x.shape[0]
|
||||
|
||||
# $\Theta = {\theta_i = 10000^{-\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
|
||||
theta = 1.0 / (self.base ** (torch.arange(0, self.d, 2).float() / self.d)).to(x.device)
|
||||
|
||||
# Create position indexes `[0, 1, ..., seq_len - 1]`
|
||||
seq_idx = torch.arange(seq_len, device=x.device).float().to(x.device)
|
||||
|
||||
# Calculate the product of position index and $\theta_i$
|
||||
idx_theta = torch.einsum("n,d->nd", seq_idx, theta)
|
||||
|
||||
# Concatenate so that for row $m$ we have
|
||||
# $[m \theta_0, m \theta_1, ..., m \theta_{\frac{d}{2}}, m \theta_0, m \theta_1, ..., m \theta_{\frac{d}{2}}]$
|
||||
idx_theta2 = torch.cat([idx_theta, idx_theta], dim=1)
|
||||
|
||||
# Cache them
|
||||
self.cos_cached = idx_theta2.cos()[:, None, None, :]
|
||||
self.sin_cached = idx_theta2.sin()[:, None, None, :]
|
||||
|
||||
def _neg_half(self, x: torch.Tensor):
|
||||
# $\frac{d}{2}$
|
||||
d_2 = self.d // 2
|
||||
|
||||
# Calculate $[-x^{(\frac{d}{2} + 1)}, -x^{(\frac{d}{2} + 2)}, ..., -x^{(d)}, x^{(1)}, x^{(2)}, ..., x^{(\frac{d}{2})}]$
|
||||
return torch.cat([-x[:, :, :, d_2:], x[:, :, :, :d_2]], dim=-1)
|
||||
|
||||
def forward(self, x: torch.Tensor):
|
||||
"""
|
||||
* `x` is the Tensor at the head of a key or a query with shape `[seq_len, batch_size, n_heads, d]`
|
||||
"""
|
||||
# Cache $\cos$ and $\sin$ values
|
||||
x = rearrange(x, "b h t d -> t b h d")
|
||||
|
||||
self._build_cache(x)
|
||||
|
||||
# Split the features, we can choose to apply rotary embeddings only to a partial set of features.
|
||||
x_rope, x_pass = x[..., : self.d], x[..., self.d :]
|
||||
|
||||
# Calculate
|
||||
# $[-x^{(\frac{d}{2} + 1)}, -x^{(\frac{d}{2} + 2)}, ..., -x^{(d)}, x^{(1)}, x^{(2)}, ..., x^{(\frac{d}{2})}]$
|
||||
neg_half_x = self._neg_half(x_rope)
|
||||
|
||||
x_rope = (x_rope * self.cos_cached[: x.shape[0]]) + (neg_half_x * self.sin_cached[: x.shape[0]])
|
||||
|
||||
return rearrange(torch.cat((x_rope, x_pass), dim=-1), "t b h d -> b h t d")
|
||||
|
||||
|
||||
class MultiHeadAttention(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
channels,
|
||||
out_channels,
|
||||
n_heads,
|
||||
heads_share=True,
|
||||
p_dropout=0.0,
|
||||
proximal_bias=False,
|
||||
proximal_init=False,
|
||||
):
|
||||
super().__init__()
|
||||
assert channels % n_heads == 0
|
||||
|
||||
self.channels = channels
|
||||
self.out_channels = out_channels
|
||||
self.n_heads = n_heads
|
||||
self.heads_share = heads_share
|
||||
self.proximal_bias = proximal_bias
|
||||
self.p_dropout = p_dropout
|
||||
self.attn = None
|
||||
|
||||
self.k_channels = channels // n_heads
|
||||
self.conv_q = torch.nn.Conv1d(channels, channels, 1)
|
||||
self.conv_k = torch.nn.Conv1d(channels, channels, 1)
|
||||
self.conv_v = torch.nn.Conv1d(channels, channels, 1)
|
||||
|
||||
# from https://nn.labml.ai/transformers/rope/index.html
|
||||
self.query_rotary_pe = RotaryPositionalEmbeddings(self.k_channels * 0.5)
|
||||
self.key_rotary_pe = RotaryPositionalEmbeddings(self.k_channels * 0.5)
|
||||
|
||||
self.conv_o = torch.nn.Conv1d(channels, out_channels, 1)
|
||||
self.drop = torch.nn.Dropout(p_dropout)
|
||||
|
||||
torch.nn.init.xavier_uniform_(self.conv_q.weight)
|
||||
torch.nn.init.xavier_uniform_(self.conv_k.weight)
|
||||
if proximal_init:
|
||||
self.conv_k.weight.data.copy_(self.conv_q.weight.data)
|
||||
self.conv_k.bias.data.copy_(self.conv_q.bias.data)
|
||||
torch.nn.init.xavier_uniform_(self.conv_v.weight)
|
||||
|
||||
def forward(self, x, c, attn_mask=None):
|
||||
q = self.conv_q(x)
|
||||
k = self.conv_k(c)
|
||||
v = self.conv_v(c)
|
||||
|
||||
x, self.attn = self.attention(q, k, v, mask=attn_mask)
|
||||
|
||||
x = self.conv_o(x)
|
||||
return x
|
||||
|
||||
def attention(self, query, key, value, mask=None):
|
||||
b, d, t_s, t_t = (*key.size(), query.size(2))
|
||||
query = rearrange(query, "b (h c) t-> b h t c", h=self.n_heads)
|
||||
key = rearrange(key, "b (h c) t-> b h t c", h=self.n_heads)
|
||||
value = rearrange(value, "b (h c) t-> b h t c", h=self.n_heads)
|
||||
|
||||
query = self.query_rotary_pe(query)
|
||||
key = self.key_rotary_pe(key)
|
||||
|
||||
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels)
|
||||
|
||||
if self.proximal_bias:
|
||||
assert t_s == t_t, "Proximal bias is only available for self-attention."
|
||||
scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
|
||||
if mask is not None:
|
||||
scores = scores.masked_fill(mask == 0, -1e4)
|
||||
p_attn = torch.nn.functional.softmax(scores, dim=-1)
|
||||
p_attn = self.drop(p_attn)
|
||||
output = torch.matmul(p_attn, value)
|
||||
output = output.transpose(2, 3).contiguous().view(b, d, t_t)
|
||||
return output, p_attn
|
||||
|
||||
@staticmethod
|
||||
def _attention_bias_proximal(length):
|
||||
r = torch.arange(length, dtype=torch.float32)
|
||||
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
|
||||
return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
|
||||
|
||||
|
||||
class FFN(nn.Module):
|
||||
def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0.0):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
|
||||
self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
|
||||
self.conv_2 = torch.nn.Conv1d(filter_channels, out_channels, kernel_size, padding=kernel_size // 2)
|
||||
self.drop = torch.nn.Dropout(p_dropout)
|
||||
|
||||
def forward(self, x, x_mask):
|
||||
x = self.conv_1(x * x_mask)
|
||||
x = torch.relu(x)
|
||||
x = self.drop(x)
|
||||
x = self.conv_2(x * x_mask)
|
||||
return x * x_mask
|
||||
|
||||
|
||||
class Encoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size=1,
|
||||
p_dropout=0.0,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__()
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
|
||||
self.drop = torch.nn.Dropout(p_dropout)
|
||||
self.attn_layers = torch.nn.ModuleList()
|
||||
self.norm_layers_1 = torch.nn.ModuleList()
|
||||
self.ffn_layers = torch.nn.ModuleList()
|
||||
self.norm_layers_2 = torch.nn.ModuleList()
|
||||
for _ in range(self.n_layers):
|
||||
self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
|
||||
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
||||
self.ffn_layers.append(
|
||||
FFN(
|
||||
hidden_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
kernel_size,
|
||||
p_dropout=p_dropout,
|
||||
)
|
||||
)
|
||||
self.norm_layers_2.append(LayerNorm(hidden_channels))
|
||||
|
||||
def forward(self, x, x_mask):
|
||||
attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
|
||||
for i in range(self.n_layers):
|
||||
x = x * x_mask
|
||||
y = self.attn_layers[i](x, x, attn_mask)
|
||||
y = self.drop(y)
|
||||
x = self.norm_layers_1[i](x + y)
|
||||
y = self.ffn_layers[i](x, x_mask)
|
||||
y = self.drop(y)
|
||||
x = self.norm_layers_2[i](x + y)
|
||||
x = x * x_mask
|
||||
return x
|
||||
|
||||
|
||||
class TextEncoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
encoder_type,
|
||||
encoder_params,
|
||||
duration_predictor_params,
|
||||
n_vocab,
|
||||
n_spks=1,
|
||||
spk_emb_dim=128,
|
||||
):
|
||||
super().__init__()
|
||||
self.encoder_type = encoder_type
|
||||
self.n_vocab = n_vocab
|
||||
self.n_feats = encoder_params.n_feats
|
||||
self.n_channels = encoder_params.n_channels
|
||||
self.spk_emb_dim = spk_emb_dim
|
||||
self.n_spks = n_spks
|
||||
|
||||
self.emb = torch.nn.Embedding(n_vocab, self.n_channels)
|
||||
torch.nn.init.normal_(self.emb.weight, 0.0, self.n_channels**-0.5)
|
||||
|
||||
if encoder_params.prenet:
|
||||
self.prenet = ConvReluNorm(
|
||||
self.n_channels,
|
||||
self.n_channels,
|
||||
self.n_channels,
|
||||
kernel_size=5,
|
||||
n_layers=3,
|
||||
p_dropout=0.5,
|
||||
)
|
||||
else:
|
||||
self.prenet = lambda x, x_mask: x
|
||||
|
||||
self.encoder = Encoder(
|
||||
encoder_params.n_channels + (spk_emb_dim if n_spks > 1 else 0),
|
||||
encoder_params.filter_channels,
|
||||
encoder_params.n_heads,
|
||||
encoder_params.n_layers,
|
||||
encoder_params.kernel_size,
|
||||
encoder_params.p_dropout,
|
||||
)
|
||||
|
||||
self.proj_m = torch.nn.Conv1d(self.n_channels + (spk_emb_dim if n_spks > 1 else 0), self.n_feats, 1)
|
||||
self.proj_w = DurationPredictor(
|
||||
self.n_channels + (spk_emb_dim if n_spks > 1 else 0),
|
||||
duration_predictor_params.filter_channels_dp,
|
||||
duration_predictor_params.kernel_size,
|
||||
duration_predictor_params.p_dropout,
|
||||
)
|
||||
|
||||
def forward(self, x, x_lengths, spks=None):
|
||||
"""Run forward pass to the transformer based encoder and duration predictor
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): text input
|
||||
shape: (batch_size, max_text_length)
|
||||
x_lengths (torch.Tensor): text input lengths
|
||||
shape: (batch_size,)
|
||||
spks (torch.Tensor, optional): speaker ids. Defaults to None.
|
||||
shape: (batch_size,)
|
||||
|
||||
Returns:
|
||||
mu (torch.Tensor): average output of the encoder
|
||||
shape: (batch_size, n_feats, max_text_length)
|
||||
logw (torch.Tensor): log duration predicted by the duration predictor
|
||||
shape: (batch_size, 1, max_text_length)
|
||||
x_mask (torch.Tensor): mask for the text input
|
||||
shape: (batch_size, 1, max_text_length)
|
||||
"""
|
||||
x = self.emb(x) * math.sqrt(self.n_channels)
|
||||
x = torch.transpose(x, 1, -1)
|
||||
x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
||||
|
||||
x = self.prenet(x, x_mask)
|
||||
if self.n_spks > 1:
|
||||
x = torch.cat([x, spks.unsqueeze(-1).repeat(1, 1, x.shape[-1])], dim=1)
|
||||
x = self.encoder(x, x_mask)
|
||||
mu = self.proj_m(x) * x_mask
|
||||
|
||||
x_dp = torch.detach(x)
|
||||
logw = self.proj_w(x_dp, x_mask)
|
||||
|
||||
return mu, logw, x_mask
|
||||
@@ -1,316 +0,0 @@
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from diffusers.models.attention import (
|
||||
GEGLU,
|
||||
GELU,
|
||||
AdaLayerNorm,
|
||||
AdaLayerNormZero,
|
||||
ApproximateGELU,
|
||||
)
|
||||
from diffusers.models.attention_processor import Attention
|
||||
from diffusers.models.lora import LoRACompatibleLinear
|
||||
from diffusers.utils.torch_utils import maybe_allow_in_graph
|
||||
|
||||
|
||||
class SnakeBeta(nn.Module):
|
||||
"""
|
||||
A modified Snake function which uses separate parameters for the magnitude of the periodic components
|
||||
Shape:
|
||||
- Input: (B, C, T)
|
||||
- Output: (B, C, T), same shape as the input
|
||||
Parameters:
|
||||
- alpha - trainable parameter that controls frequency
|
||||
- beta - trainable parameter that controls magnitude
|
||||
References:
|
||||
- This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
|
||||
https://arxiv.org/abs/2006.08195
|
||||
Examples:
|
||||
>>> a1 = snakebeta(256)
|
||||
>>> x = torch.randn(256)
|
||||
>>> x = a1(x)
|
||||
"""
|
||||
|
||||
def __init__(self, in_features, out_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True):
|
||||
"""
|
||||
Initialization.
|
||||
INPUT:
|
||||
- in_features: shape of the input
|
||||
- alpha - trainable parameter that controls frequency
|
||||
- beta - trainable parameter that controls magnitude
|
||||
alpha is initialized to 1 by default, higher values = higher-frequency.
|
||||
beta is initialized to 1 by default, higher values = higher-magnitude.
|
||||
alpha will be trained along with the rest of your model.
|
||||
"""
|
||||
super().__init__()
|
||||
self.in_features = out_features if isinstance(out_features, list) else [out_features]
|
||||
self.proj = LoRACompatibleLinear(in_features, out_features)
|
||||
|
||||
# initialize alpha
|
||||
self.alpha_logscale = alpha_logscale
|
||||
if self.alpha_logscale: # log scale alphas initialized to zeros
|
||||
self.alpha = nn.Parameter(torch.zeros(self.in_features) * alpha)
|
||||
self.beta = nn.Parameter(torch.zeros(self.in_features) * alpha)
|
||||
else: # linear scale alphas initialized to ones
|
||||
self.alpha = nn.Parameter(torch.ones(self.in_features) * alpha)
|
||||
self.beta = nn.Parameter(torch.ones(self.in_features) * alpha)
|
||||
|
||||
self.alpha.requires_grad = alpha_trainable
|
||||
self.beta.requires_grad = alpha_trainable
|
||||
|
||||
self.no_div_by_zero = 0.000000001
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
Forward pass of the function.
|
||||
Applies the function to the input elementwise.
|
||||
SnakeBeta ∶= x + 1/b * sin^2 (xa)
|
||||
"""
|
||||
x = self.proj(x)
|
||||
if self.alpha_logscale:
|
||||
alpha = torch.exp(self.alpha)
|
||||
beta = torch.exp(self.beta)
|
||||
else:
|
||||
alpha = self.alpha
|
||||
beta = self.beta
|
||||
|
||||
x = x + (1.0 / (beta + self.no_div_by_zero)) * torch.pow(torch.sin(x * alpha), 2)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class FeedForward(nn.Module):
|
||||
r"""
|
||||
A feed-forward layer.
|
||||
|
||||
Parameters:
|
||||
dim (`int`): The number of channels in the input.
|
||||
dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
|
||||
mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
|
||||
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
|
||||
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
|
||||
final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
dim_out: Optional[int] = None,
|
||||
mult: int = 4,
|
||||
dropout: float = 0.0,
|
||||
activation_fn: str = "geglu",
|
||||
final_dropout: bool = False,
|
||||
):
|
||||
super().__init__()
|
||||
inner_dim = int(dim * mult)
|
||||
dim_out = dim_out if dim_out is not None else dim
|
||||
|
||||
if activation_fn == "gelu":
|
||||
act_fn = GELU(dim, inner_dim)
|
||||
if activation_fn == "gelu-approximate":
|
||||
act_fn = GELU(dim, inner_dim, approximate="tanh")
|
||||
elif activation_fn == "geglu":
|
||||
act_fn = GEGLU(dim, inner_dim)
|
||||
elif activation_fn == "geglu-approximate":
|
||||
act_fn = ApproximateGELU(dim, inner_dim)
|
||||
elif activation_fn == "snakebeta":
|
||||
act_fn = SnakeBeta(dim, inner_dim)
|
||||
|
||||
self.net = nn.ModuleList([])
|
||||
# project in
|
||||
self.net.append(act_fn)
|
||||
# project dropout
|
||||
self.net.append(nn.Dropout(dropout))
|
||||
# project out
|
||||
self.net.append(LoRACompatibleLinear(inner_dim, dim_out))
|
||||
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
|
||||
if final_dropout:
|
||||
self.net.append(nn.Dropout(dropout))
|
||||
|
||||
def forward(self, hidden_states):
|
||||
for module in self.net:
|
||||
hidden_states = module(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
@maybe_allow_in_graph
|
||||
class BasicTransformerBlock(nn.Module):
|
||||
r"""
|
||||
A basic Transformer block.
|
||||
|
||||
Parameters:
|
||||
dim (`int`): The number of channels in the input and output.
|
||||
num_attention_heads (`int`): The number of heads to use for multi-head attention.
|
||||
attention_head_dim (`int`): The number of channels in each head.
|
||||
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
|
||||
cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
|
||||
only_cross_attention (`bool`, *optional*):
|
||||
Whether to use only cross-attention layers. In this case two cross attention layers are used.
|
||||
double_self_attention (`bool`, *optional*):
|
||||
Whether to use two self-attention layers. In this case no cross attention layers are used.
|
||||
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
|
||||
num_embeds_ada_norm (:
|
||||
obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
|
||||
attention_bias (:
|
||||
obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
num_attention_heads: int,
|
||||
attention_head_dim: int,
|
||||
dropout=0.0,
|
||||
cross_attention_dim: Optional[int] = None,
|
||||
activation_fn: str = "geglu",
|
||||
num_embeds_ada_norm: Optional[int] = None,
|
||||
attention_bias: bool = False,
|
||||
only_cross_attention: bool = False,
|
||||
double_self_attention: bool = False,
|
||||
upcast_attention: bool = False,
|
||||
norm_elementwise_affine: bool = True,
|
||||
norm_type: str = "layer_norm",
|
||||
final_dropout: bool = False,
|
||||
):
|
||||
super().__init__()
|
||||
self.only_cross_attention = only_cross_attention
|
||||
|
||||
self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
|
||||
self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
|
||||
|
||||
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
|
||||
raise ValueError(
|
||||
f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
|
||||
f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
|
||||
)
|
||||
|
||||
# Define 3 blocks. Each block has its own normalization layer.
|
||||
# 1. Self-Attn
|
||||
if self.use_ada_layer_norm:
|
||||
self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
|
||||
elif self.use_ada_layer_norm_zero:
|
||||
self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
|
||||
else:
|
||||
self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
|
||||
self.attn1 = Attention(
|
||||
query_dim=dim,
|
||||
heads=num_attention_heads,
|
||||
dim_head=attention_head_dim,
|
||||
dropout=dropout,
|
||||
bias=attention_bias,
|
||||
cross_attention_dim=cross_attention_dim if only_cross_attention else None,
|
||||
upcast_attention=upcast_attention,
|
||||
)
|
||||
|
||||
# 2. Cross-Attn
|
||||
if cross_attention_dim is not None or double_self_attention:
|
||||
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
|
||||
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
|
||||
# the second cross attention block.
|
||||
self.norm2 = (
|
||||
AdaLayerNorm(dim, num_embeds_ada_norm)
|
||||
if self.use_ada_layer_norm
|
||||
else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
|
||||
)
|
||||
self.attn2 = Attention(
|
||||
query_dim=dim,
|
||||
cross_attention_dim=cross_attention_dim if not double_self_attention else None,
|
||||
heads=num_attention_heads,
|
||||
dim_head=attention_head_dim,
|
||||
dropout=dropout,
|
||||
bias=attention_bias,
|
||||
upcast_attention=upcast_attention,
|
||||
# scale_qk=False, # uncomment this to not to use flash attention
|
||||
) # is self-attn if encoder_hidden_states is none
|
||||
else:
|
||||
self.norm2 = None
|
||||
self.attn2 = None
|
||||
|
||||
# 3. Feed-forward
|
||||
self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
|
||||
self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout)
|
||||
|
||||
# let chunk size default to None
|
||||
self._chunk_size = None
|
||||
self._chunk_dim = 0
|
||||
|
||||
def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int):
|
||||
# Sets chunk feed-forward
|
||||
self._chunk_size = chunk_size
|
||||
self._chunk_dim = dim
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.FloatTensor,
|
||||
attention_mask: Optional[torch.FloatTensor] = None,
|
||||
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
||||
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
||||
timestep: Optional[torch.LongTensor] = None,
|
||||
cross_attention_kwargs: Dict[str, Any] = None,
|
||||
class_labels: Optional[torch.LongTensor] = None,
|
||||
):
|
||||
# Notice that normalization is always applied before the real computation in the following blocks.
|
||||
# 1. Self-Attention
|
||||
if self.use_ada_layer_norm:
|
||||
norm_hidden_states = self.norm1(hidden_states, timestep)
|
||||
elif self.use_ada_layer_norm_zero:
|
||||
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
|
||||
hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
|
||||
)
|
||||
else:
|
||||
norm_hidden_states = self.norm1(hidden_states)
|
||||
|
||||
cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
|
||||
|
||||
attn_output = self.attn1(
|
||||
norm_hidden_states,
|
||||
encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
|
||||
attention_mask=encoder_attention_mask if self.only_cross_attention else attention_mask,
|
||||
**cross_attention_kwargs,
|
||||
)
|
||||
if self.use_ada_layer_norm_zero:
|
||||
attn_output = gate_msa.unsqueeze(1) * attn_output
|
||||
hidden_states = attn_output + hidden_states
|
||||
|
||||
# 2. Cross-Attention
|
||||
if self.attn2 is not None:
|
||||
norm_hidden_states = (
|
||||
self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
|
||||
)
|
||||
|
||||
attn_output = self.attn2(
|
||||
norm_hidden_states,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
attention_mask=encoder_attention_mask,
|
||||
**cross_attention_kwargs,
|
||||
)
|
||||
hidden_states = attn_output + hidden_states
|
||||
|
||||
# 3. Feed-forward
|
||||
norm_hidden_states = self.norm3(hidden_states)
|
||||
|
||||
if self.use_ada_layer_norm_zero:
|
||||
norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
|
||||
|
||||
if self._chunk_size is not None:
|
||||
# "feed_forward_chunk_size" can be used to save memory
|
||||
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
|
||||
raise ValueError(
|
||||
f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
|
||||
)
|
||||
|
||||
num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
|
||||
ff_output = torch.cat(
|
||||
[self.ff(hid_slice) for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)],
|
||||
dim=self._chunk_dim,
|
||||
)
|
||||
else:
|
||||
ff_output = self.ff(norm_hidden_states)
|
||||
|
||||
if self.use_ada_layer_norm_zero:
|
||||
ff_output = gate_mlp.unsqueeze(1) * ff_output
|
||||
|
||||
hidden_states = ff_output + hidden_states
|
||||
|
||||
return hidden_states
|
||||
@@ -1,305 +0,0 @@
|
||||
# Modified from CosyVoice https://github.com/FunAudioLLM/CosyVoice
|
||||
#
|
||||
# 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.
|
||||
|
||||
import logging
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torchaudio as ta
|
||||
from functools import lru_cache
|
||||
from typing import Optional
|
||||
from omegaconf import DictConfig
|
||||
|
||||
from ..s3tokenizer import S3_SR, SPEECH_VOCAB_SIZE, S3Tokenizer
|
||||
from .const import S3GEN_SR
|
||||
from .flow import CausalMaskedDiffWithXvec
|
||||
from .xvector import CAMPPlus
|
||||
from .utils.mel import mel_spectrogram
|
||||
from .f0_predictor import ConvRNNF0Predictor
|
||||
from .hifigan import HiFTGenerator
|
||||
from .transformer.upsample_encoder import UpsampleConformerEncoder
|
||||
from .flow_matching import CausalConditionalCFM
|
||||
from .decoder import ConditionalDecoder
|
||||
|
||||
|
||||
def drop_invalid_tokens(x):
|
||||
assert len(x.shape) <= 2 and x.shape[0] == 1, "only batch size of one allowed for now"
|
||||
return x[x < SPEECH_VOCAB_SIZE]
|
||||
|
||||
|
||||
# TODO: global resampler cache
|
||||
@lru_cache(100)
|
||||
def get_resampler(src_sr, dst_sr, device):
|
||||
return ta.transforms.Resample(src_sr, dst_sr).to(device)
|
||||
|
||||
|
||||
class S3Token2Mel(torch.nn.Module):
|
||||
"""
|
||||
CosyVoice2's CFM decoder maps S3 speech tokens to mel-spectrograms.
|
||||
|
||||
TODO: make these modules configurable?
|
||||
"""
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.tokenizer = S3Tokenizer("speech_tokenizer_v2_25hz")
|
||||
self.mel_extractor = mel_spectrogram # TODO: make it a torch module?
|
||||
self.speaker_encoder = CAMPPlus() # use default args
|
||||
|
||||
encoder = UpsampleConformerEncoder(
|
||||
output_size=512,
|
||||
attention_heads=8,
|
||||
linear_units=2048,
|
||||
num_blocks=6,
|
||||
dropout_rate=0.1,
|
||||
positional_dropout_rate=0.1,
|
||||
attention_dropout_rate=0.1,
|
||||
normalize_before=True,
|
||||
input_layer='linear',
|
||||
pos_enc_layer_type='rel_pos_espnet',
|
||||
selfattention_layer_type='rel_selfattn',
|
||||
input_size=512,
|
||||
use_cnn_module=False,
|
||||
macaron_style=False,
|
||||
)
|
||||
|
||||
estimator = ConditionalDecoder(
|
||||
in_channels=320,
|
||||
out_channels=80,
|
||||
causal=True,
|
||||
channels=[256],
|
||||
dropout=0.0,
|
||||
attention_head_dim=64,
|
||||
n_blocks=4,
|
||||
num_mid_blocks=12,
|
||||
num_heads=8,
|
||||
act_fn='gelu',
|
||||
)
|
||||
cfm_params = DictConfig({
|
||||
"sigma_min": 1e-06,
|
||||
"solver": 'euler',
|
||||
"t_scheduler": 'cosine',
|
||||
"training_cfg_rate": 0.2,
|
||||
"inference_cfg_rate": 0.7,
|
||||
"reg_loss_type": 'l1',
|
||||
})
|
||||
decoder = CausalConditionalCFM(
|
||||
spk_emb_dim=80,
|
||||
cfm_params=cfm_params,
|
||||
estimator=estimator,
|
||||
)
|
||||
|
||||
self.flow = CausalMaskedDiffWithXvec(
|
||||
encoder=encoder,
|
||||
decoder=decoder
|
||||
)
|
||||
|
||||
self.resamplers = {}
|
||||
|
||||
@property
|
||||
def device(self):
|
||||
params = self.tokenizer.parameters()
|
||||
return next(params).device
|
||||
|
||||
def embed_ref(
|
||||
self,
|
||||
ref_wav: torch.Tensor,
|
||||
ref_sr: int,
|
||||
device="auto",
|
||||
ref_fade_out=True,
|
||||
):
|
||||
device = self.device if device == "auto" else device
|
||||
if isinstance(ref_wav, np.ndarray):
|
||||
ref_wav = torch.from_numpy(ref_wav).float()
|
||||
|
||||
if ref_wav.device != device:
|
||||
ref_wav = ref_wav.to(device)
|
||||
|
||||
if len(ref_wav.shape) == 1:
|
||||
ref_wav = ref_wav.unsqueeze(0) # (B, L)
|
||||
|
||||
if ref_wav.size(1) > 10 * ref_sr:
|
||||
print("WARNING: cosydec received ref longer than 10s")
|
||||
|
||||
ref_wav_24 = ref_wav
|
||||
if ref_sr != S3GEN_SR:
|
||||
ref_wav_24 = get_resampler(ref_sr, S3GEN_SR, device)(ref_wav)
|
||||
|
||||
ref_mels_24 = self.mel_extractor(ref_wav_24).transpose(1, 2).to(device)
|
||||
ref_mels_24_len = None
|
||||
|
||||
# Resample to 16kHz
|
||||
ref_wav_16 = get_resampler(ref_sr, S3_SR, device)(ref_wav).to(device)
|
||||
|
||||
# Speaker embedding
|
||||
ref_x_vector = self.speaker_encoder.inference(ref_wav_16)
|
||||
|
||||
# Tokenize 16khz reference
|
||||
ref_speech_tokens, ref_speech_token_lens = self.tokenizer(ref_wav_16)
|
||||
|
||||
# Make sure mel_len = 2 * stoken_len (happens when the input is not padded to multiple of 40ms)
|
||||
if ref_mels_24.shape[1] != 2 * ref_speech_tokens.shape[1]:
|
||||
logging.warning(
|
||||
"Reference mel length is not equal to 2 * reference token length.\n"
|
||||
)
|
||||
ref_speech_tokens = ref_speech_tokens[:, :ref_mels_24.shape[1] // 2]
|
||||
ref_speech_token_lens[0] = ref_speech_tokens.shape[1]
|
||||
|
||||
return dict(
|
||||
prompt_token=ref_speech_tokens.to(device),
|
||||
prompt_token_len=ref_speech_token_lens,
|
||||
prompt_feat=ref_mels_24,
|
||||
prompt_feat_len=ref_mels_24_len,
|
||||
embedding=ref_x_vector,
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
speech_tokens: torch.LongTensor,
|
||||
# locally-computed ref embedding (mutex with ref_dict)
|
||||
ref_wav: Optional[torch.Tensor],
|
||||
ref_sr: Optional[int],
|
||||
# pre-computed ref embedding (prod API)
|
||||
ref_dict: Optional[dict] = None,
|
||||
finalize: bool = False,
|
||||
):
|
||||
"""
|
||||
Generate waveforms from S3 speech tokens and a reference waveform, which the speaker timbre is inferred from.
|
||||
|
||||
NOTE:
|
||||
- The speaker encoder accepts 16 kHz waveform.
|
||||
- S3TokenizerV2 accepts 16 kHz waveform.
|
||||
- The mel-spectrogram for the reference assumes 24 kHz input signal.
|
||||
- This function is designed for batch_size=1 only.
|
||||
|
||||
Args
|
||||
----
|
||||
- `speech_tokens`: S3 speech tokens [B=1, T]
|
||||
- `ref_wav`: reference waveform (`torch.Tensor` with shape=[B=1, T])
|
||||
- `ref_sr`: reference sample rate
|
||||
- `finalize`: whether streaming is finished or not. Note that if False, the last 3 tokens will be ignored.
|
||||
"""
|
||||
assert (ref_wav is None) ^ (ref_dict is None), f"Must provide exactly one of ref_wav or ref_dict (got {ref_wav} and {ref_dict})"
|
||||
|
||||
if ref_dict is None:
|
||||
ref_dict = self.embed_ref(ref_wav, ref_sr)
|
||||
else:
|
||||
# type/device casting (all values will be numpy if it's from a prod API call)
|
||||
for rk in list(ref_dict):
|
||||
if isinstance(ref_dict[rk], np.ndarray):
|
||||
ref_dict[rk] = torch.from_numpy(ref_dict[rk])
|
||||
if torch.is_tensor(ref_dict[rk]):
|
||||
ref_dict[rk] = ref_dict[rk].to(self.device)
|
||||
|
||||
if len(speech_tokens.shape) == 1:
|
||||
speech_tokens = speech_tokens.unsqueeze(0)
|
||||
|
||||
# assert speech_tokens.shape[0] == 1, "only batch size of one allowed for now"
|
||||
speech_token_lens = torch.LongTensor([speech_tokens.size(1)]).to(self.device)
|
||||
|
||||
output_mels, _ = self.flow.inference(
|
||||
token=speech_tokens,
|
||||
token_len=speech_token_lens,
|
||||
finalize=finalize,
|
||||
**ref_dict,
|
||||
)
|
||||
return output_mels
|
||||
|
||||
|
||||
class S3Token2Wav(S3Token2Mel):
|
||||
"""
|
||||
The decoder of CosyVoice2 is a concat of token-to-mel (CFM) and a mel-to-waveform (HiFiGAN) modules.
|
||||
|
||||
TODO: make these modules configurable?
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
f0_predictor = ConvRNNF0Predictor()
|
||||
self.mel2wav = HiFTGenerator(
|
||||
sampling_rate=S3GEN_SR,
|
||||
upsample_rates=[8, 5, 3],
|
||||
upsample_kernel_sizes=[16, 11, 7],
|
||||
source_resblock_kernel_sizes=[7, 7, 11],
|
||||
source_resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
|
||||
f0_predictor=f0_predictor,
|
||||
)
|
||||
|
||||
# silence out a few ms and fade audio in to reduce artifacts
|
||||
n_trim = S3GEN_SR // 50 # 20ms = half of a frame
|
||||
trim_fade = torch.zeros(2 * n_trim)
|
||||
trim_fade[n_trim:] = (torch.cos(torch.linspace(torch.pi, 0, n_trim)) + 1) / 2
|
||||
self.register_buffer("trim_fade", trim_fade, persistent=False) # (buffers get automatic device casting)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
speech_tokens,
|
||||
# locally-computed ref embedding (mutex with ref_dict)
|
||||
ref_wav: Optional[torch.Tensor],
|
||||
ref_sr: Optional[int],
|
||||
# pre-computed ref embedding (prod API)
|
||||
ref_dict: Optional[dict] = None,
|
||||
finalize: bool = False
|
||||
):
|
||||
output_mels = super().forward(speech_tokens, ref_wav=ref_wav, ref_sr=ref_sr, ref_dict=ref_dict, finalize=finalize)
|
||||
|
||||
# TODO jrm: ignoring the speed control (mel interpolation) and the HiFTGAN caching mechanisms for now.
|
||||
hift_cache_source = torch.zeros(1, 1, 0).to(self.device)
|
||||
|
||||
output_wavs, *_ = self.mel2wav.inference(speech_feat=output_mels, cache_source=hift_cache_source)
|
||||
|
||||
if not self.training:
|
||||
# NOTE: ad-hoc method to reduce "spillover" from the reference clip.
|
||||
output_wavs[:, :len(self.trim_fade)] *= self.trim_fade
|
||||
|
||||
return output_wavs
|
||||
|
||||
@torch.inference_mode()
|
||||
def flow_inference(
|
||||
self,
|
||||
speech_tokens,
|
||||
# locally-computed ref embedding (mutex with ref_dict)
|
||||
ref_wav: Optional[torch.Tensor] = None,
|
||||
ref_sr: Optional[int] = None,
|
||||
# pre-computed ref embedding (prod API)
|
||||
ref_dict: Optional[dict] = None,
|
||||
finalize: bool = False,
|
||||
):
|
||||
return super().forward(speech_tokens, ref_wav=ref_wav, ref_sr=ref_sr, ref_dict=ref_dict, finalize=finalize)
|
||||
|
||||
@torch.inference_mode()
|
||||
def hift_inference(self, speech_feat, cache_source: torch.Tensor = None):
|
||||
if cache_source is None:
|
||||
cache_source = torch.zeros(1, 1, 0).to(self.device)
|
||||
return self.mel2wav.inference(speech_feat=speech_feat, cache_source=cache_source)
|
||||
|
||||
@torch.inference_mode()
|
||||
def inference(
|
||||
self,
|
||||
speech_tokens,
|
||||
# locally-computed ref embedding (mutex with ref_dict)
|
||||
ref_wav: Optional[torch.Tensor] = None,
|
||||
ref_sr: Optional[int] = None,
|
||||
# pre-computed ref embedding (prod API)
|
||||
ref_dict: Optional[dict] = None,
|
||||
cache_source: torch.Tensor = None, # NOTE: this arg is for streaming, it can probably be removed here
|
||||
finalize: bool = True,
|
||||
):
|
||||
output_mels = self.flow_inference(speech_tokens, ref_wav=ref_wav, ref_sr=ref_sr, ref_dict=ref_dict, finalize=finalize)
|
||||
output_wavs, output_sources = self.hift_inference(output_mels, cache_source)
|
||||
|
||||
# NOTE: ad-hoc method to reduce "spillover" from the reference clip.
|
||||
output_wavs[:, :len(self.trim_fade)] *= self.trim_fade
|
||||
|
||||
return output_wavs, output_sources
|
||||
@@ -1,84 +0,0 @@
|
||||
# Copyright (c) 2020 Johns Hopkins University (Shinji Watanabe)
|
||||
# 2020 Northwestern Polytechnical University (Pengcheng Guo)
|
||||
# 2020 Mobvoi Inc (Binbin Zhang)
|
||||
# 2024 Alibaba Inc (Xiang Lyu)
|
||||
#
|
||||
# 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.
|
||||
"""Swish() activation function for Conformer."""
|
||||
|
||||
import torch
|
||||
from torch import nn, sin, pow
|
||||
from torch.nn import Parameter
|
||||
|
||||
|
||||
class Swish(torch.nn.Module):
|
||||
"""Construct an Swish object."""
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
"""Return Swish activation function."""
|
||||
return x * torch.sigmoid(x)
|
||||
|
||||
|
||||
# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license.
|
||||
# LICENSE is in incl_licenses directory.
|
||||
class Snake(nn.Module):
|
||||
'''
|
||||
Implementation of a sine-based periodic activation function
|
||||
Shape:
|
||||
- Input: (B, C, T)
|
||||
- Output: (B, C, T), same shape as the input
|
||||
Parameters:
|
||||
- alpha - trainable parameter
|
||||
References:
|
||||
- This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
|
||||
https://arxiv.org/abs/2006.08195
|
||||
Examples:
|
||||
>>> a1 = snake(256)
|
||||
>>> x = torch.randn(256)
|
||||
>>> x = a1(x)
|
||||
'''
|
||||
def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
|
||||
'''
|
||||
Initialization.
|
||||
INPUT:
|
||||
- in_features: shape of the input
|
||||
- alpha: trainable parameter
|
||||
alpha is initialized to 1 by default, higher values = higher-frequency.
|
||||
alpha will be trained along with the rest of your model.
|
||||
'''
|
||||
super(Snake, self).__init__()
|
||||
self.in_features = in_features
|
||||
|
||||
# initialize alpha
|
||||
self.alpha_logscale = alpha_logscale
|
||||
if self.alpha_logscale: # log scale alphas initialized to zeros
|
||||
self.alpha = Parameter(torch.zeros(in_features) * alpha)
|
||||
else: # linear scale alphas initialized to ones
|
||||
self.alpha = Parameter(torch.ones(in_features) * alpha)
|
||||
|
||||
self.alpha.requires_grad = alpha_trainable
|
||||
|
||||
self.no_div_by_zero = 0.000000001
|
||||
|
||||
def forward(self, x):
|
||||
'''
|
||||
Forward pass of the function.
|
||||
Applies the function to the input elementwise.
|
||||
Snake ∶= x + 1/a * sin^2 (xa)
|
||||
'''
|
||||
alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
|
||||
if self.alpha_logscale:
|
||||
alpha = torch.exp(alpha)
|
||||
x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
|
||||
|
||||
return x
|
||||
@@ -1,330 +0,0 @@
|
||||
# Copyright (c) 2019 Shigeki Karita
|
||||
# 2020 Mobvoi Inc (Binbin Zhang)
|
||||
# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn)
|
||||
# 2024 Alibaba Inc (Xiang Lyu)
|
||||
#
|
||||
# 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.
|
||||
"""Multi-Head Attention layer definition."""
|
||||
|
||||
import math
|
||||
from typing import Tuple
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
|
||||
class MultiHeadedAttention(nn.Module):
|
||||
"""Multi-Head Attention layer.
|
||||
|
||||
Args:
|
||||
n_head (int): The number of heads.
|
||||
n_feat (int): The number of features.
|
||||
dropout_rate (float): Dropout rate.
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
n_head: int,
|
||||
n_feat: int,
|
||||
dropout_rate: float,
|
||||
key_bias: bool = True):
|
||||
"""Construct an MultiHeadedAttention object."""
|
||||
super().__init__()
|
||||
assert n_feat % n_head == 0
|
||||
# We assume d_v always equals d_k
|
||||
self.d_k = n_feat // n_head
|
||||
self.h = n_head
|
||||
self.linear_q = nn.Linear(n_feat, n_feat)
|
||||
self.linear_k = nn.Linear(n_feat, n_feat, bias=key_bias)
|
||||
self.linear_v = nn.Linear(n_feat, n_feat)
|
||||
self.linear_out = nn.Linear(n_feat, n_feat)
|
||||
self.dropout = nn.Dropout(p=dropout_rate)
|
||||
|
||||
def forward_qkv(
|
||||
self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
"""Transform query, key and value.
|
||||
|
||||
Args:
|
||||
query (torch.Tensor): Query tensor (#batch, time1, size).
|
||||
key (torch.Tensor): Key tensor (#batch, time2, size).
|
||||
value (torch.Tensor): Value tensor (#batch, time2, size).
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Transformed query tensor, size
|
||||
(#batch, n_head, time1, d_k).
|
||||
torch.Tensor: Transformed key tensor, size
|
||||
(#batch, n_head, time2, d_k).
|
||||
torch.Tensor: Transformed value tensor, size
|
||||
(#batch, n_head, time2, d_k).
|
||||
|
||||
"""
|
||||
n_batch = query.size(0)
|
||||
q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
|
||||
k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
|
||||
v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
|
||||
q = q.transpose(1, 2) # (batch, head, time1, d_k)
|
||||
k = k.transpose(1, 2) # (batch, head, time2, d_k)
|
||||
v = v.transpose(1, 2) # (batch, head, time2, d_k)
|
||||
|
||||
return q, k, v
|
||||
|
||||
def forward_attention(
|
||||
self,
|
||||
value: torch.Tensor,
|
||||
scores: torch.Tensor,
|
||||
mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool)
|
||||
) -> torch.Tensor:
|
||||
"""Compute attention context vector.
|
||||
|
||||
Args:
|
||||
value (torch.Tensor): Transformed value, size
|
||||
(#batch, n_head, time2, d_k).
|
||||
scores (torch.Tensor): Attention score, size
|
||||
(#batch, n_head, time1, time2).
|
||||
mask (torch.Tensor): Mask, size (#batch, 1, time2) or
|
||||
(#batch, time1, time2), (0, 0, 0) means fake mask.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Transformed value (#batch, time1, d_model)
|
||||
weighted by the attention score (#batch, time1, time2).
|
||||
|
||||
"""
|
||||
n_batch = value.size(0)
|
||||
# NOTE(xcsong): When will `if mask.size(2) > 0` be True?
|
||||
# 1. onnx(16/4) [WHY? Because we feed real cache & real mask for the
|
||||
# 1st chunk to ease the onnx export.]
|
||||
# 2. pytorch training
|
||||
if mask.size(2) > 0: # time2 > 0
|
||||
mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2)
|
||||
# For last chunk, time2 might be larger than scores.size(-1)
|
||||
mask = mask[:, :, :, :scores.size(-1)] # (batch, 1, *, time2)
|
||||
scores = scores.masked_fill(mask, -float('inf'))
|
||||
attn = torch.softmax(scores, dim=-1).masked_fill(
|
||||
mask, 0.0) # (batch, head, time1, time2)
|
||||
# NOTE(xcsong): When will `if mask.size(2) > 0` be False?
|
||||
# 1. onnx(16/-1, -1/-1, 16/0)
|
||||
# 2. jit (16/-1, -1/-1, 16/0, 16/4)
|
||||
else:
|
||||
attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2)
|
||||
|
||||
p_attn = self.dropout(attn)
|
||||
x = torch.matmul(p_attn, value) # (batch, head, time1, d_k)
|
||||
x = (x.transpose(1, 2).contiguous().view(n_batch, -1,
|
||||
self.h * self.d_k)
|
||||
) # (batch, time1, d_model)
|
||||
|
||||
return self.linear_out(x) # (batch, time1, d_model)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
query: torch.Tensor,
|
||||
key: torch.Tensor,
|
||||
value: torch.Tensor,
|
||||
mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
|
||||
pos_emb: torch.Tensor = torch.empty(0),
|
||||
cache: torch.Tensor = torch.zeros((0, 0, 0, 0))
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
"""Compute scaled dot product attention.
|
||||
|
||||
Args:
|
||||
query (torch.Tensor): Query tensor (#batch, time1, size).
|
||||
key (torch.Tensor): Key tensor (#batch, time2, size).
|
||||
value (torch.Tensor): Value tensor (#batch, time2, size).
|
||||
mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
|
||||
(#batch, time1, time2).
|
||||
1.When applying cross attention between decoder and encoder,
|
||||
the batch padding mask for input is in (#batch, 1, T) shape.
|
||||
2.When applying self attention of encoder,
|
||||
the mask is in (#batch, T, T) shape.
|
||||
3.When applying self attention of decoder,
|
||||
the mask is in (#batch, L, L) shape.
|
||||
4.If the different position in decoder see different block
|
||||
of the encoder, such as Mocha, the passed in mask could be
|
||||
in (#batch, L, T) shape. But there is no such case in current
|
||||
CosyVoice.
|
||||
cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
|
||||
where `cache_t == chunk_size * num_decoding_left_chunks`
|
||||
and `head * d_k == size`
|
||||
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Output tensor (#batch, time1, d_model).
|
||||
torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
|
||||
where `cache_t == chunk_size * num_decoding_left_chunks`
|
||||
and `head * d_k == size`
|
||||
|
||||
"""
|
||||
q, k, v = self.forward_qkv(query, key, value)
|
||||
|
||||
# NOTE(xcsong):
|
||||
# when export onnx model, for 1st chunk, we feed
|
||||
# cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode)
|
||||
# or cache(1, head, real_cache_t, d_k * 2) (16/4 mode).
|
||||
# In all modes, `if cache.size(0) > 0` will alwayse be `True`
|
||||
# and we will always do splitting and
|
||||
# concatnation(this will simplify onnx export). Note that
|
||||
# it's OK to concat & split zero-shaped tensors(see code below).
|
||||
# when export jit model, for 1st chunk, we always feed
|
||||
# cache(0, 0, 0, 0) since jit supports dynamic if-branch.
|
||||
# >>> a = torch.ones((1, 2, 0, 4))
|
||||
# >>> b = torch.ones((1, 2, 3, 4))
|
||||
# >>> c = torch.cat((a, b), dim=2)
|
||||
# >>> torch.equal(b, c) # True
|
||||
# >>> d = torch.split(a, 2, dim=-1)
|
||||
# >>> torch.equal(d[0], d[1]) # True
|
||||
if cache.size(0) > 0:
|
||||
key_cache, value_cache = torch.split(cache,
|
||||
cache.size(-1) // 2,
|
||||
dim=-1)
|
||||
k = torch.cat([key_cache, k], dim=2)
|
||||
v = torch.cat([value_cache, v], dim=2)
|
||||
# NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's
|
||||
# non-trivial to calculate `next_cache_start` here.
|
||||
new_cache = torch.cat((k, v), dim=-1)
|
||||
|
||||
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
|
||||
return self.forward_attention(v, scores, mask), new_cache
|
||||
|
||||
|
||||
class RelPositionMultiHeadedAttention(MultiHeadedAttention):
|
||||
"""Multi-Head Attention layer with relative position encoding.
|
||||
Paper: https://arxiv.org/abs/1901.02860
|
||||
Args:
|
||||
n_head (int): The number of heads.
|
||||
n_feat (int): The number of features.
|
||||
dropout_rate (float): Dropout rate.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
n_head: int,
|
||||
n_feat: int,
|
||||
dropout_rate: float,
|
||||
key_bias: bool = True):
|
||||
"""Construct an RelPositionMultiHeadedAttention object."""
|
||||
super().__init__(n_head, n_feat, dropout_rate, key_bias)
|
||||
# linear transformation for positional encoding
|
||||
self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
|
||||
# these two learnable bias are used in matrix c and matrix d
|
||||
# as described in https://arxiv.org/abs/1901.02860 Section 3.3
|
||||
self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
|
||||
self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
|
||||
torch.nn.init.xavier_uniform_(self.pos_bias_u)
|
||||
torch.nn.init.xavier_uniform_(self.pos_bias_v)
|
||||
|
||||
def rel_shift(self, x: torch.Tensor) -> torch.Tensor:
|
||||
"""Compute relative positional encoding.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1).
|
||||
time1 means the length of query vector.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Output tensor.
|
||||
|
||||
"""
|
||||
zero_pad = torch.zeros((x.size()[0], x.size()[1], x.size()[2], 1),
|
||||
device=x.device,
|
||||
dtype=x.dtype)
|
||||
x_padded = torch.cat([zero_pad, x], dim=-1)
|
||||
|
||||
x_padded = x_padded.view(x.size()[0],
|
||||
x.size()[1],
|
||||
x.size(3) + 1, x.size(2))
|
||||
x = x_padded[:, :, 1:].view_as(x)[
|
||||
:, :, :, : x.size(-1) // 2 + 1
|
||||
] # only keep the positions from 0 to time2
|
||||
return x
|
||||
|
||||
def forward(
|
||||
self,
|
||||
query: torch.Tensor,
|
||||
key: torch.Tensor,
|
||||
value: torch.Tensor,
|
||||
mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
|
||||
pos_emb: torch.Tensor = torch.empty(0),
|
||||
cache: torch.Tensor = torch.zeros((0, 0, 0, 0))
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
"""Compute 'Scaled Dot Product Attention' with rel. positional encoding.
|
||||
Args:
|
||||
query (torch.Tensor): Query tensor (#batch, time1, size).
|
||||
key (torch.Tensor): Key tensor (#batch, time2, size).
|
||||
value (torch.Tensor): Value tensor (#batch, time2, size).
|
||||
mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
|
||||
(#batch, time1, time2), (0, 0, 0) means fake mask.
|
||||
pos_emb (torch.Tensor): Positional embedding tensor
|
||||
(#batch, time2, size).
|
||||
cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
|
||||
where `cache_t == chunk_size * num_decoding_left_chunks`
|
||||
and `head * d_k == size`
|
||||
Returns:
|
||||
torch.Tensor: Output tensor (#batch, time1, d_model).
|
||||
torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
|
||||
where `cache_t == chunk_size * num_decoding_left_chunks`
|
||||
and `head * d_k == size`
|
||||
"""
|
||||
q, k, v = self.forward_qkv(query, key, value)
|
||||
q = q.transpose(1, 2) # (batch, time1, head, d_k)
|
||||
|
||||
# NOTE(xcsong):
|
||||
# when export onnx model, for 1st chunk, we feed
|
||||
# cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode)
|
||||
# or cache(1, head, real_cache_t, d_k * 2) (16/4 mode).
|
||||
# In all modes, `if cache.size(0) > 0` will alwayse be `True`
|
||||
# and we will always do splitting and
|
||||
# concatnation(this will simplify onnx export). Note that
|
||||
# it's OK to concat & split zero-shaped tensors(see code below).
|
||||
# when export jit model, for 1st chunk, we always feed
|
||||
# cache(0, 0, 0, 0) since jit supports dynamic if-branch.
|
||||
# >>> a = torch.ones((1, 2, 0, 4))
|
||||
# >>> b = torch.ones((1, 2, 3, 4))
|
||||
# >>> c = torch.cat((a, b), dim=2)
|
||||
# >>> torch.equal(b, c) # True
|
||||
# >>> d = torch.split(a, 2, dim=-1)
|
||||
# >>> torch.equal(d[0], d[1]) # True
|
||||
if cache.size(0) > 0:
|
||||
key_cache, value_cache = torch.split(cache,
|
||||
cache.size(-1) // 2,
|
||||
dim=-1)
|
||||
k = torch.cat([key_cache, k], dim=2)
|
||||
v = torch.cat([value_cache, v], dim=2)
|
||||
# NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's
|
||||
# non-trivial to calculate `next_cache_start` here.
|
||||
new_cache = torch.cat((k, v), dim=-1)
|
||||
|
||||
n_batch_pos = pos_emb.size(0)
|
||||
p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
|
||||
p = p.transpose(1, 2) # (batch, head, time1, d_k)
|
||||
|
||||
# (batch, head, time1, d_k)
|
||||
q_with_bias_u = (q + self.pos_bias_u.to(q.device)).transpose(1, 2)
|
||||
# (batch, head, time1, d_k)
|
||||
q_with_bias_v = (q + self.pos_bias_v.to(q.device)).transpose(1, 2)
|
||||
|
||||
# compute attention score
|
||||
# first compute matrix a and matrix c
|
||||
# as described in https://arxiv.org/abs/1901.02860 Section 3.3
|
||||
# (batch, head, time1, time2)
|
||||
matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
|
||||
|
||||
# compute matrix b and matrix d
|
||||
# (batch, head, time1, time2)
|
||||
matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
|
||||
# NOTE(Xiang Lyu): Keep rel_shift since espnet rel_pos_emb is used
|
||||
if matrix_ac.shape != matrix_bd.shape:
|
||||
matrix_bd = self.rel_shift(matrix_bd)
|
||||
|
||||
scores = (matrix_ac + matrix_bd) / math.sqrt(
|
||||
self.d_k) # (batch, head, time1, time2)
|
||||
|
||||
return self.forward_attention(v, scores, mask), new_cache
|
||||
-145
@@ -1,145 +0,0 @@
|
||||
# Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu)
|
||||
# 2024 Alibaba Inc (Xiang Lyu)
|
||||
#
|
||||
# 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.
|
||||
# Modified from ESPnet(https://github.com/espnet/espnet)
|
||||
"""ConvolutionModule definition."""
|
||||
|
||||
from typing import Tuple
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
|
||||
class ConvolutionModule(nn.Module):
|
||||
"""ConvolutionModule in Conformer model."""
|
||||
|
||||
def __init__(self,
|
||||
channels: int,
|
||||
kernel_size: int = 15,
|
||||
activation: nn.Module = nn.ReLU(),
|
||||
norm: str = "batch_norm",
|
||||
causal: bool = False,
|
||||
bias: bool = True):
|
||||
"""Construct an ConvolutionModule object.
|
||||
Args:
|
||||
channels (int): The number of channels of conv layers.
|
||||
kernel_size (int): Kernel size of conv layers.
|
||||
causal (int): Whether use causal convolution or not
|
||||
"""
|
||||
super().__init__()
|
||||
|
||||
self.pointwise_conv1 = nn.Conv1d(
|
||||
channels,
|
||||
2 * channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
bias=bias,
|
||||
)
|
||||
# self.lorder is used to distinguish if it's a causal convolution,
|
||||
# if self.lorder > 0: it's a causal convolution, the input will be
|
||||
# padded with self.lorder frames on the left in forward.
|
||||
# else: it's a symmetrical convolution
|
||||
if causal:
|
||||
padding = 0
|
||||
self.lorder = kernel_size - 1
|
||||
else:
|
||||
# kernel_size should be an odd number for none causal convolution
|
||||
assert (kernel_size - 1) % 2 == 0
|
||||
padding = (kernel_size - 1) // 2
|
||||
self.lorder = 0
|
||||
self.depthwise_conv = nn.Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
stride=1,
|
||||
padding=padding,
|
||||
groups=channels,
|
||||
bias=bias,
|
||||
)
|
||||
|
||||
assert norm in ['batch_norm', 'layer_norm']
|
||||
if norm == "batch_norm":
|
||||
self.use_layer_norm = False
|
||||
self.norm = nn.BatchNorm1d(channels)
|
||||
else:
|
||||
self.use_layer_norm = True
|
||||
self.norm = nn.LayerNorm(channels)
|
||||
|
||||
self.pointwise_conv2 = nn.Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
bias=bias,
|
||||
)
|
||||
self.activation = activation
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
|
||||
cache: torch.Tensor = torch.zeros((0, 0, 0)),
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
"""Compute convolution module.
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor (#batch, time, channels).
|
||||
mask_pad (torch.Tensor): used for batch padding (#batch, 1, time),
|
||||
(0, 0, 0) means fake mask.
|
||||
cache (torch.Tensor): left context cache, it is only
|
||||
used in causal convolution (#batch, channels, cache_t),
|
||||
(0, 0, 0) meas fake cache.
|
||||
Returns:
|
||||
torch.Tensor: Output tensor (#batch, time, channels).
|
||||
"""
|
||||
# exchange the temporal dimension and the feature dimension
|
||||
x = x.transpose(1, 2) # (#batch, channels, time)
|
||||
|
||||
# mask batch padding
|
||||
if mask_pad.size(2) > 0: # time > 0
|
||||
x.masked_fill_(~mask_pad, 0.0)
|
||||
|
||||
if self.lorder > 0:
|
||||
if cache.size(2) == 0: # cache_t == 0
|
||||
x = nn.functional.pad(x, (self.lorder, 0), 'constant', 0.0)
|
||||
else:
|
||||
assert cache.size(0) == x.size(0) # equal batch
|
||||
assert cache.size(1) == x.size(1) # equal channel
|
||||
x = torch.cat((cache, x), dim=2)
|
||||
assert (x.size(2) > self.lorder)
|
||||
new_cache = x[:, :, -self.lorder:]
|
||||
else:
|
||||
# It's better we just return None if no cache is required,
|
||||
# However, for JIT export, here we just fake one tensor instead of
|
||||
# None.
|
||||
new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
|
||||
|
||||
# GLU mechanism
|
||||
x = self.pointwise_conv1(x) # (batch, 2*channel, dim)
|
||||
x = nn.functional.glu(x, dim=1) # (batch, channel, dim)
|
||||
|
||||
# 1D Depthwise Conv
|
||||
x = self.depthwise_conv(x)
|
||||
if self.use_layer_norm:
|
||||
x = x.transpose(1, 2)
|
||||
x = self.activation(self.norm(x))
|
||||
if self.use_layer_norm:
|
||||
x = x.transpose(1, 2)
|
||||
x = self.pointwise_conv2(x)
|
||||
# mask batch padding
|
||||
if mask_pad.size(2) > 0: # time > 0
|
||||
x.masked_fill_(~mask_pad, 0.0)
|
||||
|
||||
return x.transpose(1, 2), new_cache
|
||||
@@ -1,294 +0,0 @@
|
||||
# Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu)
|
||||
# 2024 Alibaba Inc (Xiang Lyu)
|
||||
#
|
||||
# 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.
|
||||
# Modified from ESPnet(https://github.com/espnet/espnet)
|
||||
"""Positonal Encoding Module."""
|
||||
|
||||
import math
|
||||
from typing import Tuple, Union
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import numpy as np
|
||||
|
||||
|
||||
class PositionalEncoding(torch.nn.Module):
|
||||
"""Positional encoding.
|
||||
|
||||
:param int d_model: embedding dim
|
||||
:param float dropout_rate: dropout rate
|
||||
:param int max_len: maximum input length
|
||||
|
||||
PE(pos, 2i) = sin(pos/(10000^(2i/dmodel)))
|
||||
PE(pos, 2i+1) = cos(pos/(10000^(2i/dmodel)))
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
d_model: int,
|
||||
dropout_rate: float,
|
||||
max_len: int = 5000,
|
||||
reverse: bool = False):
|
||||
"""Construct an PositionalEncoding object."""
|
||||
super().__init__()
|
||||
self.d_model = d_model
|
||||
self.xscale = math.sqrt(self.d_model)
|
||||
self.dropout = torch.nn.Dropout(p=dropout_rate)
|
||||
self.max_len = max_len
|
||||
|
||||
self.pe = torch.zeros(self.max_len, self.d_model)
|
||||
position = torch.arange(0, self.max_len,
|
||||
dtype=torch.float32).unsqueeze(1)
|
||||
div_term = torch.exp(
|
||||
torch.arange(0, self.d_model, 2, dtype=torch.float32) *
|
||||
-(math.log(10000.0) / self.d_model))
|
||||
self.pe[:, 0::2] = torch.sin(position * div_term)
|
||||
self.pe[:, 1::2] = torch.cos(position * div_term)
|
||||
self.pe = self.pe.unsqueeze(0)
|
||||
|
||||
def forward(self,
|
||||
x: torch.Tensor,
|
||||
offset: Union[int, torch.Tensor] = 0) \
|
||||
-> Tuple[torch.Tensor, torch.Tensor]:
|
||||
"""Add positional encoding.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input. Its shape is (batch, time, ...)
|
||||
offset (int, torch.tensor): position offset
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Encoded tensor. Its shape is (batch, time, ...)
|
||||
torch.Tensor: for compatibility to RelPositionalEncoding
|
||||
"""
|
||||
|
||||
self.pe = self.pe.to(x.device)
|
||||
pos_emb = self.position_encoding(offset, x.size(1), False)
|
||||
x = x * self.xscale + pos_emb
|
||||
return self.dropout(x), self.dropout(pos_emb)
|
||||
|
||||
def position_encoding(self,
|
||||
offset: Union[int, torch.Tensor],
|
||||
size: int,
|
||||
apply_dropout: bool = True) -> torch.Tensor:
|
||||
""" For getting encoding in a streaming fashion
|
||||
|
||||
Attention!!!!!
|
||||
we apply dropout only once at the whole utterance level in a none
|
||||
streaming way, but will call this function several times with
|
||||
increasing input size in a streaming scenario, so the dropout will
|
||||
be applied several times.
|
||||
|
||||
Args:
|
||||
offset (int or torch.tensor): start offset
|
||||
size (int): required size of position encoding
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Corresponding encoding
|
||||
"""
|
||||
# How to subscript a Union type:
|
||||
# https://github.com/pytorch/pytorch/issues/69434
|
||||
if isinstance(offset, int):
|
||||
assert offset + size <= self.max_len
|
||||
pos_emb = self.pe[:, offset:offset + size]
|
||||
elif isinstance(offset, torch.Tensor) and offset.dim() == 0: # scalar
|
||||
assert offset + size <= self.max_len
|
||||
pos_emb = self.pe[:, offset:offset + size]
|
||||
else: # for batched streaming decoding on GPU
|
||||
assert torch.max(offset) + size <= self.max_len
|
||||
index = offset.unsqueeze(1) + \
|
||||
torch.arange(0, size).to(offset.device) # B X T
|
||||
flag = index > 0
|
||||
# remove negative offset
|
||||
index = index * flag
|
||||
pos_emb = F.embedding(index, self.pe[0]) # B X T X d_model
|
||||
|
||||
if apply_dropout:
|
||||
pos_emb = self.dropout(pos_emb)
|
||||
return pos_emb
|
||||
|
||||
|
||||
class RelPositionalEncoding(PositionalEncoding):
|
||||
"""Relative positional encoding module.
|
||||
See : Appendix B in https://arxiv.org/abs/1901.02860
|
||||
Args:
|
||||
d_model (int): Embedding dimension.
|
||||
dropout_rate (float): Dropout rate.
|
||||
max_len (int): Maximum input length.
|
||||
"""
|
||||
|
||||
def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000):
|
||||
"""Initialize class."""
|
||||
super().__init__(d_model, dropout_rate, max_len, reverse=True)
|
||||
|
||||
def forward(self,
|
||||
x: torch.Tensor,
|
||||
offset: Union[int, torch.Tensor] = 0) \
|
||||
-> Tuple[torch.Tensor, torch.Tensor]:
|
||||
"""Compute positional encoding.
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor (batch, time, `*`).
|
||||
Returns:
|
||||
torch.Tensor: Encoded tensor (batch, time, `*`).
|
||||
torch.Tensor: Positional embedding tensor (1, time, `*`).
|
||||
"""
|
||||
self.pe = self.pe.to(x.device)
|
||||
x = x * self.xscale
|
||||
pos_emb = self.position_encoding(offset, x.size(1), False)
|
||||
return self.dropout(x), self.dropout(pos_emb)
|
||||
|
||||
|
||||
class WhisperPositionalEncoding(PositionalEncoding):
|
||||
""" Sinusoids position encoding used in openai-whisper.encoder
|
||||
"""
|
||||
|
||||
def __init__(self, d_model: int, dropout_rate: float, max_len: int = 1500):
|
||||
super().__init__(d_model, dropout_rate, max_len)
|
||||
self.xscale = 1.0
|
||||
log_timescale_increment = np.log(10000) / (d_model // 2 - 1)
|
||||
inv_timescales = torch.exp(-log_timescale_increment *
|
||||
torch.arange(d_model // 2))
|
||||
scaled_time = torch.arange(max_len)[:, np.newaxis] * \
|
||||
inv_timescales[np.newaxis, :]
|
||||
pe = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
|
||||
delattr(self, "pe")
|
||||
self.register_buffer("pe", pe.unsqueeze(0))
|
||||
|
||||
|
||||
class LearnablePositionalEncoding(PositionalEncoding):
|
||||
""" Learnable position encoding used in openai-whisper.decoder
|
||||
"""
|
||||
|
||||
def __init__(self, d_model: int, dropout_rate: float, max_len: int = 448):
|
||||
super().__init__(d_model, dropout_rate, max_len)
|
||||
# NOTE(xcsong): overwrite self.pe & self.xscale
|
||||
self.pe = torch.nn.Parameter(torch.empty(1, max_len, d_model))
|
||||
self.xscale = 1.0
|
||||
|
||||
|
||||
class NoPositionalEncoding(torch.nn.Module):
|
||||
""" No position encoding
|
||||
"""
|
||||
|
||||
def __init__(self, d_model: int, dropout_rate: float):
|
||||
super().__init__()
|
||||
self.d_model = d_model
|
||||
self.dropout = torch.nn.Dropout(p=dropout_rate)
|
||||
|
||||
def forward(self,
|
||||
x: torch.Tensor,
|
||||
offset: Union[int, torch.Tensor] = 0) \
|
||||
-> Tuple[torch.Tensor, torch.Tensor]:
|
||||
""" Just return zero vector for interface compatibility
|
||||
"""
|
||||
pos_emb = torch.zeros(1, x.size(1), self.d_model).to(x.device)
|
||||
return self.dropout(x), pos_emb
|
||||
|
||||
def position_encoding(self, offset: Union[int, torch.Tensor],
|
||||
size: int) -> torch.Tensor:
|
||||
return torch.zeros(1, size, self.d_model)
|
||||
|
||||
|
||||
class EspnetRelPositionalEncoding(torch.nn.Module):
|
||||
"""Relative positional encoding module (new implementation).
|
||||
|
||||
Details can be found in https://github.com/espnet/espnet/pull/2816.
|
||||
|
||||
See : Appendix B in https://arxiv.org/abs/1901.02860
|
||||
|
||||
Args:
|
||||
d_model (int): Embedding dimension.
|
||||
dropout_rate (float): Dropout rate.
|
||||
max_len (int): Maximum input length.
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000):
|
||||
"""Construct an PositionalEncoding object."""
|
||||
super(EspnetRelPositionalEncoding, self).__init__()
|
||||
self.d_model = d_model
|
||||
self.xscale = math.sqrt(self.d_model)
|
||||
self.dropout = torch.nn.Dropout(p=dropout_rate)
|
||||
self.pe = None
|
||||
self.extend_pe(torch.tensor(0.0).expand(1, max_len))
|
||||
|
||||
def extend_pe(self, x: torch.Tensor):
|
||||
"""Reset the positional encodings."""
|
||||
if self.pe is not None:
|
||||
# self.pe contains both positive and negative parts
|
||||
# the length of self.pe is 2 * input_len - 1
|
||||
if self.pe.size(1) >= x.size(1) * 2 - 1:
|
||||
if self.pe.dtype != x.dtype or self.pe.device != x.device:
|
||||
self.pe = self.pe.to(dtype=x.dtype, device=x.device)
|
||||
return
|
||||
# Suppose `i` means to the position of query vecotr and `j` means the
|
||||
# position of key vector. We use position relative positions when keys
|
||||
# are to the left (i>j) and negative relative positions otherwise (i<j).
|
||||
pe_positive = torch.zeros(x.size(1), self.d_model)
|
||||
pe_negative = torch.zeros(x.size(1), self.d_model)
|
||||
position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
|
||||
div_term = torch.exp(
|
||||
torch.arange(0, self.d_model, 2, dtype=torch.float32)
|
||||
* -(math.log(10000.0) / self.d_model)
|
||||
)
|
||||
pe_positive[:, 0::2] = torch.sin(position * div_term)
|
||||
pe_positive[:, 1::2] = torch.cos(position * div_term)
|
||||
pe_negative[:, 0::2] = torch.sin(-1 * position * div_term)
|
||||
pe_negative[:, 1::2] = torch.cos(-1 * position * div_term)
|
||||
|
||||
# Reserve the order of positive indices and concat both positive and
|
||||
# negative indices. This is used to support the shifting trick
|
||||
# as in https://arxiv.org/abs/1901.02860
|
||||
pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
|
||||
pe_negative = pe_negative[1:].unsqueeze(0)
|
||||
pe = torch.cat([pe_positive, pe_negative], dim=1)
|
||||
self.pe = pe.to(device=x.device, dtype=x.dtype)
|
||||
|
||||
def forward(self, x: torch.Tensor, offset: Union[int, torch.Tensor] = 0) \
|
||||
-> Tuple[torch.Tensor, torch.Tensor]:
|
||||
"""Add positional encoding.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor (batch, time, `*`).
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Encoded tensor (batch, time, `*`).
|
||||
|
||||
"""
|
||||
self.extend_pe(x)
|
||||
x = x * self.xscale
|
||||
pos_emb = self.position_encoding(size=x.size(1), offset=offset)
|
||||
return self.dropout(x), self.dropout(pos_emb)
|
||||
|
||||
def position_encoding(self,
|
||||
offset: Union[int, torch.Tensor],
|
||||
size: int) -> torch.Tensor:
|
||||
""" For getting encoding in a streaming fashion
|
||||
|
||||
Attention!!!!!
|
||||
we apply dropout only once at the whole utterance level in a none
|
||||
streaming way, but will call this function several times with
|
||||
increasing input size in a streaming scenario, so the dropout will
|
||||
be applied several times.
|
||||
|
||||
Args:
|
||||
offset (int or torch.tensor): start offset
|
||||
size (int): required size of position encoding
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Corresponding encoding
|
||||
"""
|
||||
pos_emb = self.pe[
|
||||
:,
|
||||
self.pe.size(1) // 2 - size + 1: self.pe.size(1) // 2 + size,
|
||||
]
|
||||
return pos_emb
|
||||
-236
@@ -1,236 +0,0 @@
|
||||
# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu)
|
||||
# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn)
|
||||
#
|
||||
# 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.
|
||||
# Modified from ESPnet(https://github.com/espnet/espnet)
|
||||
"""Encoder self-attention layer definition."""
|
||||
|
||||
from typing import Optional, Tuple
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
|
||||
class TransformerEncoderLayer(nn.Module):
|
||||
"""Encoder layer module.
|
||||
|
||||
Args:
|
||||
size (int): Input dimension.
|
||||
self_attn (torch.nn.Module): Self-attention module instance.
|
||||
`MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
|
||||
instance can be used as the argument.
|
||||
feed_forward (torch.nn.Module): Feed-forward module instance.
|
||||
`PositionwiseFeedForward`, instance can be used as the argument.
|
||||
dropout_rate (float): Dropout rate.
|
||||
normalize_before (bool):
|
||||
True: use layer_norm before each sub-block.
|
||||
False: to use layer_norm after each sub-block.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
size: int,
|
||||
self_attn: torch.nn.Module,
|
||||
feed_forward: torch.nn.Module,
|
||||
dropout_rate: float,
|
||||
normalize_before: bool = True,
|
||||
):
|
||||
"""Construct an EncoderLayer object."""
|
||||
super().__init__()
|
||||
self.self_attn = self_attn
|
||||
self.feed_forward = feed_forward
|
||||
self.norm1 = nn.LayerNorm(size, eps=1e-12)
|
||||
self.norm2 = nn.LayerNorm(size, eps=1e-12)
|
||||
self.dropout = nn.Dropout(dropout_rate)
|
||||
self.size = size
|
||||
self.normalize_before = normalize_before
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
mask: torch.Tensor,
|
||||
pos_emb: torch.Tensor,
|
||||
mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
|
||||
att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
|
||||
cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
"""Compute encoded features.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): (#batch, time, size)
|
||||
mask (torch.Tensor): Mask tensor for the input (#batch, time，time),
|
||||
(0, 0, 0) means fake mask.
|
||||
pos_emb (torch.Tensor): just for interface compatibility
|
||||
to ConformerEncoderLayer
|
||||
mask_pad (torch.Tensor): does not used in transformer layer,
|
||||
just for unified api with conformer.
|
||||
att_cache (torch.Tensor): Cache tensor of the KEY & VALUE
|
||||
(#batch=1, head, cache_t1, d_k * 2), head * d_k == size.
|
||||
cnn_cache (torch.Tensor): Convolution cache in conformer layer
|
||||
(#batch=1, size, cache_t2), not used here, it's for interface
|
||||
compatibility to ConformerEncoderLayer.
|
||||
Returns:
|
||||
torch.Tensor: Output tensor (#batch, time, size).
|
||||
torch.Tensor: Mask tensor (#batch, time, time).
|
||||
torch.Tensor: att_cache tensor,
|
||||
(#batch=1, head, cache_t1 + time, d_k * 2).
|
||||
torch.Tensor: cnn_cahce tensor (#batch=1, size, cache_t2).
|
||||
|
||||
"""
|
||||
residual = x
|
||||
if self.normalize_before:
|
||||
x = self.norm1(x)
|
||||
x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb=pos_emb, cache=att_cache)
|
||||
x = residual + self.dropout(x_att)
|
||||
if not self.normalize_before:
|
||||
x = self.norm1(x)
|
||||
|
||||
residual = x
|
||||
if self.normalize_before:
|
||||
x = self.norm2(x)
|
||||
x = residual + self.dropout(self.feed_forward(x))
|
||||
if not self.normalize_before:
|
||||
x = self.norm2(x)
|
||||
|
||||
fake_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
|
||||
return x, mask, new_att_cache, fake_cnn_cache
|
||||
|
||||
|
||||
class ConformerEncoderLayer(nn.Module):
|
||||
"""Encoder layer module.
|
||||
Args:
|
||||
size (int): Input dimension.
|
||||
self_attn (torch.nn.Module): Self-attention module instance.
|
||||
`MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
|
||||
instance can be used as the argument.
|
||||
feed_forward (torch.nn.Module): Feed-forward module instance.
|
||||
`PositionwiseFeedForward` instance can be used as the argument.
|
||||
feed_forward_macaron (torch.nn.Module): Additional feed-forward module
|
||||
instance.
|
||||
`PositionwiseFeedForward` instance can be used as the argument.
|
||||
conv_module (torch.nn.Module): Convolution module instance.
|
||||
`ConvlutionModule` instance can be used as the argument.
|
||||
dropout_rate (float): Dropout rate.
|
||||
normalize_before (bool):
|
||||
True: use layer_norm before each sub-block.
|
||||
False: use layer_norm after each sub-block.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
size: int,
|
||||
self_attn: torch.nn.Module,
|
||||
feed_forward: Optional[nn.Module] = None,
|
||||
feed_forward_macaron: Optional[nn.Module] = None,
|
||||
conv_module: Optional[nn.Module] = None,
|
||||
dropout_rate: float = 0.1,
|
||||
normalize_before: bool = True,
|
||||
):
|
||||
"""Construct an EncoderLayer object."""
|
||||
super().__init__()
|
||||
self.self_attn = self_attn
|
||||
self.feed_forward = feed_forward
|
||||
self.feed_forward_macaron = feed_forward_macaron
|
||||
self.conv_module = conv_module
|
||||
self.norm_ff = nn.LayerNorm(size, eps=1e-12) # for the FNN module
|
||||
self.norm_mha = nn.LayerNorm(size, eps=1e-12) # for the MHA module
|
||||
if feed_forward_macaron is not None:
|
||||
self.norm_ff_macaron = nn.LayerNorm(size, eps=1e-12)
|
||||
self.ff_scale = 0.5
|
||||
else:
|
||||
self.ff_scale = 1.0
|
||||
if self.conv_module is not None:
|
||||
self.norm_conv = nn.LayerNorm(size, eps=1e-12) # for the CNN module
|
||||
self.norm_final = nn.LayerNorm(
|
||||
size, eps=1e-12) # for the final output of the block
|
||||
self.dropout = nn.Dropout(dropout_rate)
|
||||
self.size = size
|
||||
self.normalize_before = normalize_before
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
mask: torch.Tensor,
|
||||
pos_emb: torch.Tensor,
|
||||
mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
|
||||
att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
|
||||
cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
"""Compute encoded features.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): (#batch, time, size)
|
||||
mask (torch.Tensor): Mask tensor for the input (#batch, time，time),
|
||||
(0, 0, 0) means fake mask.
|
||||
pos_emb (torch.Tensor): positional encoding, must not be None
|
||||
for ConformerEncoderLayer.
|
||||
mask_pad (torch.Tensor): batch padding mask used for conv module.
|
||||
(#batch, 1，time), (0, 0, 0) means fake mask.
|
||||
att_cache (torch.Tensor): Cache tensor of the KEY & VALUE
|
||||
(#batch=1, head, cache_t1, d_k * 2), head * d_k == size.
|
||||
cnn_cache (torch.Tensor): Convolution cache in conformer layer
|
||||
(#batch=1, size, cache_t2)
|
||||
Returns:
|
||||
torch.Tensor: Output tensor (#batch, time, size).
|
||||
torch.Tensor: Mask tensor (#batch, time, time).
|
||||
torch.Tensor: att_cache tensor,
|
||||
(#batch=1, head, cache_t1 + time, d_k * 2).
|
||||
torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2).
|
||||
"""
|
||||
|
||||
# whether to use macaron style
|
||||
if self.feed_forward_macaron is not None:
|
||||
residual = x
|
||||
if self.normalize_before:
|
||||
x = self.norm_ff_macaron(x)
|
||||
x = residual + self.ff_scale * self.dropout(
|
||||
self.feed_forward_macaron(x))
|
||||
if not self.normalize_before:
|
||||
x = self.norm_ff_macaron(x)
|
||||
|
||||
# multi-headed self-attention module
|
||||
residual = x
|
||||
if self.normalize_before:
|
||||
x = self.norm_mha(x)
|
||||
x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb,
|
||||
att_cache)
|
||||
x = residual + self.dropout(x_att)
|
||||
if not self.normalize_before:
|
||||
x = self.norm_mha(x)
|
||||
|
||||
# convolution module
|
||||
# Fake new cnn cache here, and then change it in conv_module
|
||||
new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
|
||||
if self.conv_module is not None:
|
||||
residual = x
|
||||
if self.normalize_before:
|
||||
x = self.norm_conv(x)
|
||||
x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache)
|
||||
x = residual + self.dropout(x)
|
||||
|
||||
if not self.normalize_before:
|
||||
x = self.norm_conv(x)
|
||||
|
||||
# feed forward module
|
||||
residual = x
|
||||
if self.normalize_before:
|
||||
x = self.norm_ff(x)
|
||||
|
||||
x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
|
||||
if not self.normalize_before:
|
||||
x = self.norm_ff(x)
|
||||
|
||||
if self.conv_module is not None:
|
||||
x = self.norm_final(x)
|
||||
|
||||
return x, mask, new_att_cache, new_cnn_cache
|
||||
-115
@@ -1,115 +0,0 @@
|
||||
# Copyright (c) 2019 Shigeki Karita
|
||||
# 2020 Mobvoi Inc (Binbin Zhang)
|
||||
#
|
||||
# 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.
|
||||
"""Positionwise feed forward layer definition."""
|
||||
|
||||
import torch
|
||||
|
||||
|
||||
class PositionwiseFeedForward(torch.nn.Module):
|
||||
"""Positionwise feed forward layer.
|
||||
|
||||
FeedForward are appied on each position of the sequence.
|
||||
The output dim is same with the input dim.
|
||||
|
||||
Args:
|
||||
idim (int): Input dimenstion.
|
||||
hidden_units (int): The number of hidden units.
|
||||
dropout_rate (float): Dropout rate.
|
||||
activation (torch.nn.Module): Activation function
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
idim: int,
|
||||
hidden_units: int,
|
||||
dropout_rate: float,
|
||||
activation: torch.nn.Module = torch.nn.ReLU(),
|
||||
):
|
||||
"""Construct a PositionwiseFeedForward object."""
|
||||
super(PositionwiseFeedForward, self).__init__()
|
||||
self.w_1 = torch.nn.Linear(idim, hidden_units)
|
||||
self.activation = activation
|
||||
self.dropout = torch.nn.Dropout(dropout_rate)
|
||||
self.w_2 = torch.nn.Linear(hidden_units, idim)
|
||||
|
||||
def forward(self, xs: torch.Tensor) -> torch.Tensor:
|
||||
"""Forward function.
|
||||
|
||||
Args:
|
||||
xs: input tensor (B, L, D)
|
||||
Returns:
|
||||
output tensor, (B, L, D)
|
||||
"""
|
||||
return self.w_2(self.dropout(self.activation(self.w_1(xs))))
|
||||
|
||||
|
||||
class MoEFFNLayer(torch.nn.Module):
|
||||
"""
|
||||
Mixture of expert with Positionwise feed forward layer
|
||||
See also figure 1 in https://arxiv.org/pdf/2305.15663.pdf
|
||||
The output dim is same with the input dim.
|
||||
|
||||
Modified from https://github.com/Lightning-AI/lit-gpt/pull/823
|
||||
https://github.com/mistralai/mistral-src/blob/b46d6/moe_one_file_ref.py#L203-L219
|
||||
Args:
|
||||
n_expert: number of expert.
|
||||
n_expert_per_token: The actual number of experts used for each frame
|
||||
idim (int): Input dimenstion.
|
||||
hidden_units (int): The number of hidden units.
|
||||
dropout_rate (float): Dropout rate.
|
||||
activation (torch.nn.Module): Activation function
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
n_expert: int,
|
||||
n_expert_per_token: int,
|
||||
idim: int,
|
||||
hidden_units: int,
|
||||
dropout_rate: float,
|
||||
activation: torch.nn.Module = torch.nn.ReLU(),
|
||||
):
|
||||
super(MoEFFNLayer, self).__init__()
|
||||
self.gate = torch.nn.Linear(idim, n_expert, bias=False)
|
||||
self.experts = torch.nn.ModuleList(
|
||||
PositionwiseFeedForward(idim, hidden_units, dropout_rate,
|
||||
activation) for _ in range(n_expert))
|
||||
self.n_expert_per_token = n_expert_per_token
|
||||
|
||||
def forward(self, xs: torch.Tensor) -> torch.Tensor:
|
||||
"""Foward function.
|
||||
Args:
|
||||
xs: input tensor (B, L, D)
|
||||
Returns:
|
||||
output tensor, (B, L, D)
|
||||
|
||||
"""
|
||||
B, L, D = xs.size(
|
||||
) # batch size, sequence length, embedding dimension (idim)
|
||||
xs = xs.view(-1, D) # (B*L, D)
|
||||
router = self.gate(xs) # (B*L, n_expert)
|
||||
logits, indices = torch.topk(
|
||||
router, self.n_expert_per_token
|
||||
) # probs:(B*L, n_expert), indices: (B*L, n_expert)
|
||||
weights = torch.nn.functional.softmax(
|
||||
logits, dim=1,
|
||||
dtype=torch.float).to(dtype=xs.dtype) # (B*L, n_expert_per_token)
|
||||
output = torch.zeros_like(xs) # (B*L, D)
|
||||
for i, expert in enumerate(self.experts):
|
||||
mask = indices == i
|
||||
batch_idx, ith_expert = torch.where(mask)
|
||||
output[batch_idx] += weights[batch_idx, ith_expert, None] * expert(
|
||||
xs[batch_idx])
|
||||
return output.view(B, L, D)
|
||||
-383
@@ -1,383 +0,0 @@
|
||||
# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu)
|
||||
# 2024 Alibaba Inc (Xiang Lyu)
|
||||
#
|
||||
# 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.
|
||||
# Modified from ESPnet(https://github.com/espnet/espnet)
|
||||
"""Subsampling layer definition."""
|
||||
|
||||
from typing import Tuple, Union
|
||||
|
||||
import torch
|
||||
|
||||
|
||||
class BaseSubsampling(torch.nn.Module):
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.right_context = 0
|
||||
self.subsampling_rate = 1
|
||||
|
||||
def position_encoding(self, offset: Union[int, torch.Tensor],
|
||||
size: int) -> torch.Tensor:
|
||||
return self.pos_enc.position_encoding(offset, size)
|
||||
|
||||
|
||||
class EmbedinigNoSubsampling(BaseSubsampling):
|
||||
"""Embedding input without subsampling
|
||||
"""
|
||||
|
||||
def __init__(self, idim: int, odim: int, dropout_rate: float,
|
||||
pos_enc_class: torch.nn.Module):
|
||||
super().__init__()
|
||||
self.embed = torch.nn.Embedding(idim, odim)
|
||||
self.pos_enc = pos_enc_class
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
x_mask: torch.Tensor,
|
||||
offset: Union[int, torch.Tensor] = 0
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
"""Input x.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor (#batch, time, idim).
|
||||
x_mask (torch.Tensor): Input mask (#batch, 1, time).
|
||||
|
||||
Returns:
|
||||
torch.Tensor: linear input tensor (#batch, time', odim),
|
||||
where time' = time .
|
||||
torch.Tensor: linear input mask (#batch, 1, time'),
|
||||
where time' = time .
|
||||
|
||||
"""
|
||||
x = self.embed(x)
|
||||
x, pos_emb = self.pos_enc(x, offset)
|
||||
return x, pos_emb, x_mask
|
||||
|
||||
|
||||
class LinearNoSubsampling(BaseSubsampling):
|
||||
"""Linear transform the input without subsampling
|
||||
|
||||
Args:
|
||||
idim (int): Input dimension.
|
||||
odim (int): Output dimension.
|
||||
dropout_rate (float): Dropout rate.
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, idim: int, odim: int, dropout_rate: float,
|
||||
pos_enc_class: torch.nn.Module):
|
||||
"""Construct an linear object."""
|
||||
super().__init__()
|
||||
self.out = torch.nn.Sequential(
|
||||
torch.nn.Linear(idim, odim),
|
||||
torch.nn.LayerNorm(odim, eps=1e-5),
|
||||
torch.nn.Dropout(dropout_rate),
|
||||
)
|
||||
self.pos_enc = pos_enc_class
|
||||
self.right_context = 0
|
||||
self.subsampling_rate = 1
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
x_mask: torch.Tensor,
|
||||
offset: Union[int, torch.Tensor] = 0
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
"""Input x.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor (#batch, time, idim).
|
||||
x_mask (torch.Tensor): Input mask (#batch, 1, time).
|
||||
|
||||
Returns:
|
||||
torch.Tensor: linear input tensor (#batch, time', odim),
|
||||
where time' = time .
|
||||
torch.Tensor: linear input mask (#batch, 1, time'),
|
||||
where time' = time .
|
||||
|
||||
"""
|
||||
x = self.out(x)
|
||||
x, pos_emb = self.pos_enc(x, offset)
|
||||
return x, pos_emb, x_mask
|
||||
|
||||
|
||||
class Conv1dSubsampling2(BaseSubsampling):
|
||||
"""Convolutional 1D subsampling (to 1/2 length).
|
||||
It is designed for Whisper, ref:
|
||||
https://github.com/openai/whisper/blob/main/whisper/model.py
|
||||
|
||||
Args:
|
||||
idim (int): Input dimension.
|
||||
odim (int): Output dimension.
|
||||
dropout_rate (float): Dropout rate.
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, idim: int, odim: int, dropout_rate: float,
|
||||
pos_enc_class: torch.nn.Module):
|
||||
"""Construct an Conv1dSubsampling2 object."""
|
||||
super().__init__()
|
||||
self.conv = torch.nn.Sequential(
|
||||
torch.nn.Conv1d(idim, odim, kernel_size=3, padding=1),
|
||||
torch.nn.GELU(),
|
||||
torch.nn.Conv1d(odim, odim, kernel_size=3, stride=2, padding=1),
|
||||
torch.nn.GELU(),
|
||||
)
|
||||
self.pos_enc = pos_enc_class
|
||||
# The right context for every conv layer is computed by:
|
||||
# (kernel_size - 1) * frame_rate_of_this_layer
|
||||
self.subsampling_rate = 2
|
||||
# 4 = (3 - 1) * 1 + (3 - 1) * 1
|
||||
self.right_context = 4
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
x_mask: torch.Tensor,
|
||||
offset: Union[int, torch.Tensor] = 0
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
"""Subsample x.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor (#batch, time, idim).
|
||||
x_mask (torch.Tensor): Input mask (#batch, 1, time).
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Subsampled tensor (#batch, time', odim),
|
||||
where time' = time // 2.
|
||||
torch.Tensor: Subsampled mask (#batch, 1, time'),
|
||||
where time' = time // 2.
|
||||
torch.Tensor: positional encoding
|
||||
|
||||
"""
|
||||
time = x.size(1)
|
||||
x = x.transpose(1, 2) # (b, f, t)
|
||||
x = self.conv(x)
|
||||
x = x.transpose(1, 2) # (b, t, f)
|
||||
x, pos_emb = self.pos_enc(x, offset)
|
||||
return x, pos_emb, x_mask[:, :, (time + 1) % 2::2]
|
||||
|
||||
|
||||
class Conv2dSubsampling4(BaseSubsampling):
|
||||
"""Convolutional 2D subsampling (to 1/4 length).
|
||||
|
||||
Args:
|
||||
idim (int): Input dimension.
|
||||
odim (int): Output dimension.
|
||||
dropout_rate (float): Dropout rate.
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, idim: int, odim: int, dropout_rate: float,
|
||||
pos_enc_class: torch.nn.Module):
|
||||
"""Construct an Conv2dSubsampling4 object."""
|
||||
super().__init__()
|
||||
self.conv = torch.nn.Sequential(
|
||||
torch.nn.Conv2d(1, odim, 3, 2),
|
||||
torch.nn.ReLU(),
|
||||
torch.nn.Conv2d(odim, odim, 3, 2),
|
||||
torch.nn.ReLU(),
|
||||
)
|
||||
self.out = torch.nn.Sequential(
|
||||
torch.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim))
|
||||
self.pos_enc = pos_enc_class
|
||||
# The right context for every conv layer is computed by:
|
||||
# (kernel_size - 1) * frame_rate_of_this_layer
|
||||
self.subsampling_rate = 4
|
||||
# 6 = (3 - 1) * 1 + (3 - 1) * 2
|
||||
self.right_context = 6
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
x_mask: torch.Tensor,
|
||||
offset: Union[int, torch.Tensor] = 0
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
"""Subsample x.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor (#batch, time, idim).
|
||||
x_mask (torch.Tensor): Input mask (#batch, 1, time).
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Subsampled tensor (#batch, time', odim),
|
||||
where time' = time // 4.
|
||||
torch.Tensor: Subsampled mask (#batch, 1, time'),
|
||||
where time' = time // 4.
|
||||
torch.Tensor: positional encoding
|
||||
|
||||
"""
|
||||
x = x.unsqueeze(1) # (b, c=1, t, f)
|
||||
x = self.conv(x)
|
||||
b, c, t, f = x.size()
|
||||
x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
|
||||
x, pos_emb = self.pos_enc(x, offset)
|
||||
return x, pos_emb, x_mask[:, :, 2::2][:, :, 2::2]
|
||||
|
||||
|
||||
class Conv2dSubsampling6(BaseSubsampling):
|
||||
"""Convolutional 2D subsampling (to 1/6 length).
|
||||
Args:
|
||||
idim (int): Input dimension.
|
||||
odim (int): Output dimension.
|
||||
dropout_rate (float): Dropout rate.
|
||||
pos_enc (torch.nn.Module): Custom position encoding layer.
|
||||
"""
|
||||
|
||||
def __init__(self, idim: int, odim: int, dropout_rate: float,
|
||||
pos_enc_class: torch.nn.Module):
|
||||
"""Construct an Conv2dSubsampling6 object."""
|
||||
super().__init__()
|
||||
self.conv = torch.nn.Sequential(
|
||||
torch.nn.Conv2d(1, odim, 3, 2),
|
||||
torch.nn.ReLU(),
|
||||
torch.nn.Conv2d(odim, odim, 5, 3),
|
||||
torch.nn.ReLU(),
|
||||
)
|
||||
self.linear = torch.nn.Linear(odim * (((idim - 1) // 2 - 2) // 3),
|
||||
odim)
|
||||
self.pos_enc = pos_enc_class
|
||||
# 10 = (3 - 1) * 1 + (5 - 1) * 2
|
||||
self.subsampling_rate = 6
|
||||
self.right_context = 10
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
x_mask: torch.Tensor,
|
||||
offset: Union[int, torch.Tensor] = 0
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
"""Subsample x.
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor (#batch, time, idim).
|
||||
x_mask (torch.Tensor): Input mask (#batch, 1, time).
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Subsampled tensor (#batch, time', odim),
|
||||
where time' = time // 6.
|
||||
torch.Tensor: Subsampled mask (#batch, 1, time'),
|
||||
where time' = time // 6.
|
||||
torch.Tensor: positional encoding
|
||||
"""
|
||||
x = x.unsqueeze(1) # (b, c, t, f)
|
||||
x = self.conv(x)
|
||||
b, c, t, f = x.size()
|
||||
x = self.linear(x.transpose(1, 2).contiguous().view(b, t, c * f))
|
||||
x, pos_emb = self.pos_enc(x, offset)
|
||||
return x, pos_emb, x_mask[:, :, 2::2][:, :, 4::3]
|
||||
|
||||
|
||||
class Conv2dSubsampling8(BaseSubsampling):
|
||||
"""Convolutional 2D subsampling (to 1/8 length).
|
||||
|
||||
Args:
|
||||
idim (int): Input dimension.
|
||||
odim (int): Output dimension.
|
||||
dropout_rate (float): Dropout rate.
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, idim: int, odim: int, dropout_rate: float,
|
||||
pos_enc_class: torch.nn.Module):
|
||||
"""Construct an Conv2dSubsampling8 object."""
|
||||
super().__init__()
|
||||
self.conv = torch.nn.Sequential(
|
||||
torch.nn.Conv2d(1, odim, 3, 2),
|
||||
torch.nn.ReLU(),
|
||||
torch.nn.Conv2d(odim, odim, 3, 2),
|
||||
torch.nn.ReLU(),
|
||||
torch.nn.Conv2d(odim, odim, 3, 2),
|
||||
torch.nn.ReLU(),
|
||||
)
|
||||
self.linear = torch.nn.Linear(
|
||||
odim * ((((idim - 1) // 2 - 1) // 2 - 1) // 2), odim)
|
||||
self.pos_enc = pos_enc_class
|
||||
self.subsampling_rate = 8
|
||||
# 14 = (3 - 1) * 1 + (3 - 1) * 2 + (3 - 1) * 4
|
||||
self.right_context = 14
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
x_mask: torch.Tensor,
|
||||
offset: Union[int, torch.Tensor] = 0
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
"""Subsample x.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor (#batch, time, idim).
|
||||
x_mask (torch.Tensor): Input mask (#batch, 1, time).
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Subsampled tensor (#batch, time', odim),
|
||||
where time' = time // 8.
|
||||
torch.Tensor: Subsampled mask (#batch, 1, time'),
|
||||
where time' = time // 8.
|
||||
torch.Tensor: positional encoding
|
||||
"""
|
||||
x = x.unsqueeze(1) # (b, c, t, f)
|
||||
x = self.conv(x)
|
||||
b, c, t, f = x.size()
|
||||
x = self.linear(x.transpose(1, 2).contiguous().view(b, t, c * f))
|
||||
x, pos_emb = self.pos_enc(x, offset)
|
||||
return x, pos_emb, x_mask[:, :, 2::2][:, :, 2::2][:, :, 2::2]
|
||||
|
||||
|
||||
class LegacyLinearNoSubsampling(BaseSubsampling):
|
||||
"""Linear transform the input without subsampling
|
||||
|
||||
Args:
|
||||
idim (int): Input dimension.
|
||||
odim (int): Output dimension.
|
||||
dropout_rate (float): Dropout rate.
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, idim: int, odim: int, dropout_rate: float,
|
||||
pos_enc_class: torch.nn.Module):
|
||||
"""Construct an linear object."""
|
||||
super().__init__()
|
||||
self.out = torch.nn.Sequential(
|
||||
torch.nn.Linear(idim, odim),
|
||||
torch.nn.LayerNorm(odim, eps=1e-5),
|
||||
torch.nn.Dropout(dropout_rate),
|
||||
torch.nn.ReLU(),
|
||||
)
|
||||
self.pos_enc = pos_enc_class
|
||||
self.right_context = 0
|
||||
self.subsampling_rate = 1
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
x_mask: torch.Tensor,
|
||||
offset: Union[int, torch.Tensor] = 0
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
"""Input x.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor (#batch, time, idim).
|
||||
x_mask (torch.Tensor): Input mask (#batch, 1, time).
|
||||
|
||||
Returns:
|
||||
torch.Tensor: linear input tensor (#batch, time', odim),
|
||||
where time' = time .
|
||||
torch.Tensor: linear input mask (#batch, 1, time'),
|
||||
where time' = time .
|
||||
|
||||
"""
|
||||
x = self.out(x)
|
||||
x, pos_emb = self.pos_enc(x, offset)
|
||||
return x, pos_emb, x_mask
|
||||
-318
@@ -1,318 +0,0 @@
|
||||
# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu)
|
||||
# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn)
|
||||
# 2024 Alibaba Inc (Xiang Lyu)
|
||||
#
|
||||
# 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.
|
||||
# Modified from ESPnet(https://github.com/espnet/espnet)
|
||||
"""Encoder definition."""
|
||||
from typing import Tuple
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
from .convolution import ConvolutionModule
|
||||
from .encoder_layer import ConformerEncoderLayer
|
||||
from .positionwise_feed_forward import PositionwiseFeedForward
|
||||
from ..utils.class_utils import (
|
||||
COSYVOICE_EMB_CLASSES,
|
||||
COSYVOICE_SUBSAMPLE_CLASSES,
|
||||
COSYVOICE_ATTENTION_CLASSES,
|
||||
COSYVOICE_ACTIVATION_CLASSES,
|
||||
)
|
||||
from ..utils.mask import make_pad_mask
|
||||
from ..utils.mask import add_optional_chunk_mask
|
||||
|
||||
|
||||
class Upsample1D(nn.Module):
|
||||
"""A 1D upsampling layer with an optional convolution.
|
||||
|
||||
Parameters:
|
||||
channels (`int`):
|
||||
number of channels in the inputs and outputs.
|
||||
use_conv (`bool`, default `False`):
|
||||
option to use a convolution.
|
||||
use_conv_transpose (`bool`, default `False`):
|
||||
option to use a convolution transpose.
|
||||
out_channels (`int`, optional):
|
||||
number of output channels. Defaults to `channels`.
|
||||
"""
|
||||
|
||||
def __init__(self, channels: int, out_channels: int, stride: int = 2):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.out_channels = out_channels
|
||||
self.stride = stride
|
||||
# In this mode, first repeat interpolate, than conv with stride=1
|
||||
self.conv = nn.Conv1d(self.channels, self.out_channels, stride * 2 + 1, stride=1, padding=0)
|
||||
|
||||
def forward(self, inputs: torch.Tensor, input_lengths: torch.Tensor):
|
||||
outputs = F.interpolate(inputs, scale_factor=float(self.stride), mode="nearest")
|
||||
outputs = F.pad(outputs, (self.stride * 2, 0), value=0.0)
|
||||
outputs = self.conv(outputs)
|
||||
return outputs, input_lengths * self.stride
|
||||
|
||||
|
||||
class PreLookaheadLayer(nn.Module):
|
||||
def __init__(self, channels: int, pre_lookahead_len: int = 1):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.pre_lookahead_len = pre_lookahead_len
|
||||
self.conv1 = nn.Conv1d(
|
||||
channels, channels,
|
||||
kernel_size=pre_lookahead_len + 1,
|
||||
stride=1, padding=0,
|
||||
)
|
||||
self.conv2 = nn.Conv1d(
|
||||
channels, channels,
|
||||
kernel_size=3, stride=1, padding=0,
|
||||
)
|
||||
|
||||
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
inputs: (batch_size, seq_len, channels)
|
||||
"""
|
||||
outputs = inputs.transpose(1, 2).contiguous()
|
||||
# look ahead
|
||||
outputs = F.pad(outputs, (0, self.pre_lookahead_len), mode='constant', value=0.0)
|
||||
outputs = F.leaky_relu(self.conv1(outputs))
|
||||
# outputs
|
||||
outputs = F.pad(outputs, (2, 0), mode='constant', value=0.0)
|
||||
outputs = self.conv2(outputs)
|
||||
outputs = outputs.transpose(1, 2).contiguous()
|
||||
|
||||
# residual connection
|
||||
outputs = outputs + inputs
|
||||
return outputs
|
||||
|
||||
|
||||
class UpsampleConformerEncoder(torch.nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
input_size: int = 512,
|
||||
output_size: int = 512,
|
||||
attention_heads: int = 8,
|
||||
linear_units: int = 2048,
|
||||
num_blocks: int = 6,
|
||||
dropout_rate: float = 0.1,
|
||||
positional_dropout_rate: float = 0.1,
|
||||
attention_dropout_rate: float = 0.1,
|
||||
input_layer: str = "linear",
|
||||
pos_enc_layer_type: str = "rel_pos_espnet",
|
||||
normalize_before: bool = True,
|
||||
static_chunk_size: int = 0,
|
||||
use_dynamic_chunk: bool = False,
|
||||
global_cmvn: torch.nn.Module = None,
|
||||
use_dynamic_left_chunk: bool = False,
|
||||
positionwise_conv_kernel_size: int = 1,
|
||||
macaron_style: bool = False,
|
||||
selfattention_layer_type: str = "rel_selfattn",
|
||||
activation_type: str = "swish",
|
||||
use_cnn_module: bool = False,
|
||||
cnn_module_kernel: int = 15,
|
||||
causal: bool = False,
|
||||
cnn_module_norm: str = "batch_norm",
|
||||
key_bias: bool = True,
|
||||
gradient_checkpointing: bool = False,
|
||||
):
|
||||
"""
|
||||
Args:
|
||||
input_size (int): input dim
|
||||
output_size (int): dimension of attention
|
||||
attention_heads (int): the number of heads of multi head attention
|
||||
linear_units (int): the hidden units number of position-wise feed
|
||||
forward
|
||||
num_blocks (int): the number of decoder blocks
|
||||
dropout_rate (float): dropout rate
|
||||
attention_dropout_rate (float): dropout rate in attention
|
||||
positional_dropout_rate (float): dropout rate after adding
|
||||
positional encoding
|
||||
input_layer (str): input layer type.
|
||||
optional [linear, conv2d, conv2d6, conv2d8]
|
||||
pos_enc_layer_type (str): Encoder positional encoding layer type.
|
||||
opitonal [abs_pos, scaled_abs_pos, rel_pos, no_pos]
|
||||
normalize_before (bool):
|
||||
True: use layer_norm before each sub-block of a layer.
|
||||
False: use layer_norm after each sub-block of a layer.
|
||||
static_chunk_size (int): chunk size for static chunk training and
|
||||
decoding
|
||||
use_dynamic_chunk (bool): whether use dynamic chunk size for
|
||||
training or not, You can only use fixed chunk(chunk_size > 0)
|
||||
or dyanmic chunk size(use_dynamic_chunk = True)
|
||||
global_cmvn (Optional[torch.nn.Module]): Optional GlobalCMVN module
|
||||
use_dynamic_left_chunk (bool): whether use dynamic left chunk in
|
||||
dynamic chunk training
|
||||
key_bias: whether use bias in attention.linear_k, False for whisper models.
|
||||
gradient_checkpointing: rerunning a forward-pass segment for each
|
||||
checkpointed segment during backward.
|
||||
"""
|
||||
super().__init__()
|
||||
self._output_size = output_size
|
||||
|
||||
self.global_cmvn = global_cmvn
|
||||
self.embed = COSYVOICE_SUBSAMPLE_CLASSES[input_layer](
|
||||
input_size,
|
||||
output_size,
|
||||
dropout_rate,
|
||||
COSYVOICE_EMB_CLASSES[pos_enc_layer_type](output_size,
|
||||
positional_dropout_rate),
|
||||
)
|
||||
|
||||
self.normalize_before = normalize_before
|
||||
self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5)
|
||||
self.static_chunk_size = static_chunk_size
|
||||
self.use_dynamic_chunk = use_dynamic_chunk
|
||||
self.use_dynamic_left_chunk = use_dynamic_left_chunk
|
||||
self.gradient_checkpointing = 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.pre_lookahead_layer = PreLookaheadLayer(channels=512, pre_lookahead_len=3)
|
||||
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)
|
||||
])
|
||||
self.up_layer = Upsample1D(channels=512, out_channels=512, stride=2)
|
||||
self.up_embed = COSYVOICE_SUBSAMPLE_CLASSES[input_layer](
|
||||
input_size,
|
||||
output_size,
|
||||
dropout_rate,
|
||||
COSYVOICE_EMB_CLASSES[pos_enc_layer_type](output_size,
|
||||
positional_dropout_rate),
|
||||
)
|
||||
self.up_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(4)
|
||||
])
|
||||
|
||||
def output_size(self) -> int:
|
||||
return self._output_size
|
||||
|
||||
def forward(
|
||||
self,
|
||||
xs: torch.Tensor,
|
||||
xs_lens: torch.Tensor,
|
||||
decoding_chunk_size: int = 0,
|
||||
num_decoding_left_chunks: int = -1,
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
"""Embed positions in tensor.
|
||||
|
||||
Args:
|
||||
xs: padded input tensor (B, T, D)
|
||||
xs_lens: input length (B)
|
||||
decoding_chunk_size: decoding chunk size for dynamic chunk
|
||||
0: default for training, use random dynamic chunk.
|
||||
<0: for decoding, use full chunk.
|
||||
>0: for decoding, use fixed chunk size as set.
|
||||
num_decoding_left_chunks: number of left chunks, this is for decoding,
|
||||
the chunk size is decoding_chunk_size.
|
||||
>=0: use num_decoding_left_chunks
|
||||
<0: use all left chunks
|
||||
Returns:
|
||||
encoder output tensor xs, and subsampled masks
|
||||
xs: padded output tensor (B, T' ~= T/subsample_rate, D)
|
||||
masks: torch.Tensor batch padding mask after subsample
|
||||
(B, 1, T' ~= T/subsample_rate)
|
||||
NOTE(xcsong):
|
||||
We pass the `__call__` method of the modules instead of `forward` to the
|
||||
checkpointing API because `__call__` attaches all the hooks of the module.
|
||||
https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2
|
||||
"""
|
||||
T = xs.size(1)
|
||||
masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T)
|
||||
if self.global_cmvn is not None:
|
||||
xs = self.global_cmvn(xs)
|
||||
xs, pos_emb, masks = self.embed(xs, masks)
|
||||
mask_pad = masks # (B, 1, T/subsample_rate)
|
||||
chunk_masks = add_optional_chunk_mask(xs, masks,
|
||||
self.use_dynamic_chunk,
|
||||
self.use_dynamic_left_chunk,
|
||||
decoding_chunk_size,
|
||||
self.static_chunk_size,
|
||||
num_decoding_left_chunks)
|
||||
# lookahead + conformer encoder
|
||||
xs = self.pre_lookahead_layer(xs)
|
||||
xs = self.forward_layers(xs, chunk_masks, pos_emb, mask_pad)
|
||||
|
||||
# upsample + conformer encoder
|
||||
xs = xs.transpose(1, 2).contiguous()
|
||||
xs, xs_lens = self.up_layer(xs, xs_lens)
|
||||
xs = xs.transpose(1, 2).contiguous()
|
||||
T = xs.size(1)
|
||||
masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T)
|
||||
xs, pos_emb, masks = self.up_embed(xs, masks)
|
||||
mask_pad = masks # (B, 1, T/subsample_rate)
|
||||
chunk_masks = add_optional_chunk_mask(xs, masks,
|
||||
self.use_dynamic_chunk,
|
||||
self.use_dynamic_left_chunk,
|
||||
decoding_chunk_size,
|
||||
self.static_chunk_size * self.up_layer.stride,
|
||||
num_decoding_left_chunks)
|
||||
xs = self.forward_up_layers(xs, chunk_masks, pos_emb, mask_pad)
|
||||
|
||||
if self.normalize_before:
|
||||
xs = self.after_norm(xs)
|
||||
# Here we assume the mask is not changed in encoder layers, so just
|
||||
# return the masks before encoder layers, and the masks will be used
|
||||
# for cross attention with decoder later
|
||||
return xs, masks
|
||||
|
||||
def forward_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor,
|
||||
pos_emb: torch.Tensor,
|
||||
mask_pad: torch.Tensor) -> torch.Tensor:
|
||||
for layer in self.encoders:
|
||||
xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
|
||||
return xs
|
||||
|
||||
def forward_up_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor,
|
||||
pos_emb: torch.Tensor,
|
||||
mask_pad: torch.Tensor) -> torch.Tensor:
|
||||
for layer in self.up_encoders:
|
||||
xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
|
||||
return xs
|
||||
@@ -1,71 +0,0 @@
|
||||
# Copyright [2023-11-28] <sxc19@mails.tsinghua.edu.cn, Xingchen Song>
|
||||
# 2024 Alibaba Inc (authors: Xiang Lyu)
|
||||
#
|
||||
# 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.
|
||||
import torch
|
||||
|
||||
from ..transformer.activation import Swish
|
||||
from ..transformer.subsampling import (
|
||||
LinearNoSubsampling,
|
||||
EmbedinigNoSubsampling,
|
||||
Conv1dSubsampling2,
|
||||
Conv2dSubsampling4,
|
||||
Conv2dSubsampling6,
|
||||
Conv2dSubsampling8,
|
||||
)
|
||||
from ..transformer.embedding import (
|
||||
PositionalEncoding,
|
||||
RelPositionalEncoding,
|
||||
WhisperPositionalEncoding,
|
||||
LearnablePositionalEncoding,
|
||||
NoPositionalEncoding)
|
||||
from ..transformer.attention import (MultiHeadedAttention,
|
||||
RelPositionMultiHeadedAttention)
|
||||
from ..transformer.embedding import EspnetRelPositionalEncoding
|
||||
from ..transformer.subsampling import LegacyLinearNoSubsampling
|
||||
|
||||
|
||||
COSYVOICE_ACTIVATION_CLASSES = {
|
||||
"hardtanh": torch.nn.Hardtanh,
|
||||
"tanh": torch.nn.Tanh,
|
||||
"relu": torch.nn.ReLU,
|
||||
"selu": torch.nn.SELU,
|
||||
"swish": getattr(torch.nn, "SiLU", Swish),
|
||||
"gelu": torch.nn.GELU,
|
||||
}
|
||||
|
||||
COSYVOICE_SUBSAMPLE_CLASSES = {
|
||||
"linear": LinearNoSubsampling,
|
||||
"linear_legacy": LegacyLinearNoSubsampling,
|
||||
"embed": EmbedinigNoSubsampling,
|
||||
"conv1d2": Conv1dSubsampling2,
|
||||
"conv2d": Conv2dSubsampling4,
|
||||
"conv2d6": Conv2dSubsampling6,
|
||||
"conv2d8": Conv2dSubsampling8,
|
||||
'paraformer_dummy': torch.nn.Identity
|
||||
}
|
||||
|
||||
COSYVOICE_EMB_CLASSES = {
|
||||
"embed": PositionalEncoding,
|
||||
"abs_pos": PositionalEncoding,
|
||||
"rel_pos": RelPositionalEncoding,
|
||||
"rel_pos_espnet": EspnetRelPositionalEncoding,
|
||||
"no_pos": NoPositionalEncoding,
|
||||
"abs_pos_whisper": WhisperPositionalEncoding,
|
||||
"embed_learnable_pe": LearnablePositionalEncoding,
|
||||
}
|
||||
|
||||
COSYVOICE_ATTENTION_CLASSES = {
|
||||
"selfattn": MultiHeadedAttention,
|
||||
"rel_selfattn": RelPositionMultiHeadedAttention,
|
||||
}
|
||||
@@ -1,193 +0,0 @@
|
||||
# Copyright (c) 2019 Shigeki Karita
|
||||
# 2020 Mobvoi Inc (Binbin Zhang)
|
||||
# 2024 Alibaba Inc (authors: Xiang Lyu)
|
||||
#
|
||||
# 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.
|
||||
|
||||
import torch
|
||||
|
||||
'''
|
||||
def subsequent_mask(
|
||||
size: int,
|
||||
device: torch.device = torch.device("cpu"),
|
||||
) -> torch.Tensor:
|
||||
"""Create mask for subsequent steps (size, size).
|
||||
|
||||
This mask is used only in decoder which works in an auto-regressive mode.
|
||||
This means the current step could only do attention with its left steps.
|
||||
|
||||
In encoder, fully attention is used when streaming is not necessary and
|
||||
the sequence is not long. In this case, no attention mask is needed.
|
||||
|
||||
When streaming is need, chunk-based attention is used in encoder. See
|
||||
subsequent_chunk_mask for the chunk-based attention mask.
|
||||
|
||||
Args:
|
||||
size (int): size of mask
|
||||
str device (str): "cpu" or "cuda" or torch.Tensor.device
|
||||
dtype (torch.device): result dtype
|
||||
|
||||
Returns:
|
||||
torch.Tensor: mask
|
||||
|
||||
Examples:
|
||||
>>> subsequent_mask(3)
|
||||
[[1, 0, 0],
|
||||
[1, 1, 0],
|
||||
[1, 1, 1]]
|
||||
"""
|
||||
ret = torch.ones(size, size, device=device, dtype=torch.bool)
|
||||
return torch.tril(ret)
|
||||
'''
|
||||
|
||||
|
||||
def subsequent_chunk_mask(
|
||||
size: int,
|
||||
chunk_size: int,
|
||||
num_left_chunks: int = -1,
|
||||
device: torch.device = torch.device("cpu"),
|
||||
) -> torch.Tensor:
|
||||
"""Create mask for subsequent steps (size, size) with chunk size,
|
||||
this is for streaming encoder
|
||||
|
||||
Args:
|
||||
size (int): size of mask
|
||||
chunk_size (int): size of chunk
|
||||
num_left_chunks (int): number of left chunks
|
||||
<0: use full chunk
|
||||
>=0: use num_left_chunks
|
||||
device (torch.device): "cpu" or "cuda" or torch.Tensor.device
|
||||
|
||||
Returns:
|
||||
torch.Tensor: mask
|
||||
|
||||
Examples:
|
||||
>>> subsequent_chunk_mask(4, 2)
|
||||
[[1, 1, 0, 0],
|
||||
[1, 1, 0, 0],
|
||||
[1, 1, 1, 1],
|
||||
[1, 1, 1, 1]]
|
||||
"""
|
||||
# NOTE this modified implementation meets onnx export requirements, but it doesn't support num_left_chunks
|
||||
# actually this is not needed after we have inference cache implemented, will remove it later
|
||||
pos_idx = torch.arange(size, device=device)
|
||||
block_value = (torch.div(pos_idx, chunk_size, rounding_mode='trunc') + 1) * chunk_size
|
||||
ret = pos_idx.unsqueeze(0) < block_value.unsqueeze(1)
|
||||
return ret
|
||||
|
||||
|
||||
def add_optional_chunk_mask(xs: torch.Tensor,
|
||||
masks: torch.Tensor,
|
||||
use_dynamic_chunk: bool,
|
||||
use_dynamic_left_chunk: bool,
|
||||
decoding_chunk_size: int,
|
||||
static_chunk_size: int,
|
||||
num_decoding_left_chunks: int,
|
||||
enable_full_context: bool = True):
|
||||
""" Apply optional mask for encoder.
|
||||
|
||||
Args:
|
||||
xs (torch.Tensor): padded input, (B, L, D), L for max length
|
||||
mask (torch.Tensor): mask for xs, (B, 1, L)
|
||||
use_dynamic_chunk (bool): whether to use dynamic chunk or not
|
||||
use_dynamic_left_chunk (bool): whether to use dynamic left chunk for
|
||||
training.
|
||||
decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's
|
||||
0: default for training, use random dynamic chunk.
|
||||
<0: for decoding, use full chunk.
|
||||
>0: for decoding, use fixed chunk size as set.
|
||||
static_chunk_size (int): chunk size for static chunk training/decoding
|
||||
if it's greater than 0, if use_dynamic_chunk is true,
|
||||
this parameter will be ignored
|
||||
num_decoding_left_chunks: number of left chunks, this is for decoding,
|
||||
the chunk size is decoding_chunk_size.
|
||||
>=0: use num_decoding_left_chunks
|
||||
<0: use all left chunks
|
||||
enable_full_context (bool):
|
||||
True: chunk size is either [1, 25] or full context(max_len)
|
||||
False: chunk size ~ U[1, 25]
|
||||
|
||||
Returns:
|
||||
torch.Tensor: chunk mask of the input xs.
|
||||
"""
|
||||
# Whether to use chunk mask or not
|
||||
if use_dynamic_chunk:
|
||||
max_len = xs.size(1)
|
||||
if decoding_chunk_size < 0:
|
||||
chunk_size = max_len
|
||||
num_left_chunks = -1
|
||||
elif decoding_chunk_size > 0:
|
||||
chunk_size = decoding_chunk_size
|
||||
num_left_chunks = num_decoding_left_chunks
|
||||
else:
|
||||
# chunk size is either [1, 25] or full context(max_len).
|
||||
# Since we use 4 times subsampling and allow up to 1s(100 frames)
|
||||
# delay, the maximum frame is 100 / 4 = 25.
|
||||
chunk_size = torch.randint(1, max_len, (1, )).item()
|
||||
num_left_chunks = -1
|
||||
if chunk_size > max_len // 2 and enable_full_context:
|
||||
chunk_size = max_len
|
||||
else:
|
||||
chunk_size = chunk_size % 25 + 1
|
||||
if use_dynamic_left_chunk:
|
||||
max_left_chunks = (max_len - 1) // chunk_size
|
||||
num_left_chunks = torch.randint(0, max_left_chunks,
|
||||
(1, )).item()
|
||||
chunk_masks = subsequent_chunk_mask(xs.size(1), chunk_size,
|
||||
num_left_chunks,
|
||||
xs.device) # (L, L)
|
||||
chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L)
|
||||
chunk_masks = masks & chunk_masks # (B, L, L)
|
||||
elif static_chunk_size > 0:
|
||||
num_left_chunks = num_decoding_left_chunks
|
||||
chunk_masks = subsequent_chunk_mask(xs.size(1), static_chunk_size,
|
||||
num_left_chunks,
|
||||
xs.device) # (L, L)
|
||||
chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L)
|
||||
chunk_masks = masks & chunk_masks # (B, L, L)
|
||||
else:
|
||||
chunk_masks = masks
|
||||
assert chunk_masks.dtype == torch.bool
|
||||
if (chunk_masks.sum(dim=-1) == 0).sum().item() != 0:
|
||||
logging.warning('get chunk_masks all false at some timestep, force set to true, make sure they are masked in futuer computation!')
|
||||
chunk_masks[chunk_masks.sum(dim=-1)==0] = True
|
||||
return chunk_masks
|
||||
|
||||
|
||||
def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
|
||||
"""Make mask tensor containing indices of padded part.
|
||||
|
||||
See description of make_non_pad_mask.
|
||||
|
||||
Args:
|
||||
lengths (torch.Tensor): Batch of lengths (B,).
|
||||
Returns:
|
||||
torch.Tensor: Mask tensor containing indices of padded part.
|
||||
|
||||
Examples:
|
||||
>>> lengths = [5, 3, 2]
|
||||
>>> make_pad_mask(lengths)
|
||||
masks = [[0, 0, 0, 0 ,0],
|
||||
[0, 0, 0, 1, 1],
|
||||
[0, 0, 1, 1, 1]]
|
||||
"""
|
||||
batch_size = lengths.size(0)
|
||||
max_len = max_len if max_len > 0 else lengths.max().item()
|
||||
seq_range = torch.arange(0,
|
||||
max_len,
|
||||
dtype=torch.int64,
|
||||
device=lengths.device)
|
||||
seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
|
||||
seq_length_expand = lengths.unsqueeze(-1)
|
||||
mask = seq_range_expand >= seq_length_expand
|
||||
return mask
|
||||
@@ -1,81 +0,0 @@
|
||||
"""mel-spectrogram extraction in Matcha-TTS"""
|
||||
from librosa.filters import mel as librosa_mel_fn
|
||||
import torch
|
||||
import numpy as np
|
||||
|
||||
|
||||
# NOTE: they decalred these global vars
|
||||
mel_basis = {}
|
||||
hann_window = {}
|
||||
|
||||
|
||||
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
|
||||
return torch.log(torch.clamp(x, min=clip_val) * C)
|
||||
|
||||
|
||||
def spectral_normalize_torch(magnitudes):
|
||||
output = dynamic_range_compression_torch(magnitudes)
|
||||
return output
|
||||
|
||||
"""
|
||||
feat_extractor: !name:matcha.utils.audio.mel_spectrogram
|
||||
n_fft: 1920
|
||||
num_mels: 80
|
||||
sampling_rate: 24000
|
||||
hop_size: 480
|
||||
win_size: 1920
|
||||
fmin: 0
|
||||
fmax: 8000
|
||||
center: False
|
||||
|
||||
"""
|
||||
|
||||
def mel_spectrogram(y, n_fft=1920, num_mels=80, sampling_rate=24000, hop_size=480, win_size=1920,
|
||||
fmin=0, fmax=8000, center=False):
|
||||
"""Copied from https://github.com/shivammehta25/Matcha-TTS/blob/main/matcha/utils/audio.py
|
||||
Set default values according to Cosyvoice's config.
|
||||
"""
|
||||
|
||||
if isinstance(y, np.ndarray):
|
||||
y = torch.tensor(y).float()
|
||||
|
||||
if len(y.shape) == 1:
|
||||
y = y[None, ]
|
||||
|
||||
if torch.min(y) < -1.0:
|
||||
print("min value is ", torch.min(y))
|
||||
if torch.max(y) > 1.0:
|
||||
print("max value is ", torch.max(y))
|
||||
|
||||
global mel_basis, hann_window # pylint: disable=global-statement,global-variable-not-assigned
|
||||
if f"{str(fmax)}_{str(y.device)}" not in mel_basis:
|
||||
mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
|
||||
mel_basis[str(fmax) + "_" + str(y.device)] = torch.from_numpy(mel).float().to(y.device)
|
||||
hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
|
||||
|
||||
y = torch.nn.functional.pad(
|
||||
y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect"
|
||||
)
|
||||
y = y.squeeze(1)
|
||||
|
||||
spec = torch.view_as_real(
|
||||
torch.stft(
|
||||
y,
|
||||
n_fft,
|
||||
hop_length=hop_size,
|
||||
win_length=win_size,
|
||||
window=hann_window[str(y.device)],
|
||||
center=center,
|
||||
pad_mode="reflect",
|
||||
normalized=False,
|
||||
onesided=True,
|
||||
return_complex=True,
|
||||
)
|
||||
)
|
||||
|
||||
spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
|
||||
|
||||
spec = torch.matmul(mel_basis[str(fmax) + "_" + str(y.device)], spec)
|
||||
spec = spectral_normalize_torch(spec)
|
||||
|
||||
return spec
|
||||
@@ -1,428 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- encoding: utf-8 -*-
|
||||
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
|
||||
# MIT License (https://opensource.org/licenses/MIT)
|
||||
# Modified from 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker)
|
||||
|
||||
|
||||
from collections import OrderedDict
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import torch.utils.checkpoint as cp
|
||||
import torchaudio.compliance.kaldi as Kaldi
|
||||
|
||||
|
||||
def pad_list(xs, pad_value):
|
||||
"""Perform padding for the list of tensors.
|
||||
|
||||
Args:
|
||||
xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)].
|
||||
pad_value (float): Value for padding.
|
||||
|
||||
Returns:
|
||||
Tensor: Padded tensor (B, Tmax, `*`).
|
||||
|
||||
Examples:
|
||||
>>> x = [torch.ones(4), torch.ones(2), torch.ones(1)]
|
||||
>>> x
|
||||
[tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])]
|
||||
>>> pad_list(x, 0)
|
||||
tensor([[1., 1., 1., 1.],
|
||||
[1., 1., 0., 0.],
|
||||
[1., 0., 0., 0.]])
|
||||
|
||||
"""
|
||||
n_batch = len(xs)
|
||||
max_len = max(x.size(0) for x in xs)
|
||||
pad = xs[0].new(n_batch, max_len, *xs[0].size()[1:]).fill_(pad_value)
|
||||
|
||||
for i in range(n_batch):
|
||||
pad[i, : xs[i].size(0)] = xs[i]
|
||||
|
||||
return pad
|
||||
|
||||
|
||||
def extract_feature(audio):
|
||||
features = []
|
||||
feature_times = []
|
||||
feature_lengths = []
|
||||
for au in audio:
|
||||
feature = Kaldi.fbank(au.unsqueeze(0), num_mel_bins=80)
|
||||
feature = feature - feature.mean(dim=0, keepdim=True)
|
||||
features.append(feature)
|
||||
feature_times.append(au.shape[0])
|
||||
feature_lengths.append(feature.shape[0])
|
||||
# padding for batch inference
|
||||
features_padded = pad_list(features, pad_value=0)
|
||||
# features = torch.cat(features)
|
||||
return features_padded, feature_lengths, feature_times
|
||||
|
||||
|
||||
class BasicResBlock(torch.nn.Module):
|
||||
expansion = 1
|
||||
|
||||
def __init__(self, in_planes, planes, stride=1):
|
||||
super(BasicResBlock, self).__init__()
|
||||
self.conv1 = torch.nn.Conv2d(
|
||||
in_planes, planes, kernel_size=3, stride=(stride, 1), padding=1, bias=False
|
||||
)
|
||||
self.bn1 = torch.nn.BatchNorm2d(planes)
|
||||
self.conv2 = torch.nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
|
||||
self.bn2 = torch.nn.BatchNorm2d(planes)
|
||||
|
||||
self.shortcut = torch.nn.Sequential()
|
||||
if stride != 1 or in_planes != self.expansion * planes:
|
||||
self.shortcut = torch.nn.Sequential(
|
||||
torch.nn.Conv2d(
|
||||
in_planes,
|
||||
self.expansion * planes,
|
||||
kernel_size=1,
|
||||
stride=(stride, 1),
|
||||
bias=False,
|
||||
),
|
||||
torch.nn.BatchNorm2d(self.expansion * planes),
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
out = F.relu(self.bn1(self.conv1(x)))
|
||||
out = self.bn2(self.conv2(out))
|
||||
out += self.shortcut(x)
|
||||
out = F.relu(out)
|
||||
return out
|
||||
|
||||
|
||||
class FCM(torch.nn.Module):
|
||||
def __init__(self, block=BasicResBlock, num_blocks=[2, 2], m_channels=32, feat_dim=80):
|
||||
super(FCM, self).__init__()
|
||||
self.in_planes = m_channels
|
||||
self.conv1 = torch.nn.Conv2d(1, m_channels, kernel_size=3, stride=1, padding=1, bias=False)
|
||||
self.bn1 = torch.nn.BatchNorm2d(m_channels)
|
||||
|
||||
self.layer1 = self._make_layer(block, m_channels, num_blocks[0], stride=2)
|
||||
self.layer2 = self._make_layer(block, m_channels, num_blocks[0], stride=2)
|
||||
|
||||
self.conv2 = torch.nn.Conv2d(
|
||||
m_channels, m_channels, kernel_size=3, stride=(2, 1), padding=1, bias=False
|
||||
)
|
||||
self.bn2 = torch.nn.BatchNorm2d(m_channels)
|
||||
self.out_channels = m_channels * (feat_dim // 8)
|
||||
|
||||
def _make_layer(self, block, planes, num_blocks, stride):
|
||||
strides = [stride] + [1] * (num_blocks - 1)
|
||||
layers = []
|
||||
for stride in strides:
|
||||
layers.append(block(self.in_planes, planes, stride))
|
||||
self.in_planes = planes * block.expansion
|
||||
return torch.nn.Sequential(*layers)
|
||||
|
||||
def forward(self, x):
|
||||
x = x.unsqueeze(1)
|
||||
out = F.relu(self.bn1(self.conv1(x)))
|
||||
out = self.layer1(out)
|
||||
out = self.layer2(out)
|
||||
out = F.relu(self.bn2(self.conv2(out)))
|
||||
|
||||
shape = out.shape
|
||||
out = out.reshape(shape[0], shape[1] * shape[2], shape[3])
|
||||
return out
|
||||
|
||||
|
||||
def get_nonlinear(config_str, channels):
|
||||
nonlinear = torch.nn.Sequential()
|
||||
for name in config_str.split("-"):
|
||||
if name == "relu":
|
||||
nonlinear.add_module("relu", torch.nn.ReLU(inplace=True))
|
||||
elif name == "prelu":
|
||||
nonlinear.add_module("prelu", torch.nn.PReLU(channels))
|
||||
elif name == "batchnorm":
|
||||
nonlinear.add_module("batchnorm", torch.nn.BatchNorm1d(channels))
|
||||
elif name == "batchnorm_":
|
||||
nonlinear.add_module("batchnorm", torch.nn.BatchNorm1d(channels, affine=False))
|
||||
else:
|
||||
raise ValueError("Unexpected module ({}).".format(name))
|
||||
return nonlinear
|
||||
|
||||
|
||||
def statistics_pooling(x, dim=-1, keepdim=False, unbiased=True, eps=1e-2):
|
||||
mean = x.mean(dim=dim)
|
||||
std = x.std(dim=dim, unbiased=unbiased)
|
||||
stats = torch.cat([mean, std], dim=-1)
|
||||
if keepdim:
|
||||
stats = stats.unsqueeze(dim=dim)
|
||||
return stats
|
||||
|
||||
|
||||
class StatsPool(torch.nn.Module):
|
||||
def forward(self, x):
|
||||
return statistics_pooling(x)
|
||||
|
||||
|
||||
class TDNNLayer(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
kernel_size,
|
||||
stride=1,
|
||||
padding=0,
|
||||
dilation=1,
|
||||
bias=False,
|
||||
config_str="batchnorm-relu",
|
||||
):
|
||||
super(TDNNLayer, self).__init__()
|
||||
if padding < 0:
|
||||
assert (
|
||||
kernel_size % 2 == 1
|
||||
), "Expect equal paddings, but got even kernel size ({})".format(kernel_size)
|
||||
padding = (kernel_size - 1) // 2 * dilation
|
||||
self.linear = torch.nn.Conv1d(
|
||||
in_channels,
|
||||
out_channels,
|
||||
kernel_size,
|
||||
stride=stride,
|
||||
padding=padding,
|
||||
dilation=dilation,
|
||||
bias=bias,
|
||||
)
|
||||
self.nonlinear = get_nonlinear(config_str, out_channels)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.linear(x)
|
||||
x = self.nonlinear(x)
|
||||
return x
|
||||
|
||||
|
||||
class CAMLayer(torch.nn.Module):
|
||||
def __init__(
|
||||
self, bn_channels, out_channels, kernel_size, stride, padding, dilation, bias, reduction=2
|
||||
):
|
||||
super(CAMLayer, self).__init__()
|
||||
self.linear_local = torch.nn.Conv1d(
|
||||
bn_channels,
|
||||
out_channels,
|
||||
kernel_size,
|
||||
stride=stride,
|
||||
padding=padding,
|
||||
dilation=dilation,
|
||||
bias=bias,
|
||||
)
|
||||
self.linear1 = torch.nn.Conv1d(bn_channels, bn_channels // reduction, 1)
|
||||
self.relu = torch.nn.ReLU(inplace=True)
|
||||
self.linear2 = torch.nn.Conv1d(bn_channels // reduction, out_channels, 1)
|
||||
self.sigmoid = torch.nn.Sigmoid()
|
||||
|
||||
def forward(self, x):
|
||||
y = self.linear_local(x)
|
||||
context = x.mean(-1, keepdim=True) + self.seg_pooling(x)
|
||||
context = self.relu(self.linear1(context))
|
||||
m = self.sigmoid(self.linear2(context))
|
||||
return y * m
|
||||
|
||||
def seg_pooling(self, x, seg_len=100, stype="avg"):
|
||||
if stype == "avg":
|
||||
seg = F.avg_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True)
|
||||
elif stype == "max":
|
||||
seg = F.max_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True)
|
||||
else:
|
||||
raise ValueError("Wrong segment pooling type.")
|
||||
shape = seg.shape
|
||||
seg = seg.unsqueeze(-1).expand(*shape, seg_len).reshape(*shape[:-1], -1)
|
||||
seg = seg[..., : x.shape[-1]]
|
||||
return seg
|
||||
|
||||
|
||||
class CAMDenseTDNNLayer(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
bn_channels,
|
||||
kernel_size,
|
||||
stride=1,
|
||||
dilation=1,
|
||||
bias=False,
|
||||
config_str="batchnorm-relu",
|
||||
memory_efficient=False,
|
||||
):
|
||||
super(CAMDenseTDNNLayer, self).__init__()
|
||||
assert kernel_size % 2 == 1, "Expect equal paddings, but got even kernel size ({})".format(
|
||||
kernel_size
|
||||
)
|
||||
padding = (kernel_size - 1) // 2 * dilation
|
||||
self.memory_efficient = memory_efficient
|
||||
self.nonlinear1 = get_nonlinear(config_str, in_channels)
|
||||
self.linear1 = torch.nn.Conv1d(in_channels, bn_channels, 1, bias=False)
|
||||
self.nonlinear2 = get_nonlinear(config_str, bn_channels)
|
||||
self.cam_layer = CAMLayer(
|
||||
bn_channels,
|
||||
out_channels,
|
||||
kernel_size,
|
||||
stride=stride,
|
||||
padding=padding,
|
||||
dilation=dilation,
|
||||
bias=bias,
|
||||
)
|
||||
|
||||
def bn_function(self, x):
|
||||
return self.linear1(self.nonlinear1(x))
|
||||
|
||||
def forward(self, x):
|
||||
if self.training and self.memory_efficient:
|
||||
x = cp.checkpoint(self.bn_function, x)
|
||||
else:
|
||||
x = self.bn_function(x)
|
||||
x = self.cam_layer(self.nonlinear2(x))
|
||||
return x
|
||||
|
||||
|
||||
class CAMDenseTDNNBlock(torch.nn.ModuleList):
|
||||
def __init__(
|
||||
self,
|
||||
num_layers,
|
||||
in_channels,
|
||||
out_channels,
|
||||
bn_channels,
|
||||
kernel_size,
|
||||
stride=1,
|
||||
dilation=1,
|
||||
bias=False,
|
||||
config_str="batchnorm-relu",
|
||||
memory_efficient=False,
|
||||
):
|
||||
super(CAMDenseTDNNBlock, self).__init__()
|
||||
for i in range(num_layers):
|
||||
layer = CAMDenseTDNNLayer(
|
||||
in_channels=in_channels + i * out_channels,
|
||||
out_channels=out_channels,
|
||||
bn_channels=bn_channels,
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
dilation=dilation,
|
||||
bias=bias,
|
||||
config_str=config_str,
|
||||
memory_efficient=memory_efficient,
|
||||
)
|
||||
self.add_module("tdnnd%d" % (i + 1), layer)
|
||||
|
||||
def forward(self, x):
|
||||
for layer in self:
|
||||
x = torch.cat([x, layer(x)], dim=1)
|
||||
return x
|
||||
|
||||
|
||||
class TransitLayer(torch.nn.Module):
|
||||
def __init__(self, in_channels, out_channels, bias=True, config_str="batchnorm-relu"):
|
||||
super(TransitLayer, self).__init__()
|
||||
self.nonlinear = get_nonlinear(config_str, in_channels)
|
||||
self.linear = torch.nn.Conv1d(in_channels, out_channels, 1, bias=bias)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.nonlinear(x)
|
||||
x = self.linear(x)
|
||||
return x
|
||||
|
||||
|
||||
class DenseLayer(torch.nn.Module):
|
||||
def __init__(self, in_channels, out_channels, bias=False, config_str="batchnorm-relu"):
|
||||
super(DenseLayer, self).__init__()
|
||||
self.linear = torch.nn.Conv1d(in_channels, out_channels, 1, bias=bias)
|
||||
self.nonlinear = get_nonlinear(config_str, out_channels)
|
||||
|
||||
def forward(self, x):
|
||||
if len(x.shape) == 2:
|
||||
x = self.linear(x.unsqueeze(dim=-1)).squeeze(dim=-1)
|
||||
else:
|
||||
x = self.linear(x)
|
||||
x = self.nonlinear(x)
|
||||
return x
|
||||
|
||||
# @tables.register("model_classes", "CAMPPlus")
|
||||
class CAMPPlus(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
feat_dim=80,
|
||||
embedding_size=192,
|
||||
growth_rate=32,
|
||||
bn_size=4,
|
||||
init_channels=128,
|
||||
config_str="batchnorm-relu",
|
||||
memory_efficient=True,
|
||||
output_level="segment",
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.head = FCM(feat_dim=feat_dim)
|
||||
channels = self.head.out_channels
|
||||
self.output_level = output_level
|
||||
|
||||
self.xvector = torch.nn.Sequential(
|
||||
OrderedDict(
|
||||
[
|
||||
(
|
||||
"tdnn",
|
||||
TDNNLayer(
|
||||
channels,
|
||||
init_channels,
|
||||
5,
|
||||
stride=2,
|
||||
dilation=1,
|
||||
padding=-1,
|
||||
config_str=config_str,
|
||||
),
|
||||
),
|
||||
]
|
||||
)
|
||||
)
|
||||
channels = init_channels
|
||||
for i, (num_layers, kernel_size, dilation) in enumerate(
|
||||
zip((12, 24, 16), (3, 3, 3), (1, 2, 2))
|
||||
):
|
||||
block = CAMDenseTDNNBlock(
|
||||
num_layers=num_layers,
|
||||
in_channels=channels,
|
||||
out_channels=growth_rate,
|
||||
bn_channels=bn_size * growth_rate,
|
||||
kernel_size=kernel_size,
|
||||
dilation=dilation,
|
||||
config_str=config_str,
|
||||
memory_efficient=memory_efficient,
|
||||
)
|
||||
self.xvector.add_module("block%d" % (i + 1), block)
|
||||
channels = channels + num_layers * growth_rate
|
||||
self.xvector.add_module(
|
||||
"transit%d" % (i + 1),
|
||||
TransitLayer(channels, channels // 2, bias=False, config_str=config_str),
|
||||
)
|
||||
channels //= 2
|
||||
|
||||
self.xvector.add_module("out_nonlinear", get_nonlinear(config_str, channels))
|
||||
|
||||
if self.output_level == "segment":
|
||||
self.xvector.add_module("stats", StatsPool())
|
||||
self.xvector.add_module(
|
||||
"dense", DenseLayer(channels * 2, embedding_size, config_str="batchnorm_")
|
||||
)
|
||||
else:
|
||||
assert (
|
||||
self.output_level == "frame"
|
||||
), "`output_level` should be set to 'segment' or 'frame'. "
|
||||
|
||||
for m in self.modules():
|
||||
if isinstance(m, (torch.nn.Conv1d, torch.nn.Linear)):
|
||||
torch.nn.init.kaiming_normal_(m.weight.data)
|
||||
if m.bias is not None:
|
||||
torch.nn.init.zeros_(m.bias)
|
||||
|
||||
def forward(self, x):
|
||||
x = x.permute(0, 2, 1) # (B,T,F) => (B,F,T)
|
||||
x = self.head(x)
|
||||
x = self.xvector(x)
|
||||
if self.output_level == "frame":
|
||||
x = x.transpose(1, 2)
|
||||
return x
|
||||
|
||||
def inference(self, audio_list):
|
||||
speech, speech_lengths, speech_times = extract_feature(audio_list)
|
||||
results = self.forward(speech.to(torch.float32))
|
||||
return results
|
||||
@@ -1,30 +0,0 @@
|
||||
from .s3tokenizer import (
|
||||
S3_SR,
|
||||
S3_HOP,
|
||||
S3_TOKEN_HOP,
|
||||
S3_TOKEN_RATE,
|
||||
SPEECH_VOCAB_SIZE,
|
||||
S3Tokenizer,
|
||||
)
|
||||
|
||||
|
||||
SOS = SPEECH_VOCAB_SIZE
|
||||
EOS = SPEECH_VOCAB_SIZE + 1
|
||||
|
||||
|
||||
|
||||
def drop_invalid_tokens(x):
|
||||
"""Drop SoS and EoS"""
|
||||
assert len(x.shape) == 1 or (len(x.shape) == 2 and x.shape[0] == 1), "only batch size of one allowed for now"
|
||||
if SOS in x:
|
||||
s = (x == SOS).nonzero(as_tuple=True)[0].squeeze(0) + 1
|
||||
else:
|
||||
s = 0
|
||||
|
||||
if EOS in x:
|
||||
e = (x == EOS).nonzero(as_tuple=True)[0].squeeze(0)
|
||||
else:
|
||||
e = None
|
||||
|
||||
x = x[s: e]
|
||||
return x
|
||||
@@ -1,168 +0,0 @@
|
||||
from typing import List, Tuple
|
||||
|
||||
import numpy as np
|
||||
import librosa
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from s3tokenizer.utils import padding
|
||||
from s3tokenizer.model_v2 import (
|
||||
S3TokenizerV2,
|
||||
ModelConfig,
|
||||
)
|
||||
|
||||
|
||||
# Sampling rate of the inputs to S3TokenizerV2
|
||||
S3_SR = 16_000
|
||||
S3_HOP = 160 # 100 frames/sec
|
||||
S3_TOKEN_HOP = 640 # 25 tokens/sec
|
||||
S3_TOKEN_RATE = 25
|
||||
SPEECH_VOCAB_SIZE = 6561
|
||||
|
||||
|
||||
class S3Tokenizer(S3TokenizerV2):
|
||||
"""
|
||||
s3tokenizer.S3TokenizerV2 with the following changes:
|
||||
- a more integrated `forward`
|
||||
- compute `log_mel_spectrogram` using `_mel_filters` and `window` in `register_buffers`
|
||||
"""
|
||||
|
||||
ignore_state_dict_missing = ("_mel_filters", "window")
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
name: str="speech_tokenizer_v2_25hz",
|
||||
config: ModelConfig = ModelConfig()
|
||||
):
|
||||
super().__init__(name)
|
||||
|
||||
self.n_fft = 400
|
||||
_mel_filters = librosa.filters.mel(
|
||||
sr=S3_SR,
|
||||
n_fft=self.n_fft,
|
||||
n_mels=config.n_mels
|
||||
)
|
||||
self.register_buffer(
|
||||
"_mel_filters",
|
||||
torch.FloatTensor(_mel_filters),
|
||||
)
|
||||
|
||||
self.register_buffer(
|
||||
"window",
|
||||
torch.hann_window(self.n_fft),
|
||||
)
|
||||
|
||||
def pad(self, wavs, sr) -> List[torch.Tensor]:
|
||||
"""
|
||||
Given a list of wavs with the same `sample_rate`, pad them so that the length is multiple of 40ms (S3 runs at 25 token/sec).
|
||||
"""
|
||||
processed_wavs = []
|
||||
for wav in wavs:
|
||||
if isinstance(wav, np.ndarray):
|
||||
wav = torch.from_numpy(wav)
|
||||
if wav.dim() == 1:
|
||||
wav = wav.unsqueeze(0)
|
||||
|
||||
n_tokens = (wav.shape[1] / sr) * S3_TOKEN_RATE
|
||||
n_tokens = np.ceil(n_tokens)
|
||||
intended_wav_len = n_tokens * (sr / S3_TOKEN_RATE)
|
||||
intended_wav_len = int(intended_wav_len)
|
||||
wav = torch.nn.functional.pad(
|
||||
wav,
|
||||
(0, intended_wav_len - wav.shape[-1]),
|
||||
mode="constant",
|
||||
value=0
|
||||
)
|
||||
processed_wavs.append(wav)
|
||||
return processed_wavs
|
||||
|
||||
def _prepare_audio(self, wavs):
|
||||
"""Prepare a list of audios for s3tokenizer processing."""
|
||||
processed_wavs = []
|
||||
for wav in wavs:
|
||||
if isinstance(wav, np.ndarray):
|
||||
wav = torch.from_numpy(wav)
|
||||
if wav.dim() == 1:
|
||||
wav = wav.unsqueeze(0)
|
||||
|
||||
processed_wavs.append(wav)
|
||||
return processed_wavs
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(
|
||||
self,
|
||||
wavs: torch.Tensor,
|
||||
accelerator: 'Accelerator'=None,
|
||||
max_len: int=None,
|
||||
) -> Tuple[torch.Tensor, torch.LongTensor]:
|
||||
"""
|
||||
NOTE: mel-spec has a hop size of 160 points (100 frame/sec).
|
||||
FIXME: this class inherits `nn.Module` but doesn't accept `torch.Tensor` and handles a list of wavs one by one, which is unexpected.
|
||||
|
||||
Args
|
||||
----
|
||||
- `wavs`: 16 kHz speech audio
|
||||
- `max_len` max length to truncate the output sequence to (25 token/sec).
|
||||
NOTE: please pad the waveform if longer sequence is needed.
|
||||
"""
|
||||
processed_wavs = self._prepare_audio(wavs)
|
||||
mels, mel_lens = [], []
|
||||
for wav in processed_wavs:
|
||||
wav = wav.to(self.device)
|
||||
mel = self.log_mel_spectrogram(wav) # [B=1, F, T]
|
||||
if max_len is not None:
|
||||
mel = mel[..., :max_len * 4] # num_mel_frames = 4 * num_tokens
|
||||
mels.append(mel.squeeze(0))
|
||||
|
||||
mels, mel_lens = padding(mels)
|
||||
if accelerator is None:
|
||||
tokenizer = self
|
||||
else:
|
||||
tokenizer = accelerator.unwrap_model(self)
|
||||
|
||||
speech_tokens, speech_token_lens = tokenizer.quantize(mels, mel_lens.to(self.device))
|
||||
return (
|
||||
speech_tokens.long().detach(),
|
||||
speech_token_lens.long().detach(),
|
||||
)
|
||||
|
||||
def log_mel_spectrogram(
|
||||
self,
|
||||
audio: torch.Tensor,
|
||||
padding: int = 0,
|
||||
):
|
||||
"""
|
||||
Compute the log-Mel spectrogram of
|
||||
|
||||
Parameters
|
||||
----------
|
||||
audio: torch.Tensor, shape = (*)
|
||||
The path to audio or either a NumPy array or Tensor containing the
|
||||
audio waveform in 16 kHz
|
||||
|
||||
padding: int
|
||||
Number of zero samples to pad to the right
|
||||
|
||||
Returns
|
||||
-------
|
||||
torch.Tensor, shape = (128, n_frames)
|
||||
A Tensor that contains the Mel spectrogram
|
||||
"""
|
||||
if not torch.is_tensor(audio):
|
||||
audio = torch.from_numpy(audio)
|
||||
|
||||
audio = audio.to(self.device)
|
||||
if padding > 0:
|
||||
audio = F.pad(audio, (0, padding))
|
||||
stft = torch.stft(
|
||||
audio, self.n_fft, S3_HOP,
|
||||
window=self.window.to(self.device),
|
||||
return_complex=True
|
||||
)
|
||||
magnitudes = stft[..., :-1].abs()**2
|
||||
|
||||
mel_spec = self._mel_filters.to(self.device) @ magnitudes
|
||||
|
||||
log_spec = torch.clamp(mel_spec, min=1e-10).log10()
|
||||
log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
|
||||
log_spec = (log_spec + 4.0) / 4.0
|
||||
return log_spec
|
||||
@@ -1 +0,0 @@
|
||||
from .t3 import T3
|
||||
-154
@@ -1,154 +0,0 @@
|
||||
# Copyright (c) 2025 Resemble AI
|
||||
# Author: John Meade, Jeremy Hsu
|
||||
# MIT License
|
||||
import logging
|
||||
import torch
|
||||
from dataclasses import dataclass
|
||||
from types import MethodType
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class AlignmentAnalysisResult:
|
||||
# was this frame detected as being part of a noisy beginning chunk with potential hallucinations?
|
||||
false_start: bool
|
||||
# was this frame detected as being part of a long tail with potential hallucinations?
|
||||
long_tail: bool
|
||||
# was this frame detected as repeating existing text content?
|
||||
repetition: bool
|
||||
# was the alignment position of this frame too far from the previous frame?
|
||||
discontinuity: bool
|
||||
# has inference reached the end of the text tokens? eg, this remains false if inference stops early
|
||||
complete: bool
|
||||
# approximate position in the text token sequence. Can be used for generating online timestamps.
|
||||
position: int
|
||||
|
||||
|
||||
class AlignmentStreamAnalyzer:
|
||||
def __init__(self, tfmr, queue, text_tokens_slice, alignment_layer_idx=9, eos_idx=0):
|
||||
"""
|
||||
Some transformer TTS models implicitly solve text-speech alignment in one or more of their self-attention
|
||||
activation maps. This module exploits this to perform online integrity checks which streaming.
|
||||
A hook is injected into the specified attention layer, and heuristics are used to determine alignment
|
||||
position, repetition, etc.
|
||||
|
||||
NOTE: currently requires no queues.
|
||||
"""
|
||||
# self.queue = queue
|
||||
self.text_tokens_slice = (i, j) = text_tokens_slice
|
||||
self.eos_idx = eos_idx
|
||||
self.alignment = torch.zeros(0, j-i)
|
||||
# self.alignment_bin = torch.zeros(0, j-i)
|
||||
self.curr_frame_pos = 0
|
||||
self.text_position = 0
|
||||
|
||||
self.started = False
|
||||
self.started_at = None
|
||||
|
||||
self.complete = False
|
||||
self.completed_at = None
|
||||
|
||||
# Using `output_attentions=True` is incompatible with optimized attention kernels, so
|
||||
# using it for all layers slows things down too much. We can apply it to just one layer
|
||||
# by intercepting the kwargs and adding a forward hook (credit: jrm)
|
||||
self.last_aligned_attn = None
|
||||
self._add_attention_spy(tfmr, alignment_layer_idx)
|
||||
|
||||
def _add_attention_spy(self, tfmr, alignment_layer_idx):
|
||||
"""
|
||||
Adds a forward hook to a specific attention layer to collect outputs.
|
||||
Using `output_attentions=True` is incompatible with optimized attention kernels, so
|
||||
using it for all layers slows things down too much.
|
||||
(credit: jrm)
|
||||
"""
|
||||
|
||||
def attention_forward_hook(module, input, output):
|
||||
"""
|
||||
See `LlamaAttention.forward`; the output is a 3-tuple: `attn_output, attn_weights, past_key_value`.
|
||||
NOTE:
|
||||
- When `output_attentions=True`, `LlamaSdpaAttention.forward` calls `LlamaAttention.forward`.
|
||||
- `attn_output` has shape [B, H, T0, T0] for the 0th entry, and [B, H, 1, T0+i] for the rest i-th.
|
||||
"""
|
||||
step_attention = output[1].cpu() # (B, 16, N, N)
|
||||
self.last_aligned_attn = step_attention[0].mean(0) # (N, N)
|
||||
|
||||
target_layer = tfmr.layers[alignment_layer_idx].self_attn
|
||||
hook_handle = target_layer.register_forward_hook(attention_forward_hook)
|
||||
|
||||
# Backup original forward
|
||||
original_forward = target_layer.forward
|
||||
def patched_forward(self, *args, **kwargs):
|
||||
kwargs['output_attentions'] = True
|
||||
return original_forward(*args, **kwargs)
|
||||
|
||||
# TODO: how to unpatch it?
|
||||
target_layer.forward = MethodType(patched_forward, target_layer)
|
||||
|
||||
def step(self, logits):
|
||||
"""
|
||||
Emits an AlignmentAnalysisResult into the output queue, and potentially modifies the logits to force an EOS.
|
||||
"""
|
||||
# extract approximate alignment matrix chunk (1 frame at a time after the first chunk)
|
||||
aligned_attn = self.last_aligned_attn # (N, N)
|
||||
i, j = self.text_tokens_slice
|
||||
if self.curr_frame_pos == 0:
|
||||
# first chunk has conditioning info, text tokens, and BOS token
|
||||
A_chunk = aligned_attn[j:, i:j].clone().cpu() # (T, S)
|
||||
else:
|
||||
# subsequent chunks have 1 frame due to KV-caching
|
||||
A_chunk = aligned_attn[:, i:j].clone().cpu() # (1, S)
|
||||
|
||||
# TODO: monotonic masking; could have issue b/c spaces are often skipped.
|
||||
A_chunk[:, self.curr_frame_pos + 1:] = 0
|
||||
|
||||
|
||||
self.alignment = torch.cat((self.alignment, A_chunk), dim=0)
|
||||
|
||||
A = self.alignment
|
||||
T, S = A.shape
|
||||
|
||||
# update position
|
||||
cur_text_posn = A_chunk[-1].argmax()
|
||||
discontinuity = not(-4 < cur_text_posn - self.text_position < 7) # NOTE: very lenient!
|
||||
if not discontinuity:
|
||||
self.text_position = cur_text_posn
|
||||
|
||||
# Hallucinations at the start of speech show up as activations at the bottom of the attention maps!
|
||||
# To mitigate this, we just wait until there are no activations far off-diagonal in the last 2 tokens,
|
||||
# and there are some strong activations in the first few tokens.
|
||||
false_start = (not self.started) and (A[-2:, -2:].max() > 0.1 or A[:, :4].max() < 0.5)
|
||||
self.started = not false_start
|
||||
if self.started and self.started_at is None:
|
||||
self.started_at = T
|
||||
|
||||
# Is generation likely complete?
|
||||
self.complete = self.complete or self.text_position >= S - 3
|
||||
if self.complete and self.completed_at is None:
|
||||
self.completed_at = T
|
||||
|
||||
# NOTE: EOS rarely assigned activations, and second-last token is often punctuation, so use last 3 tokens.
|
||||
# NOTE: due to the false-start behaviour, we need to make sure we skip activations for the first few tokens.
|
||||
last_text_token_duration = A[15:, -3:].sum()
|
||||
|
||||
# Activations for the final token that last too long are likely hallucinations.
|
||||
long_tail = self.complete and (A[self.completed_at:, -3:].sum(dim=0).max() >= 10) # 400ms
|
||||
|
||||
# If there are activations in previous tokens after generation has completed, assume this is a repetition error.
|
||||
repetition = self.complete and (A[self.completed_at:, :-5].max(dim=1).values.sum() > 5)
|
||||
|
||||
# If a bad ending is detected, force emit EOS by modifying logits
|
||||
# NOTE: this means logits may be inconsistent with latents!
|
||||
if long_tail or repetition:
|
||||
logger.warn(f"forcing EOS token, {long_tail=}, {repetition=}")
|
||||
# (±2**15 is safe for all dtypes >= 16bit)
|
||||
logits = -(2**15) * torch.ones_like(logits)
|
||||
logits[..., self.eos_idx] = 2**15
|
||||
|
||||
# Suppress EoS to prevent early termination
|
||||
if cur_text_posn < S - 3: # FIXME: arbitrary
|
||||
logits[..., self.eos_idx] = -2**15
|
||||
|
||||
self.curr_frame_pos += 1
|
||||
return logits
|
||||
@@ -1,116 +0,0 @@
|
||||
from typing import Optional
|
||||
|
||||
import torch
|
||||
from torch import nn as nn
|
||||
from transformers import LlamaConfig, LlamaModel, LlamaPreTrainedModel, GenerationMixin
|
||||
from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
|
||||
|
||||
|
||||
class T3HuggingfaceBackend(LlamaPreTrainedModel, GenerationMixin):
|
||||
"""
|
||||
Override some HuggingFace interface methods so we can use the standard `generate` method with our
|
||||
custom embedding / logit layers.
|
||||
|
||||
NOTE: need to extend "*PreTrainedModel" to avoid re-initializing weights!
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: LlamaConfig,
|
||||
llama: LlamaModel,
|
||||
*,
|
||||
speech_enc,
|
||||
speech_head,
|
||||
latents_queue=None,
|
||||
logits_queue=None,
|
||||
alignment_stream_analyzer: 'AlignmentStreamAnalyzer'=None,
|
||||
):
|
||||
super().__init__(config)
|
||||
self.model = llama
|
||||
self.speech_enc = speech_enc
|
||||
self.speech_head = speech_head
|
||||
self._added_cond = False
|
||||
self.alignment_stream_analyzer = alignment_stream_analyzer
|
||||
|
||||
@torch.inference_mode()
|
||||
def prepare_inputs_for_generation(
|
||||
self, input_ids: torch.Tensor, decoder_cond: torch.Tensor, use_cache: bool, past_key_values=None,
|
||||
# This argument was introduced in some recent version of transformers (>=4.29.1)
|
||||
cache_position=None
|
||||
):
|
||||
"""
|
||||
This is a method used by huggingface's generate() method.
|
||||
Overridden here to apply our custom speech token embedding layer.
|
||||
|
||||
:param input_ids: (B, S) int64 tensors of input tokens.
|
||||
:param decoder_cond: (B, T, C) float32 tensor of conditioning (prefixed to <input_embeds>)
|
||||
"""
|
||||
|
||||
# Make use of the kv cache: only the last input ID is new, we trim away all the ones before
|
||||
if not use_cache:
|
||||
past_key_values = None
|
||||
if past_key_values is not None:
|
||||
input_ids = input_ids[:, -1:]
|
||||
|
||||
# custom speech token embedding layer
|
||||
inputs_embeds = self.speech_enc(input_ids)
|
||||
|
||||
# prefix decoder conditioning if applicable
|
||||
if not self._added_cond:
|
||||
assert past_key_values is not None # should be first step
|
||||
if decoder_cond.size(0) != inputs_embeds.size(0):
|
||||
decoder_cond = decoder_cond.expand(inputs_embeds.size(0), -1, -1)
|
||||
inputs_embeds = torch.cat([decoder_cond, inputs_embeds], dim=1)
|
||||
self._added_cond = True
|
||||
|
||||
return {
|
||||
"inputs_embeds": inputs_embeds,
|
||||
"past_key_values": past_key_values,
|
||||
"use_cache": use_cache,
|
||||
}
|
||||
|
||||
@torch.inference_mode()
|
||||
def forward(
|
||||
self,
|
||||
inputs_embeds: torch.Tensor,
|
||||
past_key_values: Optional[torch.Tensor]=None,
|
||||
use_cache=True,
|
||||
output_attentions=False,
|
||||
output_hidden_states=True,
|
||||
return_dict=True,
|
||||
):
|
||||
"""
|
||||
This is a method used by huggingface's generate() method.
|
||||
Overridden here to apply our custom layer norm and speech logit projection layers.
|
||||
|
||||
:param inputs_embeds: (B, S, C) float32 tensor of conditioning inputs. If past key values are given,
|
||||
S should be 1.
|
||||
"""
|
||||
is_large_input = inputs_embeds.size(1) != 1
|
||||
has_cache = past_key_values is not None and len(past_key_values) > 0
|
||||
assert not (is_large_input and has_cache)
|
||||
assert return_dict
|
||||
assert output_hidden_states
|
||||
|
||||
tfmr_out = self.model(
|
||||
inputs_embeds=inputs_embeds,
|
||||
past_key_values=past_key_values,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=True,
|
||||
)
|
||||
hidden_states = tfmr_out.hidden_states[-1] # (B, seq, dim)
|
||||
|
||||
logits = self.speech_head(hidden_states)
|
||||
# assert inputs_embeds.size(0) == 1 # (disabled for CFG)
|
||||
|
||||
# NOTE: hallucination handler may modify logits to force emit an EOS token
|
||||
# logits = self.alignment_stream_analyzer.step(logits)
|
||||
|
||||
return CausalLMOutputWithCrossAttentions(
|
||||
logits=logits,
|
||||
past_key_values=tfmr_out.past_key_values,
|
||||
hidden_states=tfmr_out.hidden_states,
|
||||
attentions=tfmr_out.attentions,
|
||||
)
|
||||
@@ -1,37 +0,0 @@
|
||||
LLAMA_520M_CONFIG_DICT = dict(
|
||||
# Arbitrary small number that won't cause problems when loading.
|
||||
# These param are unused due to custom input layers.
|
||||
vocab_size=8,
|
||||
# default params needed for loading most pretrained 1B weights
|
||||
max_position_embeddings=131072,
|
||||
hidden_size=1024,
|
||||
intermediate_size=4096,
|
||||
num_hidden_layers=30,
|
||||
num_attention_heads=16,
|
||||
attn_implementation="sdpa",
|
||||
head_dim=64,
|
||||
tie_word_embeddings=False,
|
||||
hidden_act="silu",
|
||||
attention_bias=False,
|
||||
attention_dropout=0.0,
|
||||
initializer_range=0.02,
|
||||
mlp_bias=False,
|
||||
model_type="llama",
|
||||
num_key_value_heads=16,
|
||||
pretraining_tp=1,
|
||||
rms_norm_eps=1e-05,
|
||||
rope_scaling=dict(
|
||||
factor=8.0,
|
||||
high_freq_factor=4.0,
|
||||
low_freq_factor=1.0,
|
||||
original_max_position_embeddings=8192,
|
||||
rope_type="llama3"
|
||||
),
|
||||
rope_theta=500000.0,
|
||||
torch_dtype="bfloat16",
|
||||
use_cache=True,
|
||||
)
|
||||
|
||||
LLAMA_CONFIGS = {
|
||||
"Llama_520M": LLAMA_520M_CONFIG_DICT,
|
||||
}
|
||||
@@ -1,97 +0,0 @@
|
||||
from dataclasses import dataclass
|
||||
from typing import Optional
|
||||
|
||||
import torch
|
||||
from torch import nn, Tensor
|
||||
|
||||
from .perceiver import Perceiver
|
||||
from .t3_config import T3Config
|
||||
|
||||
|
||||
@dataclass
|
||||
class T3Cond:
|
||||
"""
|
||||
Dataclass container for most / all conditioning info.
|
||||
TODO: serialization methods aren't used, keeping them around for convenience
|
||||
"""
|
||||
|
||||
speaker_emb: Tensor
|
||||
clap_emb: Optional[Tensor] = None
|
||||
cond_prompt_speech_tokens: Optional[Tensor] = None
|
||||
cond_prompt_speech_emb: Optional[Tensor] = None
|
||||
emotion_adv: Optional[Tensor] = 0.5
|
||||
|
||||
def to(self, *, device=None, dtype=None):
|
||||
"Cast to a device and dtype. Dtype casting is ignored for long/int tensors."
|
||||
for k, v in self.__dict__.items():
|
||||
if torch.is_tensor(v):
|
||||
is_fp = type(v.view(-1)[0].item()) is not int
|
||||
setattr(self, k, v.to(device=device, dtype=dtype if is_fp else None))
|
||||
return self
|
||||
|
||||
def save(self, fpath):
|
||||
torch.save(self.__dict__, fpath)
|
||||
|
||||
@staticmethod
|
||||
def load(fpath, map_location="cpu"):
|
||||
kwargs = torch.load(fpath, map_location=map_location, weights_only=True)
|
||||
return T3Cond(**kwargs)
|
||||
|
||||
|
||||
class T3CondEnc(nn.Module):
|
||||
"""
|
||||
Handle all non-text conditioning, like speaker embeddings / prompts, CLAP, emotion, etc.
|
||||
"""
|
||||
|
||||
def __init__(self, hp: T3Config):
|
||||
super().__init__()
|
||||
self.hp = hp
|
||||
if hp.encoder_type == "voice_encoder":
|
||||
self.spkr_enc = nn.Linear(hp.speaker_embed_size, hp.n_channels)
|
||||
else:
|
||||
raise NotImplementedError(str(hp.encoder_type))
|
||||
|
||||
# emotion adv
|
||||
self.emotion_adv_fc = None
|
||||
if hp.emotion_adv:
|
||||
self.emotion_adv_fc = nn.Linear(1, hp.n_channels, bias=False)
|
||||
|
||||
# perceiver resampler
|
||||
self.perceiver = None
|
||||
if hp.use_perceiver_resampler:
|
||||
self.perceiver = Perceiver()
|
||||
|
||||
def forward(self, cond: T3Cond):
|
||||
# Validate
|
||||
assert (cond.cond_prompt_speech_tokens is None) == (cond.cond_prompt_speech_emb is None), \
|
||||
"no embeddings for cond_prompt_speech_tokens"
|
||||
|
||||
# Speaker embedding projection
|
||||
cond_spkr = self.spkr_enc(cond.speaker_emb.view(-1, self.hp.speaker_embed_size))[:, None] # (B, 1, dim)
|
||||
empty = torch.zeros_like(cond_spkr[:, :0]) # (B, 0, dim)
|
||||
|
||||
# TODO CLAP
|
||||
assert cond.clap_emb is None, "clap_embed not implemented"
|
||||
cond_clap = empty # (B, 0, dim)
|
||||
|
||||
# Cond prompt
|
||||
cond_prompt_speech_emb = cond.cond_prompt_speech_emb
|
||||
if cond_prompt_speech_emb is None:
|
||||
cond_prompt_speech_emb = empty # (B, 0, dim)
|
||||
elif self.hp.use_perceiver_resampler:
|
||||
cond_prompt_speech_emb = self.perceiver(cond_prompt_speech_emb)
|
||||
|
||||
# Emotion Adv: must provide a value if this model uses emotion conditioning
|
||||
cond_emotion_adv = empty # (B, 0, dim)
|
||||
if self.hp.emotion_adv:
|
||||
assert cond.emotion_adv is not None
|
||||
cond_emotion_adv = self.emotion_adv_fc(cond.emotion_adv.view(-1, 1, 1))
|
||||
|
||||
# Concat and return
|
||||
cond_embeds = torch.cat((
|
||||
cond_spkr,
|
||||
cond_clap,
|
||||
cond_prompt_speech_emb,
|
||||
cond_emotion_adv,
|
||||
), dim=1)
|
||||
return cond_embeds
|
||||
@@ -1,32 +0,0 @@
|
||||
from typing import Union
|
||||
|
||||
import torch
|
||||
from torch import nn, Tensor
|
||||
|
||||
|
||||
class LearnedPositionEmbeddings(nn.Module):
|
||||
def __init__(self, seq_len, model_dim, init=.02):
|
||||
super().__init__()
|
||||
self.emb = nn.Embedding(seq_len, model_dim)
|
||||
# Initializing this way is standard for GPT-2
|
||||
self.emb.weight.data.normal_(mean=0.0, std=init)
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
Returns positional embeddings for index 0 up to the length of x
|
||||
"""
|
||||
sl = x.shape[1]
|
||||
return self.emb(torch.arange(0, sl, device=x.device))
|
||||
|
||||
def get_fixed_embedding(self, idx: 'Union[int, Tensor]'):
|
||||
"""
|
||||
Args:
|
||||
idx: scalar int or an integer tensor of shape (T,) or (B, T)
|
||||
Returns:
|
||||
positional embeddings for given indices, shape (B, T, dim), ie (1, 1, dim) for int input
|
||||
"""
|
||||
device = self.emb.weight.device
|
||||
idx = idx.to(device) if torch.is_tensor(idx) else torch.tensor(idx, device=device)
|
||||
idx = torch.atleast_2d(idx)
|
||||
assert idx.ndim == 2
|
||||
return self.emb(idx) # (B, T, dim)
|
||||
@@ -1,212 +0,0 @@
|
||||
# Copyright (c) 2025 Resemble AI
|
||||
# Author: Manmay Nakhashi
|
||||
# MIT License
|
||||
import math
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
from einops import rearrange
|
||||
|
||||
|
||||
class RelativePositionBias(nn.Module):
|
||||
def __init__(self, scale, causal=False, num_buckets=32, max_distance=128, heads=8):
|
||||
super().__init__()
|
||||
self.scale = scale
|
||||
self.causal = causal
|
||||
self.num_buckets = num_buckets
|
||||
self.max_distance = max_distance
|
||||
self.relative_attention_bias = nn.Embedding(num_buckets, heads)
|
||||
|
||||
@staticmethod
|
||||
def _relative_position_bucket(relative_position, causal=True, num_buckets=32, max_distance=128):
|
||||
ret = 0
|
||||
n = -relative_position
|
||||
if not causal:
|
||||
num_buckets //= 2
|
||||
ret += (n < 0).long() * num_buckets
|
||||
n = torch.abs(n)
|
||||
else:
|
||||
n = torch.max(n, torch.zeros_like(n))
|
||||
|
||||
max_exact = num_buckets // 2
|
||||
is_small = n < max_exact
|
||||
|
||||
val_if_large = max_exact + (
|
||||
torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
|
||||
).long()
|
||||
val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
|
||||
|
||||
ret += torch.where(is_small, n, val_if_large)
|
||||
return ret
|
||||
|
||||
def forward(self, qk_dots):
|
||||
i, j, device = *qk_dots.shape[-2:], qk_dots.device
|
||||
q_pos = torch.arange(i, dtype=torch.long, device=device)
|
||||
k_pos = torch.arange(j, dtype=torch.long, device=device)
|
||||
rel_pos = k_pos[None, :] - q_pos[:, None]
|
||||
rp_bucket = self._relative_position_bucket(rel_pos, causal=self.causal, num_buckets=self.num_buckets,
|
||||
max_distance=self.max_distance)
|
||||
values = self.relative_attention_bias(rp_bucket)
|
||||
bias = rearrange(values, 'i j h -> () h i j')
|
||||
return qk_dots + (bias * self.scale)
|
||||
|
||||
|
||||
class AttentionQKV(nn.Module):
|
||||
def __init__(self, n_heads, head_dim, dropout_rate=0.1, scale=None, flash=False):
|
||||
super().__init__()
|
||||
self.n_heads = n_heads
|
||||
self.head_dim = head_dim
|
||||
self.scale = scale if scale is not None else head_dim ** -0.5
|
||||
self.flash = flash
|
||||
self.dropout_rate = dropout_rate
|
||||
self.dropout = nn.Dropout(dropout_rate)
|
||||
self.flash_config = self.setup_flash_config() if flash else None
|
||||
|
||||
def setup_flash_config(self):
|
||||
# Setup flash attention configuration
|
||||
flash_config = {
|
||||
'enable_flash': True,
|
||||
'enable_math': True,
|
||||
'enable_mem_efficient': True
|
||||
}
|
||||
return flash_config
|
||||
|
||||
def forward(self, q, k, v, mask=None):
|
||||
q, k, v = [self.split_heads(tensor) for tensor in [q, k, v]]
|
||||
if self.flash:
|
||||
out = self.flash_attention(q, k, v, mask=mask)
|
||||
else:
|
||||
out = self.scaled_dot_product_attention(q, k, v, mask=mask)
|
||||
|
||||
return self.combine_heads(out)
|
||||
|
||||
def scaled_dot_product_attention(self, q, k, v, mask=None):
|
||||
sim = torch.einsum("bhlt,bhls->bhts", q, k) * self.scale
|
||||
if mask is not None:
|
||||
sim = sim.masked_fill(mask == 0, float('-inf'))
|
||||
attn = torch.softmax(sim, dim=-1)
|
||||
attn = self.dropout(attn)
|
||||
return torch.einsum("bhts,bhls->bhlt", attn, v)
|
||||
|
||||
def flash_attention(self, q, k, v, mask=None):
|
||||
config = self.flash_config if self.flash_config else {}
|
||||
with torch.backends.cuda.sdp_kernel(**config):
|
||||
out = F.scaled_dot_product_attention(
|
||||
q, k, v,
|
||||
attn_mask=mask,
|
||||
dropout_p=self.dropout_rate if self.training else 0.
|
||||
)
|
||||
return out
|
||||
|
||||
def split_heads(self, x):
|
||||
bs, length, _ = x.shape
|
||||
x = x.view(bs, length, self.n_heads, self.head_dim)
|
||||
return x.permute(0, 2, 1, 3)
|
||||
|
||||
def combine_heads(self, x):
|
||||
bs, _, length, _ = x.shape
|
||||
x = x.permute(0, 2, 1, 3).contiguous()
|
||||
return x.view(bs, length, -1)
|
||||
|
||||
|
||||
class AttentionBlock2(nn.Module):
|
||||
"""
|
||||
An attention block that allows spatial positions to attend to each other,
|
||||
using AttentionQKV and separate linear transformations for Q, K, and V.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
channels,
|
||||
num_heads=1,
|
||||
num_head_channels=-1,
|
||||
relative_pos_embeddings=False,
|
||||
flash_attention=True,
|
||||
dropout_rate=0.2,
|
||||
scale=None
|
||||
):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
|
||||
if num_head_channels == -1:
|
||||
self.num_heads = num_heads
|
||||
else:
|
||||
assert (
|
||||
channels % num_head_channels == 0
|
||||
), f"channels {channels} is not divisible by num_head_channels {num_head_channels}"
|
||||
self.num_heads = channels // num_head_channels
|
||||
|
||||
self.norm = nn.LayerNorm(channels)
|
||||
|
||||
# Separate linear layers for Q, K, and V
|
||||
self.to_q = nn.Linear(channels, channels)
|
||||
self.to_k = nn.Linear(channels, channels)
|
||||
self.to_v = nn.Linear(channels, channels)
|
||||
|
||||
self.attention = AttentionQKV(self.num_heads, channels // self.num_heads, dropout_rate=dropout_rate, flash=flash_attention, scale=scale)
|
||||
|
||||
self.proj_out = nn.Linear(channels, channels)
|
||||
|
||||
if relative_pos_embeddings:
|
||||
self.relative_pos_embeddings = RelativePositionBias(scale=(channels // self.num_heads) ** .5, causal=False, heads=num_heads, num_buckets=32, max_distance=64)
|
||||
else:
|
||||
self.relative_pos_embeddings = None
|
||||
|
||||
def forward(self, x1, x2, mask=None):
|
||||
b1, c1, *spatial1 = x1.shape
|
||||
b2, c2, *spatial2 = x2.shape
|
||||
|
||||
x1_norm = self.norm(x1)
|
||||
x2_norm = self.norm(x2)
|
||||
|
||||
q = self.to_q(x1_norm)
|
||||
k = self.to_k(x2_norm)
|
||||
v = self.to_v(x2_norm)
|
||||
|
||||
h = self.attention(q, k, v, mask=mask)
|
||||
h = self.proj_out(h)
|
||||
|
||||
return (x1 + h).reshape(b1, c1, *spatial1)
|
||||
|
||||
|
||||
class Perceiver(nn.Module):
|
||||
"""Inspired by https://arxiv.org/abs/2103.03206"""
|
||||
def __init__(self, pre_attention_query_token=32, pre_attention_query_size=1024, embedding_dim=1024, num_attn_heads=4):
|
||||
"""
|
||||
Initialize the perceiver module.
|
||||
|
||||
:param pre_attention_query_token: Number of query tokens for pre-attention
|
||||
:param pre_attention_query_size: Size of each query token
|
||||
:param embedding_dim: Dimension of the embedding space
|
||||
:param num_attn_heads: Number of attention heads
|
||||
"""
|
||||
super().__init__()
|
||||
|
||||
# Initialize the pre-attention query parameter
|
||||
self.pre_attention_query = torch.nn.Parameter(
|
||||
torch.empty(1, pre_attention_query_token, pre_attention_query_size)
|
||||
)
|
||||
|
||||
# Calculate the variance for uniform initialization
|
||||
query_variance = math.sqrt(3.0) * math.sqrt(2.0 / (pre_attention_query_token + pre_attention_query_token))
|
||||
|
||||
# Initialize the pre-attention query with uniform distribution
|
||||
self.pre_attention_query.data.uniform_(-query_variance, query_variance)
|
||||
|
||||
# Initialize the attention block
|
||||
self.attn = AttentionBlock2(embedding_dim, num_attn_heads)
|
||||
|
||||
def forward(self, h):
|
||||
"""
|
||||
Forward pass of the perceiver module.
|
||||
:param h: Input tensor
|
||||
:return: Output after applying attention mechanisms
|
||||
"""
|
||||
# Expand the pre-attention query to match the batch size of the input
|
||||
query_ = self.pre_attention_query.expand(h.shape[0], -1, -1)
|
||||
# Apply the first attention mechanism (cross-attention)
|
||||
pre_att = self.attn(query_, h)
|
||||
# Apply the second attention mechanism (self-attention)
|
||||
attn = self.attn(pre_att, pre_att)
|
||||
return attn
|
||||
@@ -1,27 +0,0 @@
|
||||
from ..llama_configs import LLAMA_CONFIGS
|
||||
|
||||
|
||||
class T3Config:
|
||||
start_text_token = 255
|
||||
stop_text_token = 0
|
||||
text_tokens_dict_size = 704
|
||||
max_text_tokens = 2048
|
||||
|
||||
start_speech_token = 6561
|
||||
stop_speech_token = 6562
|
||||
speech_tokens_dict_size = 8194
|
||||
max_speech_tokens = 4096
|
||||
|
||||
llama_config_name = "Llama_520M"
|
||||
input_pos_emb = "learned"
|
||||
speech_cond_prompt_len = 150
|
||||
|
||||
# For T3CondEnc
|
||||
encoder_type = "voice_encoder"
|
||||
speaker_embed_size = 256
|
||||
use_perceiver_resampler = True
|
||||
emotion_adv = True
|
||||
|
||||
@property
|
||||
def n_channels(self):
|
||||
return LLAMA_CONFIGS[self.llama_config_name]["hidden_size"]
|
||||
@@ -1,372 +0,0 @@
|
||||
# Copyright (c) 2025 Resemble AI
|
||||
# MIT License
|
||||
import logging
|
||||
from typing import Union, Optional, List
|
||||
|
||||
from tqdm import tqdm
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch import nn, Tensor
|
||||
from transformers import LlamaModel, LlamaConfig
|
||||
from transformers.generation.logits_process import TopPLogitsWarper, RepetitionPenaltyLogitsProcessor
|
||||
|
||||
from .modules.learned_pos_emb import LearnedPositionEmbeddings
|
||||
|
||||
from .modules.cond_enc import T3CondEnc, T3Cond
|
||||
from .modules.t3_config import T3Config
|
||||
from .llama_configs import LLAMA_CONFIGS
|
||||
from .inference.t3_hf_backend import T3HuggingfaceBackend
|
||||
from .inference.alignment_stream_analyzer import AlignmentStreamAnalyzer
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class AttrDict(dict):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super(AttrDict, self).__init__(*args, **kwargs)
|
||||
self.__dict__ = self
|
||||
|
||||
|
||||
def _ensure_BOT_EOT(text_tokens: Tensor, hp):
|
||||
B = text_tokens.size(0)
|
||||
assert (text_tokens == hp.start_text_token).int().sum() >= B, "missing start_text_token"
|
||||
assert (text_tokens == hp.stop_text_token).int().sum() >= B, "missing stop_text_token"
|
||||
|
||||
|
||||
class T3(nn.Module):
|
||||
"""
|
||||
Token-To-Token (T3) TTS model using huggingface transformer models as backbones,
|
||||
* tokenization, including start / stop tokens are always added externally to this class
|
||||
* conditioning data like CLAP, emotion, etc are all in a separate file for more modularity
|
||||
* careful! this class assumes relative positional encoding -- with absolute PE, we would at
|
||||
least want to reset the position to 0 when speech tokens begin, and optionally use a
|
||||
different PE embedding space for speech.
|
||||
"""
|
||||
|
||||
def __init__(self, hp=T3Config()):
|
||||
super().__init__()
|
||||
self.hp = hp
|
||||
self.cfg = LlamaConfig(**LLAMA_CONFIGS[hp.llama_config_name])
|
||||
self.tfmr = LlamaModel(self.cfg)
|
||||
self.dim = self.cfg.hidden_size
|
||||
self.deepspeed_patch_applied = False
|
||||
|
||||
# conditioning / embedding
|
||||
self.cond_enc = T3CondEnc(hp)
|
||||
self.text_emb = nn.Embedding(hp.text_tokens_dict_size, self.dim)
|
||||
self.speech_emb = nn.Embedding(hp.speech_tokens_dict_size, self.dim)
|
||||
|
||||
# custom position embedding
|
||||
if hp.input_pos_emb == "learned":
|
||||
max_text_seq_len = hp.max_text_tokens + 2
|
||||
self.text_pos_emb = LearnedPositionEmbeddings(max_text_seq_len, self.dim)
|
||||
|
||||
max_mel_seq_len = hp.max_speech_tokens + 2 + 2
|
||||
self.speech_pos_emb = LearnedPositionEmbeddings(max_mel_seq_len, self.dim)
|
||||
|
||||
# logit projection
|
||||
self.text_head = nn.Linear(self.cfg.hidden_size, hp.text_tokens_dict_size, bias=False)
|
||||
self.speech_head = nn.Linear(self.cfg.hidden_size, hp.speech_tokens_dict_size, bias=False)
|
||||
self.compiled = False
|
||||
|
||||
@property
|
||||
def device(self):
|
||||
return self.speech_head.weight.device
|
||||
|
||||
def prepare_conditioning(self, t3_cond: T3Cond):
|
||||
"""
|
||||
Token cond data needs to be embedded, so that needs to be here instead of in `T3CondEnc`.
|
||||
"""
|
||||
if t3_cond.cond_prompt_speech_tokens is not None and t3_cond.cond_prompt_speech_emb is None:
|
||||
t3_cond.cond_prompt_speech_emb = self.speech_emb(t3_cond.cond_prompt_speech_tokens) + \
|
||||
self.speech_pos_emb(t3_cond.cond_prompt_speech_tokens)
|
||||
return self.cond_enc(t3_cond) # (B, len_cond, dim)
|
||||
|
||||
def prepare_input_embeds(
|
||||
self,
|
||||
*,
|
||||
t3_cond: T3Cond,
|
||||
text_tokens: torch.LongTensor,
|
||||
speech_tokens: torch.LongTensor,
|
||||
):
|
||||
# prepare input embeddings (skip backbone tranformer embeddings)
|
||||
cond_emb = self.prepare_conditioning(t3_cond) # (B, len_cond, dim)
|
||||
text_emb = self.text_emb(text_tokens) # (B, len_text, dim)
|
||||
text_emb[1].zero_() # CFG uncond
|
||||
|
||||
speech_emb = self.speech_emb(speech_tokens) # (B, len_speech, dim)
|
||||
if self.hp.input_pos_emb == "learned":
|
||||
text_emb = text_emb + self.text_pos_emb(text_tokens)
|
||||
speech_emb = speech_emb + self.speech_pos_emb(speech_tokens)
|
||||
len_cond = cond_emb.size(1)
|
||||
|
||||
if cond_emb.size(0) != text_emb.size(0):
|
||||
cond_emb = cond_emb.expand(text_emb.size(0), -1, -1)
|
||||
|
||||
# concat
|
||||
embeds = torch.stack([
|
||||
torch.cat((ce, te, se))
|
||||
for ce, te, se in zip(cond_emb, text_emb, speech_emb)
|
||||
]) # (B, length, dim)
|
||||
return embeds, len_cond
|
||||
|
||||
def forward(
|
||||
self,
|
||||
*,
|
||||
t3_cond: T3Cond,
|
||||
text_tokens: torch.LongTensor,
|
||||
text_token_lens: torch.LongTensor,
|
||||
speech_tokens: torch.LongTensor,
|
||||
speech_token_lens: torch.LongTensor,
|
||||
training=False,
|
||||
):
|
||||
_ensure_BOT_EOT(text_tokens, self.hp)
|
||||
|
||||
# prepare custom input embeds
|
||||
embeds, len_cond = self.prepare_input_embeds(
|
||||
t3_cond=t3_cond,
|
||||
text_tokens=text_tokens,
|
||||
speech_tokens=speech_tokens,
|
||||
)
|
||||
|
||||
# backbone tranformer forward
|
||||
tfmr_out = self.tfmr.forward(
|
||||
input_ids=None,
|
||||
# position_ids=position_ids, # TODO? ROPE should be fine?
|
||||
inputs_embeds=embeds,
|
||||
output_hidden_states=True,
|
||||
return_dict=True,
|
||||
use_cache=(not training),
|
||||
)
|
||||
hidden_states = tfmr_out.hidden_states[-1] # final tfmr layer output, (B, seq, dim)
|
||||
|
||||
# post-processing: splice out text and speech parts of hidden states
|
||||
len_text = text_tokens.size(1)
|
||||
len_speech = speech_tokens.size(1)
|
||||
B, _, dim = hidden_states.shape
|
||||
device, dtype = hidden_states.device, hidden_states.dtype
|
||||
text_latents = torch.zeros(B, len_text, dim, dtype=dtype, device=device)
|
||||
speech_latents = torch.zeros(B, len_speech, dim, dtype=dtype, device=device)
|
||||
ttl, stl = text_token_lens, speech_token_lens
|
||||
for i in range(B):
|
||||
text_end = len_cond + ttl[i].item()
|
||||
speech_start = len_cond + text_tokens.size(1)
|
||||
speech_end = speech_start + stl[i].item()
|
||||
text_latents[i, :ttl[i]] = hidden_states[i, len_cond:text_end]
|
||||
speech_latents[i, :stl[i]] = hidden_states[i, speech_start:speech_end]
|
||||
|
||||
# logit projection
|
||||
text_logits = self.text_head(text_latents)
|
||||
speech_logits = self.speech_head(speech_latents)
|
||||
|
||||
return AttrDict(
|
||||
text_logits=text_logits,
|
||||
text_latents=text_latents,
|
||||
speech_logits=speech_logits,
|
||||
speech_latents=speech_latents,
|
||||
hidden_states=hidden_states,
|
||||
)
|
||||
|
||||
def loss(
|
||||
self,
|
||||
*,
|
||||
t3_cond: T3Cond,
|
||||
text_tokens: torch.LongTensor,
|
||||
text_token_lens: torch.LongTensor,
|
||||
speech_tokens: torch.LongTensor,
|
||||
speech_token_lens: torch.LongTensor,
|
||||
):
|
||||
"training method"
|
||||
len_text = text_tokens.size(1)
|
||||
len_speech = speech_tokens.size(1)
|
||||
assert len_text == text_token_lens.max()
|
||||
assert len_speech == speech_token_lens.max()
|
||||
|
||||
out = self.forward(
|
||||
t3_cond=t3_cond,
|
||||
text_tokens=text_tokens,
|
||||
text_token_lens=text_token_lens,
|
||||
speech_tokens=speech_tokens,
|
||||
speech_token_lens=speech_token_lens,
|
||||
training=True,
|
||||
) # (B, seq, vocab_size)
|
||||
|
||||
# Calc CCE losses
|
||||
IGNORE_ID = -100
|
||||
device = out.text_logits.device
|
||||
mask_text = torch.arange(len_text, device=device)[None] >= text_token_lens[:, None] # (B, len_text)
|
||||
mask_speech = torch.arange(len_speech, device=device)[None] >= speech_token_lens[:, None] # (B, len_speech)
|
||||
masked_text = text_tokens.masked_fill(mask_text, IGNORE_ID)
|
||||
masked_speech = speech_tokens.masked_fill(mask_speech, IGNORE_ID)
|
||||
loss_text = F.cross_entropy(out.text_logits, masked_text, ignore_index=IGNORE_ID)
|
||||
loss_speech = F.cross_entropy(out.speech_logits, masked_speech, ignore_index=IGNORE_ID)
|
||||
|
||||
return loss_text, loss_speech
|
||||
|
||||
@torch.inference_mode()
|
||||
def inference(
|
||||
self,
|
||||
*,
|
||||
t3_cond: T3Cond,
|
||||
text_tokens: Tensor,
|
||||
initial_speech_tokens: Optional[Tensor]=None,
|
||||
|
||||
# misc conditioning
|
||||
prepend_prompt_speech_tokens: Optional[Tensor]=None,
|
||||
|
||||
# HF generate args
|
||||
num_return_sequences=1,
|
||||
max_new_tokens=None,
|
||||
stop_on_eos=True,
|
||||
do_sample=True,
|
||||
temperature=0.8,
|
||||
top_p=0.8,
|
||||
length_penalty=1.0,
|
||||
repetition_penalty=2.0,
|
||||
cfg_weight=0,
|
||||
):
|
||||
"""
|
||||
Args:
|
||||
text_tokens: a 1D (unbatched) or 2D (batched) tensor.
|
||||
"""
|
||||
# Validate / sanitize inputs
|
||||
assert prepend_prompt_speech_tokens is None, "not implemented"
|
||||
_ensure_BOT_EOT(text_tokens, self.hp)
|
||||
text_tokens = torch.atleast_2d(text_tokens).to(dtype=torch.long, device=self.device)
|
||||
|
||||
# Default initial speech to a single start-of-speech token
|
||||
if initial_speech_tokens is None:
|
||||
initial_speech_tokens = self.hp.start_speech_token * torch.ones_like(text_tokens[:, :1])
|
||||
|
||||
# Prepare custom input embeds
|
||||
embeds, len_cond = self.prepare_input_embeds(
|
||||
t3_cond=t3_cond,
|
||||
text_tokens=text_tokens,
|
||||
speech_tokens=initial_speech_tokens,
|
||||
)
|
||||
|
||||
# In order to use the standard HF generate method, we need to extend some methods to inject our custom logic
|
||||
# Note the llama-specific logic. Other tfmr types can be added later.
|
||||
|
||||
self.compiled = False
|
||||
|
||||
# TODO? synchronize the expensive compile function
|
||||
# with self.compile_lock:
|
||||
if not self.compiled:
|
||||
alignment_stream_analyzer = AlignmentStreamAnalyzer(
|
||||
self.tfmr,
|
||||
None,
|
||||
text_tokens_slice=(len_cond, len_cond + text_tokens.size(-1)),
|
||||
alignment_layer_idx=9, # TODO: hparam or something?
|
||||
eos_idx=self.hp.stop_speech_token,
|
||||
)
|
||||
patched_model = T3HuggingfaceBackend(
|
||||
config=self.cfg,
|
||||
llama=self.tfmr,
|
||||
speech_enc=self.speech_emb,
|
||||
speech_head=self.speech_head,
|
||||
alignment_stream_analyzer=alignment_stream_analyzer,
|
||||
)
|
||||
self.patched_model = patched_model
|
||||
self.compiled = True
|
||||
|
||||
# # Run normal generate method, which calls our custom extended methods
|
||||
# return self.patched_model.generate(
|
||||
# inputs=initial_speech_tokens,
|
||||
# decoder_cond=embeds,
|
||||
# bos_token_id=self.hp.start_speech_token,
|
||||
# eos_token_id=(self.hp.stop_speech_token if stop_on_eos else -1),
|
||||
# pad_token_id=self.hp.stop_speech_token,
|
||||
# max_new_tokens=max_new_tokens or self.hp.max_speech_tokens,
|
||||
# num_return_sequences=num_return_sequences,
|
||||
# temperature=temperature,
|
||||
# top_p=top_p,
|
||||
# length_penalty=length_penalty,
|
||||
# repetition_penalty=repetition_penalty,
|
||||
# do_sample=do_sample,
|
||||
# # cache_implementation=None if not self.compiled else "static",
|
||||
# )
|
||||
|
||||
device = embeds.device
|
||||
|
||||
bos_token = torch.tensor([[self.hp.start_speech_token]], dtype=torch.long, device=device)
|
||||
bos_embed = self.speech_emb(bos_token) # shape: (B, 1, embed_dim)
|
||||
bos_embed = bos_embed + self.speech_pos_emb.get_fixed_embedding(0)
|
||||
|
||||
# batch_size=2 for CFG
|
||||
bos_embed = torch.cat([bos_embed, bos_embed])
|
||||
|
||||
# Combine condition and BOS token for the initial input
|
||||
inputs_embeds = torch.cat([embeds, bos_embed], dim=1)
|
||||
|
||||
# Track generated token ids; start with the BOS token.
|
||||
generated_ids = bos_token.clone()
|
||||
predicted = [] # To store the predicted tokens
|
||||
|
||||
# Instantiate the logits processors.
|
||||
top_p_warper = TopPLogitsWarper(top_p=top_p)
|
||||
repetition_penalty_processor = RepetitionPenaltyLogitsProcessor(penalty=repetition_penalty)
|
||||
|
||||
# ---- Initial Forward Pass (no kv_cache yet) ----
|
||||
output = self.patched_model(
|
||||
inputs_embeds=inputs_embeds,
|
||||
past_key_values=None,
|
||||
use_cache=True,
|
||||
output_attentions=True,
|
||||
output_hidden_states=True,
|
||||
return_dict=True,
|
||||
)
|
||||
# Initialize kv_cache with the full context.
|
||||
past = output.past_key_values
|
||||
|
||||
# ---- Generation Loop using kv_cache ----
|
||||
for i in tqdm(range(max_new_tokens), desc="Sampling", dynamic_ncols=True):
|
||||
logits = output.logits[:, -1, :]
|
||||
|
||||
# CFG
|
||||
logits_cond = logits[0:1]
|
||||
logits_uncond = logits[1:2]
|
||||
logits = logits_cond + cfg_weight * (logits_cond - logits_uncond)
|
||||
logits = logits.squeeze(1)
|
||||
|
||||
# Apply temperature scaling.
|
||||
if temperature != 1.0:
|
||||
logits = logits / temperature
|
||||
|
||||
# Apply repetition penalty and top‑p filtering.
|
||||
logits = repetition_penalty_processor(generated_ids, logits)
|
||||
logits = top_p_warper(None, logits)
|
||||
|
||||
# Convert logits to probabilities and sample the next token.
|
||||
probs = torch.softmax(logits, dim=-1)
|
||||
next_token = torch.multinomial(probs, num_samples=1) # shape: (B, 1)
|
||||
|
||||
predicted.append(next_token)
|
||||
generated_ids = torch.cat([generated_ids, next_token], dim=1)
|
||||
|
||||
# Check for EOS token.
|
||||
if next_token.view(-1) == self.hp.stop_speech_token:
|
||||
break
|
||||
|
||||
# Get embedding for the new token.
|
||||
next_token_embed = self.speech_emb(next_token)
|
||||
next_token_embed = next_token_embed + self.speech_pos_emb.get_fixed_embedding(i + 1)
|
||||
|
||||
# For CFG
|
||||
next_token_embed = torch.cat([next_token_embed, next_token_embed])
|
||||
|
||||
# Forward pass with only the new token and the cached past.
|
||||
output = self.patched_model(
|
||||
inputs_embeds=next_token_embed,
|
||||
past_key_values=past,
|
||||
output_attentions=True,
|
||||
output_hidden_states=True,
|
||||
return_dict=True,
|
||||
)
|
||||
# Update the kv_cache.
|
||||
past = output.past_key_values
|
||||
|
||||
# Concatenate all predicted tokens along the sequence dimension.
|
||||
predicted_tokens = torch.cat(predicted, dim=1) # shape: (B, num_tokens)
|
||||
return predicted_tokens
|
||||
@@ -1 +0,0 @@
|
||||
from .tokenizer import EnTokenizer
|
||||
@@ -1,50 +0,0 @@
|
||||
import logging
|
||||
|
||||
import torch
|
||||
from tokenizers import Tokenizer
|
||||
|
||||
|
||||
# Special tokens
|
||||
SOT = "[START]"
|
||||
EOT = "[STOP]"
|
||||
UNK = "[UNK]"
|
||||
SPACE = "[SPACE]"
|
||||
SPECIAL_TOKENS = [SOT, EOT, UNK, SPACE, "[PAD]", "[SEP]", "[CLS]", "[MASK]"]
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class EnTokenizer:
|
||||
def __init__(self, vocab_file_path):
|
||||
self.tokenizer: Tokenizer = Tokenizer.from_file(vocab_file_path)
|
||||
self.check_vocabset_sot_eot()
|
||||
|
||||
def check_vocabset_sot_eot(self):
|
||||
voc = self.tokenizer.get_vocab()
|
||||
assert SOT in voc
|
||||
assert EOT in voc
|
||||
|
||||
def text_to_tokens(self, text: str):
|
||||
text_tokens = self.encode(text)
|
||||
text_tokens = torch.IntTensor(text_tokens).unsqueeze(0)
|
||||
return text_tokens
|
||||
|
||||
def encode( self, txt: str, verbose=False):
|
||||
"""
|
||||
clean_text > (append `lang_id`) > replace SPACE > encode text using Tokenizer
|
||||
"""
|
||||
txt = txt.replace(' ', SPACE)
|
||||
code = self.tokenizer.encode(txt)
|
||||
ids = code.ids
|
||||
return ids
|
||||
|
||||
def decode(self, seq):
|
||||
if isinstance(seq, torch.Tensor):
|
||||
seq = seq.cpu().numpy()
|
||||
|
||||
txt: str = self.tokenizer.decode(seq,
|
||||
skip_special_tokens=False)
|
||||
txt = txt.replace(' ', '')
|
||||
txt = txt.replace(SPACE, ' ')
|
||||
txt = txt.replace(EOT, '')
|
||||
txt = txt.replace(UNK, '')
|
||||
return txt
|
||||
@@ -1 +0,0 @@
|
||||
from .voice_encoder import VoiceEncoder, VoiceEncConfig
|
||||
@@ -1,18 +0,0 @@
|
||||
class VoiceEncConfig:
|
||||
num_mels = 40
|
||||
sample_rate = 16000
|
||||
speaker_embed_size = 256
|
||||
ve_hidden_size = 256
|
||||
flatten_lstm_params = False
|
||||
n_fft = 400
|
||||
hop_size = 160
|
||||
win_size = 400
|
||||
fmax = 8000
|
||||
fmin = 0
|
||||
preemphasis = 0.
|
||||
mel_power = 2.0
|
||||
mel_type = "amp"
|
||||
normalized_mels = False
|
||||
ve_partial_frames = 160
|
||||
ve_final_relu = True
|
||||
stft_magnitude_min = 1e-4
|
||||
@@ -1,78 +0,0 @@
|
||||
from functools import lru_cache
|
||||
|
||||
from scipy import signal
|
||||
import numpy as np
|
||||
import librosa
|
||||
|
||||
|
||||
@lru_cache()
|
||||
def mel_basis(hp):
|
||||
assert hp.fmax <= hp.sample_rate // 2
|
||||
return librosa.filters.mel(
|
||||
sr=hp.sample_rate,
|
||||
n_fft=hp.n_fft,
|
||||
n_mels=hp.num_mels,
|
||||
fmin=hp.fmin,
|
||||
fmax=hp.fmax) # -> (nmel, nfreq)
|
||||
|
||||
|
||||
def preemphasis(wav, hp):
|
||||
assert hp.preemphasis != 0
|
||||
wav = signal.lfilter([1, -hp.preemphasis], [1], wav)
|
||||
wav = np.clip(wav, -1, 1)
|
||||
return wav
|
||||
|
||||
|
||||
def melspectrogram(wav, hp, pad=True):
|
||||
# Run through pre-emphasis
|
||||
if hp.preemphasis > 0:
|
||||
wav = preemphasis(wav, hp)
|
||||
assert np.abs(wav).max() - 1 < 1e-07
|
||||
|
||||
# Do the stft
|
||||
spec_complex = _stft(wav, hp, pad=pad)
|
||||
|
||||
# Get the magnitudes
|
||||
spec_magnitudes = np.abs(spec_complex)
|
||||
|
||||
if hp.mel_power != 1.0:
|
||||
spec_magnitudes **= hp.mel_power
|
||||
|
||||
# Get the mel and convert magnitudes->db
|
||||
mel = np.dot(mel_basis(hp), spec_magnitudes)
|
||||
if hp.mel_type == "db":
|
||||
mel = _amp_to_db(mel, hp)
|
||||
|
||||
# Normalise the mel from db to 0,1
|
||||
if hp.normalized_mels:
|
||||
mel = _normalize(mel, hp).astype(np.float32)
|
||||
|
||||
assert not pad or mel.shape[1] == 1 + len(wav) // hp.hop_size # Sanity check
|
||||
return mel # (M, T)
|
||||
|
||||
|
||||
def _stft(y, hp, pad=True):
|
||||
# NOTE: after 0.8, pad mode defaults to constant, setting this to reflect for
|
||||
# historical consistency and streaming-version consistency
|
||||
return librosa.stft(
|
||||
y,
|
||||
n_fft=hp.n_fft,
|
||||
hop_length=hp.hop_size,
|
||||
win_length=hp.win_size,
|
||||
center=pad,
|
||||
pad_mode="reflect",
|
||||
)
|
||||
|
||||
|
||||
def _amp_to_db(x, hp):
|
||||
return 20 * np.log10(np.maximum(hp.stft_magnitude_min, x))
|
||||
|
||||
|
||||
def _db_to_amp(x):
|
||||
return np.power(10.0, x * 0.05)
|
||||
|
||||
|
||||
def _normalize(s, hp, headroom_db=15):
|
||||
min_level_db = 20 * np.log10(hp.stft_magnitude_min)
|
||||
s = (s - min_level_db) / (-min_level_db + headroom_db)
|
||||
return s
|
||||
@@ -1,274 +0,0 @@
|
||||
# Adapted from https://github.com/CorentinJ/Real-Time-Voice-Cloning
|
||||
# MIT License
|
||||
from typing import List, Union, Optional
|
||||
|
||||
import numpy as np
|
||||
from numpy.lib.stride_tricks import as_strided
|
||||
import librosa
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch import nn, Tensor
|
||||
|
||||
from .config import VoiceEncConfig
|
||||
from .melspec import melspectrogram
|
||||
|
||||
|
||||
def pack(arrays, seq_len: int=None, pad_value=0):
|
||||
"""
|
||||
Given a list of length B of array-like objects of shapes (Ti, ...), packs them in a single tensor of
|
||||
shape (B, T, ...) by padding each individual array on the right.
|
||||
|
||||
:param arrays: a list of array-like objects of matching shapes except for the first axis.
|
||||
:param seq_len: the value of T. It must be the maximum of the lengths Ti of the arrays at
|
||||
minimum. Will default to that value if None.
|
||||
:param pad_value: the value to pad the arrays with.
|
||||
:return: a (B, T, ...) tensor
|
||||
"""
|
||||
if seq_len is None:
|
||||
seq_len = max(len(array) for array in arrays)
|
||||
else:
|
||||
assert seq_len >= max(len(array) for array in arrays)
|
||||
|
||||
# Convert lists to np.array
|
||||
if isinstance(arrays[0], list):
|
||||
arrays = [np.array(array) for array in arrays]
|
||||
|
||||
# Convert to tensor and handle device
|
||||
device = None
|
||||
if isinstance(arrays[0], torch.Tensor):
|
||||
tensors = arrays
|
||||
device = tensors[0].device
|
||||
else:
|
||||
tensors = [torch.as_tensor(array) for array in arrays]
|
||||
|
||||
# Fill the packed tensor with the array data
|
||||
packed_shape = (len(tensors), seq_len, *tensors[0].shape[1:])
|
||||
packed_tensor = torch.full(packed_shape, pad_value, dtype=tensors[0].dtype, device=device)
|
||||
|
||||
for i, tensor in enumerate(tensors):
|
||||
packed_tensor[i, :tensor.size(0)] = tensor
|
||||
|
||||
return packed_tensor
|
||||
|
||||
|
||||
def get_num_wins(
|
||||
n_frames: int,
|
||||
step: int,
|
||||
min_coverage: float,
|
||||
hp: VoiceEncConfig,
|
||||
):
|
||||
assert n_frames > 0
|
||||
win_size = hp.ve_partial_frames
|
||||
n_wins, remainder = divmod(max(n_frames - win_size + step, 0), step)
|
||||
if n_wins == 0 or (remainder + (win_size - step)) / win_size >= min_coverage:
|
||||
n_wins += 1
|
||||
target_n = win_size + step * (n_wins - 1)
|
||||
return n_wins, target_n
|
||||
|
||||
|
||||
def get_frame_step(
|
||||
overlap: float,
|
||||
rate: float,
|
||||
hp: VoiceEncConfig,
|
||||
):
|
||||
# Compute how many frames separate two partial utterances
|
||||
assert 0 <= overlap < 1
|
||||
if rate is None:
|
||||
frame_step = int(np.round(hp.ve_partial_frames * (1 - overlap)))
|
||||
else:
|
||||
frame_step = int(np.round((hp.sample_rate / rate) / hp.ve_partial_frames))
|
||||
assert 0 < frame_step <= hp.ve_partial_frames
|
||||
return frame_step
|
||||
|
||||
|
||||
def stride_as_partials(
|
||||
mel: np.ndarray,
|
||||
hp: VoiceEncConfig,
|
||||
overlap=0.5,
|
||||
rate: float=None,
|
||||
min_coverage=0.8,
|
||||
):
|
||||
"""
|
||||
Takes unscaled mels in (T, M) format
|
||||
TODO: doc
|
||||
"""
|
||||
assert 0 < min_coverage <= 1
|
||||
frame_step = get_frame_step(overlap, rate, hp)
|
||||
|
||||
# Compute how many partials can fit in the mel
|
||||
n_partials, target_len = get_num_wins(len(mel), frame_step, min_coverage, hp)
|
||||
|
||||
# Trim or pad the mel spectrogram to match the number of partials
|
||||
if target_len > len(mel):
|
||||
mel = np.concatenate((mel, np.full((target_len - len(mel), hp.num_mels), 0)))
|
||||
elif target_len < len(mel):
|
||||
mel = mel[:target_len]
|
||||
|
||||
# Ensure the numpy array data is float32 and contiguous in memory
|
||||
mel = mel.astype(np.float32, order="C")
|
||||
|
||||
# Re-arrange the array in memory to be of shape (N, P, M) with partials overlapping eachother,
|
||||
# where N is the number of partials, P is the number of frames of each partial and M the
|
||||
# number of channels of the mel spectrograms.
|
||||
shape = (n_partials, hp.ve_partial_frames, hp.num_mels)
|
||||
strides = (mel.strides[0] * frame_step, mel.strides[0], mel.strides[1])
|
||||
partials = as_strided(mel, shape, strides)
|
||||
return partials
|
||||
|
||||
|
||||
class VoiceEncoder(nn.Module):
|
||||
def __init__(self, hp=VoiceEncConfig()):
|
||||
super().__init__()
|
||||
|
||||
self.hp = hp
|
||||
|
||||
# Network definition
|
||||
self.lstm = nn.LSTM(self.hp.num_mels, self.hp.ve_hidden_size, num_layers=3, batch_first=True)
|
||||
if hp.flatten_lstm_params:
|
||||
self.lstm.flatten_parameters()
|
||||
self.proj = nn.Linear(self.hp.ve_hidden_size, self.hp.speaker_embed_size)
|
||||
|
||||
# Cosine similarity scaling (fixed initial parameter values)
|
||||
self.similarity_weight = nn.Parameter(torch.tensor([10.]), requires_grad=True)
|
||||
self.similarity_bias = nn.Parameter(torch.tensor([-5.]), requires_grad=True)
|
||||
|
||||
@property
|
||||
def device(self):
|
||||
return next(self.parameters()).device
|
||||
|
||||
def forward(self, mels: torch.FloatTensor):
|
||||
"""
|
||||
Computes the embeddings of a batch of partial utterances.
|
||||
|
||||
:param mels: a batch of unscaled mel spectrograms of same duration as a float32 tensor
|
||||
of shape (B, T, M) where T is hp.ve_partial_frames
|
||||
:return: the embeddings as a float32 tensor of shape (B, E) where E is
|
||||
hp.speaker_embed_size. Embeddings are L2-normed and thus lay in the range [-1, 1].
|
||||
"""
|
||||
if self.hp.normalized_mels and (mels.min() < 0 or mels.max() > 1):
|
||||
raise Exception(f"Mels outside [0, 1]. Min={mels.min()}, Max={mels.max()}")
|
||||
|
||||
# Pass the input through the LSTM layers
|
||||
_, (hidden, _) = self.lstm(mels)
|
||||
|
||||
# Project the final hidden state
|
||||
raw_embeds = self.proj(hidden[-1])
|
||||
if self.hp.ve_final_relu:
|
||||
raw_embeds = F.relu(raw_embeds)
|
||||
|
||||
# L2 normalize the embeddings.
|
||||
return raw_embeds / torch.linalg.norm(raw_embeds, dim=1, keepdim=True)
|
||||
|
||||
def inference(self, mels: torch.Tensor, mel_lens, overlap=0.5, rate: float=None, min_coverage=0.8, batch_size=None):
|
||||
"""
|
||||
Computes the embeddings of a batch of full utterances with gradients.
|
||||
|
||||
:param mels: (B, T, M) unscaled mels
|
||||
:return: (B, E) embeddings on CPU
|
||||
"""
|
||||
mel_lens = mel_lens.tolist() if torch.is_tensor(mel_lens) else mel_lens
|
||||
|
||||
# Compute where to split the utterances into partials
|
||||
frame_step = get_frame_step(overlap, rate, self.hp)
|
||||
n_partials, target_lens = zip(*(get_num_wins(l, frame_step, min_coverage, self.hp) for l in mel_lens))
|
||||
|
||||
# Possibly pad the mels to reach the target lengths
|
||||
len_diff = max(target_lens) - mels.size(1)
|
||||
if len_diff > 0:
|
||||
pad = torch.full((mels.size(0), len_diff, self.hp.num_mels), 0, dtype=torch.float32)
|
||||
mels = torch.cat((mels, pad.to(mels.device)), dim=1)
|
||||
|
||||
# Group all partials together so that we can batch them easily
|
||||
partials = [
|
||||
mel[i * frame_step: i * frame_step + self.hp.ve_partial_frames]
|
||||
for mel, n_partial in zip(mels, n_partials) for i in range(n_partial)
|
||||
]
|
||||
assert all(partials[0].shape == partial.shape for partial in partials)
|
||||
partials = torch.stack(partials)
|
||||
|
||||
# Forward the partials
|
||||
n_chunks = int(np.ceil(len(partials) / (batch_size or len(partials))))
|
||||
partial_embeds = torch.cat([self(batch) for batch in partials.chunk(n_chunks)], dim=0).cpu()
|
||||
|
||||
# Reduce the partial embeds into full embeds and L2-normalize them
|
||||
slices = np.concatenate(([0], np.cumsum(n_partials)))
|
||||
raw_embeds = [torch.mean(partial_embeds[start:end], dim=0) for start, end in zip(slices[:-1], slices[1:])]
|
||||
raw_embeds = torch.stack(raw_embeds)
|
||||
embeds = raw_embeds / torch.linalg.norm(raw_embeds, dim=1, keepdim=True)
|
||||
|
||||
return embeds
|
||||
|
||||
@staticmethod
|
||||
def utt_to_spk_embed(utt_embeds: np.ndarray):
|
||||
"""
|
||||
Takes an array of L2-normalized utterance embeddings, computes the mean embedding and L2-normalize it to get a
|
||||
speaker embedding.
|
||||
"""
|
||||
assert utt_embeds.ndim == 2
|
||||
utt_embeds = np.mean(utt_embeds, axis=0)
|
||||
return utt_embeds / np.linalg.norm(utt_embeds, 2)
|
||||
|
||||
@staticmethod
|
||||
def voice_similarity(embeds_x: np.ndarray, embeds_y: np.ndarray):
|
||||
"""
|
||||
Cosine similarity for L2-normalized utterance embeddings or speaker embeddings
|
||||
"""
|
||||
embeds_x = embeds_x if embeds_x.ndim == 1 else VoiceEncoder.utt_to_spk_embed(embeds_x)
|
||||
embeds_y = embeds_y if embeds_y.ndim == 1 else VoiceEncoder.utt_to_spk_embed(embeds_y)
|
||||
return embeds_x @ embeds_y
|
||||
|
||||
def embeds_from_mels(
|
||||
self, mels: Union[Tensor, List[np.ndarray]], mel_lens=None, as_spk=False, batch_size=32, **kwargs
|
||||
):
|
||||
"""
|
||||
Convenience function for deriving utterance or speaker embeddings from mel spectrograms.
|
||||
|
||||
:param mels: unscaled mels strictly within [0, 1] as either a (B, T, M) tensor or a list of (Ti, M) arrays.
|
||||
:param mel_lens: if passing mels as a tensor, individual mel lengths
|
||||
:param as_spk: whether to return utterance embeddings or a single speaker embedding
|
||||
:param kwargs: args for inference()
|
||||
|
||||
:returns: embeds as a (B, E) float32 numpy array if <as_spk> is False, else as a (E,) array
|
||||
"""
|
||||
# Load mels in memory and pack them
|
||||
if isinstance(mels, List):
|
||||
mels = [np.asarray(mel) for mel in mels]
|
||||
assert all(m.shape[1] == mels[0].shape[1] for m in mels), "Mels aren't in (B, T, M) format"
|
||||
mel_lens = [mel.shape[0] for mel in mels]
|
||||
mels = pack(mels)
|
||||
|
||||
# Embed them
|
||||
with torch.inference_mode():
|
||||
utt_embeds = self.inference(mels.to(self.device), mel_lens, batch_size=batch_size, **kwargs).numpy()
|
||||
|
||||
return self.utt_to_spk_embed(utt_embeds) if as_spk else utt_embeds
|
||||
|
||||
def embeds_from_wavs(
|
||||
self,
|
||||
wavs: List[np.ndarray],
|
||||
sample_rate,
|
||||
as_spk=False,
|
||||
batch_size=32,
|
||||
trim_top_db: Optional[float]=20,
|
||||
**kwargs
|
||||
):
|
||||
"""
|
||||
Wrapper around embeds_from_mels
|
||||
|
||||
:param trim_top_db: this argument was only added for the sake of compatibility with metavoice's implementation
|
||||
"""
|
||||
if sample_rate != self.hp.sample_rate:
|
||||
wavs = [
|
||||
librosa.resample(wav, orig_sr=sample_rate, target_sr=self.hp.sample_rate, res_type="kaiser_fast")
|
||||
for wav in wavs
|
||||
]
|
||||
|
||||
if trim_top_db:
|
||||
wavs = [librosa.effects.trim(wav, top_db=trim_top_db)[0] for wav in wavs]
|
||||
|
||||
if "rate" not in kwargs:
|
||||
kwargs["rate"] = 1.3 # Resemble's default value.
|
||||
|
||||
mels = [melspectrogram(w, self.hp).T for w in wavs]
|
||||
|
||||
return self.embeds_from_mels(mels, as_spk=as_spk, batch_size=batch_size, **kwargs)
|
||||
@@ -1,244 +0,0 @@
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
|
||||
import librosa
|
||||
import torch
|
||||
import perth
|
||||
import torch.nn.functional as F
|
||||
from huggingface_hub import hf_hub_download
|
||||
|
||||
from .models.t3 import T3
|
||||
from .models.s3tokenizer import S3_SR, drop_invalid_tokens
|
||||
from .models.s3gen import S3GEN_SR, S3Gen
|
||||
from .models.tokenizers import EnTokenizer
|
||||
from .models.voice_encoder import VoiceEncoder
|
||||
from .models.t3.modules.cond_enc import T3Cond
|
||||
|
||||
|
||||
REPO_ID = "ResembleAI/chatterbox"
|
||||
|
||||
|
||||
def punc_norm(text: str) -> str:
|
||||
"""
|
||||
Quick cleanup func for punctuation from LLMs or
|
||||
containing chars not seen often in the dataset
|
||||
"""
|
||||
if len(text) == 0:
|
||||
return "You need to add some text for me to talk."
|
||||
|
||||
# Capitalise first letter
|
||||
if text[0].islower():
|
||||
text = text[0].upper() + text[1:]
|
||||
|
||||
# Remove multiple space chars
|
||||
text = " ".join(text.split())
|
||||
|
||||
# Replace uncommon/llm punc
|
||||
punc_to_replace = [
|
||||
("...", ", "),
|
||||
("…", ", "),
|
||||
(":", ","),
|
||||
(" - ", ", "),
|
||||
(";", ", "),
|
||||
("—", "-"),
|
||||
("–", "-"),
|
||||
(" ,", ","),
|
||||
("“", "\""),
|
||||
("”", "\""),
|
||||
("‘", "'"),
|
||||
("’", "'"),
|
||||
]
|
||||
for old_char_sequence, new_char in punc_to_replace:
|
||||
text = text.replace(old_char_sequence, new_char)
|
||||
|
||||
# Add full stop if no ending punc
|
||||
text = text.rstrip(" ")
|
||||
sentence_enders = {".", "!", "?", "-", ","}
|
||||
if not any(text.endswith(p) for p in sentence_enders):
|
||||
text += "."
|
||||
|
||||
return text
|
||||
|
||||
|
||||
@dataclass
|
||||
class Conditionals:
|
||||
"""
|
||||
Conditionals for T3 and S3Gen
|
||||
- T3 conditionals:
|
||||
- speaker_emb
|
||||
- clap_emb
|
||||
- cond_prompt_speech_tokens
|
||||
- cond_prompt_speech_emb
|
||||
- emotion_adv
|
||||
- S3Gen conditionals:
|
||||
- prompt_token
|
||||
- prompt_token_len
|
||||
- prompt_feat
|
||||
- prompt_feat_len
|
||||
- embedding
|
||||
"""
|
||||
t3: T3Cond
|
||||
gen: dict
|
||||
|
||||
def to(self, device):
|
||||
self.t3 = self.t3.to(device=device)
|
||||
for k, v in self.gen.items():
|
||||
if torch.is_tensor(v):
|
||||
self.gen[k] = v.to(device=device)
|
||||
return self
|
||||
|
||||
def save(self, fpath: Path):
|
||||
arg_dict = dict(
|
||||
t3=self.t3.__dict__,
|
||||
gen=self.gen
|
||||
)
|
||||
torch.save(arg_dict, fpath)
|
||||
|
||||
@classmethod
|
||||
def load(cls, fpath, map_location="cpu"):
|
||||
kwargs = torch.load(fpath, map_location=map_location, weights_only=True)
|
||||
return cls(T3Cond(**kwargs['t3']), kwargs['gen'])
|
||||
|
||||
|
||||
class ChatterboxTTS:
|
||||
ENC_COND_LEN = 6 * S3_SR
|
||||
DEC_COND_LEN = 10 * S3GEN_SR
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
t3: T3,
|
||||
s3gen: S3Gen,
|
||||
ve: VoiceEncoder,
|
||||
tokenizer: EnTokenizer,
|
||||
device: str,
|
||||
conds: Conditionals = None,
|
||||
):
|
||||
self.sr = S3GEN_SR # sample rate of synthesized audio
|
||||
self.t3 = t3
|
||||
self.s3gen = s3gen
|
||||
self.ve = ve
|
||||
self.tokenizer = tokenizer
|
||||
self.device = device
|
||||
self.conds = conds
|
||||
self.watermarker = perth.PerthImplicitWatermarker()
|
||||
|
||||
@classmethod
|
||||
def from_local(cls, ckpt_dir, device) -> 'ChatterboxTTS':
|
||||
ckpt_dir = Path(ckpt_dir)
|
||||
|
||||
ve = VoiceEncoder()
|
||||
ve.load_state_dict(
|
||||
torch.load(ckpt_dir / "ve.pt")
|
||||
)
|
||||
ve.to(device).eval()
|
||||
|
||||
t3 = T3()
|
||||
t3_state = torch.load(ckpt_dir / "t3_cfg.pt")
|
||||
if "model" in t3_state.keys():
|
||||
t3_state = t3_state["model"][0]
|
||||
t3.load_state_dict(t3_state)
|
||||
t3.to(device).eval()
|
||||
|
||||
s3gen = S3Gen()
|
||||
s3gen.load_state_dict(
|
||||
torch.load(ckpt_dir / "s3gen.pt")
|
||||
)
|
||||
s3gen.to(device).eval()
|
||||
|
||||
tokenizer = EnTokenizer(
|
||||
str(ckpt_dir / "tokenizer.json")
|
||||
)
|
||||
|
||||
conds = None
|
||||
if (builtin_voice := ckpt_dir / "conds.pt").exists():
|
||||
conds = Conditionals.load(builtin_voice).to(device)
|
||||
|
||||
return cls(t3, s3gen, ve, tokenizer, device, conds=conds)
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, device) -> 'ChatterboxTTS':
|
||||
for fpath in ["ve.pt", "t3_cfg.pt", "s3gen.pt", "tokenizer.json", "conds.pt"]:
|
||||
local_path = hf_hub_download(repo_id=REPO_ID, filename=fpath)
|
||||
|
||||
return cls.from_local(Path(local_path).parent, device)
|
||||
|
||||
def prepare_conditionals(self, wav_fpath, exaggeration=0.5):
|
||||
## Load reference wav
|
||||
s3gen_ref_wav, _sr = librosa.load(wav_fpath, sr=S3GEN_SR)
|
||||
|
||||
ref_16k_wav = librosa.resample(s3gen_ref_wav, orig_sr=S3GEN_SR, target_sr=S3_SR)
|
||||
|
||||
s3gen_ref_wav = s3gen_ref_wav[:self.DEC_COND_LEN]
|
||||
s3gen_ref_dict = self.s3gen.embed_ref(s3gen_ref_wav, S3GEN_SR, device=self.device)
|
||||
|
||||
# Speech cond prompt tokens
|
||||
if plen := self.t3.hp.speech_cond_prompt_len:
|
||||
s3_tokzr = self.s3gen.tokenizer
|
||||
t3_cond_prompt_tokens, _ = s3_tokzr.forward([ref_16k_wav[:self.ENC_COND_LEN]], max_len=plen)
|
||||
t3_cond_prompt_tokens = torch.atleast_2d(t3_cond_prompt_tokens).to(self.device)
|
||||
|
||||
# Voice-encoder speaker embedding
|
||||
ve_embed = torch.from_numpy(self.ve.embeds_from_wavs([ref_16k_wav], sample_rate=S3_SR))
|
||||
ve_embed = ve_embed.mean(axis=0, keepdim=True).to(self.device)
|
||||
|
||||
t3_cond = T3Cond(
|
||||
speaker_emb=ve_embed,
|
||||
cond_prompt_speech_tokens=t3_cond_prompt_tokens,
|
||||
emotion_adv=exaggeration * torch.ones(1, 1, 1),
|
||||
).to(device=self.device)
|
||||
self.conds = Conditionals(t3_cond, s3gen_ref_dict)
|
||||
|
||||
def generate(
|
||||
self,
|
||||
text,
|
||||
audio_prompt_path=None,
|
||||
exaggeration=0.5,
|
||||
cfg_weight=0.5,
|
||||
temperature=0.8,
|
||||
):
|
||||
if audio_prompt_path:
|
||||
self.prepare_conditionals(audio_prompt_path, exaggeration=exaggeration)
|
||||
else:
|
||||
assert self.conds is not None, "Please `prepare_conditionals` first or specify `audio_prompt_path`"
|
||||
|
||||
# Update exaggeration if needed
|
||||
if exaggeration != self.conds.t3.emotion_adv[0, 0, 0]:
|
||||
_cond: T3Cond = self.conds.t3
|
||||
self.conds.t3 = T3Cond(
|
||||
speaker_emb=_cond.speaker_emb,
|
||||
cond_prompt_speech_tokens=_cond.cond_prompt_speech_tokens,
|
||||
emotion_adv=exaggeration * torch.ones(1, 1, 1),
|
||||
).to(device=self.device)
|
||||
|
||||
# Norm and tokenize text
|
||||
text = punc_norm(text)
|
||||
text_tokens = self.tokenizer.text_to_tokens(text).to(self.device)
|
||||
text_tokens = torch.cat([text_tokens, text_tokens], dim=0) # Need two seqs for CFG
|
||||
|
||||
sot = self.t3.hp.start_text_token
|
||||
eot = self.t3.hp.stop_text_token
|
||||
text_tokens = F.pad(text_tokens, (1, 0), value=sot)
|
||||
text_tokens = F.pad(text_tokens, (0, 1), value=eot)
|
||||
|
||||
with torch.inference_mode():
|
||||
speech_tokens = self.t3.inference(
|
||||
t3_cond=self.conds.t3,
|
||||
text_tokens=text_tokens,
|
||||
max_new_tokens=1000, # TODO: use the value in config
|
||||
temperature=temperature,
|
||||
cfg_weight=cfg_weight,
|
||||
)
|
||||
# Extract only the conditional batch.
|
||||
speech_tokens = speech_tokens[0]
|
||||
|
||||
# TODO: output becomes 1D
|
||||
speech_tokens = drop_invalid_tokens(speech_tokens)
|
||||
speech_tokens = speech_tokens.to(self.device)
|
||||
|
||||
wav, _ = self.s3gen.inference(
|
||||
speech_tokens=speech_tokens,
|
||||
ref_dict=self.conds.gen,
|
||||
)
|
||||
wav = wav.squeeze(0).detach().cpu().numpy()
|
||||
watermarked_wav = self.watermarker.apply_watermark(wav, sample_rate=self.sr)
|
||||
return torch.from_numpy(watermarked_wav).unsqueeze(0)
|
||||
@@ -1,88 +0,0 @@
|
||||
from pathlib import Path
|
||||
|
||||
import librosa
|
||||
import torch
|
||||
import perth
|
||||
from huggingface_hub import hf_hub_download
|
||||
|
||||
from .models.s3tokenizer import S3_SR
|
||||
from .models.s3gen import S3GEN_SR, S3Gen
|
||||
|
||||
|
||||
REPO_ID = "ResembleAI/chatterbox"
|
||||
|
||||
|
||||
class ChatterboxVC:
|
||||
ENC_COND_LEN = 6 * S3_SR
|
||||
DEC_COND_LEN = 10 * S3GEN_SR
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
s3gen: S3Gen,
|
||||
device: str,
|
||||
ref_dict: dict=None,
|
||||
):
|
||||
self.sr = S3GEN_SR
|
||||
self.s3gen = s3gen
|
||||
self.device = device
|
||||
self.watermarker = perth.PerthImplicitWatermarker()
|
||||
if ref_dict is None:
|
||||
self.ref_dict = None
|
||||
else:
|
||||
self.ref_dict = {
|
||||
k: v.to(device) if torch.is_tensor(v) else v
|
||||
for k, v in ref_dict.items()
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_local(cls, ckpt_dir, device) -> 'ChatterboxVC':
|
||||
ckpt_dir = Path(ckpt_dir)
|
||||
ref_dict = None
|
||||
if (builtin_voice := ckpt_dir / "conds.pt").exists():
|
||||
states = torch.load(builtin_voice)
|
||||
ref_dict = states['gen']
|
||||
|
||||
s3gen = S3Gen()
|
||||
s3gen.load_state_dict(
|
||||
torch.load(ckpt_dir / "s3gen.pt")
|
||||
)
|
||||
s3gen.to(device).eval()
|
||||
|
||||
return cls(s3gen, device, ref_dict=ref_dict)
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, device) -> 'ChatterboxVC':
|
||||
for fpath in ["s3gen.pt", "conds.pt"]:
|
||||
local_path = hf_hub_download(repo_id=REPO_ID, filename=fpath)
|
||||
|
||||
return cls.from_local(Path(local_path).parent, device)
|
||||
|
||||
def set_target_voice(self, wav_fpath):
|
||||
## Load reference wav
|
||||
s3gen_ref_wav, _sr = librosa.load(wav_fpath, sr=S3GEN_SR)
|
||||
|
||||
s3gen_ref_wav = s3gen_ref_wav[:self.DEC_COND_LEN]
|
||||
self.ref_dict = self.s3gen.embed_ref(s3gen_ref_wav, S3GEN_SR, device=self.device)
|
||||
|
||||
def generate(
|
||||
self,
|
||||
audio,
|
||||
target_voice_path=None,
|
||||
):
|
||||
if target_voice_path:
|
||||
self.set_target_voice(target_voice_path)
|
||||
else:
|
||||
assert self.ref_dict is not None, "Please `prepare_conditionals` first or specify `target_voice_path`"
|
||||
|
||||
with torch.inference_mode():
|
||||
audio_16, _ = librosa.load(audio, sr=S3_SR)
|
||||
audio_16 = torch.from_numpy(audio_16).float().to(self.device)[None, ]
|
||||
|
||||
s3_tokens, _ = self.s3gen.tokenizer(audio_16)
|
||||
wav, _ = self.s3gen.inference(
|
||||
speech_tokens=s3_tokens,
|
||||
ref_dict=self.ref_dict,
|
||||
)
|
||||
wav = wav.squeeze(0).detach().cpu().numpy()
|
||||
watermarked_wav = self.watermarker.apply_watermark(wav, sample_rate=self.sr)
|
||||
return torch.from_numpy(watermarked_wav).unsqueeze(0)
|
||||
@@ -1 +0,0 @@
|
||||
pip
|
||||
@@ -1,123 +0,0 @@
|
||||
Metadata-Version: 2.4
|
||||
Name: chatterbox-tts
|
||||
Version: 0.1.1
|
||||
Summary: Chatterbox: Open Source TTS and Voice Conversion by Resemble AI
|
||||
Author-email: resemble-ai <engineering@resemble.ai>
|
||||
License: MIT License
|
||||
|
||||
Copyright (c) 2025 Resemble AI
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
Project-URL: Homepage, https://github.com/resemble-ai/chatterbox
|
||||
Project-URL: Repository, https://github.com/resemble-ai/chatterbox
|
||||
Requires-Python: >=3.8
|
||||
Description-Content-Type: text/markdown
|
||||
License-File: LICENSE
|
||||
Requires-Dist: numpy==1.26.0
|
||||
Requires-Dist: resampy==0.4.3
|
||||
Requires-Dist: librosa==0.10.0
|
||||
Requires-Dist: s3tokenizer
|
||||
Requires-Dist: torch==2.6.0
|
||||
Requires-Dist: torchaudio==2.6.0
|
||||
Requires-Dist: transformers==4.46.3
|
||||
Requires-Dist: diffusers==0.29.0
|
||||
Requires-Dist: resemble-perth==1.0.1
|
||||
Requires-Dist: omegaconf==2.3.0
|
||||
Requires-Dist: conformer==0.3.2
|
||||
Dynamic: license-file
|
||||
|
||||
|
||||
<img width="1200" alt="cb-big2" src="https://github.com/user-attachments/assets/bd8c5f03-e91d-4ee5-b680-57355da204d1" />
|
||||
|
||||
# Chatterbox TTS
|
||||
|
||||
[](https://resemble-ai.github.io/chatterbox_demopage/)
|
||||
[](https://huggingface.co/spaces/ResembleAI/Chatterbox)
|
||||
[](https://podonos.com/resembleai/chatterbox)
|
||||
|
||||
_Made with ♥️ by <img width="100" alt="resemble-logo-horizontal" src="https://github.com/user-attachments/assets/35cf756b-3506-4943-9c72-c05ddfa4e525" />_
|
||||
|
||||
We're excited to introduce Chatterbox, [Resemble AI's](https://resemble.ai) first production-grade open source TTS model. Licensed under MIT, Chatterbox has been benchmarked against leading closed-source systems like ElevenLabs, and is consistently preferred in side-by-side evaluations.
|
||||
|
||||
Whether you're working on memes, videos, games, or AI agents, Chatterbox brings your content to life. It's also the first open source TTS model to support **emotion exaggeration control**, a powerful feature that makes your voices stand out. Try it now on our [Hugging Face Gradio app.](https://huggingface.co/spaces/ResembleAI/Chatterbox)
|
||||
|
||||
If you like the model but need to scale or tune it for higher accuracy, check out our competitively priced TTS service (<a href="https://resemble.ai">link</a>). It delivers reliable performance with ultra-low latency of sub 200ms—ideal for production use in agents, applications, or interactive media.
|
||||
|
||||
# Key Details
|
||||
- SoTA zeroshot TTS
|
||||
- 0.5B Llama backbone
|
||||
- Unique exaggeration/intensity control
|
||||
- Ultra-stable with alignment-informed inference
|
||||
- Trained on 0.5M hours of cleaned data
|
||||
- Watermarked outputs
|
||||
- Easy voice conversion script
|
||||
- [Outperforms ElevenLabs](https://podonos.com/resembleai/chatterbox)
|
||||
|
||||
# Tips
|
||||
- **General Use (TTS and Voice Agents):**
|
||||
- The default settings (`exaggeration=0.5`, `cfg=0.5`) work well for most prompts.
|
||||
- If the reference speaker has a fast speaking style, lowering `cfg` to around `0.3` can improve pacing.
|
||||
|
||||
- **Expressive or Dramatic Speech:**
|
||||
- Try lower `cfg` values (e.g. `~0.3`) and increase `exaggeration` to around `0.7` or higher.
|
||||
- Higher `exaggeration` tends to speed up speech; reducing `cfg` helps compensate with slower, more deliberate pacing.
|
||||
|
||||
|
||||
# Installation
|
||||
```
|
||||
conda create -yn chatterbox python=3.11
|
||||
conda activate chatterbox
|
||||
|
||||
pip install https://github.com/resemble-ai/chatterbox.git
|
||||
```
|
||||
|
||||
# Pip Install
|
||||
```
|
||||
pip install chatterbox-tts==0.1
|
||||
```
|
||||
|
||||
# Usage
|
||||
```python
|
||||
import torchaudio as ta
|
||||
from chatterbox.tts import ChatterboxTTS
|
||||
|
||||
model = ChatterboxTTS.from_pretrained(device="cuda")
|
||||
|
||||
text = "Ezreal and Jinx teamed up with Ahri, Yasuo, and Teemo to take down the enemy's Nexus in an epic late-game pentakill."
|
||||
wav = model.generate(text)
|
||||
ta.save("test-1.wav", wav, model.sr)
|
||||
|
||||
# If you want to synthesize with a different voice, specify the audio prompt
|
||||
AUDIO_PROMPT_PATH="YOUR_FILE.wav"
|
||||
wav = model.generate(text, audio_prompt_path=AUDIO_PROMPT_PATH)
|
||||
ta.save("test-2.wav", wav, model.sr)
|
||||
```
|
||||
See `example_tts.py` for more examples.
|
||||
|
||||
# Acknowledgements
|
||||
- [Cosyvoice](https://github.com/FunAudioLLM/CosyVoice)
|
||||
- [HiFT-GAN](https://github.com/yl4579/HiFTNet)
|
||||
- [Llama 3](https://github.com/meta-llama/llama3)
|
||||
|
||||
# Built-in PerTh Watermarking for Responsible AI
|
||||
|
||||
Every audio file generated by Chatterbox includes [Resemble AI's Perth (Perceptual Threshold) Watermarker](https://github.com/resemble-ai/perth) - imperceptible neural watermarks that survive MP3 compression, audio editing, and common manipulations while maintaining nearly 100% detection accuracy.
|
||||
|
||||
# Disclaimer
|
||||
Don't use this model to do bad things. Prompts are sourced from freely available data on the internet.
|
||||
@@ -1,97 +0,0 @@
|
||||
chatterbox/__init__.py,sha256=G7pCEKWSBQeJapa5aiZs5jCuGBZNOqPus1So-L5twek,60
|
||||
chatterbox/__pycache__/__init__.cpython-311.pyc,,
|
||||
chatterbox/__pycache__/tts.cpython-311.pyc,,
|
||||
chatterbox/__pycache__/vc.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/__init__.py,sha256=HrmLeFxIFQLlFWdtoOZa-_2PoiVK4wO2-pcU22pScww,68
|
||||
chatterbox/models/s3gen/__pycache__/__init__.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/__pycache__/const.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/__pycache__/decoder.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/__pycache__/f0_predictor.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/__pycache__/flow.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/__pycache__/flow_matching.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/__pycache__/hifigan.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/__pycache__/s3gen.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/__pycache__/xvector.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/const.py,sha256=n6hu6U3jNUuOMvjQnZZ0iRapql0jJi5KJ-qDdvsOFlo,17
|
||||
chatterbox/models/s3gen/decoder.py,sha256=pUjwl6FcuJKJtJiXj3h2za39mZBL1OSPzqUiWqmaJL4,12709
|
||||
chatterbox/models/s3gen/f0_predictor.py,sha256=kXgl7sNTuWAbn3rwwONgPrEGHYtkFTkiXnjJENe8IlU,1993
|
||||
chatterbox/models/s3gen/flow.py,sha256=YwXF1LbpcnxlsuOlb-2vqx55hZW_XzZERYvhWURZ6Wk,10655
|
||||
chatterbox/models/s3gen/flow_matching.py,sha256=WJjSo-pBIwEmF3ynM58Z69ezKfV1siD6FMurgt70Xg0,10074
|
||||
chatterbox/models/s3gen/hifigan.py,sha256=5J9HIzI5N0k-z19sfxk09Xdm70bHTq98YL9Fahxv6Jc,17652
|
||||
chatterbox/models/s3gen/matcha/__pycache__/decoder.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/matcha/__pycache__/flow_matching.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/matcha/__pycache__/text_encoder.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/matcha/__pycache__/transformer.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/matcha/decoder.py,sha256=qfNd97PrHH3vuHRGJrMu8FBKUZoGbzJA_ekDk6ObNIg,14435
|
||||
chatterbox/models/s3gen/matcha/flow_matching.py,sha256=VmFLkqrVL3E8GYrgidR-an3xNFDzz10RRPZfs0w1EH0,4556
|
||||
chatterbox/models/s3gen/matcha/text_encoder.py,sha256=oemTrSGdld7oZOAQ2EFecFerthMaIVUAk9g9sCebAcs,14965
|
||||
chatterbox/models/s3gen/matcha/transformer.py,sha256=Zv8gktl0qZslG52OXAi7zszq7fr0HivYrytLx6Vty-o,13237
|
||||
chatterbox/models/s3gen/s3gen.py,sha256=pRk1SDJLKEkkDzYy40fY9-tOjwmi4bttuWgzI1HlSUQ,11060
|
||||
chatterbox/models/s3gen/transformer/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
|
||||
chatterbox/models/s3gen/transformer/__pycache__/__init__.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/transformer/__pycache__/activation.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/transformer/__pycache__/attention.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/transformer/__pycache__/convolution.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/transformer/__pycache__/embedding.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/transformer/__pycache__/encoder_layer.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/transformer/__pycache__/positionwise_feed_forward.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/transformer/__pycache__/subsampling.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/transformer/__pycache__/upsample_encoder.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/transformer/activation.py,sha256=pKlsrqn3sFERKG3l6nYL39-cTlNEj1NCCFfcBKUEQMI,3089
|
||||
chatterbox/models/s3gen/transformer/attention.py,sha256=KMOPHj6hrqwVJumPg0nZ8GjiXx9q4kbGC63D1UDWJ5Q,14415
|
||||
chatterbox/models/s3gen/transformer/convolution.py,sha256=619B8ySpciXHO5xDCvi7IxvXc4bvGEULsP0yn0aatOE,5230
|
||||
chatterbox/models/s3gen/transformer/embedding.py,sha256=NkbnSHg_5sBptCTMnL_oG8VKXM_qGucEHfOYpgPMzT4,11399
|
||||
chatterbox/models/s3gen/transformer/encoder_layer.py,sha256=GSBYK-LJt894Nee1ORGOweudqPLHEcYlf4WYs3kpUbk,9602
|
||||
chatterbox/models/s3gen/transformer/positionwise_feed_forward.py,sha256=boA447zIyght3KUI-5udQL86uYvrq89clJNdAyMp0Pg,4219
|
||||
chatterbox/models/s3gen/transformer/subsampling.py,sha256=MfwDR6hRq8EgXf1M9oCZwMQWWJw-maB7JQ6GMM7OGdA,12666
|
||||
chatterbox/models/s3gen/transformer/upsample_encoder.py,sha256=F5T_AbZ4eRrWZdewwJmUSOdg_B3LO8ggjeX1hRDB9ow,13694
|
||||
chatterbox/models/s3gen/utils/__pycache__/class_utils.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/utils/__pycache__/mask.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/utils/__pycache__/mel.cpython-311.pyc,,
|
||||
chatterbox/models/s3gen/utils/class_utils.py,sha256=AvPLOlXuEkwnl3ckWwkGH6FagYLm59d2L4-kXApjGUg,2334
|
||||
chatterbox/models/s3gen/utils/mask.py,sha256=6uQPJ001lBt4lWs-H8izamma4vmLKAPUc1CJyvtVmO0,7637
|
||||
chatterbox/models/s3gen/utils/mel.py,sha256=OnjR5GtIndnMvu21J6A0Aj6Qfx2wPda23_dx4yc4jX8,2364
|
||||
chatterbox/models/s3gen/xvector.py,sha256=x8dZkgy_GqyfEuChXBvz042lHKnT6np7U890DIb-F_o,14141
|
||||
chatterbox/models/s3tokenizer/__init__.py,sha256=c1fOmgNJuuUejoyrzdAbqFNN1qI3PB4JIpHstxZBT-A,595
|
||||
chatterbox/models/s3tokenizer/__pycache__/__init__.cpython-311.pyc,,
|
||||
chatterbox/models/s3tokenizer/__pycache__/s3tokenizer.cpython-311.pyc,,
|
||||
chatterbox/models/s3tokenizer/s3tokenizer.py,sha256=durp-VsUYIxIR8nXrPzwgTjDZWQLDUWJ3gHnQrDl60M,5155
|
||||
chatterbox/models/t3/__init__.py,sha256=z-zBx999IjmBV9QGBav6ZPMO-baXGQ3E9Xsikpn3BTg,19
|
||||
chatterbox/models/t3/__pycache__/__init__.cpython-311.pyc,,
|
||||
chatterbox/models/t3/__pycache__/llama_configs.cpython-311.pyc,,
|
||||
chatterbox/models/t3/__pycache__/t3.cpython-311.pyc,,
|
||||
chatterbox/models/t3/inference/__pycache__/alignment_stream_analyzer.cpython-311.pyc,,
|
||||
chatterbox/models/t3/inference/__pycache__/t3_hf_backend.cpython-311.pyc,,
|
||||
chatterbox/models/t3/inference/alignment_stream_analyzer.py,sha256=e9vmXVoK20hzKAzcnyXNHknnA-eKMyGKifn7oKs_9gw,6850
|
||||
chatterbox/models/t3/inference/t3_hf_backend.py,sha256=qHrgRNl3dYmsUxNguz-Qlq3-ckz4S5LKb0MG9AXeYd0,4271
|
||||
chatterbox/models/t3/llama_configs.py,sha256=Ahl8MW0CNZqW4c9sPHqwz1XsFF4WVSTp7db-kDhKT3Q,1005
|
||||
chatterbox/models/t3/modules/__pycache__/cond_enc.cpython-311.pyc,,
|
||||
chatterbox/models/t3/modules/__pycache__/learned_pos_emb.cpython-311.pyc,,
|
||||
chatterbox/models/t3/modules/__pycache__/perceiver.cpython-311.pyc,,
|
||||
chatterbox/models/t3/modules/__pycache__/t3_config.cpython-311.pyc,,
|
||||
chatterbox/models/t3/modules/cond_enc.py,sha256=2BKIAwkUIxQQUcryld5iCBE9qYf-U33lVmxLhOGd-Ec,3293
|
||||
chatterbox/models/t3/modules/learned_pos_emb.py,sha256=LsWwnOlNcSYenCWDOgj2Zr1uLVQ5sjFBI-A1wC34t68,1099
|
||||
chatterbox/models/t3/modules/perceiver.py,sha256=-cSwEwZS6U0Fhz6tkx7NOZqd1kS3oX7fG3bX1_K_v0I,7719
|
||||
chatterbox/models/t3/modules/t3_config.py,sha256=6MLr-S3jjiVMm71a6Bq6PZUx69fX0kEB0c3kd5cwfDg,647
|
||||
chatterbox/models/t3/t3.py,sha256=XsnAURvYBiJKhuFj6zzDiHpGrgKxbAlLIur5mLkz3rg,14530
|
||||
chatterbox/models/tokenizers/__init__.py,sha256=gbKCJQwD_r0P5vF6CGvBaIluJy_NTkNj2wGynG-wWdU,35
|
||||
chatterbox/models/tokenizers/__pycache__/__init__.cpython-311.pyc,,
|
||||
chatterbox/models/tokenizers/__pycache__/tokenizer.cpython-311.pyc,,
|
||||
chatterbox/models/tokenizers/tokenizer.py,sha256=7rjUZMy0Xs_p1hELRGo_B5itxTEtfd9MqsAO9HoFxM0,1367
|
||||
chatterbox/models/voice_encoder/__init__.py,sha256=rfG873TpDa4Py1wpRRA8vAvZrxXMm-Qoj5YK19Is5ng,56
|
||||
chatterbox/models/voice_encoder/__pycache__/__init__.cpython-311.pyc,,
|
||||
chatterbox/models/voice_encoder/__pycache__/config.cpython-311.pyc,,
|
||||
chatterbox/models/voice_encoder/__pycache__/melspec.cpython-311.pyc,,
|
||||
chatterbox/models/voice_encoder/__pycache__/voice_encoder.cpython-311.pyc,,
|
||||
chatterbox/models/voice_encoder/config.py,sha256=C0HXlKFv2Ukps3_SKdxh02NBeKEQRqa1g04QkQmo48g,406
|
||||
chatterbox/models/voice_encoder/melspec.py,sha256=D1_qFzhBxRUPpuqSNhYHmMjuJK-6ck1l2mHc54pNSOg,1940
|
||||
chatterbox/models/voice_encoder/voice_encoder.py,sha256=SxLp1Jy7fOgeZzHALU83qbdj73QZ26lafmbqB41JqcM,10555
|
||||
chatterbox/tts.py,sha256=cQkPVSqgZw3-SvzQR5YcnwhVb7qIWNDm557n2waqWBY,7711
|
||||
chatterbox/vc.py,sha256=Ye9US7-7DLy9LPQPztAt3UHT2UlR43rjP2pm-YgqSaQ,2701
|
||||
chatterbox_tts-0.1.1.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
|
||||
chatterbox_tts-0.1.1.dist-info/METADATA,sha256=CTT4EWudKoqrsijprkxhPsKbai8Kl7kZcQcaRfaDHmw,5896
|
||||
chatterbox_tts-0.1.1.dist-info/RECORD,,
|
||||
chatterbox_tts-0.1.1.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
|
||||
chatterbox_tts-0.1.1.dist-info/WHEEL,sha256=_zCd3N1l69ArxyTb8rzEoP9TpbYXkqRFSNOD5OuxnTs,91
|
||||
chatterbox_tts-0.1.1.dist-info/licenses/LICENSE,sha256=QkjpEKkohJ_lgVoPkjbhf6B3aNlbkZMhJ1LEZLk9bKo,1067
|
||||
chatterbox_tts-0.1.1.dist-info/top_level.txt,sha256=rBjAeGn5vfA69esbcnInQtvn4XUfS4U-hxvZi63HDDg,11
|
||||
@@ -1,5 +0,0 @@
|
||||
Wheel-Version: 1.0
|
||||
Generator: setuptools (80.9.0)
|
||||
Root-Is-Purelib: true
|
||||
Tag: py3-none-any
|
||||
|
||||
@@ -1,21 +0,0 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2025 Resemble AI
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
@@ -1 +0,0 @@
|
||||
chatterbox
|
||||
Reference in New Issue
Block a user