mirror of https://github.com/coqui-ai/TTS.git
Add support for hifigan and streaming
This commit is contained in:
WeberJulian
parent
2150136210
commit
e7a91befdd
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@ -172,7 +172,7 @@ class GPT(nn.Module):
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"heads": list(self.text_head.parameters()) + list(self.mel_head.parameters()),
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}
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def init_gpt_for_inference(self, kv_cache=True):
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def init_gpt_for_inference(self, kv_cache=True, use_deepspeed=False):
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seq_length = self.max_prompt_tokens + self.max_mel_tokens + self.max_text_tokens + 1
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gpt_config = GPT2Config(
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vocab_size=self.max_mel_tokens,
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@ -195,6 +195,17 @@ class GPT(nn.Module):
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)
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self.gpt.wte = self.mel_embedding
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if use_deepspeed:
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import deepspeed
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self.ds_engine = deepspeed.init_inference(
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model=self.gpt_inference.half(), # Transformers models
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mp_size=1, # Number of GPU
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dtype=torch.float32, # desired data type of output
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replace_method="auto", # Lets DS autmatically identify the layer to replace
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replace_with_kernel_inject=True, # replace the model with the kernel injector
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)
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self.gpt_inference = self.ds_engine.module.eval()
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def set_inputs_and_targets(self, input, start_token, stop_token):
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inp = F.pad(input, (1, 0), value=start_token)
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tar = F.pad(input, (0, 1), value=stop_token)
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@ -543,3 +554,14 @@ class GPT(nn.Module):
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if "return_dict_in_generate" in hf_generate_kwargs:
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return gen.sequences[:, gpt_inputs.shape[1] :], gen
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return gen[:, gpt_inputs.shape[1] :]
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def get_generator(self, fake_inputs, **hf_generate_kwargs):
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return self.gpt_inference.generate_stream(
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fake_inputs,
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bos_token_id=self.start_audio_token,
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pad_token_id=self.stop_audio_token,
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eos_token_id=self.stop_audio_token,
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max_length=self.max_mel_tokens * 2 + self.max_prompt_tokens + self.max_text_tokens,
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do_stream=True,
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**hf_generate_kwargs,
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)
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@ -0,0 +1,742 @@
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import torch
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from torch import nn
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from torch.nn import Conv1d, ConvTranspose1d
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from torch.nn import functional as F
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from torch.nn.utils import remove_weight_norm, weight_norm
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import torchaudio
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from TTS.utils.io import load_fsspec
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LRELU_SLOPE = 0.1
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def get_padding(k, d):
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return int((k * d - d) / 2)
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class ResBlock1(torch.nn.Module):
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"""Residual Block Type 1. It has 3 convolutional layers in each convolutional block.
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Network::
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x -> lrelu -> conv1_1 -> conv1_2 -> conv1_3 -> z -> lrelu -> conv2_1 -> conv2_2 -> conv2_3 -> o -> + -> o
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|--------------------------------------------------------------------------------------------------|
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Args:
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channels (int): number of hidden channels for the convolutional layers.
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kernel_size (int): size of the convolution filter in each layer.
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dilations (list): list of dilation value for each conv layer in a block.
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"""
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def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
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super().__init__()
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self.convs1 = nn.ModuleList(
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[
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1]),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[2],
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padding=get_padding(kernel_size, dilation[2]),
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)
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),
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]
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)
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self.convs2 = nn.ModuleList(
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[
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=1,
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padding=get_padding(kernel_size, 1),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=1,
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padding=get_padding(kernel_size, 1),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=1,
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padding=get_padding(kernel_size, 1),
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)
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),
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]
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)
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def forward(self, x):
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"""
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Args:
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x (Tensor): input tensor.
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Returns:
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Tensor: output tensor.
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Shapes:
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x: [B, C, T]
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"""
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for c1, c2 in zip(self.convs1, self.convs2):
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xt = F.leaky_relu(x, LRELU_SLOPE)
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xt = c1(xt)
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xt = F.leaky_relu(xt, LRELU_SLOPE)
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xt = c2(xt)
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x = xt + x
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return x
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def remove_weight_norm(self):
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for l in self.convs1:
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remove_weight_norm(l)
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for l in self.convs2:
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remove_weight_norm(l)
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class ResBlock2(torch.nn.Module):
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"""Residual Block Type 2. It has 1 convolutional layers in each convolutional block.
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Network::
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x -> lrelu -> conv1-> -> z -> lrelu -> conv2-> o -> + -> o
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|---------------------------------------------------|
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Args:
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channels (int): number of hidden channels for the convolutional layers.
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kernel_size (int): size of the convolution filter in each layer.
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dilations (list): list of dilation value for each conv layer in a block.
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"""
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def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
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super().__init__()
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self.convs = nn.ModuleList(
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[
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1]),
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)
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),
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]
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)
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def forward(self, x):
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for c in self.convs:
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xt = F.leaky_relu(x, LRELU_SLOPE)
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xt = c(xt)
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x = xt + x
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return x
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def remove_weight_norm(self):
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for l in self.convs:
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remove_weight_norm(l)
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class HifiganGenerator(torch.nn.Module):
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def __init__(
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self,
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in_channels,
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out_channels,
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resblock_type,
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resblock_dilation_sizes,
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resblock_kernel_sizes,
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upsample_kernel_sizes,
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upsample_initial_channel,
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upsample_factors,
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inference_padding=5,
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cond_channels=0,
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conv_pre_weight_norm=True,
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conv_post_weight_norm=True,
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conv_post_bias=True,
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cond_in_each_up_layer=False,
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):
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r"""HiFiGAN Generator with Multi-Receptive Field Fusion (MRF)
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Network:
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x -> lrelu -> upsampling_layer -> resblock1_k1x1 -> z1 -> + -> z_sum / #resblocks -> lrelu -> conv_post_7x1 -> tanh -> o
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.. -> zI ---|
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resblockN_kNx1 -> zN ---'
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Args:
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in_channels (int): number of input tensor channels.
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out_channels (int): number of output tensor channels.
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resblock_type (str): type of the `ResBlock`. '1' or '2'.
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resblock_dilation_sizes (List[List[int]]): list of dilation values in each layer of a `ResBlock`.
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resblock_kernel_sizes (List[int]): list of kernel sizes for each `ResBlock`.
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upsample_kernel_sizes (List[int]): list of kernel sizes for each transposed convolution.
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upsample_initial_channel (int): number of channels for the first upsampling layer. This is divided by 2
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for each consecutive upsampling layer.
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upsample_factors (List[int]): upsampling factors (stride) for each upsampling layer.
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inference_padding (int): constant padding applied to the input at inference time. Defaults to 5.
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"""
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super().__init__()
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self.inference_padding = inference_padding
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self.num_kernels = len(resblock_kernel_sizes)
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self.num_upsamples = len(upsample_factors)
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self.cond_in_each_up_layer = cond_in_each_up_layer
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# initial upsampling layers
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self.conv_pre = weight_norm(
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Conv1d(in_channels, upsample_initial_channel, 7, 1, padding=3)
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)
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resblock = ResBlock1 if resblock_type == "1" else ResBlock2
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# upsampling layers
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self.ups = nn.ModuleList()
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for i, (u, k) in enumerate(zip(upsample_factors, upsample_kernel_sizes)):
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self.ups.append(
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weight_norm(
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ConvTranspose1d(
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upsample_initial_channel // (2**i),
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upsample_initial_channel // (2 ** (i + 1)),
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k,
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u,
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padding=(k - u) // 2,
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)
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)
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)
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# MRF blocks
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self.resblocks = nn.ModuleList()
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for i in range(len(self.ups)):
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ch = upsample_initial_channel // (2 ** (i + 1))
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for _, (k, d) in enumerate(
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zip(resblock_kernel_sizes, resblock_dilation_sizes)
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):
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self.resblocks.append(resblock(ch, k, d))
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# post convolution layer
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self.conv_post = weight_norm(
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Conv1d(ch, out_channels, 7, 1, padding=3, bias=conv_post_bias)
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)
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if cond_channels > 0:
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self.cond_layer = nn.Conv1d(cond_channels, upsample_initial_channel, 1)
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if not conv_pre_weight_norm:
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remove_weight_norm(self.conv_pre)
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if not conv_post_weight_norm:
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remove_weight_norm(self.conv_post)
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if self.cond_in_each_up_layer:
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self.conds = nn.ModuleList()
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for i in range(len(self.ups)):
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ch = upsample_initial_channel // (2 ** (i + 1))
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self.conds.append(nn.Conv1d(cond_channels, ch, 1))
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def forward(self, x, g=None):
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"""
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Args:
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x (Tensor): feature input tensor.
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g (Tensor): global conditioning input tensor.
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Returns:
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Tensor: output waveform.
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Shapes:
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x: [B, C, T]
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Tensor: [B, 1, T]
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"""
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o = self.conv_pre(x)
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if hasattr(self, "cond_layer"):
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o = o + self.cond_layer(g)
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for i in range(self.num_upsamples):
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o = F.leaky_relu(o, LRELU_SLOPE)
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o = self.ups[i](o)
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if self.cond_in_each_up_layer:
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o = o + self.conds[i](g)
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z_sum = None
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for j in range(self.num_kernels):
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if z_sum is None:
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z_sum = self.resblocks[i * self.num_kernels + j](o)
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else:
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z_sum += self.resblocks[i * self.num_kernels + j](o)
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o = z_sum / self.num_kernels
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o = F.leaky_relu(o)
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o = self.conv_post(o)
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o = torch.tanh(o)
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return o
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@torch.no_grad()
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def inference(self, c):
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"""
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Args:
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x (Tensor): conditioning input tensor.
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Returns:
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Tensor: output waveform.
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Shapes:
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x: [B, C, T]
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Tensor: [B, 1, T]
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"""
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c = c.to(self.conv_pre.weight.device)
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c = torch.nn.functional.pad(
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c, (self.inference_padding, self.inference_padding), "replicate"
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)
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return self.forward(c)
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def remove_weight_norm(self):
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print("Removing weight norm...")
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for l in self.ups:
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remove_weight_norm(l)
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for l in self.resblocks:
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l.remove_weight_norm()
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remove_weight_norm(self.conv_pre)
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remove_weight_norm(self.conv_post)
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def load_checkpoint(
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self, config, checkpoint_path, eval=False, cache=False
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): # pylint: disable=unused-argument, redefined-builtin
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state = torch.load(checkpoint_path, map_location=torch.device("cpu"))
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self.load_state_dict(state["model"])
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if eval:
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self.eval()
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assert not self.training
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self.remove_weight_norm()
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class SELayer(nn.Module):
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def __init__(self, channel, reduction=8):
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super(SELayer, self).__init__()
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.fc = nn.Sequential(
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nn.Linear(channel, channel // reduction),
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nn.ReLU(inplace=True),
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nn.Linear(channel // reduction, channel),
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nn.Sigmoid(),
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)
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def forward(self, x):
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b, c, _, _ = x.size()
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y = self.avg_pool(x).view(b, c)
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y = self.fc(y).view(b, c, 1, 1)
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return x * y
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class SEBasicBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=8):
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super(SEBasicBlock, self).__init__()
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self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.relu = nn.ReLU(inplace=True)
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self.se = SELayer(planes, reduction)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.relu(out)
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out = self.bn1(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.se(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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def set_init_dict(model_dict, checkpoint_state, c):
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# Partial initialization: if there is a mismatch with new and old layer, it is skipped.
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for k, v in checkpoint_state.items():
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if k not in model_dict:
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print(" | > Layer missing in the model definition: {}".format(k))
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# 1. filter out unnecessary keys
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pretrained_dict = {k: v for k, v in checkpoint_state.items() if k in model_dict}
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# 2. filter out different size layers
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pretrained_dict = {k: v for k, v in pretrained_dict.items() if v.numel() == model_dict[k].numel()}
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# 3. skip reinit layers
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if c.has("reinit_layers") and c.reinit_layers is not None:
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for reinit_layer_name in c.reinit_layers:
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pretrained_dict = {k: v for k, v in pretrained_dict.items() if reinit_layer_name not in k}
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# 4. overwrite entries in the existing state dict
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model_dict.update(pretrained_dict)
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print(" | > {} / {} layers are restored.".format(len(pretrained_dict), len(model_dict)))
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return model_dict
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class PreEmphasis(nn.Module):
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def __init__(self, coefficient=0.97):
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super().__init__()
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self.coefficient = coefficient
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self.register_buffer("filter", torch.FloatTensor([-self.coefficient, 1.0]).unsqueeze(0).unsqueeze(0))
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def forward(self, x):
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assert len(x.size()) == 2
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x = torch.nn.functional.pad(x.unsqueeze(1), (1, 0), "reflect")
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return torch.nn.functional.conv1d(x, self.filter).squeeze(1)
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class ResNetSpeakerEncoder(nn.Module):
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"""This is copied from 🐸TTS to remove it from the dependencies.
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"""
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# pylint: disable=W0102
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def __init__(
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self,
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input_dim=64,
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proj_dim=512,
|
||||
layers=[3, 4, 6, 3],
|
||||
num_filters=[32, 64, 128, 256],
|
||||
encoder_type="ASP",
|
||||
log_input=False,
|
||||
use_torch_spec=False,
|
||||
audio_config=None,
|
||||
):
|
||||
super(ResNetSpeakerEncoder, self).__init__()
|
||||
|
||||
self.encoder_type = encoder_type
|
||||
self.input_dim = input_dim
|
||||
self.log_input = log_input
|
||||
self.use_torch_spec = use_torch_spec
|
||||
self.audio_config = audio_config
|
||||
self.proj_dim = proj_dim
|
||||
|
||||
self.conv1 = nn.Conv2d(1, num_filters[0], kernel_size=3, stride=1, padding=1)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.bn1 = nn.BatchNorm2d(num_filters[0])
|
||||
|
||||
self.inplanes = num_filters[0]
|
||||
self.layer1 = self.create_layer(SEBasicBlock, num_filters[0], layers[0])
|
||||
self.layer2 = self.create_layer(SEBasicBlock, num_filters[1], layers[1], stride=(2, 2))
|
||||
self.layer3 = self.create_layer(SEBasicBlock, num_filters[2], layers[2], stride=(2, 2))
|
||||
self.layer4 = self.create_layer(SEBasicBlock, num_filters[3], layers[3], stride=(2, 2))
|
||||
|
||||
self.instancenorm = nn.InstanceNorm1d(input_dim)
|
||||
|
||||
if self.use_torch_spec:
|
||||
self.torch_spec = torch.nn.Sequential(
|
||||
PreEmphasis(audio_config["preemphasis"]),
|
||||
torchaudio.transforms.MelSpectrogram(
|
||||
sample_rate=audio_config["sample_rate"],
|
||||
n_fft=audio_config["fft_size"],
|
||||
win_length=audio_config["win_length"],
|
||||
hop_length=audio_config["hop_length"],
|
||||
window_fn=torch.hamming_window,
|
||||
n_mels=audio_config["num_mels"],
|
||||
),
|
||||
)
|
||||
|
||||
else:
|
||||
self.torch_spec = None
|
||||
|
||||
outmap_size = int(self.input_dim / 8)
|
||||
|
||||
self.attention = nn.Sequential(
|
||||
nn.Conv1d(num_filters[3] * outmap_size, 128, kernel_size=1),
|
||||
nn.ReLU(),
|
||||
nn.BatchNorm1d(128),
|
||||
nn.Conv1d(128, num_filters[3] * outmap_size, kernel_size=1),
|
||||
nn.Softmax(dim=2),
|
||||
)
|
||||
|
||||
if self.encoder_type == "SAP":
|
||||
out_dim = num_filters[3] * outmap_size
|
||||
elif self.encoder_type == "ASP":
|
||||
out_dim = num_filters[3] * outmap_size * 2
|
||||
else:
|
||||
raise ValueError("Undefined encoder")
|
||||
|
||||
self.fc = nn.Linear(out_dim, proj_dim)
|
||||
|
||||
self._init_layers()
|
||||
|
||||
def _init_layers(self):
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.Conv2d):
|
||||
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
|
||||
elif isinstance(m, nn.BatchNorm2d):
|
||||
nn.init.constant_(m.weight, 1)
|
||||
nn.init.constant_(m.bias, 0)
|
||||
|
||||
def create_layer(self, block, planes, blocks, stride=1):
|
||||
downsample = None
|
||||
if stride != 1 or self.inplanes != planes * block.expansion:
|
||||
downsample = nn.Sequential(
|
||||
nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
|
||||
nn.BatchNorm2d(planes * block.expansion),
|
||||
)
|
||||
|
||||
layers = []
|
||||
layers.append(block(self.inplanes, planes, stride, downsample))
|
||||
self.inplanes = planes * block.expansion
|
||||
for _ in range(1, blocks):
|
||||
layers.append(block(self.inplanes, planes))
|
||||
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
# pylint: disable=R0201
|
||||
def new_parameter(self, *size):
|
||||
out = nn.Parameter(torch.FloatTensor(*size))
|
||||
nn.init.xavier_normal_(out)
|
||||
return out
|
||||
|
||||
def forward(self, x, l2_norm=False):
|
||||
"""Forward pass of the model.
|
||||
|
||||
Args:
|
||||
x (Tensor): Raw waveform signal or spectrogram frames. If input is a waveform, `torch_spec` must be `True`
|
||||
to compute the spectrogram on-the-fly.
|
||||
l2_norm (bool): Whether to L2-normalize the outputs.
|
||||
|
||||
Shapes:
|
||||
- x: :math:`(N, 1, T_{in})` or :math:`(N, D_{spec}, T_{in})`
|
||||
"""
|
||||
x.squeeze_(1)
|
||||
# if you torch spec compute it otherwise use the mel spec computed by the AP
|
||||
if self.use_torch_spec:
|
||||
x = self.torch_spec(x)
|
||||
|
||||
if self.log_input:
|
||||
x = (x + 1e-6).log()
|
||||
x = self.instancenorm(x).unsqueeze(1)
|
||||
|
||||
x = self.conv1(x)
|
||||
x = self.relu(x)
|
||||
x = self.bn1(x)
|
||||
|
||||
x = self.layer1(x)
|
||||
x = self.layer2(x)
|
||||
x = self.layer3(x)
|
||||
x = self.layer4(x)
|
||||
|
||||
x = x.reshape(x.size()[0], -1, x.size()[-1])
|
||||
|
||||
w = self.attention(x)
|
||||
|
||||
if self.encoder_type == "SAP":
|
||||
x = torch.sum(x * w, dim=2)
|
||||
elif self.encoder_type == "ASP":
|
||||
mu = torch.sum(x * w, dim=2)
|
||||
sg = torch.sqrt((torch.sum((x**2) * w, dim=2) - mu**2).clamp(min=1e-5))
|
||||
x = torch.cat((mu, sg), 1)
|
||||
|
||||
x = x.view(x.size()[0], -1)
|
||||
x = self.fc(x)
|
||||
|
||||
if l2_norm:
|
||||
x = torch.nn.functional.normalize(x, p=2, dim=1)
|
||||
return x
|
||||
|
||||
def load_checkpoint(
|
||||
self,
|
||||
checkpoint_path: str,
|
||||
eval: bool = False,
|
||||
use_cuda: bool = False,
|
||||
criterion=None,
|
||||
cache=False,
|
||||
):
|
||||
state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"), cache=cache)
|
||||
try:
|
||||
self.load_state_dict(state["model"])
|
||||
print(" > Model fully restored. ")
|
||||
except (KeyError, RuntimeError) as error:
|
||||
# If eval raise the error
|
||||
if eval:
|
||||
raise error
|
||||
|
||||
print(" > Partial model initialization.")
|
||||
model_dict = self.state_dict()
|
||||
model_dict = set_init_dict(model_dict, state["model"])
|
||||
self.load_state_dict(model_dict)
|
||||
del model_dict
|
||||
|
||||
# load the criterion for restore_path
|
||||
if criterion is not None and "criterion" in state:
|
||||
try:
|
||||
criterion.load_state_dict(state["criterion"])
|
||||
except (KeyError, RuntimeError) as error:
|
||||
print(" > Criterion load ignored because of:", error)
|
||||
|
||||
if use_cuda:
|
||||
self.cuda()
|
||||
if criterion is not None:
|
||||
criterion = criterion.cuda()
|
||||
|
||||
if eval:
|
||||
self.eval()
|
||||
assert not self.training
|
||||
|
||||
if not eval:
|
||||
return criterion, state["step"]
|
||||
return criterion
|
||||
|
||||
class HifiDecoder(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
input_sample_rate=22050,
|
||||
output_sample_rate=24000,
|
||||
output_hop_length=256,
|
||||
ar_mel_length_compression=1024,
|
||||
decoder_input_dim=1024,
|
||||
resblock_type_decoder="1",
|
||||
resblock_dilation_sizes_decoder=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
|
||||
resblock_kernel_sizes_decoder=[3, 7, 11],
|
||||
upsample_rates_decoder=[8, 8, 2, 2],
|
||||
upsample_initial_channel_decoder=512,
|
||||
upsample_kernel_sizes_decoder=[16, 16, 4, 4],
|
||||
d_vector_dim=512,
|
||||
cond_d_vector_in_each_upsampling_layer=True,
|
||||
speaker_encoder_audio_config={
|
||||
"fft_size": 512,
|
||||
"win_length": 400,
|
||||
"hop_length": 160,
|
||||
"sample_rate": 16000,
|
||||
"preemphasis": 0.97,
|
||||
"num_mels": 64,
|
||||
},
|
||||
):
|
||||
super().__init__()
|
||||
self.input_sample_rate = input_sample_rate
|
||||
self.output_sample_rate = output_sample_rate
|
||||
self.output_hop_length = output_hop_length
|
||||
self.ar_mel_length_compression = ar_mel_length_compression
|
||||
self.speaker_encoder_audio_config = speaker_encoder_audio_config
|
||||
self.waveform_decoder = HifiganGenerator(
|
||||
decoder_input_dim,
|
||||
1,
|
||||
resblock_type_decoder,
|
||||
resblock_dilation_sizes_decoder,
|
||||
resblock_kernel_sizes_decoder,
|
||||
upsample_kernel_sizes_decoder,
|
||||
upsample_initial_channel_decoder,
|
||||
upsample_rates_decoder,
|
||||
inference_padding=0,
|
||||
cond_channels=d_vector_dim,
|
||||
conv_pre_weight_norm=False,
|
||||
conv_post_weight_norm=False,
|
||||
conv_post_bias=False,
|
||||
cond_in_each_up_layer=cond_d_vector_in_each_upsampling_layer,
|
||||
)
|
||||
self.speaker_encoder = ResNetSpeakerEncoder(
|
||||
input_dim=64,
|
||||
proj_dim=512,
|
||||
log_input=True,
|
||||
use_torch_spec=True,
|
||||
audio_config=speaker_encoder_audio_config,
|
||||
)
|
||||
|
||||
@property
|
||||
def device(self):
|
||||
return next(self.parameters()).device
|
||||
|
||||
def forward(self, latents, g=None):
|
||||
"""
|
||||
Args:
|
||||
x (Tensor): feature input tensor (GPT latent).
|
||||
g (Tensor): global conditioning input tensor.
|
||||
|
||||
Returns:
|
||||
Tensor: output waveform.
|
||||
|
||||
Shapes:
|
||||
x: [B, C, T]
|
||||
Tensor: [B, 1, T]
|
||||
"""
|
||||
|
||||
z = torch.nn.functional.interpolate(
|
||||
latents.transpose(1, 2),
|
||||
scale_factor=[self.ar_mel_length_compression / self.output_hop_length],
|
||||
mode="linear",
|
||||
).squeeze(1)
|
||||
# upsample to the right sr
|
||||
if self.output_sample_rate != self.input_sample_rate:
|
||||
z = torch.nn.functional.interpolate(
|
||||
z,
|
||||
scale_factor=[self.output_sample_rate / self.input_sample_rate],
|
||||
mode="linear",
|
||||
).squeeze(0)
|
||||
o = self.waveform_decoder(z, g=g)
|
||||
return o
|
||||
|
||||
@torch.no_grad()
|
||||
def inference(self, c, g):
|
||||
"""
|
||||
Args:
|
||||
x (Tensor): feature input tensor (GPT latent).
|
||||
g (Tensor): global conditioning input tensor.
|
||||
|
||||
Returns:
|
||||
Tensor: output waveform.
|
||||
|
||||
Shapes:
|
||||
x: [B, C, T]
|
||||
Tensor: [B, 1, T]
|
||||
"""
|
||||
return self.forward(c, g=g)
|
||||
|
||||
def load_checkpoint(
|
||||
self, checkpoint_path, eval=False
|
||||
): # pylint: disable=unused-argument, redefined-builtin
|
||||
state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"))
|
||||
# remove unused keys
|
||||
state = state["model"]
|
||||
states_keys = list(state.keys())
|
||||
for key in states_keys:
|
||||
if "waveform_decoder." not in key and "speaker_encoder." not in key:
|
||||
del state[key]
|
||||
|
||||
self.load_state_dict(state)
|
||||
if eval:
|
||||
self.eval()
|
||||
assert not self.training
|
||||
self.waveform_decoder.remove_weight_norm()
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -13,9 +13,12 @@ from TTS.tts.layers.xtts.diffusion import SpacedDiffusion, get_named_beta_schedu
|
|||
from TTS.tts.layers.xtts.gpt import GPT
|
||||
from TTS.tts.layers.xtts.tokenizer import VoiceBpeTokenizer
|
||||
from TTS.tts.layers.xtts.vocoder import UnivNetGenerator
|
||||
from TTS.tts.layers.xtts.hifigan_decoder import HifiDecoder
|
||||
from TTS.tts.layers.xtts.stream_generator import init_stream_support
|
||||
from TTS.tts.models.base_tts import BaseTTS
|
||||
from TTS.utils.io import load_fsspec
|
||||
|
||||
init_stream_support()
|
||||
|
||||
def load_audio(audiopath, sr=22050):
|
||||
"""
|
||||
|
|
@ -266,6 +269,15 @@ class XttsArgs(Coqpit):
|
|||
diff_layer_drop: int = 0
|
||||
diff_unconditioned_percentage: int = 0
|
||||
|
||||
# HifiGAN Decoder params
|
||||
input_sample_rate: int = 22050
|
||||
output_sample_rate: int = 24000
|
||||
output_hop_length: int = 256
|
||||
ar_mel_length_compression: int = 1024
|
||||
decoder_input_dim: int = 1024
|
||||
d_vector_dim: int = 512
|
||||
cond_d_vector_in_each_upsampling_layer: bool = True
|
||||
|
||||
# constants
|
||||
duration_const: int = 102400
|
||||
|
||||
|
|
@ -322,6 +334,16 @@ class Xtts(BaseTTS):
|
|||
stop_audio_token=self.args.gpt_stop_audio_token,
|
||||
)
|
||||
|
||||
self.hifigan_decoder = HifiDecoder(
|
||||
input_sample_rate=self.args.input_sample_rate,
|
||||
output_sample_rate=self.args.output_sample_rate,
|
||||
output_hop_length=self.args.output_hop_length,
|
||||
ar_mel_length_compression=self.args.ar_mel_length_compression,
|
||||
decoder_input_dim=self.args.decoder_input_dim,
|
||||
d_vector_dim=self.args.d_vector_dim,
|
||||
cond_d_vector_in_each_upsampling_layer=self.args.cond_d_vector_in_each_upsampling_layer,
|
||||
)
|
||||
|
||||
self.diffusion_decoder = DiffusionTts(
|
||||
model_channels=self.args.diff_model_channels,
|
||||
num_layers=self.args.diff_num_layers,
|
||||
|
|
@ -393,16 +415,30 @@ class Xtts(BaseTTS):
|
|||
diffusion_latent = diffusion.get_conditioning(diffusion_conds)
|
||||
return diffusion_latent
|
||||
|
||||
def get_speaker_embedding(
|
||||
self,
|
||||
audio_path
|
||||
):
|
||||
wav, sr = torchaudio.load(audio_path)
|
||||
spk_waveform = torchaudio.functional.resample(
|
||||
wav,
|
||||
22050,
|
||||
self.hifigan_decoder.speaker_encoder_audio_config["sample_rate"],
|
||||
).to(self.device)
|
||||
speaker_embedding = self.hifigan_decoder.speaker_encoder.forward(
|
||||
spk_waveform, l2_norm=True
|
||||
).unsqueeze(-1).to(self.device)
|
||||
return speaker_embedding
|
||||
|
||||
def get_conditioning_latents(
|
||||
self,
|
||||
audio_path,
|
||||
gpt_cond_len=3,
|
||||
):
|
||||
gpt_cond_latents = self.get_gpt_cond_latents(audio_path, length=gpt_cond_len) # [1, 1024, T]
|
||||
diffusion_cond_latents = self.get_diffusion_cond_latents(
|
||||
audio_path,
|
||||
)
|
||||
return gpt_cond_latents.to(self.device), diffusion_cond_latents.to(self.device)
|
||||
diffusion_cond_latents = self.get_diffusion_cond_latents(audio_path)
|
||||
speaker_embedding = self.get_speaker_embedding(audio_path)
|
||||
return gpt_cond_latents.to(self.device), diffusion_cond_latents.to(self.device), speaker_embedding
|
||||
|
||||
def synthesize(self, text, config, speaker_wav, language, **kwargs):
|
||||
"""Synthesize speech with the given input text.
|
||||
|
|
@ -469,6 +505,7 @@ class Xtts(BaseTTS):
|
|||
cond_free_k=2,
|
||||
diffusion_temperature=1.0,
|
||||
decoder_sampler="ddim",
|
||||
use_hifigan=True,
|
||||
**hf_generate_kwargs,
|
||||
):
|
||||
"""
|
||||
|
|
@ -535,14 +572,16 @@ class Xtts(BaseTTS):
|
|||
(
|
||||
gpt_cond_latent,
|
||||
diffusion_conditioning,
|
||||
speaker_embedding
|
||||
) = self.get_conditioning_latents(audio_path=ref_audio_path, gpt_cond_len=gpt_cond_len)
|
||||
|
||||
diffuser = load_discrete_vocoder_diffuser(
|
||||
desired_diffusion_steps=decoder_iterations,
|
||||
cond_free=cond_free,
|
||||
cond_free_k=cond_free_k,
|
||||
sampler=decoder_sampler,
|
||||
)
|
||||
if not use_hifigan:
|
||||
diffuser = load_discrete_vocoder_diffuser(
|
||||
desired_diffusion_steps=decoder_iterations,
|
||||
cond_free=cond_free,
|
||||
cond_free_k=cond_free_k,
|
||||
sampler=decoder_sampler,
|
||||
)
|
||||
|
||||
with torch.no_grad():
|
||||
self.gpt = self.gpt.to(self.device)
|
||||
|
|
@ -561,8 +600,6 @@ class Xtts(BaseTTS):
|
|||
output_attentions=False,
|
||||
**hf_generate_kwargs,
|
||||
)
|
||||
|
||||
with self.lazy_load_model(self.gpt) as gpt:
|
||||
expected_output_len = torch.tensor(
|
||||
[gpt_codes.shape[-1] * self.gpt.code_stride_len], device=text_tokens.device
|
||||
)
|
||||
|
|
@ -586,20 +623,120 @@ class Xtts(BaseTTS):
|
|||
if ctokens > 8:
|
||||
gpt_latents = gpt_latents[:, :k]
|
||||
break
|
||||
|
||||
with self.lazy_load_model(self.diffusion_decoder) as diffusion:
|
||||
mel = do_spectrogram_diffusion(
|
||||
diffusion,
|
||||
diffuser,
|
||||
gpt_latents,
|
||||
diffusion_conditioning,
|
||||
temperature=diffusion_temperature,
|
||||
)
|
||||
with self.lazy_load_model(self.vocoder) as vocoder:
|
||||
wav = vocoder.inference(mel)
|
||||
|
||||
if use_hifigan:
|
||||
with self.lazy_load_model(self.hifigan_decoder) as hifigan_decoder:
|
||||
wav = hifigan_decoder(gpt_latents, g=speaker_embedding)
|
||||
else:
|
||||
with self.lazy_load_model(self.diffusion_decoder) as diffusion:
|
||||
mel = do_spectrogram_diffusion(
|
||||
diffusion,
|
||||
diffuser,
|
||||
gpt_latents,
|
||||
diffusion_conditioning,
|
||||
temperature=diffusion_temperature,
|
||||
)
|
||||
with self.lazy_load_model(self.vocoder) as vocoder:
|
||||
wav = vocoder.inference(mel)
|
||||
|
||||
return {"wav": wav.cpu().numpy().squeeze()}
|
||||
|
||||
def inference_speaker_cond(self, ref_audio_path, gpt_cond_len=3):
|
||||
(
|
||||
gpt_cond_latent,
|
||||
diffusion_conditioning,
|
||||
speaker_embedding
|
||||
) = self.get_conditioning_latents(audio_path=ref_audio_path, gpt_cond_len=3)
|
||||
return {
|
||||
"gpt_cond_latent": gpt_cond_latent,
|
||||
"speaker_embedding": speaker_embedding,
|
||||
"diffusion_conditioning": diffusion_conditioning,
|
||||
}
|
||||
|
||||
def handle_chunks(self, wav_gen, wav_gen_prev, wav_overlap, overlap_len):
|
||||
"""Handle chunk formatting in streaming mode"""
|
||||
wav_chunk = wav_gen[:-overlap_len]
|
||||
if wav_gen_prev is not None:
|
||||
wav_chunk = wav_gen[(wav_gen_prev.shape[0] - overlap_len) : -overlap_len]
|
||||
if wav_overlap is not None:
|
||||
crossfade_wav = wav_chunk[:overlap_len]
|
||||
crossfade_wav = crossfade_wav * torch.linspace(0.0, 1.0, overlap_len).to(crossfade_wav.device)
|
||||
wav_chunk[:overlap_len] = wav_overlap * torch.linspace(1.0, 0.0, overlap_len).to(wav_overlap.device)
|
||||
wav_chunk[:overlap_len] += crossfade_wav
|
||||
wav_overlap = wav_gen[-overlap_len:]
|
||||
wav_gen_prev = wav_gen
|
||||
return wav_chunk, wav_gen_prev, wav_overlap
|
||||
|
||||
def inference_stream(
|
||||
self,
|
||||
text,
|
||||
language,
|
||||
gpt_cond_latent,
|
||||
speaker_embedding,
|
||||
diffusion_conditioning,
|
||||
# Streaming
|
||||
stream_chunk_size=20,
|
||||
overlap_wav_len=1024,
|
||||
# GPT inference
|
||||
temperature=0.65,
|
||||
length_penalty=1,
|
||||
repetition_penalty=2.0,
|
||||
top_k=50,
|
||||
top_p=0.85,
|
||||
gpt_cond_len=4,
|
||||
do_sample=True,
|
||||
# Decoder inference
|
||||
decoder_iterations=100,
|
||||
cond_free=True,
|
||||
cond_free_k=2,
|
||||
diffusion_temperature=1.0,
|
||||
decoder_sampler="ddim",
|
||||
**hf_generate_kwargs,
|
||||
):
|
||||
text = f"[{language}]{text.strip().lower()}"
|
||||
text_tokens = torch.IntTensor(self.tokenizer.encode(text, lang=language)).unsqueeze(0).to(self.device)
|
||||
|
||||
fake_inputs = self.gpt.compute_embeddings(
|
||||
gpt_cond_latent.to(self.device),
|
||||
text_tokens,
|
||||
)
|
||||
gpt_generator = self.gpt.get_generator(
|
||||
fake_inputs=fake_inputs,
|
||||
top_k=top_k,
|
||||
top_p=top_p,
|
||||
temperature=temperature,
|
||||
do_sample=do_sample,
|
||||
num_beams=1,
|
||||
num_return_sequences=1,
|
||||
length_penalty=float(length_penalty),
|
||||
repetition_penalty=float(repetition_penalty),
|
||||
output_attentions=False,
|
||||
output_hidden_states=True,
|
||||
)
|
||||
|
||||
last_tokens = []
|
||||
all_latents = []
|
||||
wav_gen_prev = None
|
||||
wav_overlap = None
|
||||
is_end = False
|
||||
|
||||
while not is_end:
|
||||
try:
|
||||
x, latent = next(gpt_generator)
|
||||
last_tokens += [x]
|
||||
all_latents += [latent]
|
||||
except StopIteration:
|
||||
is_end = True
|
||||
|
||||
if is_end or (stream_chunk_size > 0 and len(last_tokens) >= stream_chunk_size):
|
||||
gpt_latents = torch.cat(all_latents, dim=0)[None, :]
|
||||
wav_gen = self.hifigan_decoder(gpt_latents, g=speaker_embedding.to(self.device))
|
||||
wav_chunk, wav_gen_prev, wav_overlap = self.handle_chunks(
|
||||
wav_gen.squeeze(), wav_gen_prev, wav_overlap, overlap_wav_len
|
||||
)
|
||||
last_tokens = []
|
||||
yield wav_chunk
|
||||
|
||||
def forward(self):
|
||||
raise NotImplementedError("XTTS Training is not implemented")
|
||||
|
||||
|
|
@ -616,7 +753,14 @@ class Xtts(BaseTTS):
|
|||
super().eval()
|
||||
|
||||
def load_checkpoint(
|
||||
self, config, checkpoint_dir=None, checkpoint_path=None, vocab_path=None, eval=False, strict=True
|
||||
self,
|
||||
config,
|
||||
checkpoint_dir=None,
|
||||
checkpoint_path=None,
|
||||
vocab_path=None,
|
||||
eval=False,
|
||||
strict=True,
|
||||
use_deepspeed=False
|
||||
):
|
||||
"""
|
||||
Loads a checkpoint from disk and initializes the model's state and tokenizer.
|
||||
|
|
@ -636,8 +780,8 @@ class Xtts(BaseTTS):
|
|||
model_path = checkpoint_path or os.path.join(checkpoint_dir, "model.pth")
|
||||
vocab_path = vocab_path or os.path.join(checkpoint_dir, "vocab.json")
|
||||
|
||||
if os.path.exists(os.path.join(checkpoint_dir, "vocab.json")):
|
||||
self.tokenizer = VoiceBpeTokenizer(vocab_file=os.path.join(checkpoint_dir, "vocab.json"))
|
||||
if os.path.exists(vocab_path):
|
||||
self.tokenizer = VoiceBpeTokenizer(vocab_file=vocab_path)
|
||||
|
||||
self.init_models()
|
||||
if eval:
|
||||
|
|
@ -645,10 +789,11 @@ class Xtts(BaseTTS):
|
|||
self.load_state_dict(load_fsspec(model_path, map_location=self.device)["model"], strict=strict)
|
||||
|
||||
if eval:
|
||||
self.gpt.init_gpt_for_inference(kv_cache=self.args.kv_cache)
|
||||
self.gpt.init_gpt_for_inference(kv_cache=self.args.kv_cache, use_deepspeed=use_deepspeed)
|
||||
self.gpt.eval()
|
||||
self.diffusion_decoder.eval()
|
||||
self.vocoder.eval()
|
||||
self.hifigan_decoder.eval()
|
||||
|
||||
def train_step(self):
|
||||
raise NotImplementedError("XTTS Training is not implemented")
|
||||
|
|
|
|||
Loading…
Reference in New Issue