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fix vocoder train

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lyuxiang.lx
2025-03-07 16:39:13 +08:00
parent fcc054f64e
commit a69b7e275d
12 changed files with 108 additions and 17 deletions
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3
cosyvoice/cli/model.py
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@@ -299,7 +299,8 @@ class CosyVoice2Model(CosyVoiceModel):
self.flow.half()
self.token_hop_len = self.flow.encoder.static_chunk_size
# flow decoder required_cache_size
self.flow_decoder_required_cache_size = self.flow.decoder.estimator.num_decoding_left_chunks * self.flow.decoder.estimator.static_chunk_size
# TODO 基模型训练时没有设置num_decoding_left_chunks，需要重新训一下才能指定flow_decoder_required_cache_size
self.flow_decoder_required_cache_size = 999
# hift cache
self.mel_cache_len = 8
self.source_cache_len = int(self.mel_cache_len * 480)

4
cosyvoice/flow/flow.py
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@@ -91,7 +91,7 @@ class MaskedDiffWithXvec(torch.nn.Module):
conds = conds.transpose(1, 2)
mask = (~make_pad_mask(feat_len)).to(h)
# NOTE 这一句应该是不需要的，应该h已经过length_regulator跟feat一样的shape了
# NOTE this is unnecessary, feat/h already same shape
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(),
@@ -117,7 +117,7 @@ class MaskedDiffWithXvec(torch.nn.Module):
embedding = F.normalize(embedding, dim=1)
embedding = self.spk_embed_affine_layer(embedding)
# concat text and prompt_text
# concat speech token and prompt speech token
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)

1
cosyvoice/flow/length_regulator.py
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@@ -51,6 +51,7 @@ class InterpolateRegulator(nn.Module):
def inference(self, x1, x2, mel_len1, mel_len2, input_frame_rate=50):
# in inference mode, interploate prompt token and token(head/mid/tail) seprately, so we can get a clear separation point of mel
# NOTE 20 corresponds to token_overlap_len in cosyvoice/cli/model.py
# x in (B, T, D)
if x2.shape[1] > 40:
x2_head = F.interpolate(x2[:, :20].transpose(1, 2).contiguous(), size=int(20 / input_frame_rate * 22050 / 256), mode='linear')

91
cosyvoice/hifigan/discriminator.py
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@@ -1,13 +1,16 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
try:
from torch.nn.utils.parametrizations import weight_norm
from torch.nn.utils.parametrizations import weight_norm, spectral_norm
except ImportError:
from torch.nn.utils import weight_norm
from torch.nn.utils import weight_norm, spectral_norm
from typing import List, Optional, Tuple
from einops import rearrange
from torchaudio.transforms import Spectrogram
LRELU_SLOPE = 0.1
class MultipleDiscriminator(nn.Module):
def __init__(
@@ -141,3 +144,87 @@ class DiscriminatorR(nn.Module):
x += h
return x, fmap
class MultiResSpecDiscriminator(torch.nn.Module):
def __init__(self,
fft_sizes=[1024, 2048, 512],
hop_sizes=[120, 240, 50],
win_lengths=[600, 1200, 240],
window="hann_window"):
super(MultiResSpecDiscriminator, self).__init__()
self.discriminators = nn.ModuleList([
SpecDiscriminator(fft_sizes[0], hop_sizes[0], win_lengths[0], window),
SpecDiscriminator(fft_sizes[1], hop_sizes[1], win_lengths[1], window),
SpecDiscriminator(fft_sizes[2], hop_sizes[2], win_lengths[2], window)])
def forward(self, y, y_hat):
y_d_rs = []
y_d_gs = []
fmap_rs = []
fmap_gs = []
for i, d in enumerate(self.discriminators):
y_d_r, fmap_r = d(y)
y_d_g, fmap_g = d(y_hat)
y_d_rs.append(y_d_r)
fmap_rs.append(fmap_r)
y_d_gs.append(y_d_g)
fmap_gs.append(fmap_g)
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
def stft(x, fft_size, hop_size, win_length, window):
"""Perform STFT and convert to magnitude spectrogram.
Args:
x (Tensor): Input signal tensor (B, T).
fft_size (int): FFT size.
hop_size (int): Hop size.
win_length (int): Window length.
window (str): Window function type.
Returns:
Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
"""
x_stft = torch.stft(x, fft_size, hop_size, win_length, window, return_complex=True)
# NOTE(kan-bayashi): clamp is needed to avoid nan or inf
return torch.abs(x_stft).transpose(2, 1)
class SpecDiscriminator(nn.Module):
"""docstring for Discriminator."""
def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window", use_spectral_norm=False):
super(SpecDiscriminator, self).__init__()
norm_f = weight_norm if use_spectral_norm is False else spectral_norm
self.fft_size = fft_size
self.shift_size = shift_size
self.win_length = win_length
self.window = getattr(torch, window)(win_length)
self.discriminators = nn.ModuleList([
norm_f(nn.Conv2d(1, 32, kernel_size=(3, 9), padding=(1, 4))),
norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))),
norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))),
norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))),
norm_f(nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))),
])
self.out = norm_f(nn.Conv2d(32, 1, 3, 1, 1))
def forward(self, y):
fmap = []
y = y.squeeze(1)
y = stft(y, self.fft_size, self.shift_size, self.win_length, self.window.to(y.device))
y = y.unsqueeze(1)
for i, d in enumerate(self.discriminators):
y = d(y)
y = F.leaky_relu(y, LRELU_SLOPE)
fmap.append(y)
y = self.out(y)
fmap.append(y)
return torch.flatten(y, 1, -1), fmap

2
cosyvoice/hifigan/hifigan.py
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@@ -56,7 +56,7 @@ class HiFiGan(nn.Module):
with torch.no_grad():
generated_speech, generated_f0 = self.generator(batch, device)
# 2. calculate discriminator outputs
y_d_rs, y_d_gs, fmap_rs, fmap_gs = self.discriminator(real_speech, generated_speech)
y_d_rs, y_d_gs, fmap_rs, fmap_gs = self.discriminator(real_speech, generated_speech.detach())
# 3. calculate discriminator losses, tpr losses [Optional]
loss_disc, _, _ = discriminator_loss(y_d_rs, y_d_gs)
if self.tpr_loss_weight != 0:

3
cosyvoice/llm/llm.py
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@@ -326,7 +326,8 @@ class Qwen2LM(TransformerLM):
# unistream sequence
else:
this_lm_target = torch.tensor([IGNORE_ID] * (1 + text_token_len[i]) + speech_token[i].tolist() + [self.speech_token_size])
this_lm_input = torch.concat([self.llm_embedding.weight[self.sos_eos].reshape(1, -1), text_token_emb[i], self.llm_embedding.weight[self.task_id].reshape(1, -1), speech_token_emb[i]], dim=0)
this_lm_input = torch.concat([self.llm_embedding.weight[self.sos_eos].reshape(1, -1), text_token_emb[i],
self.llm_embedding.weight[self.task_id].reshape(1, -1), speech_token_emb[i]], dim=0)
lm_target.append(this_lm_target)
lm_input.append(this_lm_input)
lm_input_len = torch.tensor([i.size(0) for i in lm_input], dtype=torch.int32)

2
cosyvoice/utils/train_utils.py
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@@ -340,7 +340,7 @@ def log_per_save(writer, info_dict):
rank = int(os.environ.get('RANK', 0))
logging.info(
'Epoch {} Step {} CV info lr {} {} rank {}'.format(
epoch, step + 1, lr, rank, ' '.join(['{}_{}'.format(k, v) for k, v in loss_dict.items()])))
epoch, step + 1, lr, rank, ' '.join(['{} {}'.format(k, v) for k, v in loss_dict.items()])))
if writer is not None:
for k in ['epoch', 'lr']:

2
examples/libritts/cosyvoice/conf/cosyvoice.fromscratch.yaml
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@@ -147,7 +147,7 @@ hifigan: !new:cosyvoice.hifigan.hifigan.HiFiGan
generator: !ref <hift>
discriminator: !new:cosyvoice.hifigan.discriminator.MultipleDiscriminator
mpd: !new:matcha.hifigan.models.MultiPeriodDiscriminator
mrd: !new:cosyvoice.hifigan.discriminator.MultiResolutionDiscriminator
mrd: !new:cosyvoice.hifigan.discriminator.MultiResSpecDiscriminator
mel_spec_transform: [
!ref <mel_spec_transform1>
]

2
examples/libritts/cosyvoice/conf/cosyvoice.yaml
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@@ -147,7 +147,7 @@ hifigan: !new:cosyvoice.hifigan.hifigan.HiFiGan
generator: !ref <hift>
discriminator: !new:cosyvoice.hifigan.discriminator.MultipleDiscriminator
mpd: !new:matcha.hifigan.models.MultiPeriodDiscriminator
mrd: !new:cosyvoice.hifigan.discriminator.MultiResolutionDiscriminator
mrd: !new:cosyvoice.hifigan.discriminator.MultiResSpecDiscriminator
mel_spec_transform: [
!ref <mel_spec_transform1>
]

12
examples/libritts/cosyvoice2/conf/cosyvoice2.yaml
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@@ -14,8 +14,8 @@ token_frame_rate: 25
token_mel_ratio: 2
# stream related params
chunk_size: 1 # streaming inference chunk size, in second
num_decoding_left_chunks: 2 # streaming inference flow decoder left chunk size, in second
chunk_size: 2 # streaming inference chunk size, in second
num_decoding_left_chunks: 1 # streaming inference flow decoder left chunk size
# model params
# for all class/function included in this repo, we use !<name> or !<new> for intialization, so that user may find all corresponding class/function according to one single yaml.
@@ -112,11 +112,11 @@ hift: !new:cosyvoice.hifigan.generator.HiFTGenerator
# gan related module
mel_spec_transform1: !name:matcha.utils.audio.mel_spectrogram
n_fft: 1024
n_fft: 1920
num_mels: 80
sampling_rate: !ref <sample_rate>
hop_size: 256
win_size: 1024
hop_size: 480
win_size: 1920
fmin: 0
fmax: null
center: False
@@ -124,7 +124,7 @@ hifigan: !new:cosyvoice.hifigan.hifigan.HiFiGan
generator: !ref <hift>
discriminator: !new:cosyvoice.hifigan.discriminator.MultipleDiscriminator
mpd: !new:matcha.hifigan.models.MultiPeriodDiscriminator
mrd: !new:cosyvoice.hifigan.discriminator.MultiResolutionDiscriminator
mrd: !new:cosyvoice.hifigan.discriminator.MultiResSpecDiscriminator
mel_spec_transform: [
!ref <mel_spec_transform1>
]

2
examples/libritts/cosyvoice2/run.sh
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@@ -71,7 +71,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
fi
# train llm
export CUDA_VISIBLE_DEVICES="2,3,4,5,6,7"
export CUDA_VISIBLE_DEVICES="0,1,2,3"
num_gpus=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
job_id=1986
dist_backend="nccl"

1
requirements.txt
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@@ -21,6 +21,7 @@ onnxruntime-gpu==1.18.0; sys_platform == 'linux'
onnxruntime==1.18.0; sys_platform == 'darwin' or sys_platform == 'windows'
openai-whisper==20231117
protobuf==4.25
pyarrow==18.1.0
pydantic==2.7.0
pyworld==0.3.4
rich==13.7.1