Spaces:
Running on Zero
Running on Zero
File size: 7,598 Bytes
804ee23 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 | import math
import random
import torch
import torch.nn as nn
import torchaudio
from torch.nn.utils.rnn import pad_sequence
from dots_tts.modules.speaker.campplus import CAMPPlus
from dots_tts.modules.speaker.fbank import (
_SPEAKER_FBANK_N_MELS,
_SPEAKER_FBANK_SAMPLE_RATE,
extract_speaker_fbank,
)
class SpeakerXVectorFeatures(nn.Module):
"""
Speaker embedding extractor based on 3D-Speaker CAM++.
"""
def __init__(
self,
sample_rate=_SPEAKER_FBANK_SAMPLE_RATE,
campplus_embedding_size=512,
max_audio_seconds=10.0,
):
super().__init__()
self.sample_rate = sample_rate
self.max_audio_seconds = float(max_audio_seconds)
self.model = CAMPPlus(
feat_dim=_SPEAKER_FBANK_N_MELS,
embedding_size=campplus_embedding_size,
)
self.resample = None
if self.sample_rate != _SPEAKER_FBANK_SAMPLE_RATE:
self.resample = torchaudio.transforms.Resample(
orig_freq=sample_rate,
new_freq=_SPEAKER_FBANK_SAMPLE_RATE,
)
for param in self.model.parameters():
param.requires_grad = False
@staticmethod
def _normalize_lengths(lengths, batch_size, max_length, device, *, min_length):
if lengths is None:
return torch.full(
(batch_size,),
max_length,
device=device,
dtype=torch.long,
)
return lengths.to(device=device, dtype=torch.long).clamp(
min=min_length,
max=max_length,
)
def _crop_audio(self, audio, audio_lengths=None):
original_lengths = self._normalize_lengths(
audio_lengths,
audio.size(0),
audio.size(-1),
audio.device,
min_length=0,
)
if self.max_audio_seconds <= 0:
return audio, original_lengths, original_lengths, torch.zeros_like(
original_lengths
)
max_input_length = round(self.sample_rate * self.max_audio_seconds)
cropped_audio = []
cropped_lengths = []
starts = []
for index, total_length_tensor in enumerate(original_lengths):
total_length = int(total_length_tensor.item())
cropped_length = min(total_length, max_input_length)
start = (
random.randint(0, total_length - cropped_length)
if total_length > cropped_length
else 0
)
cropped_audio.append(audio[index, start : start + cropped_length])
cropped_lengths.append(cropped_length)
starts.append(start)
return pad_sequence(
cropped_audio,
batch_first=True,
padding_value=0.0,
), original_lengths, torch.tensor(
cropped_lengths,
device=audio.device,
dtype=torch.long,
), torch.tensor(starts, device=audio.device, dtype=torch.long)
def _crop_fbank(
self,
fbank,
fbank_lengths,
original_audio_lengths,
cropped_audio_lengths,
starts,
):
original_fbank_lengths = self._normalize_lengths(
fbank_lengths,
fbank.size(0),
fbank.size(1),
fbank.device,
min_length=1,
)
cropped_fbank = []
cropped_fbank_lengths = []
for index, total_feat_length_tensor in enumerate(original_fbank_lengths):
total_audio_length = int(original_audio_lengths[index].item())
total_feat_length = int(total_feat_length_tensor.item())
start_audio = int(starts[index].item())
end_audio = start_audio + int(cropped_audio_lengths[index].item())
if total_audio_length > 0:
start_feat = math.floor(
start_audio * total_feat_length / total_audio_length
)
end_feat = math.ceil(end_audio * total_feat_length / total_audio_length)
else:
start_feat = 0
end_feat = 1
start_feat = min(start_feat, total_feat_length - 1)
end_feat = min(max(end_feat, start_feat + 1), total_feat_length)
cropped_fbank.append(fbank[index, start_feat:end_feat])
cropped_fbank_lengths.append(end_feat - start_feat)
return pad_sequence(
cropped_fbank,
batch_first=True,
padding_value=0.0,
), torch.tensor(
cropped_fbank_lengths,
device=fbank.device,
dtype=torch.long,
)
def _extract_fbank_batch(self, audio, audio_lengths):
if self.resample is not None:
audio = self.resample(audio)
audio_lengths = torch.ceil(
audio_lengths.float()
* (_SPEAKER_FBANK_SAMPLE_RATE / self.sample_rate)
).long()
audio_cpu = audio.detach().cpu()
features = []
for index, valid_length_tensor in enumerate(audio_lengths):
valid_length = int(valid_length_tensor.item())
waveform = audio_cpu[index, :valid_length]
if waveform.numel() == 0:
waveform = audio_cpu.new_zeros(1)
features.append(
extract_speaker_fbank(
waveform,
sample_rate=_SPEAKER_FBANK_SAMPLE_RATE,
)
)
fbank_lengths = torch.tensor(
[feature.size(0) for feature in features],
device=audio.device,
dtype=torch.long,
)
fbank = pad_sequence(
features,
batch_first=True,
padding_value=0.0,
).to(device=audio.device, dtype=audio.dtype)
return fbank, fbank_lengths
@torch.no_grad()
@torch.autocast(enabled=False, device_type="cuda")
def forward(
self, audio, audio_lengths=None, fbank=None, fbank_lengths=None, **_kwargs
):
self.model.eval()
audio = audio.float()
if audio.dim() == 3:
if audio.size(1) != 1:
raise ValueError(
f"Speaker encoder expects mono audio, got shape {tuple(audio.shape)}."
)
audio = audio[:, 0]
elif audio.dim() != 2:
raise ValueError(
f"Speaker encoder expects a 2D or 3D audio tensor, got shape {tuple(audio.shape)}."
)
audio, original_audio_lengths, cropped_audio_lengths, starts = self._crop_audio(
audio,
audio_lengths=audio_lengths,
)
if fbank is None:
fbank, fbank_lengths = self._extract_fbank_batch(
audio,
cropped_audio_lengths,
)
else:
if not isinstance(fbank, torch.Tensor):
raise TypeError("Speaker encoder expects `fbank` to be a torch.Tensor.")
if fbank.dim() != 3 or fbank.size(0) != audio.size(0):
raise ValueError(
f"Speaker encoder expects `fbank` with shape (B, T, F) and matching batch size, got {tuple(fbank.shape)}."
)
fbank, fbank_lengths = self._crop_fbank(
fbank.to(device=audio.device, dtype=torch.float32),
fbank_lengths,
original_audio_lengths,
cropped_audio_lengths,
starts,
)
return self.model(fbank, lengths=fbank_lengths)
|