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f5fcbcd | 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 | """VoiceCLAP-Small: dual-tower CLAP using BUD-E-Whisper-Small + MiniLM.
Standalone single-file implementation. Only depends on PyTorch and
HuggingFace `transformers` (for `BertModel`, `PreTrainedModel`, and
`PretrainedConfig`).
"""
import math
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import BertConfig, BertModel, PreTrainedModel
try:
from .configuration_voiceclap import VoiceCLAPSmallConfig
except ImportError:
from configuration_voiceclap import VoiceCLAPSmallConfig
class _LayerNorm(nn.LayerNorm):
def forward(self, x):
return super().forward(x.float()).type(x.dtype)
def _sinusoids(length: int, channels: int, max_timescale: float = 10000.0) -> torch.Tensor:
assert channels % 2 == 0
log_timescale_increment = math.log(max_timescale) / (channels // 2 - 1)
inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
scaled_time = torch.arange(length)[:, None] * inv_timescales[None, :]
return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
class _MultiHeadAttention(nn.Module):
def __init__(self, n_state: int, n_head: int):
super().__init__()
self.n_head = n_head
self.query = nn.Linear(n_state, n_state)
self.key = nn.Linear(n_state, n_state, bias=False)
self.value = nn.Linear(n_state, n_state)
self.out = nn.Linear(n_state, n_state)
def forward(self, x: torch.Tensor) -> torch.Tensor:
q = self.query(x)
k = self.key(x)
v = self.value(x)
n_batch, n_ctx, n_state = q.shape
head_dim = n_state // self.n_head
q = q.view(n_batch, n_ctx, self.n_head, head_dim).transpose(1, 2)
k = k.view(n_batch, n_ctx, self.n_head, head_dim).transpose(1, 2)
v = v.view(n_batch, n_ctx, self.n_head, head_dim).transpose(1, 2)
out = F.scaled_dot_product_attention(q, k, v)
out = out.transpose(1, 2).reshape(n_batch, n_ctx, n_state)
return self.out(out)
class _ResidualAttentionBlock(nn.Module):
def __init__(self, n_state: int, n_head: int):
super().__init__()
self.attn = _MultiHeadAttention(n_state, n_head)
self.attn_ln = _LayerNorm(n_state)
n_mlp = n_state * 4
self.mlp = nn.Sequential(nn.Linear(n_state, n_mlp), nn.GELU(), nn.Linear(n_mlp, n_state))
self.mlp_ln = _LayerNorm(n_state)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x + self.attn(self.attn_ln(x))
x = x + self.mlp(self.mlp_ln(x))
return x
class _WhisperAudioEncoder(nn.Module):
"""Whisper-style audio encoder. Takes a precomputed log-mel spectrogram."""
def __init__(
self,
n_mels: int = 80,
n_ctx: int = 1500,
n_state: int = 768,
n_head: int = 12,
n_layer: int = 12,
output_dim: int = 768,
):
super().__init__()
self.conv1 = nn.Conv1d(n_mels, n_state, kernel_size=3, padding=1)
self.conv2 = nn.Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
self.register_buffer("positional_embedding", _sinusoids(n_ctx, n_state))
self.blocks = nn.ModuleList(
[_ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)]
)
self.ln_post = _LayerNorm(n_state)
self.avg_pooler = nn.AvgPool1d(kernel_size=2, stride=2)
self.proj = nn.Linear(n_state, output_dim)
def forward(self, mel: torch.Tensor) -> torch.Tensor:
# mel: (B, n_mels, T_mel)
x = F.gelu(self.conv1(mel))
x = F.gelu(self.conv2(x))
x = x.permute(0, 2, 1) # (B, T', D)
T = x.size(1)
x = x + self.positional_embedding[:T].to(dtype=x.dtype, device=x.device)
for block in self.blocks:
x = block(x)
x = x.permute(0, 2, 1)
x = self.avg_pooler(x)
x = x.permute(0, 2, 1)
x = self.ln_post(x)
x = self.proj(x)
return x
class VoiceCLAPSmall(PreTrainedModel):
config_class = VoiceCLAPSmallConfig
def __init__(self, config: VoiceCLAPSmallConfig):
super().__init__(config)
self.audio_encoder = _WhisperAudioEncoder(
n_mels=config.n_mels,
n_ctx=config.n_ctx,
n_state=config.n_state,
n_head=config.n_head,
n_layer=config.n_layer,
output_dim=config.embed_dim,
)
self.audio_proj = nn.Sequential(
nn.Linear(config.embed_dim, config.embed_dim),
nn.GELU(),
nn.Linear(config.embed_dim, config.embed_dim),
)
bert_config = BertConfig(
vocab_size=config.text_vocab_size,
hidden_size=config.text_hidden_dim,
num_hidden_layers=config.text_num_layers,
num_attention_heads=config.text_num_heads,
intermediate_size=config.text_intermediate_size,
max_position_embeddings=config.text_max_position_embeddings,
layer_norm_eps=config.text_layer_norm_eps,
pad_token_id=config.text_pad_token_id,
)
self.text_encoder = BertModel(bert_config, add_pooling_layer=False)
self.text_proj = nn.Sequential(
nn.Linear(config.text_hidden_dim, config.text_proj_hidden, bias=False),
nn.GELU(),
nn.Linear(config.text_proj_hidden, config.embed_dim, bias=False),
)
self.logit_scale = nn.Parameter(torch.zeros(()))
self.logit_bias = nn.Parameter(torch.zeros(()))
# Mel filterbank used by encode_waveform / compute_log_mel.
# 80 mel bins x 201 freq bins for n_fft=400, sr=16000 (Whisper-style).
self.register_buffer(
"mel_filters",
torch.zeros(config.n_mels, 201),
persistent=True,
)
self.post_init()
@torch.no_grad()
def compute_log_mel(
self, waveform: torch.Tensor, sample_rate: int = 16000
) -> torch.Tensor:
"""Whisper-style log-mel spectrogram. waveform: (B, T) or (T,) at 16 kHz.
Returns (B, n_mels, T_mel). Matches the training-time preprocessing
bit-exactly so embeddings reproduce the published results.
"""
if sample_rate != 16000:
raise ValueError(f"sample_rate must be 16000, got {sample_rate}")
if waveform.dim() == 1:
waveform = waveform.unsqueeze(0)
device = self.mel_filters.device
waveform = waveform.to(device=device, dtype=torch.float32)
window = torch.hann_window(400, device=device)
stft = torch.stft(waveform, n_fft=400, hop_length=160, window=window, return_complex=True)
magnitudes = stft[..., :-1].abs() ** 2
mel = self.mel_filters.to(magnitudes.dtype) @ magnitudes
log_spec = torch.clamp(mel, min=1e-10).log10()
log_spec = torch.maximum(log_spec, log_spec.amax(dim=(-2, -1), keepdim=True) - 8.0)
log_spec = (log_spec + 4.0) / 4.0
return log_spec
def encode_waveform(self, waveform: torch.Tensor, sample_rate: int = 16000) -> torch.Tensor:
"""Encode raw 16 kHz waveform; calls ``compute_log_mel`` then ``encode_audio``."""
mel = self.compute_log_mel(waveform, sample_rate=sample_rate)
return self.encode_audio(mel)
def encode_audio(self, mel: torch.Tensor) -> torch.Tensor:
feats = self.audio_encoder(mel) # (B, T', D)
feats = feats.mean(dim=1) # clip-level mean
feats = self.audio_proj(feats)
return F.normalize(feats, dim=-1)
def encode_text(
self,
input_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
if attention_mask is None:
attention_mask = (input_ids != self.config.text_pad_token_id).long()
out = self.text_encoder(input_ids=input_ids, attention_mask=attention_mask)
hidden = out.last_hidden_state # (B, T, H)
mask = attention_mask.unsqueeze(-1).to(hidden.dtype)
pooled = (hidden * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1e-9)
feats = self.text_proj(pooled)
return F.normalize(feats, dim=-1)
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