import copy import functools import math import random from typing import Optional, Tuple import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from torch import distributed from transformers.models.wav2vec2.modeling_wav2vec2 import _compute_mask_indices, _sample_negative_indices from transformers.deepspeed import is_deepspeed_zero3_enabled from models.arch_util import ResBlock from trainer.networks import register_model from utils.util import checkpoint class Mel2Vec2FeatureProjection(nn.Module): def __init__(self, inner_dim, dropout): super().__init__() self.layer_norm = nn.LayerNorm(inner_dim, eps=1e-5) self.projection = nn.Linear(inner_dim, inner_dim) self.dropout = nn.Dropout(dropout) def forward(self, hidden_states): # non-projected hidden states are needed for quantization norm_hidden_states = self.layer_norm(hidden_states) hidden_states = self.projection(norm_hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states, norm_hidden_states # Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->Wav2Vec2 class Wav2Vec2Attention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads if (self.head_dim * num_heads) != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {num_heads})." ) self.scaling = self.head_dim**-0.5 self.is_decoder = is_decoder self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None bsz, tgt_len, _ = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_states = past_key_value[0] value_states = past_key_value[1] elif is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, bsz) value_states = self._shape(self.v_proj(key_value_states), -1, bsz) elif past_key_value is not None: # reuse k, v, self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_states, value_states) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.view(*proj_shape) value_states = value_states.view(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if layer_head_mask is not None: if layer_head_mask.size() != (self.num_heads,): raise ValueError( f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}" ) attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to be reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be # partitioned aross GPUs when using tensor-parallelism. attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped, past_key_value class Wav2Vec2FeedForward(nn.Module): def __init__(self, hidden_size, intermediate_size, dropout): super().__init__() self.intermediate_dropout = nn.Dropout(dropout) self.intermediate_dense = nn.Linear(hidden_size, intermediate_size) self.intermediate_act_fn = F.gelu self.output_dense = nn.Linear(intermediate_size, hidden_size) self.output_dropout = nn.Dropout(dropout) def forward(self, hidden_states): hidden_states = self.intermediate_dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) hidden_states = self.intermediate_dropout(hidden_states) hidden_states = self.output_dense(hidden_states) hidden_states = self.output_dropout(hidden_states) return hidden_states class Wav2Vec2EncoderLayer(nn.Module): def __init__(self, hidden_size, dropout): super().__init__() self.attention = Wav2Vec2Attention( embed_dim=hidden_size, num_heads=hidden_size//64, dropout=dropout, is_decoder=False, ) self.dropout = nn.Dropout(dropout) self.layer_norm = nn.LayerNorm(hidden_size, eps=1e-5) self.feed_forward = Wav2Vec2FeedForward(hidden_size, hidden_size*2, dropout) self.final_layer_norm = nn.LayerNorm(hidden_size, eps=1e-5) def forward(self, hidden_states, attention_mask=None, output_attentions=False): attn_residual = hidden_states hidden_states = self.layer_norm(hidden_states) hidden_states, attn_weights, _ = self.attention( hidden_states, attention_mask=attention_mask, output_attentions=output_attentions ) hidden_states = self.dropout(hidden_states) hidden_states = attn_residual + hidden_states hidden_states = hidden_states + self.feed_forward(self.final_layer_norm(hidden_states)) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs class Wav2Vec2SamePadLayer(nn.Module): def __init__(self, num_conv_pos_embeddings): super().__init__() self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0 def forward(self, hidden_states): if self.num_pad_remove > 0: hidden_states = hidden_states[:, :, : -self.num_pad_remove] return hidden_states from torch.nn.utils.weight_norm import WeightNorm def __deepcopy__(self, memo): # save and delete all weightnorm weights on self weights = {} for hook in self._forward_pre_hooks.values(): if isinstance(hook, WeightNorm): weights[hook.name] = getattr(self, hook.name) delattr(self, hook.name) # remove this deepcopy method, restoring the object's original one if necessary __deepcopy__ = self.__deepcopy__ if self.orig_deepcopy: self.__deepcopy__ = self.orig_deepcopy else: del self.__deepcopy__ # actually do the copy result = copy.deepcopy(self) # restore weights and method on self for name, value in weights.items(): setattr(self, name, value) self.__deepcopy__ = __deepcopy__ return result class Wav2Vec2PositionalConvEmbedding(nn.Module): def __init__(self, hidden_size, num_conv_pos_embeddings=128, num_conv_pos_embedding_groups=16): super().__init__() self.conv = nn.Conv1d( hidden_size, hidden_size, kernel_size=num_conv_pos_embeddings, padding=num_conv_pos_embeddings // 2, groups=num_conv_pos_embedding_groups, ) # Fix weightnorm deepcopy; see: https://github.com/pytorch/pytorch/issues/28594 self.conv.orig_deepcopy = getattr(Wav2Vec2PositionalConvEmbedding, '__deepcopy__', None) self.conv.__deepcopy__ = __deepcopy__.__get__(self.conv, self.conv.__class__) if is_deepspeed_zero3_enabled(): import deepspeed with deepspeed.zero.GatheredParameters(self.conv.weight, modifier_rank=0): self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2) deepspeed.zero.register_external_parameter(self, self.conv.weight_v) deepspeed.zero.register_external_parameter(self, self.conv.weight_g) else: self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2) self.padding = Wav2Vec2SamePadLayer(num_conv_pos_embeddings) self.activation = F.gelu def forward(self, hidden_states): hidden_states = hidden_states.transpose(1, 2) hidden_states = self.conv(hidden_states) hidden_states = self.padding(hidden_states) hidden_states = self.activation(hidden_states) hidden_states = hidden_states.transpose(1, 2) return hidden_states class Wav2Vec2Encoder(nn.Module): def __init__(self, hidden_size, dropout, num_layers, layerdrop): super().__init__() self.pos_conv_embed = Wav2Vec2PositionalConvEmbedding(hidden_size) self.layer_norm = nn.LayerNorm(hidden_size, eps=1e-5) self.dropout = nn.Dropout(dropout) self.layers = nn.ModuleList([Wav2Vec2EncoderLayer(hidden_size, dropout) for _ in range(num_layers)]) self.layerdrop = layerdrop def forward( self, hidden_states, attention_mask=None, output_hidden_states=False, ): all_hidden_states = () if output_hidden_states else None if attention_mask is not None: # make sure padded tokens output 0 hidden_states[~attention_mask] = 0.0 # extend attention_mask attention_mask = (1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype)) * -10000.0 attention_mask = attention_mask.expand( attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1] ) position_embeddings = self.pos_conv_embed(hidden_states) hidden_states = hidden_states + position_embeddings hidden_states = self.dropout(hidden_states) deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled() for layer in self.layers: if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = np.random.uniform(0, 1) skip_the_layer = True if self.training and (dropout_probability < self.layerdrop) else False if not skip_the_layer or deepspeed_zero3_is_enabled: layer_fn = functools.partial(layer, attention_mask=attention_mask) layer_outputs = checkpoint(layer_fn, hidden_states) hidden_states = layer_outputs[0] hidden_states = self.layer_norm(hidden_states) return hidden_states class Mel2Vec(nn.Module): def __init__(self, mel_input_channels=256, inner_dim=1024, layers=24, dropout=.1, layerdrop=0, mask_time_prob=.65, mask_time_length=10, disable_custom_linear_init=False, linear_init_scale=.02, feature_producer_type='standard', ): super().__init__() if feature_producer_type == 'standard': self.input_blocks = nn.Sequential(nn.Conv1d(mel_input_channels, inner_dim//2, kernel_size=5, padding=2, stride=2), nn.GroupNorm(num_groups=8, num_channels=inner_dim//2, affine=True), nn.GELU(), ResBlock(dims=1, channels=inner_dim//2, dropout=dropout), ResBlock(dims=1, channels=inner_dim//2, dropout=dropout), ResBlock(dims=1, channels=inner_dim//2, dropout=dropout), nn.Conv1d(inner_dim//2, inner_dim, kernel_size=3, padding=1, stride=2), nn.GELU(), ResBlock(dims=1, channels=inner_dim, dropout=dropout), ResBlock(dims=1, channels=inner_dim, dropout=dropout), ResBlock(dims=1, channels=inner_dim, dropout=dropout), ResBlock(dims=1, channels=inner_dim, dropout=dropout), ResBlock(dims=1, channels=inner_dim, dropout=dropout), ) self.dim_reduction_mult = 4 elif feature_producer_type == 'voice_8x': self.input_blocks = nn.Sequential(nn.Conv1d(mel_input_channels, inner_dim//4, kernel_size=5, padding=2, stride=2), nn.GroupNorm(num_groups=8, num_channels=inner_dim//4, affine=True), nn.GELU(), ResBlock(dims=1, channels=inner_dim//4, dropout=dropout), ResBlock(dims=1, channels=inner_dim//4, dropout=dropout), nn.Conv1d(inner_dim//4, inner_dim//2, kernel_size=3, padding=1, stride=2), nn.GELU(), ResBlock(dims=1, channels=inner_dim//2, dropout=dropout), ResBlock(dims=1, channels=inner_dim//2, dropout=dropout), ResBlock(dims=1, channels=inner_dim//2, dropout=dropout), nn.Conv1d(inner_dim//2, inner_dim, kernel_size=3, padding=1, stride=2), nn.GELU(), ResBlock(dims=1, channels=inner_dim, dropout=dropout), ResBlock(dims=1, channels=inner_dim, dropout=dropout), ResBlock(dims=1, channels=inner_dim, dropout=dropout), ResBlock(dims=1, channels=inner_dim, dropout=dropout), ResBlock(dims=1, channels=inner_dim, dropout=dropout), ) self.dim_reduction_mult = 8 else: assert False, f"feature_producer_type={feature_producer_type} not supported" self.projector = Mel2Vec2FeatureProjection(inner_dim, dropout) self.masked_spec_embed = nn.Parameter(torch.rand(inner_dim,)) self.encoder = Wav2Vec2Encoder(inner_dim, dropout, layers, layerdrop) self.mask_time_prob = mask_time_prob self.mask_time_length = mask_time_length self.disable_custom_linear_init = disable_custom_linear_init self.linear_init_scale = linear_init_scale self.mel_dim = mel_input_channels self.apply(self.init) def init(self, module): """Initialize the weights""" # gumbel softmax requires special init if isinstance(module, Wav2Vec2PositionalConvEmbedding): nn.init.normal_( module.conv.weight, mean=0, std=2 * math.sqrt(1 / (module.conv.kernel_size[0] * module.conv.in_channels)), ) nn.init.constant_(module.conv.bias, 0) elif isinstance(module, Mel2Vec2FeatureProjection): k = math.sqrt(1 / module.projection.in_features) nn.init.uniform_(module.projection.weight, a=-k, b=k) nn.init.uniform_(module.projection.bias, a=-k, b=k) elif isinstance(module, nn.Linear): if self.disable_custom_linear_init: return module.weight.data.normal_(mean=0.0, std=self.linear_init_scale) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)): module.bias.data.zero_() module.weight.data.fill_(1.0) elif isinstance(module, nn.Conv1d): nn.init.kaiming_normal_(module.weight) if module.bias is not None: k = math.sqrt(module.groups / (module.in_channels * module.kernel_size[0])) nn.init.uniform_(module.bias, a=-k, b=k) def apply_masking( self, hidden_states: torch.FloatTensor, mask_time_indices: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.LongTensor] = None, ): """ Masks extracted features along time axis and/or along feature axis according to [SpecAugment](https://arxiv.org/abs/1904.08779). """ # generate indices & apply SpecAugment along time axis batch_size, sequence_length, hidden_size = hidden_states.size() if mask_time_indices is not None: # apply SpecAugment along time axis with given mask_time_indices hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype) elif self.mask_time_prob > 0 and self.training: mask_time_indices = _compute_mask_indices( (batch_size, sequence_length), mask_prob=self.mask_time_prob, mask_length=self.mask_time_length, attention_mask=attention_mask, min_masks=self.mask_time_min_masks, ) mask_time_indices = torch.tensor(mask_time_indices, device=hidden_states.device, dtype=torch.bool) hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype) return hidden_states def forward(self, mel, mask_time_indices=None, return_projections=False): proj = self.input_blocks(mel).permute(0,2,1) proj, norm_proj = self.projector(proj) # Mask projections h = self.apply_masking(proj, mask_time_indices) h = self.encoder(h) if return_projections: return h, norm_proj return h class Wav2Vec2GumbelVectorQuantizer(nn.Module): """ Vector quantization using gumbel softmax. See `[CATEGORICAL REPARAMETERIZATION WITH GUMBEL-SOFTMAX](https://arxiv.org/pdf/1611.01144.pdf) for more information. """ def __init__(self, proj_dim=1024, codevector_dim=512, num_codevector_groups=2, num_codevectors_per_group=320): super().__init__() self.codevector_dim = codevector_dim self.num_groups = num_codevector_groups self.num_vars = num_codevectors_per_group self.num_codevectors = num_codevector_groups * num_codevectors_per_group if codevector_dim % self.num_groups != 0: raise ValueError( f"`codevector_dim {codevector_dim} must be divisible " f"by `num_codevector_groups` {num_codevector_groups} for concatenation" ) # storage for codebook variables (codewords) self.codevectors = nn.Parameter( torch.FloatTensor(1, self.num_groups * self.num_vars, codevector_dim // self.num_groups) ) self.weight_proj = nn.Linear(proj_dim, self.num_groups * self.num_vars) # can be decayed for training self.temperature = 2 # Parameters init. self.weight_proj.weight.data.normal_(mean=0.0, std=1) self.weight_proj.bias.data.zero_() nn.init.uniform_(self.codevectors) @staticmethod def _compute_perplexity(probs, mask=None): if mask is not None: mask_extended = mask.flatten()[:, None, None].expand(probs.shape) probs = torch.where(mask_extended, probs, torch.zeros_like(probs)) marginal_probs = probs.sum(dim=0) / mask.sum() else: marginal_probs = probs.mean(dim=0) perplexity = torch.exp(-torch.sum(marginal_probs * torch.log(marginal_probs + 1e-7), dim=-1)).sum() return perplexity def get_codes(self, hidden_states): batch_size, sequence_length, hidden_size = hidden_states.shape # project to codevector dim hidden_states = self.weight_proj(hidden_states) hidden_states = hidden_states.view(batch_size * sequence_length * self.num_groups, -1) codevector_idx = hidden_states.argmax(dim=-1) idxs = codevector_idx.view(batch_size, sequence_length, self.num_groups) return idxs def forward(self, hidden_states, mask_time_indices=None, return_probs=False): batch_size, sequence_length, hidden_size = hidden_states.shape # project to codevector dim hidden_states = self.weight_proj(hidden_states) hidden_states = hidden_states.view(batch_size * sequence_length * self.num_groups, -1) if self.training: # sample code vector probs via gumbel in differentiable way codevector_probs = nn.functional.gumbel_softmax( hidden_states.float(), tau=self.temperature, hard=True ).type_as(hidden_states) # compute perplexity codevector_soft_dist = torch.softmax( hidden_states.view(batch_size * sequence_length, self.num_groups, -1).float(), dim=-1 ) perplexity = self._compute_perplexity(codevector_soft_dist, mask_time_indices) else: # take argmax in non-differentiable way # compute hard codevector distribution (one hot) codevector_idx = hidden_states.argmax(dim=-1) codevector_probs = hidden_states.new_zeros(*hidden_states.shape).scatter_( -1, codevector_idx.view(-1, 1), 1.0 ) codevector_probs = codevector_probs.view(batch_size * sequence_length, self.num_groups, -1) perplexity = self._compute_perplexity(codevector_probs, mask_time_indices) codevector_probs = codevector_probs.view(batch_size * sequence_length, -1) # use probs to retrieve codevectors codevectors_per_group = codevector_probs.unsqueeze(-1) * self.codevectors codevectors = ( codevectors_per_group.view(batch_size * sequence_length, self.num_groups, self.num_vars, -1) .sum(-2) .view(batch_size, sequence_length, -1) ) if return_probs: return codevectors, perplexity, codevector_probs.view(batch_size, sequence_length, self.num_groups, self.num_vars) return codevectors, perplexity class ContrastiveTrainingWrapper(nn.Module): def __init__(self, inner_dim=1024, dropout=.1, mask_time_prob=.65, mask_time_length=6, num_negatives=100, max_gumbel_temperature=2.0, min_gumbel_temperature=.5, gumbel_temperature_decay=.999995, codebook_size=320, codebook_groups=2, freq_mask_percent=0, inp_length_multiplier=256, do_reconstruction_loss=False, **kwargs): super().__init__() self.m2v = Mel2Vec(inner_dim=inner_dim, dropout=dropout, mask_time_prob=mask_time_prob, mask_time_length=mask_time_length, **kwargs) self.num_negatives = num_negatives self.mask_time_prob = mask_time_prob self.mask_time_length = mask_time_length self.max_gumbel_temperature = max_gumbel_temperature self.min_gumbel_temperature = min_gumbel_temperature self.gumbel_temperature_decay = gumbel_temperature_decay self.freq_mask_percent = freq_mask_percent self.quantizer = Wav2Vec2GumbelVectorQuantizer(inner_dim, num_codevector_groups=codebook_groups, num_codevectors_per_group=codebook_size) self.num_losses_record = [] self.inp_length_factor = inp_length_multiplier # make sure that project_hid & project_q are initialized like normal linear layers self.project_hid = nn.Linear(inner_dim, self.quantizer.codevector_dim) self.project_q = nn.Linear(self.quantizer.codevector_dim, self.quantizer.codevector_dim) self.reconstruction = do_reconstruction_loss if do_reconstruction_loss: blocks = [[ResBlock(dims=1, channels=inner_dim, dropout=dropout), ResBlock(dims=1, channels=inner_dim, dropout=dropout, use_conv=True, up=True)] for _ in range(int(math.log2(self.m2v.dim_reduction_mult)))] blocks = sum(blocks, []) blocks.append(nn.Conv1d(inner_dim, self.m2v.mel_dim, kernel_size=3, padding=1)) self.reconstruction_net = nn.Sequential( nn.Conv1d(self.quantizer.codevector_dim, inner_dim, kernel_size=3, padding=1), *blocks ) @staticmethod def compute_contrastive_logits( target_features: torch.FloatTensor, negative_features: torch.FloatTensor, predicted_features: torch.FloatTensor, temperature: int = 0.1, ): """ Compute logits for contrastive loss based using cosine similarity as the distance measure between `[positive_feature, negative_features]` and `[predicted_features]`. Additionally, temperature can be applied. """ target_features = torch.cat([target_features, negative_features], dim=0) logits = torch.cosine_similarity(predicted_features.float(), target_features.float(), dim=-1).type_as( target_features ) # apply temperature logits = logits / temperature return logits def update_for_step(self, step, *args): self.quantizer.temperature = max( self.max_gumbel_temperature * self.gumbel_temperature_decay**step, self.min_gumbel_temperature, ) def get_grad_norm_parameter_groups(self): groups = { 'projector': list(self.m2v.input_blocks.parameters()) + list(self.m2v.projector.parameters()), 'encoder': list(self.m2v.encoder.parameters()), 'output_blocks': list(self.project_hid.parameters()) + list(self.project_q.parameters()), } return groups def get_codes(self, mel, project=False): proj = self.m2v.input_blocks(mel).permute(0,2,1) _, proj = self.m2v.projector(proj) if project: proj, _ = self.quantizer(proj) return proj else: return self.quantizer.get_codes(proj) def reconstruct(self, mel): proj = self.m2v.input_blocks(mel).permute(0,2,1) _, proj = self.m2v.projector(proj) quantized_features, codevector_perplexity = self.quantizer(proj) quantized_features = self.project_q(quantized_features) reconstruction = self.reconstruction_net(quantized_features.permute(0,2,1)) return reconstruction def forward(self, mel, inp_lengths=None): mel = mel[:, :, :-1] # The MEL computation always pads with 1, throwing off optimal tensor math. features_shape = (mel.shape[0], mel.shape[-1]//self.m2v.dim_reduction_mult) orig_mel = mel # Frequency masking freq_mask_width = int(random.random() * self.freq_mask_percent * mel.shape[1]) if freq_mask_width >= 2: freq_start = random.randint(0, mel.shape[1]-freq_mask_width) mel[:, freq_start:freq_start+freq_mask_width] = 0 # Build input masks from inp_lengths if possible. attention_mask = torch.ones(features_shape, device=mel.device, dtype=torch.long) if inp_lengths is not None: inp_lengths = inp_lengths // (self.inp_length_factor*self.m2v.dim_reduction_mult) for i, l in enumerate(inp_lengths): attention_mask[i, l:] = 0 mask_time_indices = _compute_mask_indices(features_shape, self.mask_time_prob, self.mask_time_length, attention_mask=attention_mask) sampled_negative_indices = torch.tensor(_sample_negative_indices(features_shape, self.num_negatives, mask_time_indices=mask_time_indices), device=mel.device) mask_time_indices = torch.tensor(mask_time_indices, device=mel.device) outputs, proj = self.m2v(mel, mask_time_indices, return_projections=True) # 1. project all transformed features (including masked) to final vq dim transformer_features = self.project_hid(outputs) # 2. quantize all (unmasked) extracted features and project to final vq dim quantized_features, codevector_perplexity = self.quantizer( proj, mask_time_indices=mask_time_indices ) quantized_features = self.project_q(quantized_features) batch_size, sequence_length, hidden_size = quantized_features.shape # 3. sample K negatives (distractors) quantized states for contrastive loss # if attention_mask is passed, make sure that padded feature vectors cannot be sampled # sample negative quantized vectors BTC => (BxT)C negative_quantized_features = quantized_features.view(-1, hidden_size)[ sampled_negative_indices.long().view(-1) ] negative_quantized_features = negative_quantized_features.view( batch_size, sequence_length, -1, hidden_size ).permute(2, 0, 1, 3) # 4. compute logits, corresponding to `logs = sim(c_t, [q_t, \sim{q}_t]) / \kappa` # of equation (3) in https://arxiv.org/pdf/2006.11477.pdf logits = self.compute_contrastive_logits( quantized_features[None, :], negative_quantized_features, transformer_features, .1, ) # 5. if a negative vector is identical to the positive (i.e. when codebook utilization is low), # its cosine similarity will be masked neg_is_pos = (quantized_features == negative_quantized_features).all(-1) if neg_is_pos.any(): logits[1:][neg_is_pos] = float("-inf") # 6. compute contrastive loss \mathbf{L}_m = cross_entropy(logs) = # -log(exp(sim(c_t, q_t)/\kappa) / \sum_{\sim{q}} exp(sim(c_t, \sim{q})/\kappa)) logits = logits.transpose(0, 2).reshape(-1, logits.size(0)) target = ((1 - mask_time_indices.long()) * -100).transpose(0, 1).flatten() contrastive_loss = nn.functional.cross_entropy(logits.float(), target, reduction="mean") # 7. compute diversity loss: \mathbf{L}_d num_codevectors = self.quantizer.num_codevectors diversity_loss = (num_codevectors - codevector_perplexity) / num_codevectors if self.reconstruction: reconstruction = self.reconstruction_net(quantized_features.permute(0,2,1)) reconstruction_loss = F.mse_loss(reconstruction, orig_mel) return contrastive_loss, diversity_loss, reconstruction_loss return contrastive_loss, diversity_loss @register_model def register_mel2vec_pretraining(opt_net, opt): return ContrastiveTrainingWrapper(**opt_net['kwargs']) @register_model def register_mel2vec(opt_net, opt): return Mel2Vec(**opt_net['kwargs']) if __name__ == '__main__': model = ContrastiveTrainingWrapper(freq_mask_percent=.5, do_reconstruction_loss=True, feature_producer_type='residual') mel = torch.randn((2,256,401)) print(model(mel))