diffposetalk/wav2vec2.py

from packaging import version
from typing import Optional, Tuple

import numpy as np
import torch
import torch.nn.functional as F
import transformers
from transformers import Wav2Vec2Model
from transformers.modeling_outputs import BaseModelOutput

_CONFIG_FOR_DOC = 'Wav2Vec2Config'


# the implementation of Wav2Vec2Model is borrowed from
# https://huggingface.co/transformers/_modules/transformers/models/wav2vec2/modeling_wav2vec2.html#Wav2Vec2Model
# initialize our encoder with the pre-trained wav2vec 2.0 weights.
def _compute_mask_indices(shape: Tuple[int, int], mask_prob: float, mask_length: int,
                          attention_mask: Optional[torch.Tensor] = None, min_masks: int = 0, ) -> np.ndarray:
    bsz, all_sz = shape
    mask = np.full((bsz, all_sz), False)

    all_num_mask = int(mask_prob * all_sz / float(mask_length) + np.random.rand())
    all_num_mask = max(min_masks, all_num_mask)
    mask_idcs = []
    padding_mask = attention_mask.ne(1) if attention_mask is not None else None
    for i in range(bsz):
        if padding_mask is not None:
            sz = all_sz - padding_mask[i].long().sum().item()
            num_mask = int(mask_prob * sz / float(mask_length) + np.random.rand())
            num_mask = max(min_masks, num_mask)
        else:
            sz = all_sz
            num_mask = all_num_mask

        lengths = np.full(num_mask, mask_length)

        if sum(lengths) == 0:
            lengths[0] = min(mask_length, sz - 1)

        min_len = min(lengths)
        if sz - min_len <= num_mask:
            min_len = sz - num_mask - 1

        mask_idc = np.random.choice(sz - min_len, num_mask, replace=False)
        mask_idc = np.asarray([mask_idc[j] + offset for j in range(len(mask_idc)) for offset in range(lengths[j])])
        mask_idcs.append(np.unique(mask_idc[mask_idc < sz]))

    min_len = min([len(m) for m in mask_idcs])
    for i, mask_idc in enumerate(mask_idcs):
        if len(mask_idc) > min_len:
            mask_idc = np.random.choice(mask_idc, min_len, replace=False)
        mask[i, mask_idc] = True
    return mask


# linear interpolation layer
def linear_interpolation(features, input_fps, output_fps, output_len=None):
    # features: (N, C, L)
    seq_len = features.shape[2] / float(input_fps)
    if output_len is None:
        output_len = int(seq_len * output_fps)
    output_features = F.interpolate(features, size=output_len, align_corners=False, mode='linear')
    return output_features


class Wav2Vec2Model(Wav2Vec2Model):
    def __init__(self, config):
        super().__init__(config)
        self.is_old_version = version.parse(transformers.__version__) < version.parse('4.7.0')

    def forward(self, input_values, output_fps=25, attention_mask=None, output_attentions=None,
                output_hidden_states=None, return_dict=None, frame_num=None):
        self.config.output_attentions = True
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        hidden_states = self.feature_extractor(input_values)  # (N, C, L)
        # Resample the audio feature @ 50 fps to `output_fps`.
        if frame_num is not None:
            hidden_states_len = round(frame_num * 50 / output_fps)
            hidden_states = hidden_states[:, :, :hidden_states_len]
        hidden_states = linear_interpolation(hidden_states, 50, output_fps, output_len=frame_num)
        hidden_states = hidden_states.transpose(1, 2)  # (N, L, C)

        if attention_mask is not None:
            output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1))
            attention_mask = torch.zeros(hidden_states.shape[:2], dtype=hidden_states.dtype,
                                         device=hidden_states.device)
            attention_mask[(torch.arange(attention_mask.shape[0], device=hidden_states.device), output_lengths - 1)] = 1
            attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool()

        if self.is_old_version:
            hidden_states = self.feature_projection(hidden_states)
        else:
            hidden_states = self.feature_projection(hidden_states)[0]

        if self.config.apply_spec_augment and self.training:
            batch_size, sequence_length, hidden_size = hidden_states.size()
            if self.config.mask_time_prob > 0:
                mask_time_indices = _compute_mask_indices((batch_size, sequence_length), self.config.mask_time_prob,
                                                          self.config.mask_time_length, attention_mask=attention_mask,
                                                          min_masks=2, )
                hidden_states[torch.from_numpy(mask_time_indices)] = self.masked_spec_embed.to(hidden_states.dtype)
            if self.config.mask_feature_prob > 0:
                mask_feature_indices = _compute_mask_indices((batch_size, hidden_size), self.config.mask_feature_prob,
                                                             self.config.mask_feature_length, )
                mask_feature_indices = torch.from_numpy(mask_feature_indices).to(hidden_states.device)
                hidden_states[mask_feature_indices[:, None].expand(-1, sequence_length, -1)] = 0
        encoder_outputs = self.encoder(hidden_states, attention_mask=attention_mask,
                                       output_attentions=output_attentions, output_hidden_states=output_hidden_states,
                                       return_dict=return_dict, )
        hidden_states = encoder_outputs[0]
        if not return_dict:
            return (hidden_states,) + encoder_outputs[1:]

        return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=encoder_outputs.hidden_states,
                               attentions=encoder_outputs.attentions, )