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import copy
import logging
import math
import os
import librosa
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers.configuration_utils import ConfigMixin
from diffusers.loaders.single_file_model import FromOriginalModelMixin
from diffusers.models.modeling_utils import ModelMixin
from einops import rearrange
from transformers import Wav2Vec2Config, Wav2Vec2FeatureExtractor
from transformers import Wav2Vec2Model as Wav2Vec2Model_base
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutput
from transformers.models.wav2vec2.modeling_wav2vec2 import (
Wav2Vec2PositionalConvEmbedding, Wav2Vec2SamePadLayer)
def linear_interpolation(features, seq_len):
features = features.transpose(1, 2)
output_features = F.interpolate(features, size=seq_len, align_corners=True, mode='linear')
return output_features.transpose(1, 2)
def _Wav2Vec2PositionalConvEmbedding_init_hack_(self, config):
super(Wav2Vec2PositionalConvEmbedding, self).__init__()
self.conv = nn.Conv1d(
config.hidden_size,
config.hidden_size,
kernel_size=config.num_conv_pos_embeddings,
padding=config.num_conv_pos_embeddings // 2,
groups=config.num_conv_pos_embedding_groups,
)
weight_norm = nn.utils.weight_norm
if hasattr(nn.utils.parametrizations, "weight_norm"):
weight_norm = nn.utils.parametrizations.weight_norm
self.conv = weight_norm(self.conv, name="weight", dim=2)
self.padding = Wav2Vec2SamePadLayer(config.num_conv_pos_embeddings)
self.activation = ACT2FN[config.feat_extract_activation]
Wav2Vec2PositionalConvEmbedding.__init__ = _Wav2Vec2PositionalConvEmbedding_init_hack_
# the implementation of Wav2Vec2Model is borrowed from
# https://github.com/huggingface/transformers/blob/HEAD/src/transformers/models/wav2vec2/modeling_wav2vec2.py
# initialize our encoder with the pre-trained wav2vec 2.0 weights.
class Wav2Vec2Mode(Wav2Vec2Model_base):
def __init__(self, config: Wav2Vec2Config):
config.attn_implementation = "eager"
super().__init__(config)
def forward(
self,
input_values,
seq_len,
attention_mask=None,
mask_time_indices=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
self.config._attn_implementation = "eager"
self.config.output_attentions = True
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
extract_features = self.feature_extractor(input_values)
extract_features = extract_features.transpose(1, 2)
extract_features = linear_interpolation(extract_features, seq_len=seq_len)
if attention_mask is not None:
# compute reduced attention_mask corresponding to feature vectors
attention_mask = self._get_feature_vector_attention_mask(
extract_features.shape[1], attention_mask, add_adapter=False
)
hidden_states, extract_features = self.feature_projection(extract_features)
hidden_states = self._mask_hidden_states(
hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask
)
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 self.adapter is not None:
hidden_states = self.adapter(hidden_states)
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,
)
def feature_extract(
self,
input_values,
seq_len,
):
extract_features = self.feature_extractor(input_values)
extract_features = extract_features.transpose(1, 2)
extract_features = linear_interpolation(extract_features, seq_len=seq_len)
return extract_features
def encode(
self,
extract_features,
attention_mask=None,
mask_time_indices=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
self.config.output_attentions = True
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
if attention_mask is not None:
# compute reduced attention_mask corresponding to feature vectors
attention_mask = self._get_feature_vector_attention_mask(
extract_features.shape[1], attention_mask, add_adapter=False
)
hidden_states, extract_features = self.feature_projection(extract_features)
hidden_states = self._mask_hidden_states(
hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask
)
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 self.adapter is not None:
hidden_states = self.adapter(hidden_states)
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,
)
class Wav2Vec2ModelWrapper(nn.Module):
def __init__(self, config_path, device='cuda', prefix='wav2vec2.'):
super(Wav2Vec2ModelWrapper, self).__init__()
config, model_kwargs = Wav2Vec2Config.from_pretrained(
config_path,
return_unused_kwargs=True,
force_download=False,
local_files_only=True,
)
model_path = os.path.join(config_path, 'pytorch_model.bin')
state_dict = torch.load(model_path, map_location=device)
config.name_or_path = config_path
config = copy.deepcopy(config) # We do not want to modify the config inplace in from_pretrained.
# config = Wav2Vec2Mode._autoset_attn_implementation(config, use_flash_attention_2=False)
# init model
with torch.device('meta'):
model = Wav2Vec2Mode(config)
# load checkpoint
logging.info(f'loading {model_path}')
if prefix is not None:
state_dict = {i.replace(prefix, ''):state_dict[i] for i in state_dict}
model.tie_weights()
m, u = model.load_state_dict(state_dict, assign=True, strict=False)
model.tie_weights()
model.eval()
self.model = model
@property
def feature_extractor(self):
return self.model.feature_extractor
@property
def dtype(self):
return next(self.model.parameters()).dtype
@property
def device(self):
return next(self.model.parameters()).device
def forward(
self,
input_values,
seq_len,
attention_mask=None,
mask_time_indices=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return self.model(
input_values,
seq_len,
attention_mask=attention_mask,
mask_time_indices=mask_time_indices,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
def feature_extract(
self,
input_values,
seq_len,
):
extract_features = self.feature_extractor(input_values)
extract_features = extract_features.transpose(1, 2)
extract_features = linear_interpolation(extract_features, seq_len=seq_len)
return self.model.feature_extract(
input_values,
seq_len
)
def encode(
self,
extract_features,
attention_mask=None,
mask_time_indices=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return self.model.encode(
extract_features,
attention_mask=attention_mask,
mask_time_indices=mask_time_indices,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
class LongCatVideoAudioEncoder(ModelMixin, ConfigMixin, FromOriginalModelMixin):
"""Audio encoder for LongCatVideo Avatar pipeline.
This class provides a clean interface for audio feature extraction,
similar to FantasyTalkingAudioEncoder but with LongCatVideo-specific
audio preprocessing (loudness normalization, noise floor, transient smoothing).
Uses existing Wav2Vec2ModelWrapper and Wav2Vec2FeatureExtractor internally.
"""
def __init__(self, config_path, device='cpu', prefix='wav2vec2.'):
super(LongCatVideoAudioEncoder, self).__init__()
# Use existing Wav2Vec2ModelWrapper
self.audio_encoder = Wav2Vec2ModelWrapper(config_path, device=device, prefix=prefix)
# Use existing Wav2Vec2FeatureExtractor
self.wav2vec_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(config_path)
@property
def dtype(self):
return self.audio_encoder.dtype
@property
def device(self):
return self.audio_encoder.device
def _loudness_norm(self, audio_array, sr=16000, lufs=-23, threshold=100):
"""Normalize audio loudness to target LUFS."""
import pyloudnorm as pyln
meter = pyln.Meter(sr)
loudness = meter.integrated_loudness(audio_array)
if abs(loudness) > threshold:
return audio_array
normalized_audio = pyln.normalize.loudness(audio_array, loudness, lufs)
return normalized_audio
def _add_noise_floor(self, audio, noise_db=-45):
"""Add noise floor to audio."""
noise_amp = 10 ** (noise_db / 20)
noise = np.random.randn(len(audio)) * noise_amp
return audio + noise
def _smooth_transients(self, audio, sr=16000):
"""Smooth audio transients using low-pass filter."""
import scipy.signal as ss
b, a = ss.butter(3, 3000 / (sr / 2))
return ss.lfilter(b, a, audio)
def _preprocess_audio(self, speech_array, sample_rate=16000):
"""Apply LongCatVideo-specific audio preprocessing."""
speech_array = self._loudness_norm(speech_array, sample_rate)
speech_array = self._add_noise_floor(speech_array)
speech_array = self._smooth_transients(speech_array, sample_rate)
return speech_array
@torch.no_grad()
def _extract_embedding(self, speech_array, sample_rate, num_frames, audio_stride=2):
"""Core method to extract audio embedding from preprocessed speech array.
Args:
speech_array: Preprocessed audio array.
sample_rate: Audio sample rate.
num_frames: Number of video frames.
audio_stride: Audio stride for sliding window.
Returns:
Audio embeddings tensor of shape [1, num_frames, 5, 12, 768].
"""
seq_len = int(audio_stride * num_frames)
# wav2vec_feature_extractor
audio_feature = np.squeeze(
self.wav2vec_feature_extractor(speech_array, sampling_rate=sample_rate).input_values
)
audio_feature = torch.from_numpy(audio_feature).float().to(device=self.device, dtype=self.dtype)
audio_feature = audio_feature.unsqueeze(0)
# audio embedding using Wav2Vec2ModelWrapper
embeddings = self.audio_encoder(audio_feature, seq_len=seq_len, output_hidden_states=True)
audio_emb = torch.stack(embeddings.hidden_states[1:], dim=1).squeeze(0)
audio_emb = rearrange(audio_emb, "b s d -> s b d").contiguous() # T, 12, 768
# Prepare audio embedding with sliding window
indices = torch.arange(2 * 2 + 1) - 2 # [-2, -1, 0, 1, 2]
audio_start_idx = 0
audio_end_idx = audio_start_idx + audio_stride * num_frames
center_indices = torch.arange(audio_start_idx, audio_end_idx, audio_stride).unsqueeze(1) + \
indices.unsqueeze(0)
center_indices = torch.clamp(center_indices, min=0, max=audio_emb.shape[0] - 1)
audio_emb = audio_emb[center_indices][None, ...] # [1, num_frames, 5, 12, 768]
return audio_emb
def extract_audio_feat(
self,
audio_path,
num_frames=49,
fps=16,
sr=16000,
audio_stride=2
):
"""Extract audio features from audio file.
Args:
audio_path: Path to audio file.
num_frames: Number of video frames.
fps: Video frames per second.
sr: Audio sample rate.
audio_stride: Audio stride for sliding window.
Returns:
Audio embeddings tensor of shape [1, num_frames, 5, 12, 768].
"""
# Load audio
speech_array, sample_rate = librosa.load(audio_path, sr=sr)
# Pad audio to target length
generate_duration = num_frames / fps
source_duration = len(speech_array) / sample_rate
added_sample_nums = math.ceil((generate_duration - source_duration) * sample_rate)
if added_sample_nums > 0:
speech_array = np.append(speech_array, [0.] * added_sample_nums)
# Preprocess and extract embedding
speech_array = self._preprocess_audio(speech_array, sample_rate)
return self._extract_embedding(speech_array, sample_rate, num_frames, audio_stride)
def extract_audio_feat_without_file_load(
self,
audio_segment,
sample_rate,
num_frames=49,
audio_stride=2
):
"""Extract audio features from audio array without file loading.
Args:
audio_segment: Audio array (numpy array).
sample_rate: Audio sample rate.
num_frames: Number of video frames.
audio_stride: Audio stride for sliding window.
Returns:
Audio embeddings tensor of shape [1, num_frames, 5, 12, 768].
"""
# Preprocess and extract embedding
speech_array = self._preprocess_audio(audio_segment, sample_rate)
return self._extract_embedding(speech_array, sample_rate, num_frames, audio_stride) |