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# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
# Modified from InfiniteTalk original implementation
import librosa
import torch
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 TransformersWav2Vec2Model
from transformers.modeling_outputs import BaseModelOutput
def linear_interpolation(features, seq_len):
"""Linear interpolation for audio features."""
features = features.transpose(1, 2)
output_features = F.interpolate(features, size=seq_len, align_corners=True, mode='linear')
return output_features.transpose(1, 2)
class Wav2Vec2Model(TransformersWav2Vec2Model):
"""
Custom Wav2Vec2Model that supports seq_len parameter for time alignment.
This matches the official InfiniteTalk implementation.
"""
def __init__(self, config: Wav2Vec2Config):
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,
):
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,
)
class FlashHeadAudioEncoder(ModelMixin, ConfigMixin, FromOriginalModelMixin):
"""
Audio encoder for InfiniteTalk model.
Uses Wav2Vec2Model (not Wav2Vec2ForCTC) to extract audio features,
matching the original InfiniteTalk implementation.
"""
def __init__(self, pretrained_model_path="facebook/wav2vec2-base-960h", device='cpu'):
super(FlashHeadAudioEncoder, self).__init__()
# Load pretrained model
self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(pretrained_model_path)
self.model = Wav2Vec2Model.from_pretrained(pretrained_model_path, output_attentions=True)
# Freeze feature extractor
self.model.feature_extractor._freeze_parameters()
self.model = self.model.to(device)
self.model.eval()
# Video frame rate
self.video_rate = 25 # InfiniteTalk uses 25 fps
def extract_audio_feat(
self,
audio_path,
return_all_layers=True,
sr=16000,
video_length=None,
):
"""
Extract audio features from audio file.
Args:
audio_path: Path to audio file
return_all_layers: Whether to return all hidden states (default True for InfiniteTalk)
sr: Sample rate (default 16000)
video_length: Target video length in frames
Returns:
Audio features tensor
"""
# Load audio
audio_input, sample_rate = librosa.load(audio_path, sr=sr)
# Calculate video_length if not provided
if video_length is None:
audio_duration = len(audio_input) / sr
video_length = int(audio_duration * self.video_rate)
# Extract features
input_values = self.feature_extractor(
audio_input, sampling_rate=sample_rate, return_tensors="pt"
).input_values
# Inference
with torch.no_grad():
res = self.model(
input_values.to(self.model.device),
seq_len=video_length, # Custom Wav2Vec2Model supports seq_len
output_hidden_states=True
)
if return_all_layers:
# Stack all hidden states (excluding embedding layer)
feat = torch.stack(res.hidden_states[1:], dim=1).squeeze(0)
feat = rearrange(feat, "b s d -> s b d")
else:
feat = res.hidden_states[-1]
return feat
def extract_audio_feat_without_file_load(
self,
audio_array,
sample_rate,
return_all_layers=True,
video_length=None
):
"""
Extract audio features from audio array (streaming mode).
Matches official FlashHead preprocess_audio logic.
Args:
audio_array: Audio array (numpy or torch)
sample_rate: Sample rate of the audio
return_all_layers: Whether to return all hidden states
video_length: Target video length in frames
Returns:
Audio features tensor [T, num_layers, dim]
"""
# Convert to numpy if tensor
if isinstance(audio_array, torch.Tensor):
audio_array = audio_array.cpu().numpy()
# Extract features
input_values = self.feature_extractor(
audio_array, sampling_rate=sample_rate, return_tensors="pt"
).input_values
# Calculate video_length if not provided
if video_length is None:
audio_duration = len(audio_array) / sample_rate
video_length = int(audio_duration * self.video_rate)
# Inference
with torch.no_grad():
res = self.model(
input_values.to(self.model.device),
seq_len=video_length,
output_hidden_states=True
)
if return_all_layers:
feat = torch.stack(res.hidden_states[1:], dim=1).squeeze(0)
feat = rearrange(feat, "b s d -> s b d")
else:
feat = res.hidden_states[-1]
return feat
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