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# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from transformers import PretrainedConfig, Qwen2AudioEncoder, Qwen2AudioForConditionalGeneration
from .audio_encoder import AudioTower
class Qwen2AudioTower(AudioTower):
def __init__(self, model_name_or_path: str, config: PretrainedConfig):
super().__init__(model_name_or_path, config)
self.audio_tower = Qwen2AudioEncoder.from_pretrained(model_name_or_path, attn_implementation="flash_attention_2")
self.is_loaded = True
self.audio_chunk_unit_duration = 30
self.audio_chunk_unit_length = 3000
def forward(self, sounds):
if type(sounds) is list:
sound_features = []
audio_output_lengths = []
for sound in sounds:
if hasattr(sound, "input_features") or (type(sound) is dict and "input_features" in sound):
sound = sound["input_features"]
sound_feature = self.forward_audio_tower_batch(sound)
sound_feature = sound_feature.to(sound.dtype)
sound_features.append(sound_feature)
audio_output_lengths.append(sound_feature.shape[1])
if len(sound_features) > 0:
sound_features = torch.cat(sound_features, dim=1).squeeze(0)
else:
raise NotImplementedError("Not implemented for this encoder")
return sound_features, audio_output_lengths
def forward_audio_tower_batch(self, inp):
"""
Process long audio input by splitting into fixed-size chunks (30 seconds),
padding if needed, batching them together, and processing through the audio tower.
Args:
inp: Tensor of shape (batch_size, n_mels, seq_len)
Returns:
Tensor of shape (batch_size, num_chunks * chunk_seq_len, hidden_size)
"""
batch_size, n_mels, seq_len = inp.shape
chunk_length = self.audio_chunk_unit_length
num_chunks = (seq_len + chunk_length - 1) // chunk_length # Ceiling division
padded_chunks = []
for i in range(num_chunks):
start_idx = i * chunk_length
end_idx = min(start_idx + chunk_length, seq_len)
# Extract and pad chunk if necessary
chunk = inp[:, :, start_idx:end_idx]
if chunk.shape[2] < chunk_length:
pad_len = chunk_length - chunk.shape[2]
chunk = torch.nn.functional.pad(chunk, (0, pad_len), mode='constant', value=0)
padded_chunks.append(chunk)
# Stack chunks along batch dimension
all_chunks = torch.cat(padded_chunks, dim=0).reshape(batch_size * num_chunks, n_mels, chunk_length)
# Forward pass through the audio tower
chunk_outputs = self.audio_tower(all_chunks)
hidden_states = chunk_outputs.last_hidden_state
# Reshape back to (batch_size, num_chunks * seq_len', hidden_size)
_, chunk_seq_len, hidden_size = hidden_states.shape
hidden_states = hidden_states.reshape(batch_size, num_chunks * chunk_seq_len, hidden_size)
return hidden_states
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