diff --git a/docs/source/models/supported_models.md b/docs/source/models/supported_models.md
index 7777545b8b3c1..8c5f6836d6aa8 100644
--- a/docs/source/models/supported_models.md
+++ b/docs/source/models/supported_models.md
@@ -710,7 +710,7 @@ See [this page](#generative-models) for more information on how to use generativ
   - `Qwen/Qwen2-Audio-7B-Instruct`
   -
   - ✅︎
-  -
+  - ✅︎
 * - `Qwen2VLForConditionalGeneration`
   - Qwen2-VL
   - T + I<sup>E+</sup> + V<sup>E+</sup>
@@ -724,7 +724,7 @@ See [this page](#generative-models) for more information on how to use generativ
   - `fixie-ai/ultravox-v0_3`
   -
   - ✅︎
-  -
+  - ✅︎
 ```
 
 <sup>E</sup> Pre-computed embeddings can be inputted for this modality.  
diff --git a/vllm/model_executor/models/qwen2_audio.py b/vllm/model_executor/models/qwen2_audio.py
index 576b01776e5de..7012ddc66cd9c 100644
--- a/vllm/model_executor/models/qwen2_audio.py
+++ b/vllm/model_executor/models/qwen2_audio.py
@@ -335,13 +335,16 @@ def _process_audio_input(self,
         selected_audio_feature = audio_outputs.last_hidden_state
         audio_features = self.multi_modal_projector(selected_audio_feature)
         num_audios, max_audio_tokens, embed_dim = audio_features.shape
+        audio_output_lengths = audio_output_lengths.unsqueeze(1)
         audio_features_mask = torch.arange(max_audio_tokens).expand(
-            num_audios, max_audio_tokens
-        ).to(audio_output_lengths.device) < audio_output_lengths.unsqueeze(1)
+            num_audios, max_audio_tokens).to(
+                audio_output_lengths.device) < audio_output_lengths
         masked_audio_features = audio_features[audio_features_mask].view(
             -1, embed_dim)
 
-        return masked_audio_features
+        # Split to tuple of embeddings for individual audio input.
+        return torch.split(masked_audio_features,
+                           audio_output_lengths.flatten().tolist())
 
     def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
         audio_input = self._parse_and_validate_audio_input(**kwargs)
diff --git a/vllm/model_executor/models/ultravox.py b/vllm/model_executor/models/ultravox.py
index ba823acecbb56..ecafd157b1d61 100644
--- a/vllm/model_executor/models/ultravox.py
+++ b/vllm/model_executor/models/ultravox.py
@@ -1,6 +1,5 @@
 # Adapted from https://github.com/fixie-ai/ultravox/blob/ecd58c4041030bae2ad15aa6bcf04ab43199ea02/ultravox/model/ultravox_model.py
 """PyTorch Ultravox model."""
-
 import math
 from functools import cached_property
 from typing import (Any, Iterable, List, Literal, Mapping, Optional, Set,
@@ -14,6 +13,7 @@
 from transformers.models.whisper import WhisperFeatureExtractor
 from transformers.models.whisper.modeling_whisper import WhisperEncoder
 
+from vllm import envs
 from vllm.attention import AttentionMetadata
 from vllm.config import VllmConfig
 from vllm.model_executor.layers.activation import SiluAndMul, get_act_fn
@@ -35,8 +35,11 @@
 from .interfaces import SupportsMultiModal, SupportsPP
 from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
                     init_vllm_registered_model, maybe_prefix,
+                    merge_multimodal_embeddings,
                     merge_multimodal_embeddings_from_map)
 
+_AUDIO_PLACEHOLDER_OVERRIDE = "<|reserved_special_token_0|>"
+_AUDIO_PLACEHOLDER_TOKEN = 128002
 _AUDIO_TOKENS_PER_SECOND = 6.25
 
 
@@ -64,7 +67,14 @@ def _get_hf_processor(
         # Ignored in initialization
         sampling_rate: Optional[int] = None,
     ) -> ProcessorMixin:
-        return self.ctx.get_hf_processor()
+        hf_processor = self.ctx.get_hf_processor()
+
+        # NOTE: Ultravox processing definition uses '<|eot_id|>' as the
+        # placeholder that will cause confusion with the actual end of turn
+        # token, thus we override placeholder with a reserved special
+        # token.
+        hf_processor.audio_token_replacement = _AUDIO_PLACEHOLDER_OVERRIDE
+        return hf_processor
 
     def _get_feature_extractor(
         self,
@@ -465,11 +475,15 @@ def get_input_embeddings(
         inputs_embeds = self.language_model.get_input_embeddings(input_ids)
         if multimodal_embeddings is not None:
 
-            # TODO(ywang96): use merge_multimodal_embeddings after
-            # v0 is deprecated
-            merge_multimodal_embeddings_from_map(
-                inputs_embeds, multimodal_embeddings,
-                attn_metadata.multi_modal_placeholder_index_maps["audio"])
+            # TODO(ywang96): remove this block after v0 is deprecated.
+            if not envs.VLLM_USE_V1:
+                merge_multimodal_embeddings_from_map(
+                    inputs_embeds, multimodal_embeddings,
+                    attn_metadata.multi_modal_placeholder_index_maps["audio"])
+            else:
+                inputs_embeds = merge_multimodal_embeddings(
+                    input_ids, inputs_embeds, multimodal_embeddings,
+                    _AUDIO_PLACEHOLDER_TOKEN)
         return inputs_embeds
 
     def forward(self,