feat: add model higgs-audio

Author: yanbasic

src/emd/models/__init__.py

@@ -17,6 +17,7 @@
     vlms,
     comfyui,
     asr,
+    audio,
     embeddings,
     reranks,
     custom,

src/emd/models/audio/__init__.py

@@ -0,0 +1 @@
+from . import higgs_audio

src/emd/models/audio/higgs_audio.py

@@ -0,0 +1,38 @@
+from .. import Model
+from ..engines import vllm_higgs_audio_engine091
+from ..model_series import HIGGS_AUDIO_SERIES
+from ..instances import (
+    g5d48xlarge_instance,
+    local_instance
+)
+from ..services import (
+    sagemaker_service,
+    sagemaker_async_service,
+    ecs_service,
+    local_service
+)
+from ..frameworks import fastapi_framework
+from emd.models.utils.constants import ModelType
+
+Model.register(
+    dict(
+        model_id="bosonai-higgs-audio-v2-generation-3B-base",
+        model_type=ModelType.AUDIO,
+        description="Higgs Audio v2 Generation 3B Base is a powerful multimodal audio generation model that supports voice cloning, smart voice generation, and multi-speaker synthesis. Built on vLLM engine with OpenAI-compatible API for text-to-speech and audio generation tasks.",
+        application_scenario="voice cloning, text-to-speech, audio generation, multi-speaker synthesis, smart voice generation",
+        supported_engines=[vllm_higgs_audio_engine091],
+        supported_instances=[
+            g5d48xlarge_instance, local_instance
+        ],
+        supported_services=[
+            sagemaker_service, local_service
+        ],
+        supported_frameworks=[
+            fastapi_framework
+        ],
+        allow_china_region=True,
+        huggingface_model_id="bosonai/higgs-audio-v2-generation-3B-base",
+        require_huggingface_token=False,
+        need_prepare_model=False,
+    )
+)

src/emd/models/engines.py

@@ -555,6 +555,17 @@ class KtransformersEngine(OpenAICompitableEngine):
     "description": "VLLM v0.9.1 engine for dots.ocr multilingual document parsing model with flash-attn support and eager execution for custom models"
 })
 
+# VLLM Engine v0.9.1 for Higgs Audio
+vllm_higgs_audio_engine091 = VllmEngine(**{
+    **vllm_engine064.model_dump(),
+    "engine_dockerfile_config": {"VERSION":"v0.9.1"},
+    "dockerfile_name": "Dockerfile_higgs_audio",
+    "engine_cls": "vllm.higgs_audio_backend.HiggsAudioBackend",
+    "environment_variables": "export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True",
+    "default_cli_args": " --shm-size=30gb",
+    "description": "VLLM v0.9.1 engine for Higgs Audio v2 Generation 3B Base multimodal audio generation model using native Docker entrypoint"
+})
+
 custom_engine = Engine(**{
             "engine_type":EngineType.CUSTOM,
 })

src/emd/models/model_series.py

@@ -163,3 +163,9 @@
     description="dots.ocr is a powerful, multilingual document parser that unifies layout detection and content recognition within a single vision-language model while maintaining good reading order. Despite its compact 1.7B-parameter LLM foundation, it achieves state-of-the-art(SOTA) performance on text, tables, and reading order tasks with multilingual support for over 100 languages.",
     reference_link="https://github.com/rednote-hilab/dots.ocr"
 )
+
+HIGGS_AUDIO_SERIES = ModelSeries(
+    model_series_name=ModelSeriesType.HIGGS_AUDIO,
+    description="Higgs Audio v2 Generation is a powerful multimodal audio generation model that supports voice cloning, smart voice generation, and multi-speaker synthesis. Built on advanced neural architectures, it provides high-quality text-to-speech capabilities with support for various audio generation tasks including voice cloning and multi-speaker scenarios.",
+    reference_link="https://huggingface.co/bosonai/higgs-audio-v2-generation-3B-base"
+)

src/emd/models/utils/constants.py

@@ -142,6 +142,7 @@ class ModelType(ConstantBase):
     VLM = "vlm"
     EMBEDDING = "embedding"
     VIDEO = "video"
+    AUDIO = "audio"
 
 class ServiceCode(ConstantBase):
     SAGEMAKER = "sagemaker"
@@ -236,3 +237,4 @@ class ModelSeriesType(ConstantBase):
     DEEPSEEK_v3 = "deepseek v3"
     BAICHUAN = "baichuan"
     DOTS_OCR = "dots_ocr"
+    HIGGS_AUDIO = "higgs_audio"

src/pipeline/backend/vllm/Dockerfile_higgs_audio

@@ -0,0 +1,10 @@
+FROM public.ecr.aws/aws-gcr-solutions/dmaa/higgs-audio-vllm:latest AS base
+
+WORKDIR /opt/ml/code
+
+# Copy the workspace from the base image itself
+COPY /vllm-workspace/ /opt/ml/code/
+
+EXPOSE 8080
+
+ENTRYPOINT ["python3", "-m", "vllm.entrypoints.bosonai.api_server"]

src/pipeline/backend/vllm/__init__.py

@@ -0,0 +1,5 @@
+# VLLM Backend Module
+from .vllm_backend import VLLMBackend
+from .higgs_audio_backend import HiggsAudioBackend
+
+__all__ = ['VLLMBackend', 'HiggsAudioBackend']

src/pipeline/backend/vllm/higgs_audio_backend.py

@@ -0,0 +1,86 @@
+import httpx
+import sys
+import os
+from emd.models.utils.constants import ModelType
+import inspect
+from backend.backend import OpenAICompitableProxyBackendBase
+from emd.utils.logger_utils import get_logger
+
+logger = get_logger(__name__)
+
+class HiggsAudioBackend(OpenAICompitableProxyBackendBase):
+    """
+    Higgs Audio Backend that uses the Docker image's native entrypoint
+    instead of the standard vLLM serve command.
+    
+    This backend is specifically designed for the Higgs Audio v2 Generation 3B Base model
+    which provides its own API server via the pre-built Docker image with entrypoint:
+    ["python3", "-m", "vllm.entrypoints.bosonai.api_server"]
+    """
+
+    def before_start(self,model_dir=None):
+        logger.info(f"before_startbefore_startbefore_startbefore_startbefore_start")
+
+    def create_proxy_server_start_command(self, model_path):
+        return f'python3 -m vllm.entrypoints.bosonai.api_server --served-model-name higgs-audio-v2-generation-3B-base --model bosonai/higgs-audio-v2-generation-3B-base --audio-tokenizer-type bosonai/higgs-audio-v2-tokenizer --limit-mm-per-prompt audio=50 --max-model-len 8192 --tensor-parallel-size 8 --pipeline-parallel-size 1 --port 8000 --gpu-memory-utilization 0.65 --disable-mm-preprocessor-cache'
+
+    def openai_create_helper(self, fn: callable, request: dict):
+        """
+        Helper method to handle OpenAI-compatible API calls with extra parameters.
+        """
+        sig = inspect.signature(fn)
+        extra_body = request.get("extra_body", {})
+        extra_params = {k: request.pop(k) for k in list(request.keys()) if k not in sig.parameters}
+        extra_body.update(extra_params)
+        request['extra_body'] = extra_body
+        return fn(**request)
+
+    def invoke(self, request):
+        """
+        Invoke the Higgs Audio model with OpenAI-compatible API.
+        Supports audio modalities for voice cloning, smart voice generation, and multi-speaker synthesis.
+        """
+        # Transform input to Higgs Audio format
+        request = self._transform_request(request)
+        
+        logger.info(f"Higgs Audio request: {request}")
+        
+        # Handle different model types - Higgs Audio is primarily for audio generation
+        if self.model_type == ModelType.AUDIO:
+            # Use chat completions endpoint for audio generation
+            response = self.openai_create_helper(self.client.chat.completions.create, request)
+        else:
+            # Fallback to standard chat completions
+            response = self.openai_create_helper(self.client.chat.completions.create, request)
+            
+        logger.info(f"Higgs Audio response: {response}, request: {request}")
+
+        if request.get("stream", False):
+            return self._transform_streaming_response(response)
+        else:
+            return self._transform_response(response)
+
+    async def ainvoke(self, request):
+        """
+        Async invoke the Higgs Audio model with OpenAI-compatible API.
+        """
+        # Transform input to Higgs Audio format
+        request = self._transform_request(request)
+        
+        logger.info(f"Higgs Audio async request: {request}")
+        
+        # Handle different model types - Higgs Audio is primarily for audio generation
+        if self.model_type == ModelType.AUDIO:
+            # Use chat completions endpoint for audio generation
+            response = await self.openai_create_helper(self.async_client.chat.completions.create, request)
+        else:
+            # Fallback to standard chat completions
+            response = await self.openai_create_helper(self.async_client.chat.completions.create, request)
+            
+        logger.info(f"Higgs Audio async response: {response}, request: {request}")
+
+        if request.get("stream", False):
+            logger.info(f"Higgs Audio streaming response: {response}")
+            return await self._atransform_streaming_response(response)
+        else:
+            return await self._atransform_response(response)