Refactor model config

docs/assistant_server_contract.md

@@ -27,8 +27,7 @@ produce `ConversationResult`. The server name must be registered in
 ## Quick checklist
 
 - [ ] Subclass `AbstractAssistantServer`
-- [ ] Assert `isinstance(self.pipeline_config, SpeechToSpeechConfig)` (or the correct
-      config type) in `__init__`
+- [ ] Read `self.pipeline_config.s2s_params` (a `ModelConfig` field) in `__init__`
 - [ ] Expose `ws://localhost:{self.port}/ws` accepting Twilio-framed audio
 - [ ] Override `_audio_sample_rate` to match the recording sample rate
 - [ ] Populate `self.user_audio_buffer` and `self.assistant_audio_buffer` during
@@ -58,26 +57,19 @@ Call `super().__init__(**kwargs)` first. The base class sets up:
 | `self._audio_buffer` | `bytearray` | Mixed audio (leave empty — base class mixes automatically) |
 | `self._audio_sample_rate` | `int` | Recording sample rate, default 24000 |
 
-After calling `super().__init__()`, narrow the config type and initialize state:
+After calling `super().__init__()`, read params and initialize state:
 
 ```python
 def __init__(self, **kwargs):
     super().__init__(**kwargs)
-    if isinstance(self.pipeline_config, SpeechToSpeechConfig):
-        s2s_params = self.pipeline_config.s2s_params
-    else:
-        logger.error("Pipeline config is not SpeechToSpeechConfig")
-        return
+    s2s_params = self.pipeline_config.s2s_params or {}
     self._model = s2s_params["model"] # model is required in the s2s params config
     self._audio_sample_rate = SAMPLE_RATE  # match  recording rate
     self._fw_log: FrameworkLogWriter | None = None
     self._metrics_log: MetricsLogWriter | None = None
     # ... other setup
 ```
 
-The assertion fails fast with a clear message rather than an obscure `AttributeError`
-later.
-
 ---
 
 ## 2. `start()` — server startup
@@ -352,16 +344,13 @@ async def save_outputs(self) -> None:
 
 ## 9. Config type
 
-`pipeline_config` arrives as a union type from the orchestrator. For s2s models it
-will be `SpeechToSpeechConfig`, which exposes:
+`pipeline_config` is a `ModelConfig` instance from the orchestrator. For s2s models, it exposes:
 
 ```python
 self.pipeline_config.s2s            # model identifier string
 self.pipeline_config.s2s_params     # dict of additional params (api_key, voice, model, etc.)
 ```
 
-Return if the config is not `SpeechToSpeechConfig`.
-
 Server should be documented in the relevant `configs/` YAML with `framework:
 my_framework` and `model: {s2s: my-model-id, s2s_params: {...}}`.
 
@@ -402,18 +391,14 @@ from eva.assistant.audio_bridge import (
     sync_buffer_to_position,
 )
 from eva.assistant.base_server import INITIAL_MESSAGE, AbstractAssistantServer
-from eva.models.config import SpeechToSpeechConfig
+from eva.models.config import ModelConfig
 
 
 class MyFrameworkAssistantServer(AbstractAssistantServer):
 
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
-        if isinstance(self.pipeline_config, SpeechToSpeechConfig):
-            s2s_params = self.pipeline_config.s2s_params
-        else:
-            logger.error("Pipeline config is not SpeechToSpeechConfig")
-            return
+        s2s_params = self.pipeline_config.s2s_params or {}
         self._model = s2s_params["model"]
 
 

src/eva/assistant/base_server.py

@@ -19,7 +19,7 @@
 from eva.assistant.audio_bridge import FrameworkLogWriter, MetricsLogWriter
 from eva.assistant.tools.tool_executor import ToolExecutor
 from eva.models.agents import AgentConfig
-from eva.models.config import AudioLLMConfig, PipelineConfig, SpeechToSpeechConfig
+from eva.models.config import ModelConfig
 from eva.utils.audio_utils import save_pcm_as_wav
 from eva.utils.logging import get_logger
 
@@ -43,7 +43,7 @@ class AbstractAssistantServer(ABC):
     def __init__(
         self,
         current_date_time: str,
-        pipeline_config: PipelineConfig | SpeechToSpeechConfig | AudioLLMConfig,
+        pipeline_config: ModelConfig,
         agent: AgentConfig,
         agent_config_path: str,
         scenario_db_path: str,

src/eva/assistant/elevenlabs_server.py

@@ -17,7 +17,6 @@
 import json
 import time
 from pathlib import Path
-from typing import Any
 
 try:
     import audioop
@@ -45,7 +44,7 @@
 from eva.assistant.base_server import INITIAL_MESSAGE, AbstractAssistantServer
 from eva.assistant.elevenlabs_audio_interface import TwilioAudioBridge
 from eva.models.agents import AgentConfig
-from eva.models.config import SpeechToSpeechConfig
+from eva.models.config import ModelConfig
 from eva.utils.logging import get_logger
 from eva.utils.prompt_manager import PromptManager
 
@@ -113,7 +112,7 @@ class ElevenLabsAssistantServer(AbstractAssistantServer):
     def __init__(
         self,
         current_date_time: str,
-        pipeline_config: SpeechToSpeechConfig,
+        pipeline_config: ModelConfig,
         agent: AgentConfig,
         agent_config_path: str,
         scenario_db_path: str,
@@ -135,12 +134,7 @@ def __init__(
         # Recording sample rate (ElevenLabs operates at 16 kHz)
         self._audio_sample_rate = _RECORDING_SAMPLE_RATE
 
-        s2s_params: dict[str, Any] = {}
-        if isinstance(self.pipeline_config, SpeechToSpeechConfig):
-            s2s_params = self.pipeline_config.s2s_params
-        else:
-            logger.error("Pipeline config is not SpeechToSpeechConfig")
-            return
+        s2s_params = self.pipeline_config.s2s_params or {}
         self.s2s_params = s2s_params
         self._model = s2s_params.get("model", "elevenlabs")
 

src/eva/assistant/gemini_live_server.py

@@ -38,7 +38,7 @@
 )
 from eva.assistant.base_server import INITIAL_MESSAGE, AbstractAssistantServer
 from eva.models.agents import AgentConfig
-from eva.models.config import SpeechToSpeechConfig
+from eva.models.config import ModelConfig
 from eva.utils.logging import get_logger
 from eva.utils.prompt_manager import PromptManager
 
@@ -154,7 +154,7 @@ class GeminiLiveAssistantServer(AbstractAssistantServer):
     def __init__(
         self,
         current_date_time: str,
-        pipeline_config: SpeechToSpeechConfig,
+        pipeline_config: ModelConfig,
         agent: AgentConfig,
         agent_config_path: str,
         scenario_db_path: str,
@@ -177,12 +177,7 @@ def __init__(
         self._audio_sample_rate = _RECORDING_SAMPLE_RATE
 
         # Gemini model name from s2s_params or default
-        s2s_params: dict[str, Any] = {}
-        if isinstance(self.pipeline_config, SpeechToSpeechConfig):
-            s2s_params = self.pipeline_config.s2s_params
-        else:
-            logger.error("Pipeline config is not SpeechToSpeechConfig")
-            return
+        s2s_params = self.pipeline_config.s2s_params or {}
         self._model = s2s_params["model"]
         self._voice = s2s_params.get("voice", "Kore")
         self._language_code = s2s_params.get("language_code", "en-US")

src/eva/assistant/openai_realtime_server.py

@@ -27,7 +27,6 @@
     sync_buffer_to_position,
 )
 from eva.assistant.base_server import INITIAL_MESSAGE, AbstractAssistantServer
-from eva.models.config import SpeechToSpeechConfig
 from eva.utils.logging import get_logger
 from eva.utils.prompt_manager import PromptManager
 
@@ -104,12 +103,7 @@ def __init__(self, **kwargs: Any) -> None:
         # User speech start timestamp from audio_interface (source of truth)
         self._audio_interface_speech_start_ts: str | None = None
 
-        if isinstance(self.pipeline_config, SpeechToSpeechConfig):
-            s2s_params = self.pipeline_config.s2s_params
-        else:
-            logger.error("Pipeline config is not SpeechToSpeechConfig")
-            return
-
+        s2s_params = self.pipeline_config.s2s_params or {}
         self._model: str = s2s_params["model"]
 
     async def start(self) -> None:

src/eva/assistant/pipecat_server.py

@@ -61,7 +61,7 @@
 )
 from eva.assistant.services.llm import LiteLLMClient
 from eva.models.agents import AgentConfig
-from eva.models.config import AudioLLMConfig, PipelineConfig, SpeechToSpeechConfig
+from eva.models.config import ModelConfig, PipelineType
 from eva.utils.logging import get_logger
 
 logger = get_logger(__name__)
@@ -88,7 +88,7 @@ class PipecatAssistantServer(AbstractAssistantServer):
     def __init__(
         self,
         current_date_time: str,
-        pipeline_config: PipelineConfig | SpeechToSpeechConfig | AudioLLMConfig,
+        pipeline_config: ModelConfig,
         agent: AgentConfig,
         agent_config_path: str,
         scenario_db_path: str,
@@ -225,7 +225,7 @@ async def _handle_session(self, websocket) -> None:
             audio_llm_processor = None
             audio_llm_audio_collector = None
             alm_client = None
-            if isinstance(self.pipeline_config, SpeechToSpeechConfig):
+            if self.pipeline_config.pipeline_type == PipelineType.S2S:
                 realtime_llm = create_realtime_llm_service(
                     self.pipeline_config.s2s,
                     self.pipeline_config.s2s_params,
@@ -256,7 +256,7 @@ async def _realtime_tool_handler(params) -> None:
                 realtime_llm.register_function(function_name=None, handler=_realtime_tool_handler)
                 stt = None
                 tts = None
-            elif isinstance(self.pipeline_config, AudioLLMConfig):
+            elif self.pipeline_config.pipeline_type == PipelineType.AUDIO_LLM:
                 # Audio-LLM mode: model handles STT+LLM, TTS still needed
                 stt = None
                 tts = create_tts_service(
@@ -338,8 +338,8 @@ async def _realtime_tool_handler(params) -> None:
             # Create Audio-LLM components now that context/user_aggregator are available
             audio_llm_audio_collector = None
             audio_llm_processor = None
-            if isinstance(self.pipeline_config, AudioLLMConfig):
-                assert alm_client is not None  # Set in AudioLLMConfig branch above
+            if self.pipeline_config.pipeline_type == PipelineType.AUDIO_LLM:
+                assert alm_client is not None  # Set in AUDIO_LLM branch above
                 audio_llm_audio_collector = AudioLLMUserAudioCollector(
                     context, user_aggregator, pre_speech_secs=VAD_PRE_SPEECH_BUFFER_SECS
                 )
@@ -392,7 +392,7 @@ async def on_user_transcription(text: str, timestamp: str, turn_id: int | None)
             )
             # Create agent processor (pipeline mode only — realtime handles LLM internally)
             agent_processor = None
-            if isinstance(self.pipeline_config, PipelineConfig):
+            if self.pipeline_config.pipeline_type == PipelineType.CASCADE:
                 agent_processor = BenchmarkAgentProcessor(
                     current_date_time=self.current_date_time,
                     agent=self.agent,
@@ -589,7 +589,7 @@ async def on_client_connected(transport, client):
             await audiobuffer.start_recording()
 
             # Send initial greeting
-            if isinstance(self.pipeline_config, SpeechToSpeechConfig):
+            if self.pipeline_config.pipeline_type == PipelineType.S2S:
                 await task.queue_frames([LLMRunFrame()])
             else:
                 await task.queue_frames([TTSSpeakFrame(INITIAL_MESSAGE)])
@@ -643,13 +643,13 @@ async def on_user_turn_stopped(aggregator, strategy, message: UserTurnStoppedMes
             logger.info(f"User turn stopped - complete transcript: '{message.content}'")
             logger.debug(f"Turn timestamp: {message.timestamp}, user_id: {message.user_id}")
 
-            if isinstance(self.pipeline_config, PipelineConfig):
+            if self.pipeline_config.pipeline_type == PipelineType.CASCADE:
                 # STT provides real transcript text — save it now
                 await self._save_transcript_message_from_turn(
                     role="user", content=message.content, timestamp=message.timestamp
                 )
                 await agent_processor.process_complete_user_turn(message.content)
-            elif isinstance(self.pipeline_config, AudioLLMConfig) and audio_llm_processor:
+            elif self.pipeline_config.pipeline_type == PipelineType.AUDIO_LLM and audio_llm_processor:
                 # No STT → message.content is empty.
                 # Processing is triggered by LLMContextFrame flow through ParallelPipeline
                 # (AudioLLMUserAudioCollector pushes LLMContextFrame on UserStoppedSpeakingFrame)
@@ -721,7 +721,7 @@ def _save_transcript(self) -> None:
         For pipeline mode, only write if not already written incrementally.
         """
         transcript_path = self.output_dir / "transcript.jsonl"
-        if isinstance(self.pipeline_config, SpeechToSpeechConfig) or not transcript_path.exists():
+        if self.pipeline_config.pipeline_type == PipelineType.S2S or not transcript_path.exists():
             self.audit_log.save_transcript_jsonl(transcript_path)
 
     async def save_outputs(self) -> None:

src/eva/metrics/runner.py

@@ -149,7 +149,7 @@ def _load_agent_config(self) -> dict[str, Any]:
 
         config_data = json.loads(config_path.read_text())
 
-        # Determine pipeline type from config (fallback to False for legacy runs)
+        # Determine pipeline type from config
         model_data = config_data.get("model", {})
         self._pipeline_type = get_pipeline_type(model_data) if model_data else PipelineType.CASCADE
 

src/eva/models/__init__.py

@@ -7,10 +7,9 @@
     AgentToolParameter,
 )
 from eva.models.config import (
-    AudioLLMConfig,
-    PipelineConfig,
+    ModelConfig,
+    PipelineType,
     RunConfig,
-    SpeechToSpeechConfig,
 )
 from eva.models.record import (
     AgentOverride,
@@ -37,9 +36,8 @@
     "AgentOverride",
     # Config models
     "RunConfig",
-    "PipelineConfig",
-    "SpeechToSpeechConfig",
-    "AudioLLMConfig",
+    "ModelConfig",
+    "PipelineType",
     # Result models
     "ConversationResult",
     "MetricScore",