add assistant gender

.env.example

@@ -97,22 +97,29 @@ EVA_MODEL__TTS=cartesia
 #x pipeline_mode=LLM,AudioLLM
 EVA_MODEL__TTS_PARAMS='{"api_key": "your_cartesia_api_key", "model": "sonic"}'
 
+#i TTS gender for the voice pipeline. For use in gendered languages to ensure the LLM generates speech
+#i which matches the gender of the TTS voice.
+#d enum
+#e M,F,none
+#x pipeline_mode=LLM,AudioLLM
+#v EVA_MODEL__ASSISTANT_GENDER=none
+
 # --- LLM mode: cascade latency optimizations ---
 #i Prompt a model-generated lead-in before a tool call: 'off' or 'auto'.
 #d enum
 #e off,auto
-#x pipeline_mode=LLM
-# EVA_MODEL__PRE_TOOL_SPEECH=off
+#x pipeline_mode=LLM,AudioLLM
+#v EVA_MODEL__PRE_TOOL_SPEECH=off
 
 #i Stream Chat Completions output to TTS sentence-by-sentence; Responses API falls back with a warning.
 #d bool
-#x pipeline_mode=LLM
-# EVA_MODEL__LLM_STREAMING=false
+#x pipeline_mode=LLM,AudioLLM
+#v EVA_MODEL__LLM_STREAMING=false
 
 #i Forward provider parallel_tool_calls when tools are present; leave unset for defaults.
 #d bool
-#x pipeline_mode=LLM
-# EVA_MODEL__PARALLEL_TOOL_CALLS=false
+#x pipeline_mode=LLM,AudioLLM
+#v EVA_MODEL__PARALLEL_TOOL_CALLS=false
 
 # --- S2S mode ---
 #i Speech-to-speech model name.

configs/prompts/simulation.yaml

@@ -69,9 +69,13 @@ agent:
 
   pre_tool_speech: |
     ## Responsiveness
-    Before calling a tool, say one brief sentence first. For lookups, use a short lead-in (e.g. "Let me pull that up."). Before any action that is irreversible or has real-world consequences, state what you'll do, get confirmation, then call the tool. 
+    Before calling a tool, say one brief sentence first. For lookups, use a short lead-in (e.g. "Let me pull that up."). Before any action that is irreversible or has real-world consequences, state what you'll do, get confirmation, then call the tool.
     If calling multiple tools at once, only one lead-in is needed.
 
+  gender_instruction: |
+    ## Speaking Gender
+    You are speaking as a {gender_word} voice. In languages with grammatical gender, consistently use {gender_word} forms (verb conjugations, adjectives, self-references) for yourself throughout the conversation.
+
 audio_llm_agent:
   system_prompt: |
     You are an AI voice assistant on a live phone call.

src/eva/assistant/agentic/audio_llm_system.py

@@ -42,6 +42,7 @@ def __init__(
         pre_tool_speech: str = "off",
         llm_streaming: bool = False,
         full_audio_context: bool = False,
+        assistant_gender: str | None = None,
     ):
         super().__init__(
             current_date_time=current_date_time,
@@ -52,6 +53,7 @@ def __init__(
             output_dir=output_dir,
             pre_tool_speech=pre_tool_speech,
             llm_streaming=llm_streaming,
+            assistant_gender=assistant_gender,
         )
         self.alm_client: BaseALMClient = alm_client
         # When True, every user turn is sent as audio (full audio context). When False
@@ -69,6 +71,12 @@ def __init__(
         # Reuse the shared pre-tool lead-in prompt appended to the system prompt.
         if self.pre_tool_speech == "auto":
             self.system_prompt += "\n\n" + self.prompt_manager.get_prompt("agent.pre_tool_speech")
+        # Reuse the shared gender instruction appended to the system prompt.
+        if self.assistant_gender is not None:
+            gender_word = "male" if self.assistant_gender == "M" else "female"
+            self.system_prompt += "\n\n" + self.prompt_manager.get_prompt(
+                "agent.gender_instruction", gender_word=gender_word
+            )
 
         # Per-turn audio history: list of (audio_bytes, sample_rate)
         # One entry per user turn, aligned 1:1 with the user messages in the conversation history

src/eva/assistant/agentic/system.py

@@ -106,6 +106,7 @@ def __init__(
         output_dir: Path | None = None,  # Output directory for performance stats
         pre_tool_speech: str = "off",
         llm_streaming: bool = False,
+        assistant_gender: str | None = None,
     ):
         """Initialize the agentic system.
 
@@ -118,6 +119,8 @@ def __init__(
             output_dir: Optional output directory for saving performance stats
             pre_tool_speech: Lead-in mode ('off'|'auto')
             llm_streaming: Stream LLM output sentence-by-sentence
+            assistant_gender: Speaking gender for the assistant ('M'|'F'); None adds no
+                gender instruction (for gender-neutral languages like English)
         """
         self.agent = agent
         self.tool_handler = tool_handler
@@ -127,6 +130,7 @@ def __init__(
         self.current_date_time = current_date_time
         self.pre_tool_speech = pre_tool_speech
         self.llm_streaming = llm_streaming
+        self.assistant_gender = assistant_gender
         self._warned_responses_streaming_fallback = False
 
         self.prompt_manager = PromptManager()
@@ -143,6 +147,11 @@ def __init__(
         )
         if self.pre_tool_speech == "auto":
             self.system_prompt += "\n\n" + self.prompt_manager.get_prompt("agent.pre_tool_speech")
+        if self.assistant_gender is not None:
+            gender_word = "male" if self.assistant_gender == "M" else "female"
+            self.system_prompt += "\n\n" + self.prompt_manager.get_prompt(
+                "agent.gender_instruction", gender_word=gender_word
+            )
         # Build tools for the LLM
         self.tools = agent.build_tools_for_agent()
 

src/eva/assistant/pipecat_server.py

@@ -391,6 +391,7 @@ async def _realtime_tool_handler(params) -> None:
                     pre_tool_speech=self.pipeline_config.pre_tool_speech,
                     llm_streaming=self.pipeline_config.llm_streaming,
                     full_audio_context=self.pipeline_config.audio_llm_params.get("full_audio_context", False),
+                    assistant_gender=self.pipeline_config.assistant_gender,
                 )
                 audio_llm_processor.on_assistant_response = lambda msg: self._save_transcript_message_from_turn(
                     role="assistant", content=msg, timestamp=self._current_iso_timestamp()
@@ -444,6 +445,7 @@ async def on_user_transcription(text: str, timestamp: str, turn_id: int | None)
                     output_dir=self.output_dir,
                     pre_tool_speech=self.pipeline_config.pre_tool_speech,
                     llm_streaming=self.pipeline_config.llm_streaming,
+                    assistant_gender=self.pipeline_config.assistant_gender,
                 )
 
                 async def on_assistant_response(msg: str) -> None:

src/eva/assistant/pipeline/agent_processor.py

@@ -65,6 +65,7 @@ def __init__(
         output_dir=None,
         pre_tool_speech: str = "off",
         llm_streaming: bool = False,
+        assistant_gender: str | None = None,
         **kwargs,
     ) -> None:
         """Initialize the agent processor.
@@ -78,6 +79,8 @@ def __init__(
             output_dir: Optional output directory for saving performance stats
             pre_tool_speech: Lead-in mode ('off'|'auto')
             llm_streaming: Stream LLM output sentence-by-sentence
+            assistant_gender: Speaking gender for the assistant ('M'|'F'); None adds no
+                gender instruction
             **kwargs: Additional keyword arguments passed to FrameProcessor
 
         The turn-end fallback timer is hosted by ``UserObserver`` (on the pipeline spine), which
@@ -101,6 +104,7 @@ def __init__(
             output_dir=output_dir,
             pre_tool_speech=pre_tool_speech,
             llm_streaming=llm_streaming,
+            assistant_gender=assistant_gender,
         )
 
         # State tracking

src/eva/assistant/pipeline/audio_llm_processor.py

@@ -198,6 +198,7 @@ def __init__(
         pre_tool_speech: str = "off",
         llm_streaming: bool = False,
         full_audio_context: bool = False,
+        assistant_gender: str | None = None,
         **kwargs,
     ) -> None:
         super().__init__(**kwargs)
@@ -216,6 +217,7 @@ def __init__(
             pre_tool_speech=pre_tool_speech,
             llm_streaming=llm_streaming,
             full_audio_context=full_audio_context,
+            assistant_gender=assistant_gender,
         )
 
         # State tracking (mirrors BenchmarkAgentProcessor)

src/eva/models/config.py

@@ -211,6 +211,20 @@ def _normalize_pre_tool_speech(cls, value: str) -> str:
         None,
         description="Forward parallel_tool_calls when tools are present; None leaves provider defaults.",
     )
+    assistant_gender: Literal["M", "F"] | None = Field(
+        None,
+        description=(
+            "Assistant speaking gender ('M' or 'F'), appended as a small system prompt "
+            "instruction so gendered languages (e.g. Hindi) produce grammatically consistent "
+            "speech. CASCADE/AUDIO_LLM only — S2S providers manage their own voice/gender. "
+            "None (default) adds no instruction, for gender-neutral languages like English."
+        ),
+    )
+
+    @field_validator("assistant_gender", mode="before")
+    @classmethod
+    def _normalize_assistant_gender(cls, value: Any) -> Any:
+        return value.upper() if isinstance(value, str) else value
 
     @property
     def pipeline_type(self) -> "PipelineType":
@@ -308,6 +322,11 @@ def _validate_latency_optimizations(self) -> "ModelConfig":
                 "parallel_tool_calls applies only to the CASCADE pipeline; it will be ignored "
                 f"for pipeline_type={self.pipeline_type}."
             )
+        if self.assistant_gender is not None and self.pipeline_type == PipelineType.S2S:
+            logger.warning(
+                "assistant_gender applies only to CASCADE/AUDIO_LLM pipelines; it will be "
+                "ignored for pipeline_type=s2s (S2S providers manage their own voice/gender)."
+            )
         return self