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fix(bedrock): surface Nova Sonic user transcripts, speech events, and usage in realtime API
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
This commit is contained in:
Devin AI
parent
49affa7c01
commit
b7a7754b05
5 changed files with 383 additions and 18 deletions
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@ -7,7 +7,7 @@ Transforms between OpenAI Realtime API format and Bedrock Nova Sonic format.
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import base64
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import json
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import uuid as uuid_lib
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from typing import Any, Final
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from typing import Any, Final, cast
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from pydantic import BaseModel
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@ -20,16 +20,21 @@ from litellm.types.llms.openai import (
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OpenAIRealtimeContentPartDone,
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OpenAIRealtimeDoneEvent,
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OpenAIRealtimeEvents,
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OpenAIRealtimeInputAudioBufferSpeechEvent,
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OpenAIRealtimeInputAudioTranscriptionCompleted,
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OpenAIRealtimeInputAudioTranscriptionDelta,
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OpenAIRealtimeOutputItemDone,
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OpenAIRealtimeResponseAudioDone,
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OpenAIRealtimeResponseContentPartAdded,
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OpenAIRealtimeResponseDelta,
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OpenAIRealtimeResponseDoneObject,
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OpenAIRealtimeResponseTextDone,
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OpenAIRealtimeResponseUsage,
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OpenAIRealtimeStreamResponseBaseObject,
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OpenAIRealtimeStreamResponseOutputItemAdded,
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OpenAIRealtimeStreamSession,
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OpenAIRealtimeStreamSessionEvents,
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OpenAIRealtimeUsageTokenDetails,
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)
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from litellm.types.realtime import (
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ALL_DELTA_TYPES,
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@ -43,6 +48,27 @@ class BedrockContentEnd(BaseModel):
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stopReason: str | None = None
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class BedrockUsageTokenDetails(BaseModel):
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speechTokens: int = 0
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textTokens: int = 0
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class BedrockUsageDetailsTotal(BaseModel):
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input: BedrockUsageTokenDetails = BedrockUsageTokenDetails()
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output: BedrockUsageTokenDetails = BedrockUsageTokenDetails()
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class BedrockUsageDetails(BaseModel):
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total: BedrockUsageDetailsTotal = BedrockUsageDetailsTotal()
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class BedrockUsageEvent(BaseModel):
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totalInputTokens: int = 0
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totalOutputTokens: int = 0
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totalTokens: int = 0
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details: BedrockUsageDetails = BedrockUsageDetails()
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TRIGGER_AUDIO_SAMPLE_RATE_HERTZ: Final = 16000
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TRIGGER_AUDIO_BYTES_PER_SECOND: Final = TRIGGER_AUDIO_SAMPLE_RATE_HERTZ * 2
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TRIGGER_LEADING_SILENCE: Final = bytes(TRIGGER_AUDIO_BYTES_PER_SECOND // 2)
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@ -87,6 +113,12 @@ class BedrockRealtimeConfig(BaseRealtimeConfig):
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# Text configuration
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self.text_media_type = "text/plain"
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# Response-stream state (Bedrock events carry no role on textOutput,
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# so the USER/ASSISTANT split from contentStart is tracked here)
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self._user_transcript_active = False
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self._user_transcript_generation_stage: str | None = None
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self._latest_usage: OpenAIRealtimeResponseUsage | None = None
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def validate_environment(self, headers: dict, model: str, api_key: str | None = None) -> dict:
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"""Validate environment - no special validation needed for Bedrock."""
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return headers
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@ -691,6 +723,11 @@ class BedrockRealtimeConfig(BaseRealtimeConfig):
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role: Final = content_start.get("role")
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if role != "ASSISTANT":
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if role == "USER" and content_start.get("type") == "TEXT":
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self._user_transcript_active = True
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self._user_transcript_generation_stage = self._parse_generation_stage(
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content_start.get("additionalModelFields")
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)
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return (
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[],
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current_response_id,
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@ -700,6 +737,7 @@ class BedrockRealtimeConfig(BaseRealtimeConfig):
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)
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verbose_logger.debug("Handling ASSISTANT contentStart")
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is_new_response: Final = current_response_id is None
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# Initialize IDs if needed
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if not current_response_id:
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@ -715,7 +753,8 @@ class BedrockRealtimeConfig(BaseRealtimeConfig):
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returned_messages: Final[list[OpenAIRealtimeEvents]] = []
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# Send response.created
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# Send response.created only once per response (a response can contain
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# multiple content blocks, e.g. TEXT then AUDIO)
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response_created: Final = OpenAIRealtimeStreamResponseBaseObject(
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type="response.created",
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event_id=f"event_{uuid.uuid4()}",
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@ -727,7 +766,8 @@ class BedrockRealtimeConfig(BaseRealtimeConfig):
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"conversation_id": current_conversation_id,
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},
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)
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returned_messages.append(response_created)
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if is_new_response:
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returned_messages.append(response_created)
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# Send response.output_item.added
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output_item_added: Final = OpenAIRealtimeStreamResponseOutputItemAdded(
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@ -767,6 +807,70 @@ class BedrockRealtimeConfig(BaseRealtimeConfig):
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current_delta_type,
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)
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@staticmethod
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def _parse_generation_stage(additional_model_fields: object) -> str | None:
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if not isinstance(additional_model_fields, str):
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return None
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try:
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parsed: Final = json.loads(additional_model_fields)
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except json.JSONDecodeError:
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return None
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stage: Final = parsed.get("generationStage") if isinstance(parsed, dict) else None
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return stage if isinstance(stage, str) else None
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def transform_user_speech_event(self, is_speech_start: bool) -> tuple[OpenAIRealtimeEvents, ...]:
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"""Transform Bedrock userSpeechStart/userSpeechEnd to OpenAI speech boundary events."""
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verbose_logger.debug("Handling userSpeech%s", "Start" if is_speech_start else "End")
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speech_event: Final[OpenAIRealtimeInputAudioBufferSpeechEvent] = {
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"type": "input_audio_buffer.speech_started" if is_speech_start else "input_audio_buffer.speech_stopped",
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"event_id": f"event_{uuid.uuid4()}",
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"item_id": f"item_{uuid.uuid4()}",
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}
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return (speech_event,)
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def transform_usage_event(self, usage_event: BedrockUsageEvent) -> None:
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"""Record Bedrock usageEvent token totals for the next response.done."""
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verbose_logger.debug("Handling usageEvent")
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input_details: Final[OpenAIRealtimeUsageTokenDetails] = {
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"audio_tokens": usage_event.details.total.input.speechTokens,
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"text_tokens": usage_event.details.total.input.textTokens,
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"cached_tokens": 0,
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}
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output_details: Final[OpenAIRealtimeUsageTokenDetails] = {
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"audio_tokens": usage_event.details.total.output.speechTokens,
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"text_tokens": usage_event.details.total.output.textTokens,
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}
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latest_usage: Final[OpenAIRealtimeResponseUsage] = {
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"input_tokens": usage_event.totalInputTokens,
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"output_tokens": usage_event.totalOutputTokens,
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"total_tokens": usage_event.totalTokens,
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"input_token_details": input_details,
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"output_token_details": output_details,
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}
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self._latest_usage = latest_usage
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def transform_user_transcript_event(self, transcript: str) -> tuple[OpenAIRealtimeEvents, ...]:
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"""Transform a USER-role Bedrock textOutput (ASR transcript) to OpenAI transcription events."""
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verbose_logger.debug("Handling USER textOutput (ASR transcript)")
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item_id: Final = f"item_{uuid.uuid4()}"
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delta_event: Final[OpenAIRealtimeInputAudioTranscriptionDelta] = {
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"type": "conversation.item.input_audio_transcription.delta",
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"event_id": f"event_{uuid.uuid4()}",
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"item_id": item_id,
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"content_index": 0,
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"delta": transcript,
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}
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if self._user_transcript_generation_stage == "SPECULATIVE":
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return (delta_event,)
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completed_event: Final[OpenAIRealtimeInputAudioTranscriptionCompleted] = {
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"type": "conversation.item.input_audio_transcription.completed",
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"event_id": f"event_{uuid.uuid4()}",
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"item_id": item_id,
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"content_index": 0,
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"transcript": transcript,
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}
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return (delta_event, completed_event)
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def transform_text_output_event(
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self,
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event: dict,
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@ -985,7 +1089,14 @@ class BedrockRealtimeConfig(BaseRealtimeConfig):
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if not current_response_id or not current_conversation_id:
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return [], None, None, None
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usage_obj: Final = get_empty_usage()
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empty_usage: Final = get_empty_usage()
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zero_usage: Final[OpenAIRealtimeResponseUsage] = {
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"input_tokens": empty_usage.prompt_tokens,
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"output_tokens": empty_usage.completion_tokens,
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"total_tokens": empty_usage.total_tokens,
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}
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usage: Final = self._latest_usage or zero_usage
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self._latest_usage = None
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response_done: Final = OpenAIRealtimeDoneEvent(
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type="response.done",
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event_id=f"event_{uuid.uuid4()}",
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@ -995,11 +1106,7 @@ class BedrockRealtimeConfig(BaseRealtimeConfig):
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status="completed",
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output=[],
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conversation_id=current_conversation_id,
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usage={
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"prompt_tokens": usage_obj.prompt_tokens,
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"completion_tokens": usage_obj.completion_tokens,
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"total_tokens": usage_obj.total_tokens,
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},
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usage=dict(usage),
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),
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)
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@ -1042,8 +1149,6 @@ class BedrockRealtimeConfig(BaseRealtimeConfig):
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# Create a function call arguments done event
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# This is a custom event format that matches what clients expect
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from typing import cast
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function_call_event: Final[dict[str, Any]] = {
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"type": "response.function_call_arguments.done",
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"event_id": f"event_{uuid.uuid4()}",
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@ -1194,18 +1299,25 @@ class BedrockRealtimeConfig(BaseRealtimeConfig):
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returned_messages.extend(events)
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elif "textOutput" in event:
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events, current_delta_chunks = self.transform_text_output_event(
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event,
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current_output_item_id,
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current_response_id,
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current_delta_chunks,
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)
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returned_messages.extend(events)
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if self._user_transcript_active:
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returned_messages.extend(self.transform_user_transcript_event(event["textOutput"].get("content", "")))
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else:
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events, current_delta_chunks = self.transform_text_output_event(
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event,
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current_output_item_id,
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current_response_id,
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current_delta_chunks,
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)
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returned_messages.extend(events)
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elif "audioOutput" in event:
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events = self.transform_audio_output_event(event, current_output_item_id, current_response_id)
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returned_messages.extend(events)
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elif "contentEnd" in event and self._user_transcript_active:
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self._user_transcript_active = False
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self._user_transcript_generation_stage = None
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elif "contentEnd" in event:
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events, current_delta_chunks = self.transform_content_end_event(
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event,
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@ -1224,6 +1336,12 @@ class BedrockRealtimeConfig(BaseRealtimeConfig):
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) = self._response_done_events(current_response_id, current_conversation_id)
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returned_messages.extend(done_events)
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elif "userSpeechStart" in event or "userSpeechEnd" in event:
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returned_messages.extend(self.transform_user_speech_event("userSpeechStart" in event))
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elif "usageEvent" in event:
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self.transform_usage_event(BedrockUsageEvent.model_validate(event["usageEvent"]))
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elif "toolUse" in event:
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events, tool_call_id, tool_name = self.transform_tool_use_event(
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event, current_output_item_id, current_response_id
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@ -553,6 +553,26 @@
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"supports_response_schema": true,
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"supports_vision": true
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},
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"amazon.nova-sonic-v1:0": {
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"input_cost_per_audio_token": 3.4e-06,
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"input_cost_per_token": 6e-08,
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"litellm_provider": "bedrock",
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"mode": "realtime",
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"output_cost_per_audio_token": 1.36e-05,
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"output_cost_per_token": 2.4e-07,
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"supports_audio_input": true,
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"supports_audio_output": true
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},
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"amazon.nova-2-sonic-v1:0": {
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"input_cost_per_audio_token": 3e-06,
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"input_cost_per_token": 3.3e-07,
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"litellm_provider": "bedrock",
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"mode": "realtime",
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"output_cost_per_audio_token": 1.2e-05,
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"output_cost_per_token": 2.75e-06,
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"supports_audio_input": true,
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"supports_audio_output": true
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},
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"amazon.rerank-v1:0": {
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"input_cost_per_query": 0.001,
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"input_cost_per_token": 0.0,
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@ -2162,6 +2162,42 @@ class OpenAIRealtimeDoneEvent(TypedDict):
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type: Literal["response.done"]
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class OpenAIRealtimeInputAudioBufferSpeechEvent(TypedDict):
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type: ReadOnly[Literal["input_audio_buffer.speech_started", "input_audio_buffer.speech_stopped"]]
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event_id: ReadOnly[str]
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item_id: ReadOnly[str]
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class OpenAIRealtimeInputAudioTranscriptionDelta(TypedDict):
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type: ReadOnly[Literal["conversation.item.input_audio_transcription.delta"]]
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event_id: ReadOnly[str]
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item_id: ReadOnly[str]
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content_index: ReadOnly[int]
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delta: ReadOnly[str]
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class OpenAIRealtimeInputAudioTranscriptionCompleted(TypedDict):
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type: ReadOnly[Literal["conversation.item.input_audio_transcription.completed"]]
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event_id: ReadOnly[str]
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item_id: ReadOnly[str]
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content_index: ReadOnly[int]
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transcript: ReadOnly[str]
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class OpenAIRealtimeUsageTokenDetails(TypedDict):
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audio_tokens: ReadOnly[int]
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text_tokens: ReadOnly[int]
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cached_tokens: NotRequired[ReadOnly[int]]
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class OpenAIRealtimeResponseUsage(TypedDict):
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input_tokens: ReadOnly[int]
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output_tokens: ReadOnly[int]
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total_tokens: ReadOnly[int]
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input_token_details: NotRequired[ReadOnly[OpenAIRealtimeUsageTokenDetails]]
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output_token_details: NotRequired[ReadOnly[OpenAIRealtimeUsageTokenDetails]]
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class OpenAIRealtimeEventTypes(Enum):
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SESSION_CREATED = "session.created"
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# Beta delta event names
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@ -2199,6 +2235,9 @@ OpenAIRealtimeEvents = (
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| OpenAIRealtimeOutputItemDone
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| OpenAIRealtimeFunctionCallArgumentsDone
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| OpenAIRealtimeDoneEvent
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| OpenAIRealtimeInputAudioBufferSpeechEvent
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| OpenAIRealtimeInputAudioTranscriptionDelta
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| OpenAIRealtimeInputAudioTranscriptionCompleted
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)
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OpenAIRealtimeStreamList = list[OpenAIRealtimeEvents]
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@ -553,6 +553,26 @@
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"supports_response_schema": true,
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"supports_vision": true
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},
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"amazon.nova-sonic-v1:0": {
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"input_cost_per_audio_token": 3.4e-06,
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"input_cost_per_token": 6e-08,
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"litellm_provider": "bedrock",
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"mode": "realtime",
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"output_cost_per_audio_token": 1.36e-05,
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"output_cost_per_token": 2.4e-07,
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"supports_audio_input": true,
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"supports_audio_output": true
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},
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"amazon.nova-2-sonic-v1:0": {
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"input_cost_per_audio_token": 3e-06,
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"input_cost_per_token": 3.3e-07,
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"litellm_provider": "bedrock",
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"mode": "realtime",
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"output_cost_per_audio_token": 1.2e-05,
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"output_cost_per_token": 2.75e-06,
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"supports_audio_input": true,
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"supports_audio_output": true
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},
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"amazon.rerank-v1:0": {
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"input_cost_per_query": 0.001,
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"input_cost_per_token": 0.0,
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|
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@ -827,5 +827,173 @@ class TestBedrockRealtimeSessionEvents:
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assert event["session"]["modalities"] == ["text", "audio"]
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class TestBedrockRealtimeUserEventsAndUsage:
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"""Regression tests for #38346: USER ASR transcripts, speech boundary events,
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usage propagation, and duplicate response.created"""
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@staticmethod
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def _run(config, messages):
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logging_obj = MagicMock()
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logging_obj.litellm_trace_id = "trace_123"
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state = {
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"session_configuration_request": json.dumps({"configured": True}),
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"current_output_item_id": None,
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"current_response_id": None,
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"current_conversation_id": None,
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"current_delta_chunks": [],
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"current_item_chunks": [],
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"current_delta_type": None,
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}
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all_events = []
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for msg in messages:
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result = config.transform_realtime_response(
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json.dumps(msg),
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"amazon.nova-2-sonic-v1:0",
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logging_obj,
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realtime_response_transform_input=dict(state),
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)
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all_events.extend(result["response"])
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state.update(
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{
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"current_output_item_id": result["current_output_item_id"],
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"current_response_id": result["current_response_id"],
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"current_conversation_id": result["current_conversation_id"],
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"current_delta_chunks": result["current_delta_chunks"],
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"current_delta_type": result["current_delta_type"],
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}
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)
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return all_events
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def test_user_speech_start_and_stop_events(self):
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events = self._run(
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BedrockRealtimeConfig(),
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[{"event": {"userSpeechStart": {}}}, {"event": {"userSpeechEnd": {}}}],
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)
|
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assert [e["type"] for e in events] == [
|
||||
"input_audio_buffer.speech_started",
|
||||
"input_audio_buffer.speech_stopped",
|
||||
]
|
||||
assert all(e["event_id"] and e["item_id"] for e in events)
|
||||
|
||||
def test_user_transcript_emits_input_audio_transcription_events(self):
|
||||
events = self._run(
|
||||
BedrockRealtimeConfig(),
|
||||
[
|
||||
{
|
||||
"event": {
|
||||
"contentStart": {
|
||||
"role": "USER",
|
||||
"type": "TEXT",
|
||||
"additionalModelFields": json.dumps({"generationStage": "FINAL"}),
|
||||
}
|
||||
}
|
||||
},
|
||||
{"event": {"textOutput": {"content": "ready"}}},
|
||||
{"event": {"contentEnd": {"stopReason": "PARTIAL_TURN"}}},
|
||||
],
|
||||
)
|
||||
deltas = [e for e in events if e["type"] == "conversation.item.input_audio_transcription.delta"]
|
||||
completed = [e for e in events if e["type"] == "conversation.item.input_audio_transcription.completed"]
|
||||
assert len(deltas) == 1 and deltas[0]["delta"] == "ready"
|
||||
assert len(completed) == 1 and completed[0]["transcript"] == "ready"
|
||||
assert deltas[0]["item_id"] == completed[0]["item_id"]
|
||||
assert not any(e["type"] == "response.text.delta" for e in events)
|
||||
|
||||
def test_speculative_user_transcript_emits_delta_only(self):
|
||||
events = self._run(
|
||||
BedrockRealtimeConfig(),
|
||||
[
|
||||
{
|
||||
"event": {
|
||||
"contentStart": {
|
||||
"role": "USER",
|
||||
"type": "TEXT",
|
||||
"additionalModelFields": json.dumps({"generationStage": "SPECULATIVE"}),
|
||||
}
|
||||
}
|
||||
},
|
||||
{"event": {"textOutput": {"content": "rea"}}},
|
||||
],
|
||||
)
|
||||
assert [e["type"] for e in events] == ["conversation.item.input_audio_transcription.delta"]
|
||||
|
||||
def test_user_transcript_state_resets_on_content_end(self):
|
||||
events = self._run(
|
||||
BedrockRealtimeConfig(),
|
||||
[
|
||||
{"event": {"contentStart": {"role": "USER", "type": "TEXT"}}},
|
||||
{"event": {"contentEnd": {"stopReason": "PARTIAL_TURN"}}},
|
||||
{"event": {"contentStart": {"role": "ASSISTANT", "type": "TEXT"}}},
|
||||
{"event": {"textOutput": {"content": "Hi there"}}},
|
||||
],
|
||||
)
|
||||
text_deltas = [e for e in events if e["type"] == "response.text.delta"]
|
||||
assert len(text_deltas) == 1 and text_deltas[0]["delta"] == "Hi there"
|
||||
assert not any(e["type"].startswith("conversation.item.input_audio_transcription") for e in events)
|
||||
|
||||
def test_response_created_emitted_once_per_response(self):
|
||||
events = self._run(
|
||||
BedrockRealtimeConfig(),
|
||||
[
|
||||
{"event": {"contentStart": {"role": "ASSISTANT", "type": "TEXT"}}},
|
||||
{"event": {"textOutput": {"content": "Hi"}}},
|
||||
{"event": {"contentEnd": {"stopReason": "PARTIAL_TURN"}}},
|
||||
{"event": {"contentStart": {"role": "ASSISTANT", "type": "AUDIO"}}},
|
||||
],
|
||||
)
|
||||
assert sum(1 for e in events if e["type"] == "response.created") == 1
|
||||
|
||||
def test_usage_event_propagates_to_response_done(self):
|
||||
events = self._run(
|
||||
BedrockRealtimeConfig(),
|
||||
[
|
||||
{
|
||||
"event": {
|
||||
"usageEvent": {
|
||||
"totalInputTokens": 25,
|
||||
"totalOutputTokens": 40,
|
||||
"totalTokens": 65,
|
||||
"details": {
|
||||
"total": {
|
||||
"input": {"speechTokens": 20, "textTokens": 5},
|
||||
"output": {"speechTokens": 30, "textTokens": 10},
|
||||
}
|
||||
},
|
||||
}
|
||||
}
|
||||
},
|
||||
{"event": {"contentStart": {"role": "ASSISTANT", "type": "TEXT"}}},
|
||||
{"event": {"textOutput": {"content": "Hi"}}},
|
||||
{"event": {"contentEnd": {"stopReason": "END_TURN"}}},
|
||||
],
|
||||
)
|
||||
done_events = [e for e in events if e["type"] == "response.done"]
|
||||
assert len(done_events) == 1
|
||||
usage = done_events[0]["response"]["usage"]
|
||||
assert usage["input_tokens"] == 25
|
||||
assert usage["output_tokens"] == 40
|
||||
assert usage["total_tokens"] == 65
|
||||
assert usage["input_token_details"]["audio_tokens"] == 20
|
||||
assert usage["input_token_details"]["text_tokens"] == 5
|
||||
assert usage["output_token_details"]["audio_tokens"] == 30
|
||||
assert usage["output_token_details"]["text_tokens"] == 10
|
||||
|
||||
def test_response_done_without_usage_event_reports_zero_usage(self):
|
||||
events = self._run(
|
||||
BedrockRealtimeConfig(),
|
||||
[
|
||||
{"event": {"contentStart": {"role": "ASSISTANT", "type": "TEXT"}}},
|
||||
{"event": {"textOutput": {"content": "Hi"}}},
|
||||
{"event": {"contentEnd": {"stopReason": "END_TURN"}}},
|
||||
],
|
||||
)
|
||||
done_events = [e for e in events if e["type"] == "response.done"]
|
||||
assert len(done_events) == 1
|
||||
usage = done_events[0]["response"]["usage"]
|
||||
assert usage["input_tokens"] == 0
|
||||
assert usage["output_tokens"] == 0
|
||||
assert usage["total_tokens"] == 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
pytest.main([__file__, "-v"])
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue