mirror of
https://github.com/dograh-hq/dograh.git
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fix: fix speech to speech model transitions (#545)
* fix: fix transition logic for realtime providers * chore: run formatter * chore: generate SDK and fix other realtime providers * fix: fix ultravox node transitions
This commit is contained in:
Abhishek
GitHub
parent
348cd8427b
commit
01acf6ac30
34 changed files with 1282 additions and 617 deletions
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@ -10,6 +10,7 @@ Design mirrors ``api/services/integrations/tuner/collector.py`` exactly:
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- Build a serialisable snapshot in ``build_snapshot``
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- Return it from ``on_call_finished`` so it lands in ``workflow_run.logs``
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"""
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from __future__ import annotations
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import time
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@ -43,15 +44,22 @@ def _detect_provider(name: str, fallback: str = "unknown") -> str:
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if "gemini" in clean_name:
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return "google"
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suffixes = [
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"service", "multimodallive", "realtime",
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"vertex", "llm", "tts", "stt", "helper", "transport"
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"service",
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"multimodallive",
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"realtime",
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"vertex",
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"llm",
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"tts",
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"stt",
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"helper",
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"transport",
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]
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changed = True
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while changed:
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changed = False
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for suffix in suffixes:
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if clean_name.endswith(suffix):
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clean_name = clean_name[:-len(suffix)].rstrip("_").rstrip("-")
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clean_name = clean_name[: -len(suffix)].rstrip("_").rstrip("-")
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changed = True
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break
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return clean_name or fallback
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@ -81,13 +89,13 @@ class _UsageAccumulator:
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call_end_abs_ns: int | None = None
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# STT: timestamp of when user started speaking; None when not speaking
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_user_started_speaking_ns: int | None = field(default=None, repr=False)
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@property
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def total_duration_seconds(self) -> int:
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if self.call_end_abs_ns is None:
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return int((time.time_ns() - self.call_start_abs_ns) / 1_000_000_000)
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return int((self.call_end_abs_ns - self.call_start_abs_ns) / 1_000_000_000)
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def get_stt_audio_seconds(self) -> float:
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"""Return measured STT audio seconds accumulated from the pipeline.
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@ -109,7 +117,7 @@ class _UsageAccumulator:
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if not self._has_tts_metrics:
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self._has_tts_metrics = True
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self.tts_characters = 0 # Ignore manual count if metrics emit natively
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# Extremely robust extraction
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val = 0
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if isinstance(data, (int, float)):
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@ -120,11 +128,13 @@ class _UsageAccumulator:
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val = getattr(data, "characters", 0) or 0
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elif isinstance(data, dict):
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val = data.get("value") or data.get("characters") or 0
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try:
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self.tts_characters += int(val or 0)
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except Exception as exc:
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logger.warning("[paygent] Failed to accumulate TTS characters (val={!r}): {}", val, exc)
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logger.warning(
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"[paygent] Failed to accumulate TTS characters (val={!r}): {}", val, exc
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)
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def add_tts_manual(self, text: str) -> None:
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if not self._has_tts_metrics:
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@ -138,7 +148,9 @@ class _UsageAccumulator:
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def on_user_stopped_speaking(self) -> None:
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"""Accumulate the completed utterance duration into stt_audio_seconds."""
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if self._user_started_speaking_ns is not None:
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elapsed_s = (time.time_ns() - self._user_started_speaking_ns) / 1_000_000_000
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elapsed_s = (
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time.time_ns() - self._user_started_speaking_ns
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) / 1_000_000_000
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self.stt_audio_seconds += elapsed_s
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self._user_started_speaking_ns = None
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@ -162,9 +174,11 @@ def _google_live_usage_to_sts_metadata(usage: Dict[str, Any]) -> Dict[str, Any]:
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return None
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for k in keys:
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if isinstance(obj, dict):
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if k in obj: return obj[k]
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if k in obj:
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return obj[k]
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else:
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if hasattr(obj, k): return getattr(obj, k)
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if hasattr(obj, k):
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return getattr(obj, k)
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return None
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def _get_list(obj, *keys):
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@ -202,20 +216,36 @@ def _google_live_usage_to_sts_metadata(usage: Dict[str, Any]) -> Dict[str, Any]:
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return total
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prompt_details = _get_list(usage, "prompt_tokens_details", "promptTokensDetails")
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response_details = _get_list(usage, "response_tokens_details", "responseTokensDetails")
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tool_details = _get_list(usage, "tool_use_prompt_tokens_details", "toolUsePromptTokensDetails")
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response_details = _get_list(
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usage, "response_tokens_details", "responseTokensDetails"
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)
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tool_details = _get_list(
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usage, "tool_use_prompt_tokens_details", "toolUsePromptTokensDetails"
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)
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cache_details = _get_list(usage, "cache_tokens_details", "cacheTokensDetails")
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# input side: TEXT + DOCUMENT + AUDIO + IMAGE + VIDEO
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text_in = _modality_token_count(prompt_details, "TEXT") + _modality_token_count(tool_details, "TEXT")
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audio_in = _modality_token_count(prompt_details, "AUDIO") + _modality_token_count(tool_details, "AUDIO")
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image_in = _modality_token_count(prompt_details, "IMAGE") + _modality_token_count(tool_details, "IMAGE")
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video_in = _modality_token_count(prompt_details, "VIDEO") + _modality_token_count(tool_details, "VIDEO")
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doc_as_text = _modality_token_count(prompt_details, "DOCUMENT") + _modality_token_count(tool_details, "DOCUMENT")
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text_in = _modality_token_count(prompt_details, "TEXT") + _modality_token_count(
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tool_details, "TEXT"
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)
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audio_in = _modality_token_count(prompt_details, "AUDIO") + _modality_token_count(
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tool_details, "AUDIO"
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)
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image_in = _modality_token_count(prompt_details, "IMAGE") + _modality_token_count(
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tool_details, "IMAGE"
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)
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video_in = _modality_token_count(prompt_details, "VIDEO") + _modality_token_count(
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tool_details, "VIDEO"
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)
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doc_as_text = _modality_token_count(
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prompt_details, "DOCUMENT"
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) + _modality_token_count(tool_details, "DOCUMENT")
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text_in += doc_as_text
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# fallback aggregate mapping
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tutc = _optional_int(usage, "tool_use_prompt_token_count", "toolUsePromptTokenCount")
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tutc = _optional_int(
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usage, "tool_use_prompt_token_count", "toolUsePromptTokenCount"
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)
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if tutc is not None and not tool_details:
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text_in += int(tutc)
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@ -224,8 +254,12 @@ def _google_live_usage_to_sts_metadata(usage: Dict[str, Any]) -> Dict[str, Any]:
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text_in += int(ptc)
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# output side: TEXT + DOCUMENT + AUDIO + VIDEO + THINKING
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text_out = _modality_token_count(response_details, "TEXT") + _modality_token_count(response_details, "DOCUMENT")
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audio_out = _modality_token_count(response_details, "AUDIO") + _modality_token_count(response_details, "VIDEO")
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text_out = _modality_token_count(response_details, "TEXT") + _modality_token_count(
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response_details, "DOCUMENT"
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)
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audio_out = _modality_token_count(
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response_details, "AUDIO"
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) + _modality_token_count(response_details, "VIDEO")
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rtc = _optional_int(usage, "response_token_count", "responseTokenCount")
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if text_out == 0 and audio_out == 0 and rtc is not None:
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@ -234,35 +268,55 @@ def _google_live_usage_to_sts_metadata(usage: Dict[str, Any]) -> Dict[str, Any]:
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# Thinking / reasoning tokens (Gemini 2.5+ thinking models).
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# Emitted as a separate output modality so Paygent has full billing visibility.
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thinking_tokens = _optional_int(
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usage,
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"thoughts_token_count", "thoughtsTokenCount",
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"thinking_token_count", "thinkingTokenCount",
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) or 0
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thinking_tokens = (
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_optional_int(
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usage,
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"thoughts_token_count",
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"thoughtsTokenCount",
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"thinking_token_count",
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"thinkingTokenCount",
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)
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or 0
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)
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# Cache breakdowns
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cached_text = _modality_token_count(cache_details, "TEXT") + _modality_token_count(cache_details, "DOCUMENT")
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cached_audio = _modality_token_count(cache_details, "AUDIO") + _modality_token_count(cache_details, "VIDEO")
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cached_text = _modality_token_count(cache_details, "TEXT") + _modality_token_count(
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cache_details, "DOCUMENT"
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)
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cached_audio = _modality_token_count(
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cache_details, "AUDIO"
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) + _modality_token_count(cache_details, "VIDEO")
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cached_image = _modality_token_count(cache_details, "IMAGE")
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cached_legacy = _optional_int(usage, "cached_content_token_count", "cachedContentTokenCount")
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cached_legacy = _optional_int(
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usage, "cached_content_token_count", "cachedContentTokenCount"
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)
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# Build response payload
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out = {"schemaVersion": 1}
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# Input Side
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inp = {}
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if text_in > 0: inp["text"] = {"tokens": text_in}
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if audio_in > 0: inp["audio"] = {"tokens": audio_in}
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if image_in > 0: inp["image"] = {"tokens": image_in}
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if video_in > 0: inp["video"] = {"tokens": video_in}
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if inp: out["input"] = inp
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if text_in > 0:
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inp["text"] = {"tokens": text_in}
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if audio_in > 0:
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inp["audio"] = {"tokens": audio_in}
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if image_in > 0:
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inp["image"] = {"tokens": image_in}
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if video_in > 0:
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inp["video"] = {"tokens": video_in}
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if inp:
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out["input"] = inp
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# Output Side
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o = {}
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if text_out > 0: o["text"] = {"tokens": text_out}
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if audio_out > 0: o["audio"] = {"tokens": audio_out}
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if thinking_tokens > 0: o["thinking"] = {"tokens": thinking_tokens}
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if o: out["output"] = o
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if text_out > 0:
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o["text"] = {"tokens": text_out}
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if audio_out > 0:
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o["audio"] = {"tokens": audio_out}
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if thinking_tokens > 0:
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o["thinking"] = {"tokens": thinking_tokens}
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if o:
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out["output"] = o
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# Cached breakdown
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has_split = bool(cached_text or cached_audio or cached_image)
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@ -270,15 +324,18 @@ def _google_live_usage_to_sts_metadata(usage: Dict[str, Any]) -> Dict[str, Any]:
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out["cached"] = {"tokens": int(cached_legacy)}
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elif has_split:
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cd = {}
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if cached_text > 0: cd["text"] = {"tokens": cached_text}
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if cached_audio > 0: cd["audio"] = {"tokens": cached_audio}
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if cached_image > 0: cd["image"] = {"tokens": cached_image}
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if cd: out["cached"] = cd
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if cached_text > 0:
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cd["text"] = {"tokens": cached_text}
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if cached_audio > 0:
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cd["audio"] = {"tokens": cached_audio}
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if cached_image > 0:
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cd["image"] = {"tokens": cached_image}
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if cd:
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out["cached"] = cd
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return out
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def _openai_realtime_usage_to_sts_metadata(usage: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Pure Python translation of OpenAI Realtime usage_metadata to
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@ -292,9 +349,11 @@ def _openai_realtime_usage_to_sts_metadata(usage: Dict[str, Any]) -> Dict[str, A
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return None
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for k in keys:
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if isinstance(obj, dict):
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if k in obj: return obj[k]
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if k in obj:
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return obj[k]
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else:
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if hasattr(obj, k): return getattr(obj, k)
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if hasattr(obj, k):
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return getattr(obj, k)
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return None
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total_in = int(_get_val(usage, "input_tokens", "inputTokens") or 0)
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@ -307,17 +366,29 @@ def _openai_realtime_usage_to_sts_metadata(usage: Dict[str, Any]) -> Dict[str, A
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text_in = int(_get_val(in_details, "text_tokens", "textTokens") or 0)
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image_in = int(_get_val(in_details, "image_tokens", "imageTokens") or 0)
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cached_total = int(_get_val(usage, "cached_tokens", "cachedTokens") or _get_val(in_details, "cached_tokens", "cachedTokens") or 0)
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cached_total = int(
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_get_val(usage, "cached_tokens", "cachedTokens")
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or _get_val(in_details, "cached_tokens", "cachedTokens")
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or 0
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)
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cached_details = _get_val(in_details, "cached_tokens_details", "cachedTokensDetails") or {}
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cached_details = (
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_get_val(in_details, "cached_tokens_details", "cachedTokensDetails") or {}
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)
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cached_audio = int(_get_val(cached_details, "audio_tokens", "audioTokens") or 0)
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cached_text = int(_get_val(cached_details, "text_tokens", "textTokens") or 0)
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cached_image = int(_get_val(cached_details, "image_tokens", "imageTokens") or 0)
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if not (cached_audio or cached_text or cached_image):
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cached_audio = int(_get_val(in_details, "cached_audio_tokens", "cachedAudioTokens") or 0)
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cached_text = int(_get_val(in_details, "cached_text_tokens", "cachedTextTokens") or 0)
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cached_image = int(_get_val(in_details, "cached_image_tokens", "cachedImageTokens") or 0)
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cached_audio = int(
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_get_val(in_details, "cached_audio_tokens", "cachedAudioTokens") or 0
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)
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cached_text = int(
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_get_val(in_details, "cached_text_tokens", "cachedTextTokens") or 0
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)
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cached_image = int(
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_get_val(in_details, "cached_image_tokens", "cachedImageTokens") or 0
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)
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audio_out = int(_get_val(out_details, "audio_tokens", "audioTokens") or 0)
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text_out = int(_get_val(out_details, "text_tokens", "textTokens") or 0)
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@ -327,25 +398,36 @@ def _openai_realtime_usage_to_sts_metadata(usage: Dict[str, Any]) -> Dict[str, A
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out = {"schemaVersion": 1}
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inp = {}
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if text_in > 0: inp["text"] = {"tokens": text_in}
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if audio_in > 0: inp["audio"] = {"tokens": audio_in}
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if image_in > 0: inp["image"] = {"tokens": image_in}
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if inp: out["input"] = inp
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if text_in > 0:
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inp["text"] = {"tokens": text_in}
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if audio_in > 0:
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inp["audio"] = {"tokens": audio_in}
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if image_in > 0:
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inp["image"] = {"tokens": image_in}
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if inp:
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out["input"] = inp
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o = {}
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if text_out > 0: o["text"] = {"tokens": text_out}
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if audio_out > 0: o["audio"] = {"tokens": audio_out}
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if o: out["output"] = o
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if text_out > 0:
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o["text"] = {"tokens": text_out}
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if audio_out > 0:
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o["audio"] = {"tokens": audio_out}
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if o:
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out["output"] = o
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has_split = bool(cached_text or cached_audio or cached_image)
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if cached_total > 0 and not has_split:
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out["cached"] = {"tokens": int(cached_total)}
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elif has_split:
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cd = {}
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if cached_text > 0: cd["text"] = {"tokens": cached_text}
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if cached_audio > 0: cd["audio"] = {"tokens": cached_audio}
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if cached_image > 0: cd["image"] = {"tokens": cached_image}
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if cd: out["cached"] = cd
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if cached_text > 0:
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cd["text"] = {"tokens": cached_text}
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if cached_audio > 0:
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cd["audio"] = {"tokens": cached_audio}
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if cached_image > 0:
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cd["image"] = {"tokens": cached_image}
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if cd:
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out["cached"] = cd
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return out
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@ -359,14 +441,18 @@ def _merge_sts_metadata(existing: dict, new: dict) -> dict:
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n_val = new.get(key, {})
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if not e_val and not n_val:
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continue
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merged_cat: dict = {}
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# Prefer per-modality merge when either side has per-modality detail.
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# Only use the flat aggregate{"tokens": N} form when neither side has
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# any per-modality breakdown at all (e.g. legacy schema).
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e_has_modalities = any(m in e_val for m in ("text", "audio", "image", "video", "thinking"))
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n_has_modalities = any(m in n_val for m in ("text", "audio", "image", "video", "thinking"))
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e_has_modalities = any(
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m in e_val for m in ("text", "audio", "image", "video", "thinking")
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)
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n_has_modalities = any(
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m in n_val for m in ("text", "audio", "image", "video", "thinking")
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)
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if e_has_modalities or n_has_modalities:
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for modality in ("text", "audio", "image", "video", "thinking"):
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@ -388,20 +474,25 @@ def _merge_sts_metadata(existing: dict, new: dict) -> dict:
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if merged_cat:
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out[key] = merged_cat
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# retain any other keys, summing up numeric ones to keep metadata consistent
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for k, v in existing.items():
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if k not in ("schemaVersion", "input", "output", "cached"):
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out[k] = v
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for k, v in new.items():
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if k not in ("schemaVersion", "input", "output", "cached"):
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if k in out and isinstance(out[k], (int, float)) and isinstance(v, (int, float)):
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if (
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k in out
|
||||
and isinstance(out[k], (int, float))
|
||||
and isinstance(v, (int, float))
|
||||
):
|
||||
out[k] = out[k] + v
|
||||
else:
|
||||
out[k] = v
|
||||
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class PaygentCollector(BaseObserver):
|
||||
"""Pipecat observer that accumulates usage data for a single call.
|
||||
|
||||
|
|
@ -514,37 +605,65 @@ class PaygentCollector(BaseObserver):
|
|||
if is_sts_frame:
|
||||
# Normalise the raw provider slug so that variants like
|
||||
# "openai_realtime", "azure_realtime", etc. route correctly.
|
||||
raw_provider = (
|
||||
getattr(self, "_sts_provider", "") or getattr(self, "_llm_provider", "")
|
||||
raw_provider = getattr(
|
||||
self, "_sts_provider", ""
|
||||
) or getattr(self, "_llm_provider", "")
|
||||
provider = (
|
||||
_detect_provider(raw_provider)
|
||||
if raw_provider
|
||||
else "unknown"
|
||||
)
|
||||
provider = _detect_provider(raw_provider) if raw_provider else "unknown"
|
||||
if provider not in ("grok", "ultravox"):
|
||||
usage = item.value
|
||||
raw_metadata = getattr(usage, "raw_usage_metadata", None)
|
||||
raw_metadata = getattr(
|
||||
usage, "raw_usage_metadata", None
|
||||
)
|
||||
if raw_metadata:
|
||||
# OpenAI Realtime and Azure Realtime (azure→openai via _detect_provider)
|
||||
# share the same wire format.
|
||||
if provider in ("openai", "azure"):
|
||||
new_meta = _openai_realtime_usage_to_sts_metadata(raw_metadata)
|
||||
new_meta = (
|
||||
_openai_realtime_usage_to_sts_metadata(
|
||||
raw_metadata
|
||||
)
|
||||
)
|
||||
else:
|
||||
new_meta = _google_live_usage_to_sts_metadata(raw_metadata)
|
||||
new_meta = _google_live_usage_to_sts_metadata(
|
||||
raw_metadata
|
||||
)
|
||||
else:
|
||||
prompt_tokens = getattr(usage, "prompt_tokens", 0) or 0
|
||||
completion_tokens = getattr(usage, "completion_tokens", 0) or 0
|
||||
cached_tokens = (getattr(usage, "cache_read_input_tokens", 0) or getattr(usage, "cached_tokens", 0) or 0)
|
||||
prompt_tokens = (
|
||||
getattr(usage, "prompt_tokens", 0) or 0
|
||||
)
|
||||
completion_tokens = (
|
||||
getattr(usage, "completion_tokens", 0) or 0
|
||||
)
|
||||
cached_tokens = (
|
||||
getattr(usage, "cache_read_input_tokens", 0)
|
||||
or getattr(usage, "cached_tokens", 0)
|
||||
or 0
|
||||
)
|
||||
new_meta = {"schemaVersion": 1}
|
||||
if prompt_tokens > 0:
|
||||
new_meta.setdefault("input", {})["text"] = {"tokens": prompt_tokens}
|
||||
new_meta.setdefault("input", {})["text"] = {
|
||||
"tokens": prompt_tokens
|
||||
}
|
||||
if completion_tokens > 0:
|
||||
new_meta.setdefault("output", {})["text"] = {"tokens": completion_tokens}
|
||||
new_meta.setdefault("output", {})["text"] = {
|
||||
"tokens": completion_tokens
|
||||
}
|
||||
if cached_tokens > 0:
|
||||
new_meta["cached"] = {"tokens": cached_tokens}
|
||||
|
||||
|
||||
if hasattr(usage, "__dict__"):
|
||||
for k, v in vars(usage).items():
|
||||
if not k.startswith("_") and v is not None and k not in new_meta:
|
||||
if (
|
||||
not k.startswith("_")
|
||||
and v is not None
|
||||
and k not in new_meta
|
||||
):
|
||||
new_meta[k] = v
|
||||
|
||||
|
||||
self._acc.sts_usage_metadata = _merge_sts_metadata(
|
||||
self._acc.sts_usage_metadata or {}, new_meta
|
||||
)
|
||||
|
|
@ -579,5 +698,7 @@ class PaygentCollector(BaseObserver):
|
|||
except Exception as exc:
|
||||
logger.warning(
|
||||
"[paygent] Unexpected error processing frame {!r} in collector: {}",
|
||||
type(data.frame).__name__, exc, exc_info=True,
|
||||
type(data.frame).__name__,
|
||||
exc,
|
||||
exc_info=True,
|
||||
)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue