fix
This commit is contained in:
dhruvladia-sarvam
@@ -78,11 +78,13 @@ class SarvamSTTService(STTService):
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"""Configuration parameters for Sarvam STT service.
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Parameters:
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language: Target language for transcription. Required for all models.
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Defaults to "unknown" for saarika models and "en-IN" for saaras:v3.
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prompt: Optional prompt to guide transcription style/context.
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Only applicable to saaras:v3 models. Defaults to None.
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mode: Mode of operation for saaras:v3 models. Options: transcribe, translate,
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language: Target language for transcription.
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- saarika:v2.5: Defaults to "unknown" (auto-detect supported)
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- saaras:v2.5: Not used (auto-detects language)
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- saaras:v3: Defaults to "en-IN"
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prompt: Optional prompt to guide transcription/translation style/context.
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Only applicable to saaras models (v2.5 and v3). Defaults to None.
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mode: Mode of operation for saaras:v3 models only. Options: transcribe, translate,
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verbatim, translit, codemix. Defaults to "transcribe" for saaras:v3.
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vad_signals: Enable VAD signals in response. Defaults to None.
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high_vad_sensitivity: Enable high VAD (Voice Activity Detection) sensitivity. Defaults to None.
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@@ -111,10 +113,13 @@ class SarvamSTTService(STTService):
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Args:
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api_key: Sarvam API key for authentication.
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model: Sarvam model to use for transcription. Allowed: "saarika:v2.5", "saaras:v3".
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model: Sarvam model to use for transcription. Allowed values:
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- "saarika:v2.5": Standard STT model
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- "saaras:v2.5": STT-Translate model (auto-detects language, supports prompts)
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- "saaras:v3": Advanced STT model (supports mode and prompts)
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sample_rate: Audio sample rate. Defaults to 16000 if not specified.
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input_audio_codec: Audio codec/format of the input file. Defaults to "wav".
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mode: Mode of operation for saaras:v3 models. Options: transcribe, translate,
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mode: Mode of operation for saaras:v3 models only. Options: transcribe, translate,
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verbatim, translit, codemix. Defaults to "transcribe" for saaras:v3.
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params: Configuration parameters for Sarvam STT service.
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**kwargs: Additional arguments passed to the parent STTService.
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@@ -125,34 +130,52 @@ class SarvamSTTService(STTService):
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params = params.model_copy(update={"mode": mode})
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# Validate allowed models
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allowed_models = {"saarika:v2.5", "saaras:v3"}
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allowed_models = {"saarika:v2.5", "saaras:v3", "saaras:v2.5"}
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if model not in allowed_models:
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allowed_models_list = ", ".join(sorted(allowed_models))
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raise ValueError(f"Unsupported model '{model}'. Allowed values: {allowed_models_list}.")
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# Validate that saarika models don't accept prompt parameter
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# Validate model-specific parameter restrictions
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if "saarika" in model.lower():
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# saarika models don't accept prompt or mode
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if params.prompt is not None:
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raise ValueError(
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f"Model '{model}' does not accept prompt parameter. "
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"Prompts are only supported for saaras:v3 model."
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"Prompts are only supported for saaras models (v2.5 and v3)."
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)
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if params.mode is not None:
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raise ValueError(
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f"Model '{model}' does not accept mode parameter. "
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"Mode is only supported for saaras:v3 model."
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)
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elif model.lower() == "saaras:v2.5":
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# saaras:v2.5 supports prompt but not mode
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if params.mode is not None:
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raise ValueError(
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f"Model '{model}' does not accept mode parameter. "
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"Mode is only supported for saaras:v3 model."
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)
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if params.language is not None:
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raise ValueError(
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f"Model '{model}' does not accept language parameter. "
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"saaras:v2.5 (STT-Translate) auto-detects language."
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)
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super().__init__(sample_rate=sample_rate, **kwargs)
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self.set_model_name(model)
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self._api_key = api_key
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self._language_code: Optional[Language] = params.language
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# Set language string - both models require language_code
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# Set language string based on model type
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# - saarika:v2.5: uses language_code or defaults to "unknown"
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# - saaras:v2.5: auto-detects language (no language_code needed)
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# - saaras:v3: uses language_code or defaults to "en-IN"
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if params.language:
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self._language_string = language_to_sarvam_language(params.language)
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elif "saarika" in model.lower():
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self._language_string = "unknown"
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elif model.lower() == "saaras:v2.5":
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self._language_string = None # STT-Translate auto-detects language
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elif model.lower() == "saaras:v3":
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self._language_string = "en-IN"
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else:
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@@ -225,7 +248,17 @@ class SarvamSTTService(STTService):
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Args:
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language: The language to use for speech recognition.
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Raises:
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ValueError: If called on saaras:v2.5 model which auto-detects language.
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"""
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# saaras:v2.5 (STT-Translate) auto-detects language
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if self.model_name.lower() == "saaras:v2.5":
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raise ValueError(
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f"Model '{self.model_name}' does not accept language parameter. "
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"saaras:v2.5 (STT-Translate) auto-detects language."
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)
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logger.info(f"Switching STT language to: [{language}]")
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self._language_code = language
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self._language_string = language_to_sarvam_language(language)
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@@ -233,24 +266,24 @@ class SarvamSTTService(STTService):
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await self._connect()
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async def set_prompt(self, prompt: Optional[str]):
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"""Set the transcription prompt and reconnect.
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"""Set the transcription/translation prompt and reconnect.
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Args:
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prompt: Prompt text to guide transcription style/context.
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prompt: Prompt text to guide transcription/translation style/context.
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Pass None to clear/disable prompt.
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Only applicable to saaras:v3 model.
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Only applicable to saaras models (v2.5 and v3).
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"""
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# saarika models do not accept prompt parameter
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if "saarika" in self.model_name.lower():
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if prompt is not None:
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raise ValueError(
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f"Model '{self.model_name}' does not accept prompt parameter. "
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"Prompts are only supported for saaras:v3 model."
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"Prompts are only supported for saaras models (v2.5 and v3)."
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)
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# If prompt is None and it's saarika, just silently return (no-op)
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return
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logger.info("Updating saaras:v3 prompt.")
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logger.info(f"Updating {self.model_name} prompt.")
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self._prompt = prompt
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await self._disconnect()
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await self._connect()
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@@ -314,8 +347,13 @@ class SarvamSTTService(STTService):
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"sample_rate": self.sample_rate,
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}
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# Both saarika and saaras:v3 use the same speech_to_text_streaming endpoint
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await self._socket_client.transcribe(**method_kwargs)
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# Use appropriate method based on model type
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if self.model_name.lower() == "saaras:v2.5":
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# STT-Translate: auto-detects input language and returns translated text
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await self._socket_client.translate(**method_kwargs)
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else:
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# saarika:v2.5 and saaras:v3 use transcribe
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await self._socket_client.transcribe(**method_kwargs)
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except Exception as e:
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yield ErrorFrame(error=f"Error sending audio to Sarvam: {e}", exception=e)
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@@ -331,15 +369,18 @@ class SarvamSTTService(STTService):
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vad_signals_str = "true" if self._vad_signals else "false"
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high_vad_sensitivity_str = "true" if self._high_vad_sensitivity else "false"
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# Build connection parameters - both models use speech_to_text_streaming
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# Build common connection parameters
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connect_kwargs = {
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"model": self.model_name,
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"language_code": self._language_string, # Required for both models
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"vad_signals": vad_signals_str,
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"high_vad_sensitivity": high_vad_sensitivity_str,
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"sample_rate": str(self.sample_rate),
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}
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# Add language_code for models that require it (not saaras:v2.5 which auto-detects)
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if self._language_string is not None:
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connect_kwargs["language_code"] = self._language_string
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# Add mode for saaras:v3 only
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if self.model_name.lower() == "saaras:v3" and self._mode is not None:
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connect_kwargs["mode"] = self._mode
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@@ -353,17 +394,25 @@ class SarvamSTTService(STTService):
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pass
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return connect_fn(**kwargs)
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# Both saarika and saaras:v3 use the same speech_to_text_streaming endpoint
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self._websocket_context = _connect_with_sdk_headers(
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self._sarvam_client.speech_to_text_streaming.connect,
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**connect_kwargs,
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)
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# Choose the appropriate endpoint based on model
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if self.model_name.lower() == "saaras:v2.5":
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# STT-Translate: auto-detects input language and returns translated text
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self._websocket_context = _connect_with_sdk_headers(
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self._sarvam_client.speech_to_text_translate_streaming.connect,
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**connect_kwargs,
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)
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else:
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# saarika:v2.5 and saaras:v3 use speech_to_text_streaming
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self._websocket_context = _connect_with_sdk_headers(
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self._sarvam_client.speech_to_text_streaming.connect,
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**connect_kwargs,
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)
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# Enter the async context manager
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self._socket_client = await self._websocket_context.__aenter__()
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# Set prompt if provided (only for saaras:v3 model, after connection)
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if self._prompt is not None and self.model_name.lower() == "saaras:v3":
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# Set prompt if provided (only for saaras models, after connection)
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if self._prompt is not None and "saaras" in self.model_name.lower():
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await self._socket_client.set_prompt(self._prompt)
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# Register event handler for incoming messages
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