"""Sarvam AI Speech-to-Text service implementation. This module provides a streaming Speech-to-Text service using Sarvam AI's WebSocket-based API. It supports real-time transcription with Voice Activity Detection (VAD) and can handle multiple audio formats for Indian language speech recognition. """ import base64 from typing import Optional from loguru import logger from pydantic import BaseModel from pipecat.frames.frames import ( CancelFrame, EndFrame, ErrorFrame, Frame, StartFrame, TranscriptionFrame, UserStartedSpeakingFrame, UserStoppedSpeakingFrame, VADUserStartedSpeakingFrame, VADUserStoppedSpeakingFrame, ) from pipecat.processors.frame_processor import FrameDirection from pipecat.services.sarvam._sdk import sdk_headers from pipecat.services.stt_service import STTService from pipecat.transcriptions.language import Language, resolve_language from pipecat.utils.time import time_now_iso8601 from pipecat.utils.tracing.service_decorators import traced_stt try: from sarvamai import AsyncSarvamAI from sarvamai.core.api_error import ApiError from sarvamai.core.events import EventType except ModuleNotFoundError as e: logger.error(f"Exception: {e}") logger.error("In order to use Sarvam, you need to `pip install pipecat-ai[sarvam]`.") raise Exception(f"Missing module: {e}") def language_to_sarvam_language(language: Language) -> str: """Convert a Language enum to Sarvam's language code format. Args: language: The Language enum value to convert. Returns: The Sarvam language code string. """ # Mapping of pipecat Language enum to Sarvam language codes LANGUAGE_MAP = { Language.BN_IN: "bn-IN", Language.GU_IN: "gu-IN", Language.HI_IN: "hi-IN", Language.KN_IN: "kn-IN", Language.ML_IN: "ml-IN", Language.MR_IN: "mr-IN", Language.TA_IN: "ta-IN", Language.TE_IN: "te-IN", Language.PA_IN: "pa-IN", Language.OR_IN: "od-IN", Language.EN_IN: "en-IN", Language.AS_IN: "as-IN", } return resolve_language(language, LANGUAGE_MAP, use_base_code=False) class SarvamSTTService(STTService): """Sarvam speech-to-text service. Provides real-time speech recognition using Sarvam's WebSocket API. """ class InputParams(BaseModel): """Configuration parameters for Sarvam STT service. Parameters: language: Target language for transcription. Defaults to None (required for saarika models). prompt: Optional prompt to guide translation style/context for STT-Translate models. Only applicable to saaras (STT-Translate) models. Defaults to None. vad_signals: Enable VAD signals in response. Defaults to None. high_vad_sensitivity: Enable high VAD (Voice Activity Detection) sensitivity. Defaults to None. """ language: Optional[Language] = None prompt: Optional[str] = None vad_signals: bool = None high_vad_sensitivity: bool = None def __init__( self, *, api_key: str, model: str = "saarika:v2.5", sample_rate: Optional[int] = None, input_audio_codec: str = "wav", params: Optional[InputParams] = None, **kwargs, ): """Initialize the Sarvam STT service. Args: api_key: Sarvam API key for authentication. model: Sarvam model to use for transcription. sample_rate: Audio sample rate. Defaults to 16000 if not specified. input_audio_codec: Audio codec/format of the input file. Defaults to "wav". params: Configuration parameters for Sarvam STT service. **kwargs: Additional arguments passed to the parent STTService. """ params = params or SarvamSTTService.InputParams() # Validate that saaras models don't accept language parameter if "saaras" in model.lower(): if params.language is not None: raise ValueError( f"Model '{model}' does not accept language parameter. " "STT-Translate models auto-detect language." ) # Validate that saarika models don't accept prompt parameter if "saarika" in model.lower(): if params.prompt is not None: raise ValueError( f"Model '{model}' does not accept prompt parameter. " "Prompts are only supported for STT-Translate models" ) super().__init__(sample_rate=sample_rate, **kwargs) self.set_model_name(model) self._api_key = api_key self._language_code: Optional[Language] = params.language # For saarika models, default to "unknown" if language is not provided if params.language: self._language_string = language_to_sarvam_language(params.language) elif "saarika" in model.lower(): self._language_string = "unknown" else: self._language_string = None self._prompt = params.prompt # Store connection parameters self._vad_signals = params.vad_signals self._high_vad_sensitivity = params.high_vad_sensitivity self._input_audio_codec = input_audio_codec # Initialize Sarvam SDK client self._sdk_headers = sdk_headers() # NOTE: We avoid passing non-standard kwargs here because different sarvamai # versions expose different constructor signatures (static type checkers # complain otherwise). We instead inject headers best-effort below. self._sarvam_client = AsyncSarvamAI(api_subscription_key=api_key) for attr in ("default_headers", "_default_headers", "headers", "_headers"): d = getattr(self._sarvam_client, attr, None) if isinstance(d, dict): d.update(self._sdk_headers) break self._websocket_context = None self._socket_client = None self._receive_task = None logger.info(f"Sarvam STT initialized with SDK headers: {self._sdk_headers}") def language_to_service_language(self, language: Language) -> str: """Convert pipecat Language enum to Sarvam's language code. Args: language: The Language enum value to convert. Returns: The Sarvam language code string. """ return language_to_sarvam_language(language) def can_generate_metrics(self) -> bool: """Check if this service can generate processing metrics. Returns: True, as Sarvam service supports metrics generation. """ return True async def process_frame(self, frame: Frame, direction: FrameDirection): """Process incoming frames. Handles VAD frames for TTFB tracking when using Pipecat's VAD instead of Sarvam's built-in VAD. """ await super().process_frame(frame, direction) # Only handle VAD frames when not using Sarvam's VAD signals if not self._vad_signals: if isinstance(frame, VADUserStartedSpeakingFrame): await self._start_metrics() elif isinstance(frame, VADUserStoppedSpeakingFrame): if self._socket_client: await self._socket_client.flush() async def set_language(self, language: Language): """Set the recognition language and reconnect. Args: language: The language to use for speech recognition. """ # saaras models do not accept a language parameter if "saaras" in self.model_name.lower(): raise ValueError( f"Model '{self.model_name}' (saaras) does not accept language parameter. " "saaras models auto-detect language." ) logger.info(f"Switching STT language to: [{language}]") self._language_code = language self._language_string = language_to_sarvam_language(language) await self._disconnect() await self._connect() async def set_prompt(self, prompt: Optional[str]): """Set the translation prompt and reconnect. Args: prompt: Prompt text to guide translation style/context. Pass None to clear/disable prompt. Only applicable to STT-Translate models, not STT models. """ # saarika models do not accept prompt parameter if "saarika" in self.model_name.lower(): if prompt is not None: raise ValueError( f"Model '{self.model_name}' does not accept prompt parameter. " "Prompts are only supported for STT-Translate models." ) # If prompt is None and it's saarika, just silently return (no-op) return logger.info("Updating STT-Translate prompt.") self._prompt = prompt await self._disconnect() await self._connect() async def start(self, frame: StartFrame): """Start the Sarvam STT service. Args: frame: The start frame containing initialization parameters. """ await super().start(frame) await self._connect() async def stop(self, frame: EndFrame): """Stop the Sarvam STT service. Args: frame: The end frame. """ await super().stop(frame) await self._disconnect() async def cancel(self, frame: CancelFrame): """Cancel the Sarvam STT service. Args: frame: The cancel frame. """ await super().cancel(frame) await self._disconnect() async def run_stt(self, audio: bytes): """Send audio data to Sarvam for transcription. Args: audio: Raw audio bytes to transcribe. Yields: Frame: None (transcription results come via WebSocket callbacks). """ if not self._socket_client: logger.warning("WebSocket not connected, cannot process audio") yield None return try: # Convert audio bytes to base64 for Sarvam API audio_base64 = base64.b64encode(audio).decode("utf-8") # Convert input_audio_codec to encoding format (prepend "audio/" if needed) encoding = ( self._input_audio_codec if self._input_audio_codec.startswith("audio/") else f"audio/{self._input_audio_codec}" ) # Build method arguments method_kwargs = { "audio": audio_base64, "encoding": encoding, "sample_rate": self.sample_rate, } # Use appropriate method based on service type if "saarika" in self.model_name.lower(): # STT service await self._socket_client.transcribe(**method_kwargs) else: # STT-Translate service - auto-detects input language and returns translated text await self._socket_client.translate(**method_kwargs) except Exception as e: yield ErrorFrame(error=f"Error sending audio to Sarvam: {e}", exception=e) yield None async def _connect(self): """Connect to Sarvam WebSocket API using the SDK.""" logger.debug("Connecting to Sarvam") try: # Convert boolean parameters to string for SDK vad_signals_str = "true" if self._vad_signals else "false" high_vad_sensitivity_str = "true" if self._high_vad_sensitivity else "false" # Build common connection parameters connect_kwargs = { "model": self.model_name, "vad_signals": vad_signals_str, "high_vad_sensitivity": high_vad_sensitivity_str, "input_audio_codec": self._input_audio_codec, "sample_rate": str(self.sample_rate), } def _connect_with_sdk_headers(connect_fn, **kwargs): # Different SDK versions may use different kwarg names. for header_kw in ("headers", "additional_headers", "extra_headers"): try: return connect_fn(**kwargs, **{header_kw: self._sdk_headers}) except TypeError: pass return connect_fn(**kwargs) # Choose the appropriate service based on model if "saarika" in self.model_name.lower(): # STT service - requires language_code connect_kwargs["language_code"] = self._language_string self._websocket_context = _connect_with_sdk_headers( self._sarvam_client.speech_to_text_streaming.connect, **connect_kwargs, ) else: # STT-Translate service - auto-detects input language and returns translated text self._websocket_context = _connect_with_sdk_headers( self._sarvam_client.speech_to_text_translate_streaming.connect, **connect_kwargs, ) # Enter the async context manager self._socket_client = await self._websocket_context.__aenter__() # Set prompt if provided (only for STT-Translate models, after connection) if self._prompt is not None and "saaras" in self.model_name.lower(): await self._socket_client.set_prompt(self._prompt) # Register event handler for incoming messages def _message_handler(message): """Wrapper to handle async response handler.""" # Use Pipecat's built-in task management self.create_task(self._handle_message(message)) self._socket_client.on(EventType.MESSAGE, _message_handler) # Start receive task using Pipecat's task management self._receive_task = self.create_task(self._receive_task_handler()) logger.info("Connected to Sarvam successfully") except ApiError as e: await self.push_error(error_msg=f"Sarvam API error: {e}", exception=e) except Exception as e: self._socket_client = None self._websocket_context = None await self.push_error(error_msg=f"Failed to connect to Sarvam: {e}", exception=e) async def _disconnect(self): """Disconnect from Sarvam WebSocket API using SDK.""" if self._receive_task: await self.cancel_task(self._receive_task) self._receive_task = None if self._websocket_context and self._socket_client: try: # Exit the async context manager await self._websocket_context.__aexit__(None, None, None) except Exception as e: await self.push_error( error_msg=f"Error closing WebSocket connection: {e}", exception=e ) finally: logger.debug("Disconnected from Sarvam WebSocket") self._socket_client = None self._websocket_context = None async def _receive_task_handler(self): """Handle incoming messages from Sarvam WebSocket. This task wraps the SDK's start_listening() method which processes messages via the registered event handler callback. """ if not self._socket_client: return try: # Start listening for messages from the Sarvam SDK # Messages will be handled via the _message_handler callback await self._socket_client.start_listening() except Exception as e: await self.push_error(error_msg=f"Sarvam receive task error: {e}", exception=e) async def _handle_message(self, message): """Handle incoming WebSocket message from Sarvam SDK. Processes transcription data and VAD events from the Sarvam service. Args: message: The parsed response object from Sarvam WebSocket. """ logger.debug(f"Received response: {message}") try: if message.type == "events": # VAD event signal = message.data.signal_type timestamp = message.data.occured_at logger.debug(f"VAD Signal: {signal}, Occurred at: {timestamp}") if signal == "START_SPEECH": await self._start_metrics() logger.debug("User started speaking") await self._call_event_handler("on_speech_started") await self.broadcast_frame(UserStartedSpeakingFrame) await self.push_interruption_task_frame_and_wait() elif signal == "END_SPEECH": logger.debug("User stopped speaking") await self._call_event_handler("on_speech_stopped") await self.broadcast_frame(UserStoppedSpeakingFrame) elif message.type == "data": transcript = message.data.transcript language_code = message.data.language_code # Prefer language from message (auto-detected for translate models). Fallback to configured. if language_code: language = self._map_language_code_to_enum(language_code) elif self._language_string: language = self._map_language_code_to_enum(self._language_string) else: language = Language.HI_IN # Emit utterance end event await self._call_event_handler("on_utterance_end") if transcript and transcript.strip(): # Record tracing for this transcription event await self._handle_transcription(transcript, True, language) await self.push_frame( TranscriptionFrame( transcript, self._user_id, time_now_iso8601(), language, result=(message.dict() if hasattr(message, "dict") else str(message)), ) ) await self.stop_processing_metrics() except Exception as e: await self.push_error(error_msg=f"Failed to handle message: {e}", exception=e) await self.stop_all_metrics() @traced_stt async def _handle_transcription( self, transcript: str, is_final: bool, language: Optional[Language] = None ): """Handle a transcription result with tracing. This method is decorated with @traced_stt for observability. """ pass def _map_language_code_to_enum(self, language_code: str) -> Language: """Map Sarvam language code to pipecat Language enum.""" mapping = { "bn-IN": Language.BN_IN, "gu-IN": Language.GU_IN, "hi-IN": Language.HI_IN, "kn-IN": Language.KN_IN, "ml-IN": Language.ML_IN, "mr-IN": Language.MR_IN, "ta-IN": Language.TA_IN, "te-IN": Language.TE_IN, "pa-IN": Language.PA_IN, "od-IN": Language.OR_IN, "en-US": Language.EN_US, "en-IN": Language.EN_IN, "as-IN": Language.AS_IN, } return mapping.get(language_code, Language.HI_IN) async def _start_metrics(self): """Start processing metrics collection.""" await self.start_processing_metrics()