Remove SambaNovaSTTService

SambaNova no longer offers speech-to-text audio models.

examples/foundational/13g-sambanova-transcription.py

@@ -1,121 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-import time
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import Frame, TranscriptionFrame, UserStoppedSpeakingFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.audio.vad_processor import VADProcessor
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.sambanova.stt import SambaNovaSTTService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-STOP_SECS = 2.0
-
-
-class TranscriptionLogger(FrameProcessor):
-    """Measures transcription latency.
-
-    Uses the (intentionally) long STOP_SECS parameter to give the transcription time to finish,
-    then outputs the timing between when the VAD first classified audio input as not-speech and
-    the delivery of the last transcription frame.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self._last_transcription_time = time.time()
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, UserStoppedSpeakingFrame):
-            logger.debug(
-                f"Transcription latency: {(STOP_SECS - (time.time() - self._last_transcription_time)):.2f}"
-            )
-
-        if isinstance(frame, TranscriptionFrame):
-            self._last_transcription_time = time.time()
-
-        # Push all frames through
-        await self.push_frame(frame, direction)
-
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = SambaNovaSTTService(
-        settings=SambaNovaSTTService.Settings(
-            model="Whisper-Large-v3",
-        ),
-        api_key=os.getenv("SAMBANOVA_API_KEY"),
-    )
-
-    tl = TranscriptionLogger()
-    vad_processor = VADProcessor(
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=STOP_SECS))
-    )
-
-    pipeline = Pipeline([transport.input(), vad_processor, stt, tl])
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/14s-function-calling-sambanova.py

@@ -25,9 +25,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.llm_service import FunctionCallParams
 from pipecat.services.sambanova.llm import SambaNovaLLMService
-from pipecat.services.sambanova.stt import SambaNovaSTTService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -60,9 +60,8 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = SambaNovaSTTService(
-        model="Whisper-Large-v3",
-        api_key=os.getenv("SAMBANOVA_API_KEY"),
+    stt = DeepgramSTTService(
+        api_key=os.getenv("DEEPGRAM_API_KEY"),
     )
 
     tts = CartesiaTTSService(