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feat: Refactor code to include language parameter, model_name and use _handle_transcription method

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shreyas-sarvam
2025-10-30 19:01:04 +05:30
parent 35c48a45cf
commit e7b8da7a83
1 changed files with 28 additions and 16 deletions
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44
src/pipecat/services/sarvam/stt.py
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@@ -110,7 +110,6 @@ class SarvamSTTService(STTService):
self.set_model_name(model)
self._api_key = api_key
self._model = model
self._language_code = params.language
self._language_string = (
language_to_sarvam_language(params.language) if params.language else None
@@ -141,15 +140,22 @@ class SarvamSTTService(STTService):
"""
return True
async def set_model(self, model: str):
"""Set the Sarvam model and reconnect.
async def set_language(self, language: Language):
"""Set the recognition language and reconnect.
Args:
model: The Sarvam model name to use.
language: The language to use for speech recognition.
"""
await super().set_model(model)
logger.info(f"Switching STT model to: [{model}]")
self._model = model
# 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()
@@ -199,13 +205,13 @@ class SarvamSTTService(STTService):
audio_base64 = base64.b64encode(audio).decode("utf-8")
# Use appropriate method based on service type
if "saarika" in self._model.lower():
if "saarika" in self.model_name.lower():
# STT service
await self._socket_client.transcribe(
audio=audio_base64, encoding="audio/wav", sample_rate=self.sample_rate
)
else:
# STT-translate service
# STT-translate service - auto-detects input language and returns translated text
await self._socket_client.translate(
audio=audio_base64, encoding="audio/wav", sample_rate=self.sample_rate
)
@@ -222,21 +228,21 @@ class SarvamSTTService(STTService):
try:
# Choose the appropriate service based on model
if "saarika" in self._model.lower():
if "saarika" in self.model_name.lower():
# STT service - requires language_code
self._websocket_context = self._sarvam_client.speech_to_text_streaming.connect(
language_code=self._language_string,
model=self._model,
model=self.model_name,
vad_signals=True,
high_vad_sensitivity=True,
sample_rate=str(self.sample_rate),
input_audio_codec="wav",
)
else:
# STT-translate service - auto-detects language
# STT-translate service - auto-detects input language and returns translated text
self._websocket_context = (
self._sarvam_client.speech_to_text_translate_streaming.connect(
model=self._model,
model=self.model_name,
vad_signals=True,
high_vad_sensitivity=True,
sample_rate=str(self.sample_rate),
@@ -318,14 +324,20 @@ class SarvamSTTService(STTService):
await self.stop_ttfb_metrics()
transcript = message.data.transcript
language_code = message.data.language_code
if language_code is None:
language_code = "hi-IN"
language = self._map_language_code_to_enum(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,