We will need to gather the text, and then grab the video to the next step
Parameters:
| Name |
Type |
Description |
Default |
sender
|
|
|
required
|
**kwargs
|
|
The data passed to the signal
|
{}
|
Returns:
Source code in API/orchestrator/chain/completed_openai_speech2text.py
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77 | @receiver(completed_openai_speech2text)
def trigger_completed_openai_speech2text(sender, **kwargs):
"""
We will need to gather the text, and then grab the video to the next step
Args:
sender: The sender of the signal
**kwargs: The data passed to the signal
Returns:
"""
logger.info("OpenAI Speech2Text completed triggerred")
data = kwargs.get("data", {})
track_id = kwargs.get("track_id", None)
task_data = TaskData(**data)
params = task_data.parameters
logger.info(track_id)
# get the text
result_json = task_data.result_json
result_profile = result_json.get("result_profile", {})
text = result_profile.get("text", "")
logger.info(text)
# Currently GPT-4o can only take images, so we will try to locate the relevant images
uid = params.get("uid")
home_id = params.get("home_id")
audio_index = params.get("audio_index")
audio = DataAudio.objects.filter(
home_id=home_id, uid=uid, sequence_index=audio_index
)
if not audio:
logger.error("Audio not found")
return
if len(audio) > 1:
logger.error("Multiple audio found")
return
audio_obj = audio.first()
data_text_obj = DataText.objects.filter(audio=audio_obj).first()
if data_text_obj:
data_text_obj.text = text
data_text_obj.save()
else:
data_text_obj = DataText(
audio=audio_obj,
text=text,
)
data_text_obj.save()
if not hasattr(audio_obj, "multi_modal_conversation"):
DataMultiModalConversation.objects.create(
audio=audio_obj,
)
audio_obj.multi_modal_conversation.text = data_text_obj
audio_obj.multi_modal_conversation.save()
ClusterManager.chain_next(
track_id=track_id,
current_component="completed_openai_speech2text",
next_component_params={"sender": data_text_obj, "data": data_text_obj.__dict__},
user=sender.user,
)
|