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Runtime error
Runtime error
reduce complexity by removing AudioStreamProcessor from StreamingChatService
Browse files- respond_to_prompt_actor.py +4 -5
- streaming_chat_service.py +1 -43
respond_to_prompt_actor.py
CHANGED
@@ -12,8 +12,7 @@ class PromptToLLMActor:
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load_dotenv()
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self.input_queue = input_queue
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self.output_queue = output_queue
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self.
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self.chat_service = StreamingChatService(self.audio_processor, voice_id=voice_id)
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self.cancel_event = None
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async def run(self):
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@@ -40,8 +39,7 @@ class LLMSentanceToSpeechActor:
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load_dotenv()
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self.input_queue = input_queue
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self.output_queue = output_queue
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self.
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self.chat_service = StreamingChatService(self.audio_processor, voice_id=voice_id)
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self.cancel_event = None
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async def run(self):
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@@ -66,13 +64,14 @@ class SpeechToSpeakerActor:
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load_dotenv()
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self.input_queue = input_queue
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self.audio_processor = AudioStreamProcessor()
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self.chat_service = StreamingChatService(
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async def run(self):
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while True:
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audio_chunk = await self.input_queue.get_async()
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# print (f"Got audio chunk {len(audio_chunk)}")
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self.chat_service.enqueue_speech_bytes_to_play([audio_chunk])
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async def cancel(self):
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while not self.input_queue.empty():
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load_dotenv()
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self.input_queue = input_queue
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self.output_queue = output_queue
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self.chat_service = StreamingChatService(voice_id=voice_id)
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self.cancel_event = None
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async def run(self):
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load_dotenv()
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self.input_queue = input_queue
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self.output_queue = output_queue
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self.chat_service = StreamingChatService(voice_id=voice_id)
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self.cancel_event = None
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async def run(self):
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load_dotenv()
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self.input_queue = input_queue
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self.audio_processor = AudioStreamProcessor()
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self.chat_service = StreamingChatService(voice_id=voice_id)
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async def run(self):
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while True:
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audio_chunk = await self.input_queue.get_async()
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# print (f"Got audio chunk {len(audio_chunk)}")
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self.chat_service.enqueue_speech_bytes_to_play([audio_chunk])
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self.audio_processor.add_audio_stream([audio_chunk])
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async def cancel(self):
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while not self.input_queue.empty():
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streaming_chat_service.py
CHANGED
@@ -5,13 +5,11 @@ import os
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import torch
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import openai
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from audio_stream_processor import AudioStreamProcessor
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from speech_service import SpeechService
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class StreamingChatService:
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def __init__(self,
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self._audio_processor = audio_processor
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self._speech_service = SpeechService(voice_id=voice_id)
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self._api = api
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self._device = "cuda:0" if torch.cuda.is_available() else "cpu"
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@@ -106,43 +104,6 @@ I fell off the pink step, and I had an accident.
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if not has_letters and not has_numbers:
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return True
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return False
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def _safe_enqueue_text_to_speak(self, text_to_speak):
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if self.ignore_sentence(text_to_speak):
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return
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stream = self._speech_service.stream(text_to_speak)
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self._audio_processor.add_audio_stream(stream)
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def respond_to(self, prompt):
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self._messages.append({"role": "user", "content": prompt})
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agent_response = ""
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current_sentence = ""
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response = openai.ChatCompletion.create(
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model=self._model_id,
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messages=self._messages,
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temperature=1.0, # use 1.0 for debugging/deteministic results
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stream=True
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)
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for chunk in response:
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chunk_message = chunk['choices'][0]['delta']
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if 'content' in chunk_message:
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chunk_text = chunk_message['content']
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# print(chunk_text)
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current_sentence += chunk_text
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agent_response += chunk_text
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text_to_speak = self._should_we_send_to_voice(current_sentence)
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if text_to_speak:
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self._safe_enqueue_text_to_speak(text_to_speak)
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print(text_to_speak)
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current_sentence = current_sentence[len(text_to_speak):]
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if len(current_sentence) > 0:
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self._safe_enqueue_text_to_speak(current_sentence)
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print(current_sentence)
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self._messages.append({"role": "assistant", "content": agent_response})
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return agent_response
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async def get_responses_as_sentances_async(self, prompt, cancel_event):
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self._messages.append({"role": "user", "content": prompt})
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@@ -190,6 +151,3 @@ I fell off the pink step, and I had an accident.
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if cancel_event.is_set():
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return
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yield chunk
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def enqueue_speech_bytes_to_play(self, speech_bytes):
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self._audio_processor.add_audio_stream(speech_bytes)
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import torch
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import openai
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from speech_service import SpeechService
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class StreamingChatService:
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def __init__(self, api="openai", model_id = "gpt-3.5-turbo", voice_id="Bella"):
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self._speech_service = SpeechService(voice_id=voice_id)
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self._api = api
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self._device = "cuda:0" if torch.cuda.is_available() else "cpu"
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if not has_letters and not has_numbers:
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return True
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return False
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async def get_responses_as_sentances_async(self, prompt, cancel_event):
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self._messages.append({"role": "user", "content": prompt})
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if cancel_event.is_set():
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return
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yield chunk
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