from typing import Dict from django.utils.translation import gettext as _ from langchain_core.messages import HumanMessage from langchain_google_genai import ChatGoogleGenerativeAI from common.config.tokenizer_manage_config import TokenizerManage from models_provider.base_model_provider import MaxKBBaseModel from models_provider.impl.base_stt import BaseSpeechToText def custom_get_token_ids(text: str): tokenizer = TokenizerManage.get_tokenizer() return tokenizer.encode(text) class GeminiSpeechToText(MaxKBBaseModel, BaseSpeechToText): api_key: str model: str def __init__(self, **kwargs): super().__init__(**kwargs) self.api_key = kwargs.get('api_key') @staticmethod def is_cache_model(): return False @staticmethod def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs): optional_params = {} if 'max_tokens' in model_kwargs and model_kwargs['max_tokens'] is not None: optional_params['max_tokens'] = model_kwargs['max_tokens'] if 'temperature' in model_kwargs and model_kwargs['temperature'] is not None: optional_params['temperature'] = model_kwargs['temperature'] return GeminiSpeechToText( model=model_name, api_key=model_credential.get('api_key'), **optional_params, ) def check_auth(self): client = ChatGoogleGenerativeAI( model=self.model, google_api_key=self.api_key ) response_list = client.invoke(_('Hello')) # print(response_list) def speech_to_text(self, audio_file): client = ChatGoogleGenerativeAI( model=self.model, google_api_key=self.api_key ) audio_data = audio_file.read() msg = HumanMessage(content=[ {'type': 'text', 'text': _('convert audio to text')}, {"type": "media", 'mime_type': 'audio/mp3', "data": audio_data} ]) res = client.invoke([msg]) return res.content