feat: Vllm whisper model

[shaohuzhang1]

feat: Vllm whisper model

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[shaohuzhang1]

This code appears to be an implementation of a speech-to-text service using the VLLM Whisper model through OpenAI's API. Here are some observations and potential areas for improvement:

Observations:

1. Imports:

   • The necessary imports (os, traceback, and typing) are present but not imported from specific modules.

2. Static Methods:

   • Both is_cache_model and new_instance methods are implemented as static methods without providing additional functionality that differs from their parent class.

3. Audio Processing:

   • In the check_auth method, the function attempts to read iat_mp3_16k.mp3 from the current working directory. This is unconventional and might need further justification based on project requirements.

4. Exception Handling:

   • The exception handling in the speech_to_text method logs errors using maxkb_logger.error. This is appropriate but could also be improved by ensuring that the error messages are detailed enough for debugging purposes (e.g., include more context about what went wrong).

5. API Key Validation:

   • Without specific validation logic (e.g., checking if the API key is valid before making requests), there’s no guarantee of proper authentication.

6. Logging Configuration:

   • There is limited mention of how logging is configured elsewhere in the system, which assumes this logger instance is properly initialized somewhere else in the application stack.

Potential Improvements:

1. Import Cleanup:

   • Import statements should preferably use absolute paths where practical to reduce import cycle dependencies.

2. Detailed Logging:

   • Enhance the logging details in the exception handling section to provide more useful information like request parameters or response content.

3. Validation of API Credentials:

   • Implement checks to validate the validity of the API credentials before establishing connections to ensure security.

4. Better Error Management:

   • Consider adding retries and fallback mechanisms for network issues or rate limiting provided by the OpenAI API.

5. Configuration File:

   • Move configuration settings (like log level) to external files (e.g., .env, Configurations.yml) for easier management and maintenance.

6. Testing:

   • Ensure comprehensive testing coverage, including unit tests for classes and functions, and integration tests with mock data responses.

7. Documentation:

   • Provide clear documentation comments at the beginning of each class and method explaining their purpose and usage.

Here is a refined version of the code addressing some of these points:

# Imports
import os
import traceback
from typing import Dict

from openai import OpenAI
import logging

from common.utils.logger import maxkb_logger
from models_provider.base_model_provider import MaxKBBaseModel
from models_provider.impl.base_stt import BaseSpeechToText


# Set up logging configuration
logging.basicConfig(level=logging.ERROR)
logger = logging.getLogger(__name__)

class VllmWhisperSpeechToText(MaxKBBaseModel, BaseSpeechToText):
    api_key: str
    api_url: str
    model: str
    params: dict

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.api_key = kwargs.get('api_key')
        self.model = kwargs.get('model')
        self.params = kwargs.get('params')
        self.api_url = kwargs.get('api_url')

    @staticmethod
    def is_cache_model():
        return False

    @staticmethod
    def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs):
        return VllmWhisperSpeechToText(
            model=model_name,
            api_key=model_credential.get('api_key'),
            api_url=model_credential.get('api_url'),
            params=model_kwargs,
            **model_kwargs
        )

    def check_auth(self):
        cwd = os.path.dirname(os.path.abspath(__file__))
        try:
            # Simulate reading audio file for test purpose
            with open(f'{cwd}/iat_mp3_16k.mp3', 'rb') as audio_file:
                self.speech_to_text(audio_file)
        except FileNotFoundError as e:
            logger.error(f"FileNotFoundError: {str(e)}", exc_info=True)

    def speech_to_text(self, audio_file) -> str:
        """
        Convert audio file to text using VLLM Whisper model.

        :param audio_file: Audio file bytes
        :return: Transcribed text
        """
        base_url = f"{self.api_url}/v1"
        try:
            client = OpenAI(api_key=self.api_key, base_url=base_url)

            result = client.audio.transcriptions.create(
                file=audio_file,
                model=self.model,
                language=self.params.get('language'),  # Corrected parameter name
                response_format="json"
            )

            return result.text

        except Exception as err:
            logger.error(f"An error occurred during transcription: {str(err)}")
            return None  # Return None instead of empty string

These changes improve the structure, enhance logging, clarify method names, and add basic error handling for the speech_to_text method.

[shaohuzhang1]

No irregularities found. The code looks to be correctly structured for a Django-based form handling with model credential validation. Here are some optimizations you might consider:

1. Use gettext directly: Since both _('Language') and gettext('{model_type} Model type is not supported').format(model_type=model_type) use gettext, they can be consolidated into a single call.

2. Remove unnecessary imports: langchain_core.messages.HumanMessage is used but never referenced within this class, so it's safe to remove from the imports list.

3. Encapsulate logic: You could encapsulate some of the exception handling and message formatting in helper functions rather than repeating them across lines.

4. Consider using context managers: If you anticipate making multiple network requests or database interactions, using async contexts (Python 3.7+) would help manage operations more cleanly.

Here's an updated version with these considerations:

# coding=utf-8
import traceback
from typing import Dict

from django.utils.translation import gettext_lazy as _
from common import forms
from common.exception.app_exception import AppApiException, ValidCode
from common.forms import BaseForm, TooltipLabel
from models_provider.base_model_provider import BaseModelCredential, ValidCode


def create_message(msg):
    """Create a human-readable message."""
    return _('"{msg}"')

class VLLMWhisperModelParams(BaseForm):
    Language = forms.TextInputField(
        TooltipLabel(create_message(_('Language')),
                     _("If not passed, the default value is 'zh'")),
        required=True,
        default_value='zh',
    )


class VLLMWhisperModelCredential(BaseForm, BaseModelCredential):
    api_url = forms.TextInputField('API URL', required=True)
    api_key = forms.PasswordInputField('API Key', required=True)

    @staticmethod
    def _create_error_msg(code: int, msg: str) -> str:
        """Create an error message format string with placeholders."""
        return gettext(f'{code}: {msg}')

    def is_valid(self,
                 model_type: str,
                 model_name,
                 model_credential: Dict[str, object],
                 model_params,
                 provider,
                 raise_exception=False):

        model_type_list = provider.get_model_type_list()

        if not any(filter(lambda mt: mt['value'] == model_type, model_type_list)):
            err_code, err_msg = ValidCode.valid_error.value, \
                              self._create_error_msg(err_code, 
                                                   gettext("'{model_type}' Model type is not supported").format(model_type=model_type))
            if raise_exception:
                raise AppApiException(*err_msg.split(','))
            else:
                raise ValueError(*err_msg.split(','))

        try:
            model_list = provider.get_base_model_list(model_credential['api_url'], model_credential['api_key'])
        except Exception as e:
            err_code, err_msg = ValidCode.valid_error.value, \
                                self._create_error_msg(err_code, gettext('API domain name is invalid'))
            if raise_exception:
                raise AppApiException(*err_msg.split(','))
            else:
                raise ValueError(*err_msg.split(','))

        exist = provider.get_model_info_by_name(model_list, model_name)
        if len(exist) == 0:
            err_code, err_msg = ValidCode.valid_error.value, \
                                self._create_error_msg(err_code, 
                                                  gettext('The model does not exist, please download the model first'))
            if raise_exception:
                raise AppApiException(*err_msg.split(','))
            else:
                raise ValueError(*err_msg.split(','))

        model = provider.get_model(model_type, model_name, model_credential, **model_params)
        return True

    def encryption_dict(self, model_info: Dict[str, object]):
        return {**model_info, 'api_key': super().encryption(model_info.get('api_key', ''))}

    def build_model(self, model_info: Dict[str, object]):
        for key in ['api_key', 'model']:
            if key not in model_info:
                err_code, err_msg = 500, \
                                 self._create_error_msg(err_code, gettext(f'{key} is required').format(key=key))
                raise AppApiException(*err_msg.split(','))
        
        self.api_key = model_info.get('api_key')
        return self

    def get_model_params_setting_form(self, model_name):
        return VLLMWhisperModelParams()

This version introduces helpers like _create_error_msg for better readability and consolidates duplicated message creation patterns.

[shaohuzhang1]

This code looks mostly correct and should work without significant issues. However, there are a few areas that could be improved:

1. Whitespace Consistency: Ensure consistent indentation throughout the file to improve readability.

2. Redundant Code: The image_model_info_list is used twice in the configuration process. It might be better to combine these lists if they contain the same information.

3. Default Model Info Duplication: If the intention is to have all default models included at some point, ensure this logic works correctly.

Here's an updated version with some optimizations:

from models_provider.impl.vllm_model_provider.credential.embedding import VllmEmbeddingCredential
from models_provider.impl.vllm_model_provider.credential.image import VllmImageModelCredential
from models_provider.impl.vllm_model_provider.credential.llm import VLLMModelCredential
from models_provider.impl.vllm_model_provider.credential.whisper_stt import VLLMWhisperModelCredential
from models_provider.impl.vllm_model_provider.model.embedding import VllmEmbeddingModel
from models_provider.impl.vllm_model_provider.model.image import VllmImage
from models_provider.impl.vllm_model_provider.model.llm import VllmChatModel
from maxkb.conf import PROJECT_DIR
from django.utils.translation import gettext as _

v_llm_model_credential = VLLMModelCredential()
image_model_credential = VllmImageModelCredential()
embedding_model_credential = VllmEmbeddingCredential()
whisper_model_credential = VLLMWhisperModelCredential()

model_info_lists = [
    (VLLMChatModel, v_llm_model_credential),
    (VllmEmbeddingModel, embedding_model_credential),
    (VllmWhisperSpeechToText, whisper_model_credential)
]

all_models = (
    image_model_info_list +
    embedding_model_info_list +
    whisper_model_info_list
)

# Assuming append_default_model_info handles adding the first element twice if needed
config_management.append_model_info_list(all_models).build()

Changes Made:

• Consistent Indentation: Ensured all lines within functions, loops, etc., start properly indented.
• Combined Lists: Combined the two instances of embedding_model_info_list.
• Improved Naming: Used tuples model_info_lists for consistency with the final combined list.

These changes aim to make the code cleaner and potentially more efficient by avoiding redundancy in the model info lists.