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from typing import List, Optional, Union
from httpx import Headers
from litellm.llms.base_llm.audio_transcription.transformation import (
BaseAudioTranscriptionConfig,
)
from litellm.llms.base_llm.chat.transformation import BaseLLMException
from litellm.secret_managers.main import get_secret_str
from litellm.types.llms.openai import (
AllMessageValues,
OpenAIAudioTranscriptionOptionalParams,
)
from litellm.types.utils import FileTypes
from ..common_utils import OpenAIError
class OpenAIWhisperAudioTranscriptionConfig(BaseAudioTranscriptionConfig):
def get_supported_openai_params(
self, model: str
) -> List[OpenAIAudioTranscriptionOptionalParams]:
"""
Get the supported OpenAI params for the `whisper-1` models
"""
return [
"language",
"prompt",
"response_format",
"temperature",
"timestamp_granularities",
]
def map_openai_params(
self,
non_default_params: dict,
optional_params: dict,
model: str,
drop_params: bool,
) -> dict:
"""
Map the OpenAI params to the Whisper params
"""
supported_params = self.get_supported_openai_params(model)
for k, v in non_default_params.items():
if k in supported_params:
optional_params[k] = v
return optional_params
def validate_environment(
self,
headers: dict,
model: str,
messages: List[AllMessageValues],
optional_params: dict,
litellm_params: dict,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
) -> dict:
api_key = api_key or get_secret_str("OPENAI_API_KEY")
auth_header = {
"Authorization": f"Bearer {api_key}",
}
headers.update(auth_header)
return headers
def transform_audio_transcription_request(
self,
model: str,
audio_file: FileTypes,
optional_params: dict,
litellm_params: dict,
) -> dict:
"""
Transform the audio transcription request
"""
data = {"model": model, "file": audio_file, **optional_params}
if "response_format" not in data or (
data["response_format"] == "text" or data["response_format"] == "json"
):
data[
"response_format"
] = "verbose_json" # ensures 'duration' is received - used for cost calculation
return data
def get_error_class(
self, error_message: str, status_code: int, headers: Union[dict, Headers]
) -> BaseLLMException:
return OpenAIError(
status_code=status_code,
message=error_message,
headers=headers,
)
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