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import uuid
from typing import Any, Coroutine, Optional, Union
from openai import AsyncAzureOpenAI, AzureOpenAI
from pydantic import BaseModel
from litellm.litellm_core_utils.audio_utils.utils import get_audio_file_name
from litellm.types.utils import FileTypes
from litellm.utils import (
TranscriptionResponse,
convert_to_model_response_object,
extract_duration_from_srt_or_vtt,
)
from .azure import AzureChatCompletion
from .common_utils import AzureOpenAIError
class AzureAudioTranscription(AzureChatCompletion):
def audio_transcriptions(
self,
model: str,
audio_file: FileTypes,
optional_params: dict,
logging_obj: Any,
model_response: TranscriptionResponse,
timeout: float,
max_retries: int,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
client=None,
azure_ad_token: Optional[str] = None,
atranscription: bool = False,
litellm_params: Optional[dict] = None,
) -> Union[TranscriptionResponse, Coroutine[Any, Any, TranscriptionResponse]]:
data = {"model": model, "file": audio_file, **optional_params}
if atranscription is True:
return self.async_audio_transcriptions(
audio_file=audio_file,
data=data,
model_response=model_response,
timeout=timeout,
api_key=api_key,
api_base=api_base,
client=client,
max_retries=max_retries,
logging_obj=logging_obj,
model=model,
litellm_params=litellm_params,
)
azure_client = self.get_azure_openai_client(
api_version=api_version,
api_base=api_base,
api_key=api_key,
model=model,
_is_async=False,
client=client,
litellm_params=litellm_params,
)
if not isinstance(azure_client, AzureOpenAI):
raise AzureOpenAIError(
status_code=500,
message="azure_client is not an instance of AzureOpenAI",
)
## LOGGING
logging_obj.pre_call(
input=f"audio_file_{uuid.uuid4()}",
api_key=azure_client.api_key,
additional_args={
"headers": {"Authorization": f"Bearer {azure_client.api_key}"},
"api_base": azure_client._base_url._uri_reference,
"atranscription": True,
"complete_input_dict": data,
},
)
response = azure_client.audio.transcriptions.create(
**data, timeout=timeout # type: ignore
)
if isinstance(response, BaseModel):
stringified_response = response.model_dump()
else:
stringified_response = TranscriptionResponse(text=response).model_dump()
## LOGGING
logging_obj.post_call(
input=get_audio_file_name(audio_file),
api_key=api_key,
additional_args={"complete_input_dict": data},
original_response=stringified_response,
)
hidden_params = {"model": "whisper-1", "custom_llm_provider": "azure"}
final_response: TranscriptionResponse = convert_to_model_response_object(response_object=stringified_response, model_response_object=model_response, hidden_params=hidden_params, response_type="audio_transcription") # type: ignore
return final_response
async def async_audio_transcriptions(
self,
audio_file: FileTypes,
model: str,
data: dict,
model_response: TranscriptionResponse,
timeout: float,
logging_obj: Any,
api_version: Optional[str] = None,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
client=None,
max_retries=None,
litellm_params: Optional[dict] = None,
) -> TranscriptionResponse:
response = None
try:
async_azure_client = self.get_azure_openai_client(
api_version=api_version,
api_base=api_base,
api_key=api_key,
model=model,
_is_async=True,
client=client,
litellm_params=litellm_params,
)
if not isinstance(async_azure_client, AsyncAzureOpenAI):
raise AzureOpenAIError(
status_code=500,
message="async_azure_client is not an instance of AsyncAzureOpenAI",
)
## LOGGING
logging_obj.pre_call(
input=f"audio_file_{uuid.uuid4()}",
api_key=async_azure_client.api_key,
additional_args={
"headers": {
"Authorization": f"Bearer {async_azure_client.api_key}"
},
"api_base": async_azure_client._base_url._uri_reference,
"atranscription": True,
"complete_input_dict": data,
},
)
raw_response = (
await async_azure_client.audio.transcriptions.with_raw_response.create(
**data, timeout=timeout
)
) # type: ignore
headers = dict(raw_response.headers)
response = raw_response.parse()
if isinstance(response, BaseModel):
stringified_response = response.model_dump()
else:
stringified_response = TranscriptionResponse(text=response).model_dump()
duration = extract_duration_from_srt_or_vtt(response)
stringified_response["duration"] = duration
## LOGGING
logging_obj.post_call(
input=get_audio_file_name(audio_file),
api_key=api_key,
additional_args={
"headers": {
"Authorization": f"Bearer {async_azure_client.api_key}"
},
"api_base": async_azure_client._base_url._uri_reference,
"atranscription": True,
"complete_input_dict": data,
},
original_response=stringified_response,
)
hidden_params = {"model": "whisper-1", "custom_llm_provider": "azure"}
response = convert_to_model_response_object(
_response_headers=headers,
response_object=stringified_response,
model_response_object=model_response,
hidden_params=hidden_params,
response_type="audio_transcription",
)
if not isinstance(response, TranscriptionResponse):
raise AzureOpenAIError(
status_code=500,
message="response is not an instance of TranscriptionResponse",
)
return response
except Exception as e:
## LOGGING
logging_obj.post_call(
input=input,
api_key=api_key,
original_response=str(e),
)
raise e
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