chore: initial public snapshot for github upload
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"""
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Translates from OpenAI's `/v1/audio/transcriptions` to ElevenLabs's `/v1/speech-to-text`
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"""
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from typing import List, Optional, Union
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from httpx import Headers, Response
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import litellm
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from litellm.litellm_core_utils.audio_utils.utils import process_audio_file
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from litellm.llms.base_llm.chat.transformation import BaseLLMException
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from litellm.secret_managers.main import get_secret_str
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from litellm.types.llms.openai import (
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AllMessageValues,
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OpenAIAudioTranscriptionOptionalParams,
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)
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from litellm.types.utils import FileTypes, TranscriptionResponse
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from ...base_llm.audio_transcription.transformation import (
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AudioTranscriptionRequestData,
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BaseAudioTranscriptionConfig,
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)
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from ..common_utils import ElevenLabsException
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class ElevenLabsAudioTranscriptionConfig(BaseAudioTranscriptionConfig):
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@property
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def custom_llm_provider(self) -> str:
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return litellm.LlmProviders.ELEVENLABS.value
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def get_supported_openai_params(
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self, model: str
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) -> List[OpenAIAudioTranscriptionOptionalParams]:
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return ["language", "temperature"]
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def map_openai_params(
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self,
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non_default_params: dict,
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optional_params: dict,
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model: str,
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drop_params: bool,
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) -> dict:
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supported_params = self.get_supported_openai_params(model)
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for k, v in non_default_params.items():
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if k in supported_params:
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if k == "language":
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# Map OpenAI language format to ElevenLabs language_code
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optional_params["language_code"] = v
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else:
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optional_params[k] = v
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return optional_params
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def get_error_class(
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self, error_message: str, status_code: int, headers: Union[dict, Headers]
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) -> BaseLLMException:
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return ElevenLabsException(
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message=error_message, status_code=status_code, headers=headers
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)
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def transform_audio_transcription_request(
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self,
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model: str,
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audio_file: FileTypes,
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optional_params: dict,
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litellm_params: dict,
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) -> AudioTranscriptionRequestData:
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"""
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Transforms the audio transcription request for ElevenLabs API.
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Returns AudioTranscriptionRequestData with both form data and files.
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Returns:
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AudioTranscriptionRequestData: Structured data with form data and files
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"""
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# Use common utility to process the audio file
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processed_audio = process_audio_file(audio_file)
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# Prepare form data
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form_data = {"model_id": model}
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#########################################################
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# Add OpenAI Compatible Parameters
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#########################################################
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for key, value in optional_params.items():
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if key in self.get_supported_openai_params(model) and value is not None:
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# Convert values to strings for form data, but skip None values
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form_data[key] = str(value)
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#########################################################
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# Add Provider Specific Parameters
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#########################################################
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provider_specific_params = self.get_provider_specific_params(
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model=model,
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optional_params=optional_params,
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openai_params=self.get_supported_openai_params(model),
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)
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for key, value in provider_specific_params.items():
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form_data[key] = str(value)
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#########################################################
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#########################################################
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# Prepare files
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files = {
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"file": (
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processed_audio.filename,
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processed_audio.file_content,
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processed_audio.content_type,
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)
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}
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return AudioTranscriptionRequestData(data=form_data, files=files)
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def transform_audio_transcription_response(
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self,
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raw_response: Response,
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) -> TranscriptionResponse:
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"""
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Transforms the raw response from ElevenLabs to the TranscriptionResponse format
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"""
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try:
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response_json = raw_response.json()
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# Extract the main transcript text
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text = response_json.get("text", "")
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# Create TranscriptionResponse object
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response = TranscriptionResponse(text=text)
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# Add additional metadata matching OpenAI format
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response["task"] = "transcribe"
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response["language"] = response_json.get("language_code", "unknown")
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# Map ElevenLabs words to OpenAI format
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if "words" in response_json:
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response["words"] = []
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for word_data in response_json["words"]:
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# Only include actual words, skip spacing and audio events
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if word_data.get("type") == "word":
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response["words"].append(
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{
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"word": word_data.get("text", ""),
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"start": word_data.get("start", 0),
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"end": word_data.get("end", 0),
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}
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)
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# Store full response in hidden params
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response._hidden_params = response_json
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return response
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except Exception as e:
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raise ValueError(
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f"Error transforming ElevenLabs response: {str(e)}\nResponse: {raw_response.text}"
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)
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def get_complete_url(
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self,
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api_base: Optional[str],
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api_key: Optional[str],
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model: str,
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optional_params: dict,
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litellm_params: dict,
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stream: Optional[bool] = None,
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) -> str:
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if api_base is None:
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api_base = (
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get_secret_str("ELEVENLABS_API_BASE") or "https://api.elevenlabs.io"
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)
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api_base = api_base.rstrip("/") # Remove trailing slash if present
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# ElevenLabs speech-to-text endpoint
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url = f"{api_base}/v1/speech-to-text"
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return url
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def validate_environment(
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self,
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headers: dict,
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model: str,
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messages: List[AllMessageValues],
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optional_params: dict,
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litellm_params: dict,
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api_key: Optional[str] = None,
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api_base: Optional[str] = None,
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) -> dict:
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api_key = api_key or get_secret_str("ELEVENLABS_API_KEY")
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if api_key is None:
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raise ValueError(
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"ElevenLabs API key is required. Set ELEVENLABS_API_KEY environment variable."
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)
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auth_header = {
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"xi-api-key": api_key,
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}
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headers.update(auth_header)
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return headers
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