chore: initial snapshot for gitea/github upload
This commit is contained in:
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@@ -0,0 +1,200 @@
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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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@@ -0,0 +1,5 @@
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from litellm.llms.base_llm.chat.transformation import BaseLLMException
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class ElevenLabsException(BaseLLMException):
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pass
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@@ -0,0 +1,330 @@
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"""
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Elevenlabs Text-to-Speech transformation
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Maps OpenAI TTS spec to Elevenlabs TTS API
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"""
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from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
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from urllib.parse import urlencode
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import httpx
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from httpx import Headers
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import litellm
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from litellm.types.utils import all_litellm_params
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from litellm.llms.base_llm.chat.transformation import BaseLLMException
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from litellm.llms.base_llm.text_to_speech.transformation import (
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BaseTextToSpeechConfig,
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TextToSpeechRequestData,
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)
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from litellm.secret_managers.main import get_secret_str
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from ..common_utils import ElevenLabsException
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if TYPE_CHECKING:
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
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from litellm.types.llms.openai import HttpxBinaryResponseContent
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else:
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LiteLLMLoggingObj = Any
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HttpxBinaryResponseContent = Any
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class ElevenLabsTextToSpeechConfig(BaseTextToSpeechConfig):
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"""
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Configuration for ElevenLabs Text-to-Speech
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Reference: https://elevenlabs.io/docs/api-reference/text-to-speech/convert
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"""
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TTS_BASE_URL = "https://api.elevenlabs.io"
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TTS_ENDPOINT_PATH = "/v1/text-to-speech"
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DEFAULT_OUTPUT_FORMAT = "pcm_44100"
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VOICE_MAPPINGS = {
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"alloy": "21m00Tcm4TlvDq8ikWAM", # Rachel
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"amber": "5Q0t7uMcjvnagumLfvZi", # Paul
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"ash": "AZnzlk1XvdvUeBnXmlld", # Domi
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"august": "D38z5RcWu1voky8WS1ja", # Fin
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"blue": "2EiwWnXFnvU5JabPnv8n", # Clyde
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"coral": "9BWtsMINqrJLrRacOk9x", # Aria
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"lily": "EXAVITQu4vr4xnSDxMaL", # Sarah
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"onyx": "29vD33N1CtxCmqQRPOHJ", # Drew
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"sage": "CwhRBWXzGAHq8TQ4Fs17", # Roger
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"verse": "CYw3kZ02Hs0563khs1Fj", # Dave
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}
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# Response format mappings from OpenAI to ElevenLabs
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FORMAT_MAPPINGS = {
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"mp3": "mp3_44100_128",
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"pcm": "pcm_44100",
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"opus": "opus_48000_128",
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# ElevenLabs does not support WAV, AAC, or FLAC formats.
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}
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ELEVENLABS_QUERY_PARAMS_KEY = "__elevenlabs_query_params__"
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ELEVENLABS_VOICE_ID_KEY = "__elevenlabs_voice_id__"
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def get_supported_openai_params(self, model: str) -> list:
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"""
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ElevenLabs TTS supports these OpenAI parameters
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"""
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return ["voice", "response_format", "speed"]
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def _extract_voice_id(self, voice: str) -> str:
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"""
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Normalize the provided voice information into an ElevenLabs voice_id.
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"""
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normalized_voice = voice.strip()
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mapped_voice = self.VOICE_MAPPINGS.get(normalized_voice.lower())
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return mapped_voice or normalized_voice
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def _resolve_voice_id(
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self,
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voice: Optional[Union[str, Dict[str, Any]]],
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params: Dict[str, Any],
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) -> str:
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"""
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Determine the ElevenLabs voice_id based on provided voice input or parameters.
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"""
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mapped_voice: Optional[str] = None
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if isinstance(voice, str) and voice.strip():
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mapped_voice = self._extract_voice_id(voice)
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elif isinstance(voice, dict):
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for key in ("voice_id", "id", "name"):
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candidate = voice.get(key)
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if isinstance(candidate, str) and candidate.strip():
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mapped_voice = self._extract_voice_id(candidate)
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break
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elif voice is not None:
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mapped_voice = self._extract_voice_id(str(voice))
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if mapped_voice is None:
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voice_override = params.pop("voice_id", None)
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if isinstance(voice_override, str) and voice_override.strip():
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mapped_voice = self._extract_voice_id(voice_override)
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if mapped_voice is None:
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raise ValueError(
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"ElevenLabs voice_id is required. Pass `voice` when calling `litellm.speech()`."
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)
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return mapped_voice
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def map_openai_params(
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self,
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model: str,
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optional_params: Dict,
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voice: Optional[Union[str, Dict]] = None,
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drop_params: bool = False,
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kwargs: Optional[Dict[str, Any]] = None,
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) -> Tuple[Optional[str], Dict]:
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"""
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Map OpenAI parameters to ElevenLabs TTS parameters
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"""
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mapped_params: Dict[str, Any] = {}
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query_params: Dict[str, Any] = {}
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# Work on a copy so we don't mutate the caller's dictionary
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params = dict(optional_params) if optional_params else {}
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passthrough_kwargs: Dict[str, Any] = kwargs if kwargs is not None else {}
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# Extract voice identifier
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mapped_voice = self._resolve_voice_id(voice, params)
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# Response/output format → query parameter
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response_format = params.pop("response_format", None)
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if isinstance(response_format, str):
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mapped_format = self.FORMAT_MAPPINGS.get(response_format, response_format)
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query_params["output_format"] = mapped_format
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# ElevenLabs does not support OpenAI speed directly.
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# Drop it to avoid sending unsupported keys unless caller already provided voice_settings.
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speed = params.pop("speed", None)
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if speed is not None:
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speed_value: Optional[float]
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try:
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speed_value = float(speed)
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except (TypeError, ValueError):
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speed_value = None
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if speed_value is not None:
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if isinstance(params.get("voice_settings"), dict):
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params["voice_settings"]["speed"] = speed_value # type: ignore[index]
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else:
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params["voice_settings"] = {"speed": speed_value}
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# Instructions parameter is OpenAI-specific; omit to prevent API errors.
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params.pop("instructions", None)
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self._add_elevenlabs_specific_params(
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mapped_voice=mapped_voice,
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query_params=query_params,
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mapped_params=mapped_params,
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kwargs=passthrough_kwargs,
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remaining_params=params,
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)
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return mapped_voice, mapped_params
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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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api_key: Optional[str] = None,
|
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api_base: Optional[str] = None,
|
||||
) -> dict:
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"""
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Validate Azure environment and set up authentication headers
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"""
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api_key = (
|
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api_key
|
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or litellm.api_key
|
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or litellm.openai_key
|
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or get_secret_str("ELEVENLABS_API_KEY")
|
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)
|
||||
|
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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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headers.update(
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{
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"xi-api-key": api_key,
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"Content-Type": "application/json",
|
||||
}
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||||
)
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return headers
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def get_error_class(
|
||||
self, error_message: str, status_code: int, headers: Union[dict, Headers]
|
||||
) -> BaseLLMException:
|
||||
return ElevenLabsException(
|
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message=error_message, status_code=status_code, headers=headers
|
||||
)
|
||||
|
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def transform_text_to_speech_request(
|
||||
self,
|
||||
model: str,
|
||||
input: str,
|
||||
voice: Optional[str],
|
||||
optional_params: Dict,
|
||||
litellm_params: Dict,
|
||||
headers: dict,
|
||||
) -> TextToSpeechRequestData:
|
||||
"""
|
||||
Build the ElevenLabs TTS request payload.
|
||||
"""
|
||||
params = dict(optional_params) if optional_params else {}
|
||||
extra_body = params.pop("extra_body", None)
|
||||
|
||||
request_body: Dict[str, Any] = {
|
||||
"text": input,
|
||||
"model_id": model,
|
||||
}
|
||||
|
||||
for key, value in params.items():
|
||||
if value is None:
|
||||
continue
|
||||
request_body[key] = value
|
||||
|
||||
if isinstance(extra_body, dict):
|
||||
for key, value in extra_body.items():
|
||||
if value is None:
|
||||
continue
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||||
request_body[key] = value
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||||
|
||||
return TextToSpeechRequestData(
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||||
dict_body=request_body,
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||||
headers={"Content-Type": "application/json"},
|
||||
)
|
||||
|
||||
def _add_elevenlabs_specific_params(
|
||||
self,
|
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mapped_voice: str,
|
||||
query_params: Dict[str, Any],
|
||||
mapped_params: Dict[str, Any],
|
||||
kwargs: Optional[Dict[str, Any]],
|
||||
remaining_params: Dict[str, Any],
|
||||
) -> None:
|
||||
if kwargs is None:
|
||||
kwargs = {}
|
||||
for key, value in remaining_params.items():
|
||||
if value is None:
|
||||
continue
|
||||
mapped_params[key] = value
|
||||
|
||||
reserved_kwarg_keys = set(all_litellm_params) | {
|
||||
self.ELEVENLABS_QUERY_PARAMS_KEY,
|
||||
self.ELEVENLABS_VOICE_ID_KEY,
|
||||
"voice",
|
||||
"model",
|
||||
"response_format",
|
||||
"output_format",
|
||||
"extra_body",
|
||||
"user",
|
||||
}
|
||||
|
||||
extra_body_from_kwargs = kwargs.pop("extra_body", None)
|
||||
if isinstance(extra_body_from_kwargs, dict):
|
||||
for key, value in extra_body_from_kwargs.items():
|
||||
if value is None:
|
||||
continue
|
||||
mapped_params[key] = value
|
||||
|
||||
for key in list(kwargs.keys()):
|
||||
if key in reserved_kwarg_keys:
|
||||
continue
|
||||
value = kwargs[key]
|
||||
if value is None:
|
||||
continue
|
||||
mapped_params[key] = value
|
||||
kwargs.pop(key, None)
|
||||
|
||||
if query_params:
|
||||
kwargs[self.ELEVENLABS_QUERY_PARAMS_KEY] = query_params
|
||||
else:
|
||||
kwargs.pop(self.ELEVENLABS_QUERY_PARAMS_KEY, None)
|
||||
|
||||
kwargs[self.ELEVENLABS_VOICE_ID_KEY] = mapped_voice
|
||||
|
||||
def transform_text_to_speech_response(
|
||||
self,
|
||||
model: str,
|
||||
raw_response: httpx.Response,
|
||||
logging_obj: LiteLLMLoggingObj,
|
||||
) -> "HttpxBinaryResponseContent":
|
||||
"""
|
||||
Wrap ElevenLabs binary audio response.
|
||||
"""
|
||||
from litellm.types.llms.openai import HttpxBinaryResponseContent
|
||||
|
||||
return HttpxBinaryResponseContent(raw_response)
|
||||
|
||||
def get_complete_url(
|
||||
self,
|
||||
model: str,
|
||||
api_base: Optional[str],
|
||||
litellm_params: dict,
|
||||
) -> str:
|
||||
"""
|
||||
Construct the ElevenLabs endpoint URL, including path voice_id and query params.
|
||||
"""
|
||||
base_url = (
|
||||
api_base or get_secret_str("ELEVENLABS_API_BASE") or self.TTS_BASE_URL
|
||||
)
|
||||
base_url = base_url.rstrip("/")
|
||||
|
||||
voice_id = litellm_params.get(self.ELEVENLABS_VOICE_ID_KEY)
|
||||
if not isinstance(voice_id, str) or not voice_id.strip():
|
||||
raise ValueError(
|
||||
"ElevenLabs voice_id is required. Pass `voice` when calling `litellm.speech()`."
|
||||
)
|
||||
|
||||
url = f"{base_url}{self.TTS_ENDPOINT_PATH}/{voice_id}"
|
||||
|
||||
query_params = litellm_params.get(self.ELEVENLABS_QUERY_PARAMS_KEY, {})
|
||||
if query_params:
|
||||
url = f"{url}?{urlencode(query_params)}"
|
||||
|
||||
return url
|
||||
Reference in New Issue
Block a user