chore: initial public snapshot for github upload
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"""
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Translates from OpenAI's `/v1/audio/transcriptions` to Deepgram's `/v1/listen`
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"""
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from typing import List, Optional, Union
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from urllib.parse import urlencode
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from httpx import Headers, Response
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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 DeepgramException
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class DeepgramAudioTranscriptionConfig(BaseAudioTranscriptionConfig):
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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"]
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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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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 DeepgramException(
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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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Processes the audio file input based on its type and returns AudioTranscriptionRequestData.
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For Deepgram, the binary audio data is sent directly as the request body.
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Args:
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audio_file: Can be a file path (str), a tuple (filename, file_content), or binary data (bytes).
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Returns:
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AudioTranscriptionRequestData with binary data and no 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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# Return structured data with binary content and no files
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# For Deepgram, we send binary data directly as request body
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return AudioTranscriptionRequestData(
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data=processed_audio.file_content, files=None
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)
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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 Deepgram 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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# Get the first alternative from the first channel
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first_channel = response_json["results"]["channels"][0]
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first_alternative = first_channel["alternatives"][0]
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# Detect if diarization is active by checking if words have 'speaker' field
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has_diarization = False
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if "words" in first_alternative and len(first_alternative["words"]) > 0:
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has_diarization = "speaker" in first_alternative["words"][0]
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# Extract the transcript based on diarization mode
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if not has_diarization:
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# No diarization: use the standard transcript
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text = first_alternative["transcript"]
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elif "paragraphs" in first_alternative:
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# Diarization with paragraphs: use the pre-formatted diarized transcript
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text = first_alternative["paragraphs"]["transcript"]
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else:
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# Diarization without paragraphs: reconstruct from words
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text = self._reconstruct_diarized_transcript(first_alternative["words"])
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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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# Use detected_language if available, otherwise default to "en"
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detected_language = first_channel.get("detected_language")
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response["language"] = detected_language if detected_language else "en"
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response["duration"] = response_json["metadata"]["duration"]
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# Transform words to match OpenAI format
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if "words" in first_alternative:
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response["words"] = [
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{"word": word["word"], "start": word["start"], "end": word["end"]}
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for word in first_alternative["words"]
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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 Deepgram response: {str(e)}\nResponse: {raw_response.text}"
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)
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def _reconstruct_diarized_transcript(self, words: list) -> str:
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"""
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Reconstructs a diarized transcript from words with speaker information.
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Args:
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words: List of word objects with speaker, word, and optionally punctuated_word
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Returns:
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Formatted transcript with speaker labels
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"""
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if not words:
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return ""
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segments = []
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current_speaker = None
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current_words: list[str] = []
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for word_obj in words:
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speaker = word_obj.get("speaker")
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# Use punctuated_word if available, otherwise fall back to word
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word_text = word_obj.get("punctuated_word", word_obj.get("word", ""))
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if speaker != current_speaker:
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# New speaker: save previous segment and start new one
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if current_words:
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segments.append(
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f"Speaker {current_speaker}: {' '.join(current_words)}"
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)
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current_speaker = speaker
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current_words = [word_text]
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else:
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# Same speaker: add word to current segment
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current_words.append(word_text)
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# Add the last segment
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if current_words:
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segments.append(f"\nSpeaker {current_speaker}: {' '.join(current_words)}\n")
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return "\n".join(segments)
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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("DEEPGRAM_API_BASE") or "https://api.deepgram.com/v1"
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)
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api_base = api_base.rstrip("/") # Remove trailing slash if present
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# Build query parameters including the model
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all_query_params = {"model": model}
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# Add filtered optional parameters
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additional_params = self._build_query_params(optional_params, model)
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all_query_params.update(additional_params)
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# Construct URL with proper query string encoding
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base_url = f"{api_base}/listen"
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query_string = urlencode(all_query_params)
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url = f"{base_url}?{query_string}"
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return url
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def _format_param_value(self, value) -> str:
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"""
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Formats a parameter value for use in query string.
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Args:
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value: The parameter value to format
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Returns:
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Formatted string value
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"""
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if isinstance(value, bool):
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return str(value).lower()
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return str(value)
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def _build_query_params(self, optional_params: dict, model: str) -> dict:
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"""
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Builds a dictionary of query parameters from optional_params.
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Args:
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optional_params: Dictionary of optional parameters
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model: Model name
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Returns:
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Dictionary of filtered and formatted query parameters
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"""
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query_params = {}
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provider_specific_params = self.get_provider_specific_params(
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optional_params=optional_params,
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model=model,
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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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# Format and add the parameter
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formatted_value = self._format_param_value(value)
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query_params[key] = formatted_value
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return query_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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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("DEEPGRAM_API_KEY")
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return {
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"Authorization": f"Token {api_key}",
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}
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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 DeepgramException(BaseLLMException):
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pass
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