from __future__ import annotations from typing import TYPE_CHECKING, Literal from pydantic import BaseModel, ConfigDict, Field from faster_whisper_server.text_utils import Transcription, canonicalize_word, segments_to_text if TYPE_CHECKING: from collections.abc import Iterable import faster_whisper.transcribe # https://github.com/openai/openai-openapi/blob/master/openapi.yaml#L10909 class TranscriptionWord(BaseModel): start: float end: float word: str probability: float @classmethod def from_segments(cls, segments: Iterable[TranscriptionSegment]) -> list[TranscriptionWord]: words: list[TranscriptionWord] = [] for segment in segments: # NOTE: a temporary "fix" for https://github.com/fedirz/faster-whisper-server/issues/58. # TODO: properly address the issue assert ( segment.words is not None ), "Segment must have words. If you are using an API ensure `timestamp_granularities[]=word` is set" words.extend(segment.words) return words def offset(self, seconds: float) -> None: self.start += seconds self.end += seconds @classmethod def common_prefix(cls, a: list[TranscriptionWord], b: list[TranscriptionWord]) -> list[TranscriptionWord]: i = 0 while i < len(a) and i < len(b) and canonicalize_word(a[i].word) == canonicalize_word(b[i].word): i += 1 return a[:i] # https://github.com/openai/openai-openapi/blob/master/openapi.yaml#L10938 class TranscriptionSegment(BaseModel): id: int seek: int start: float end: float text: str tokens: list[int] temperature: float avg_logprob: float compression_ratio: float no_speech_prob: float words: list[TranscriptionWord] | None @classmethod def from_faster_whisper_segments( cls, segments: Iterable[faster_whisper.transcribe.Segment] ) -> Iterable[TranscriptionSegment]: for segment in segments: yield cls( id=segment.id, seek=segment.seek, start=segment.start, end=segment.end, text=segment.text, tokens=segment.tokens, temperature=segment.temperature or 0, # FIX: hardcoded avg_logprob=segment.avg_logprob, compression_ratio=segment.compression_ratio, no_speech_prob=segment.no_speech_prob, words=[ TranscriptionWord( start=word.start, end=word.end, word=word.word, probability=word.probability, ) for word in segment.words ] if segment.words is not None else None, ) # https://platform.openai.com/docs/api-reference/audio/json-object # https://github.com/openai/openai-openapi/blob/master/openapi.yaml#L10924 class CreateTranscriptionResponseJson(BaseModel): text: str @classmethod def from_segments(cls, segments: list[TranscriptionSegment]) -> CreateTranscriptionResponseJson: return cls(text=segments_to_text(segments)) @classmethod def from_transcription(cls, transcription: Transcription) -> CreateTranscriptionResponseJson: return cls(text=transcription.text) # https://platform.openai.com/docs/api-reference/audio/verbose-json-object # https://github.com/openai/openai-openapi/blob/master/openapi.yaml#L11007 class CreateTranscriptionResponseVerboseJson(BaseModel): task: str = "transcribe" language: str duration: float text: str words: list[TranscriptionWord] | None segments: list[TranscriptionSegment] @classmethod def from_segment( cls, segment: TranscriptionSegment, transcription_info: faster_whisper.transcribe.TranscriptionInfo ) -> CreateTranscriptionResponseVerboseJson: return cls( language=transcription_info.language, duration=segment.end - segment.start, text=segment.text, words=segment.words if transcription_info.transcription_options.word_timestamps else None, segments=[segment], ) @classmethod def from_segments( cls, segments: list[TranscriptionSegment], transcription_info: faster_whisper.transcribe.TranscriptionInfo ) -> CreateTranscriptionResponseVerboseJson: return cls( language=transcription_info.language, duration=transcription_info.duration, text=segments_to_text(segments), segments=segments, words=TranscriptionWord.from_segments(segments) if transcription_info.transcription_options.word_timestamps else None, ) @classmethod def from_transcription(cls, transcription: Transcription) -> CreateTranscriptionResponseVerboseJson: return cls( language="english", # FIX: hardcoded duration=transcription.duration, text=transcription.text, words=transcription.words, segments=[], # FIX: hardcoded ) # https://github.com/openai/openai-openapi/blob/master/openapi.yaml#L8730 class ListModelsResponse(BaseModel): data: list[Model] object: Literal["list"] = "list" # https://github.com/openai/openai-openapi/blob/master/openapi.yaml#L11146 class Model(BaseModel): id: str """The model identifier, which can be referenced in the API endpoints.""" created: int """The Unix timestamp (in seconds) when the model was created.""" object_: Literal["model"] = Field(serialization_alias="object") """The object type, which is always "model".""" owned_by: str """The organization that owns the model.""" language: list[str] = Field(default_factory=list) """List of ISO 639-3 supported by the model. It's possible that the list will be empty. This field is not a part of the OpenAI API spec and is added for convenience.""" # noqa: E501 model_config = ConfigDict( populate_by_name=True, json_schema_extra={ "examples": [ { "id": "Systran/faster-whisper-large-v3", "created": 1700732060, "object": "model", "owned_by": "Systran", }, { "id": "Systran/faster-distil-whisper-large-v3", "created": 1711378296, "object": "model", "owned_by": "Systran", }, { "id": "bofenghuang/whisper-large-v2-cv11-french-ct2", "created": 1687968011, "object": "model", "owned_by": "bofenghuang", }, ] }, ) # https://github.com/openai/openai-openapi/blob/master/openapi.yaml#L10909 TimestampGranularities = list[Literal["segment", "word"]] DEFAULT_TIMESTAMP_GRANULARITIES: TimestampGranularities = ["segment"] TIMESTAMP_GRANULARITIES_COMBINATIONS: list[TimestampGranularities] = [ [], # should be treated as ["segment"]. https://platform.openai.com/docs/api-reference/audio/createTranscription#audio-createtranscription-timestamp_granularities ["segment"], ["word"], ["word", "segment"], ["segment", "word"], # same as ["word", "segment"] but order is different ]