258 lines
11 KiB
Python
258 lines
11 KiB
Python
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# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
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from __future__ import annotations
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from typing import List, Union, Mapping, cast
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from typing_extensions import Literal
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import httpx
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from ... import _legacy_response
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from ..._types import NOT_GIVEN, Body, Query, Headers, NotGiven, FileTypes
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from ..._utils import (
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extract_files,
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maybe_transform,
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deepcopy_minimal,
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async_maybe_transform,
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)
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from ..._compat import cached_property
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from ..._resource import SyncAPIResource, AsyncAPIResource
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from ..._response import to_streamed_response_wrapper, async_to_streamed_response_wrapper
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from ...types.audio import transcription_create_params
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from ..._base_client import (
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make_request_options,
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)
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from ...types.audio.transcription import Transcription
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__all__ = ["Transcriptions", "AsyncTranscriptions"]
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class Transcriptions(SyncAPIResource):
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@cached_property
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def with_raw_response(self) -> TranscriptionsWithRawResponse:
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return TranscriptionsWithRawResponse(self)
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@cached_property
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def with_streaming_response(self) -> TranscriptionsWithStreamingResponse:
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return TranscriptionsWithStreamingResponse(self)
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def create(
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self,
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*,
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file: FileTypes,
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model: Union[str, Literal["whisper-1"]],
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language: str | NotGiven = NOT_GIVEN,
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prompt: str | NotGiven = NOT_GIVEN,
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response_format: Literal["json", "text", "srt", "verbose_json", "vtt"] | NotGiven = NOT_GIVEN,
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temperature: float | NotGiven = NOT_GIVEN,
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timestamp_granularities: List[Literal["word", "segment"]] | NotGiven = NOT_GIVEN,
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# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
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# The extra values given here take precedence over values defined on the client or passed to this method.
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extra_headers: Headers | None = None,
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extra_query: Query | None = None,
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extra_body: Body | None = None,
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timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
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) -> Transcription:
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"""
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Transcribes audio into the input language.
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Args:
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file:
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The audio file object (not file name) to transcribe, in one of these formats:
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flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm.
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model: ID of the model to use. Only `whisper-1` (which is powered by our open source
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Whisper V2 model) is currently available.
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language: The language of the input audio. Supplying the input language in
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[ISO-639-1](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) format will
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improve accuracy and latency.
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prompt: An optional text to guide the model's style or continue a previous audio
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segment. The
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[prompt](https://platform.openai.com/docs/guides/speech-to-text/prompting)
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should match the audio language.
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response_format: The format of the transcript output, in one of these options: `json`, `text`,
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`srt`, `verbose_json`, or `vtt`.
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temperature: The sampling temperature, between 0 and 1. Higher values like 0.8 will make the
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output more random, while lower values like 0.2 will make it more focused and
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deterministic. If set to 0, the model will use
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[log probability](https://en.wikipedia.org/wiki/Log_probability) to
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automatically increase the temperature until certain thresholds are hit.
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timestamp_granularities: The timestamp granularities to populate for this transcription.
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`response_format` must be set `verbose_json` to use timestamp granularities.
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Either or both of these options are supported: `word`, or `segment`. Note: There
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is no additional latency for segment timestamps, but generating word timestamps
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incurs additional latency.
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extra_headers: Send extra headers
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extra_query: Add additional query parameters to the request
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extra_body: Add additional JSON properties to the request
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timeout: Override the client-level default timeout for this request, in seconds
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"""
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body = deepcopy_minimal(
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{
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"file": file,
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"model": model,
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"language": language,
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"prompt": prompt,
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"response_format": response_format,
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"temperature": temperature,
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"timestamp_granularities": timestamp_granularities,
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}
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)
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files = extract_files(cast(Mapping[str, object], body), paths=[["file"]])
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if files:
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# It should be noted that the actual Content-Type header that will be
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# sent to the server will contain a `boundary` parameter, e.g.
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# multipart/form-data; boundary=---abc--
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extra_headers = {"Content-Type": "multipart/form-data", **(extra_headers or {})}
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return self._post(
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"/audio/transcriptions",
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body=maybe_transform(body, transcription_create_params.TranscriptionCreateParams),
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files=files,
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options=make_request_options(
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extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
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),
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cast_to=Transcription,
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)
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class AsyncTranscriptions(AsyncAPIResource):
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@cached_property
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def with_raw_response(self) -> AsyncTranscriptionsWithRawResponse:
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return AsyncTranscriptionsWithRawResponse(self)
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@cached_property
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def with_streaming_response(self) -> AsyncTranscriptionsWithStreamingResponse:
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return AsyncTranscriptionsWithStreamingResponse(self)
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async def create(
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self,
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*,
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file: FileTypes,
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model: Union[str, Literal["whisper-1"]],
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language: str | NotGiven = NOT_GIVEN,
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prompt: str | NotGiven = NOT_GIVEN,
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response_format: Literal["json", "text", "srt", "verbose_json", "vtt"] | NotGiven = NOT_GIVEN,
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temperature: float | NotGiven = NOT_GIVEN,
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timestamp_granularities: List[Literal["word", "segment"]] | NotGiven = NOT_GIVEN,
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# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
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# The extra values given here take precedence over values defined on the client or passed to this method.
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extra_headers: Headers | None = None,
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extra_query: Query | None = None,
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extra_body: Body | None = None,
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timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
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) -> Transcription:
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"""
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Transcribes audio into the input language.
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Args:
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file:
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The audio file object (not file name) to transcribe, in one of these formats:
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flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm.
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|
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model: ID of the model to use. Only `whisper-1` (which is powered by our open source
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Whisper V2 model) is currently available.
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language: The language of the input audio. Supplying the input language in
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[ISO-639-1](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) format will
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improve accuracy and latency.
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|
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prompt: An optional text to guide the model's style or continue a previous audio
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|
|
segment. The
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[prompt](https://platform.openai.com/docs/guides/speech-to-text/prompting)
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should match the audio language.
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response_format: The format of the transcript output, in one of these options: `json`, `text`,
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`srt`, `verbose_json`, or `vtt`.
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|
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temperature: The sampling temperature, between 0 and 1. Higher values like 0.8 will make the
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output more random, while lower values like 0.2 will make it more focused and
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deterministic. If set to 0, the model will use
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[log probability](https://en.wikipedia.org/wiki/Log_probability) to
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automatically increase the temperature until certain thresholds are hit.
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timestamp_granularities: The timestamp granularities to populate for this transcription.
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`response_format` must be set `verbose_json` to use timestamp granularities.
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Either or both of these options are supported: `word`, or `segment`. Note: There
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is no additional latency for segment timestamps, but generating word timestamps
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incurs additional latency.
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extra_headers: Send extra headers
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extra_query: Add additional query parameters to the request
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extra_body: Add additional JSON properties to the request
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timeout: Override the client-level default timeout for this request, in seconds
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"""
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body = deepcopy_minimal(
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{
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"file": file,
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"model": model,
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"language": language,
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"prompt": prompt,
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"response_format": response_format,
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"temperature": temperature,
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"timestamp_granularities": timestamp_granularities,
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}
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)
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files = extract_files(cast(Mapping[str, object], body), paths=[["file"]])
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if files:
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# It should be noted that the actual Content-Type header that will be
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# sent to the server will contain a `boundary` parameter, e.g.
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# multipart/form-data; boundary=---abc--
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extra_headers = {"Content-Type": "multipart/form-data", **(extra_headers or {})}
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return await self._post(
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"/audio/transcriptions",
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body=await async_maybe_transform(body, transcription_create_params.TranscriptionCreateParams),
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files=files,
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options=make_request_options(
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extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
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),
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cast_to=Transcription,
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)
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class TranscriptionsWithRawResponse:
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def __init__(self, transcriptions: Transcriptions) -> None:
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self._transcriptions = transcriptions
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self.create = _legacy_response.to_raw_response_wrapper(
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transcriptions.create,
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)
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class AsyncTranscriptionsWithRawResponse:
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def __init__(self, transcriptions: AsyncTranscriptions) -> None:
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self._transcriptions = transcriptions
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self.create = _legacy_response.async_to_raw_response_wrapper(
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transcriptions.create,
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)
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class TranscriptionsWithStreamingResponse:
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def __init__(self, transcriptions: Transcriptions) -> None:
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self._transcriptions = transcriptions
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self.create = to_streamed_response_wrapper(
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transcriptions.create,
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)
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class AsyncTranscriptionsWithStreamingResponse:
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def __init__(self, transcriptions: AsyncTranscriptions) -> None:
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self._transcriptions = transcriptions
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self.create = async_to_streamed_response_wrapper(
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transcriptions.create,
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)
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