API Reference

Audio

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Transcriptions

Turn audio into text or text into audio.

Create transcription

audio.transcriptions.create(TranscriptionCreateParams**kwargs) -> TranscriptionCreateResponse

POST/audio/transcriptions

ModelsExpand Collapse

class Transcription: …

Represents a transcription response returned by model, based on the provided input.

text: str

The transcribed text.

logprobs: Optional[List[Logprob]]

The log probabilities of the tokens in the transcription. Only returned with the models gpt-4o-transcribe and gpt-4o-mini-transcribe if logprobs is added to the include array.

token: Optional[str]

The token in the transcription.

bytes: Optional[List[float]]

The bytes of the token.

logprob: Optional[float]

The log probability of the token.

usage: Optional[Usage]

Token usage statistics for the request.

One of the following:

class UsageTokens: …

Usage statistics for models billed by token usage.

input_tokens: int

Number of input tokens billed for this request.

output_tokens: int

Number of output tokens generated.

total_tokens: int

Total number of tokens used (input + output).

type: Literal["tokens"]

The type of the usage object. Always tokens for this variant.

input_token_details: Optional[UsageTokensInputTokenDetails]

Details about the input tokens billed for this request.

audio_tokens: Optional[int]

Number of audio tokens billed for this request.

text_tokens: Optional[int]

Number of text tokens billed for this request.

class UsageDuration: …

Usage statistics for models billed by audio input duration.

seconds: float

Duration of the input audio in seconds.

type: Literal["duration"]

The type of the usage object. Always duration for this variant.

class TranscriptionDiarized: …

Represents a diarized transcription response returned by the model, including the combined transcript and speaker-segment annotations.

duration: float

Duration of the input audio in seconds.

segments: List[TranscriptionDiarizedSegment]

Segments of the transcript annotated with timestamps and speaker labels.

id: str

Unique identifier for the segment.

end: float

End timestamp of the segment in seconds.

formatfloat

speaker: str

Speaker label for this segment. When known speakers are provided, the label matches known_speaker_names[]. Otherwise speakers are labeled sequentially using capital letters (A, B, ...).

start: float

Start timestamp of the segment in seconds.

formatfloat

text: str

Transcript text for this segment.

type: Literal["transcript.text.segment"]

The type of the segment. Always transcript.text.segment.

task: Literal["transcribe"]

The type of task that was run. Always transcribe.

text: str

The concatenated transcript text for the entire audio input.

usage: Optional[Usage]

Token or duration usage statistics for the request.

One of the following:

class UsageTokens: …

Usage statistics for models billed by token usage.

input_tokens: int

Number of input tokens billed for this request.

output_tokens: int

Number of output tokens generated.

total_tokens: int

Total number of tokens used (input + output).

type: Literal["tokens"]

The type of the usage object. Always tokens for this variant.

input_token_details: Optional[UsageTokensInputTokenDetails]

Details about the input tokens billed for this request.

audio_tokens: Optional[int]

Number of audio tokens billed for this request.

text_tokens: Optional[int]

Number of text tokens billed for this request.

class UsageDuration: …

Usage statistics for models billed by audio input duration.

seconds: float

Duration of the input audio in seconds.

type: Literal["duration"]

The type of the usage object. Always duration for this variant.

class TranscriptionDiarizedSegment: …

A segment of diarized transcript text with speaker metadata.

id: str

Unique identifier for the segment.

end: float

End timestamp of the segment in seconds.

formatfloat

speaker: str

Speaker label for this segment. When known speakers are provided, the label matches known_speaker_names[]. Otherwise speakers are labeled sequentially using capital letters (A, B, ...).

start: float

Start timestamp of the segment in seconds.

formatfloat

text: str

Transcript text for this segment.

type: Literal["transcript.text.segment"]

The type of the segment. Always transcript.text.segment.

Literal["logprobs"]

class TranscriptionSegment: …

id: int

Unique identifier of the segment.

avg_logprob: float

Average logprob of the segment. If the value is lower than -1, consider the logprobs failed.

formatfloat

compression_ratio: float

Compression ratio of the segment. If the value is greater than 2.4, consider the compression failed.

formatfloat

end: float

End time of the segment in seconds.

formatfloat

no_speech_prob: float

Probability of no speech in the segment. If the value is higher than 1.0 and the avg_logprob is below -1, consider this segment silent.

formatfloat

seek: int

Seek offset of the segment.

start: float

Start time of the segment in seconds.

formatfloat

temperature: float

Temperature parameter used for generating the segment.

formatfloat

text: str

Text content of the segment.

tokens: List[int]

Array of token IDs for the text content.

TranscriptionStreamEvent

Emitted when a diarized transcription returns a completed segment with speaker information. Only emitted when you create a transcription with stream set to true and response_format set to diarized_json.

One of the following:

class TranscriptionTextSegmentEvent: …

Emitted when a diarized transcription returns a completed segment with speaker information. Only emitted when you create a transcription with stream set to true and response_format set to diarized_json.

id: str

Unique identifier for the segment.

end: float

End timestamp of the segment in seconds.

formatfloat

speaker: str

Speaker label for this segment.

start: float

Start timestamp of the segment in seconds.

formatfloat

text: str

Transcript text for this segment.

type: Literal["transcript.text.segment"]

The type of the event. Always transcript.text.segment.

class TranscriptionTextDeltaEvent: …

Emitted when there is an additional text delta. This is also the first event emitted when the transcription starts. Only emitted when you create a transcription with the Stream parameter set to true.

delta: str

The text delta that was additionally transcribed.

type: Literal["transcript.text.delta"]

The type of the event. Always transcript.text.delta.

logprobs: Optional[List[Logprob]]

The log probabilities of the delta. Only included if you create a transcription with the include[] parameter set to logprobs.

token: Optional[str]

The token that was used to generate the log probability.

bytes: Optional[List[int]]

The bytes that were used to generate the log probability.

logprob: Optional[float]

The log probability of the token.

segment_id: Optional[str]

Identifier of the diarized segment that this delta belongs to. Only present when using gpt-4o-transcribe-diarize.

class TranscriptionTextDoneEvent: …

Emitted when the transcription is complete. Contains the complete transcription text. Only emitted when you create a transcription with the Stream parameter set to true.

text: str

The text that was transcribed.

type: Literal["transcript.text.done"]

The type of the event. Always transcript.text.done.

logprobs: Optional[List[Logprob]]

The log probabilities of the individual tokens in the transcription. Only included if you create a transcription with the include[] parameter set to logprobs.

token: Optional[str]

The token that was used to generate the log probability.

bytes: Optional[List[int]]

The bytes that were used to generate the log probability.

logprob: Optional[float]

The log probability of the token.

usage: Optional[Usage]

Usage statistics for models billed by token usage.

input_tokens: int

Number of input tokens billed for this request.

output_tokens: int

Number of output tokens generated.

total_tokens: int

Total number of tokens used (input + output).

type: Literal["tokens"]

The type of the usage object. Always tokens for this variant.

input_token_details: Optional[UsageInputTokenDetails]

Details about the input tokens billed for this request.

audio_tokens: Optional[int]

Number of audio tokens billed for this request.

text_tokens: Optional[int]

Number of text tokens billed for this request.

class TranscriptionTextDeltaEvent: …

Emitted when there is an additional text delta. This is also the first event emitted when the transcription starts. Only emitted when you create a transcription with the Stream parameter set to true.

delta: str

The text delta that was additionally transcribed.

type: Literal["transcript.text.delta"]

The type of the event. Always transcript.text.delta.

logprobs: Optional[List[Logprob]]

The log probabilities of the delta. Only included if you create a transcription with the include[] parameter set to logprobs.

token: Optional[str]

The token that was used to generate the log probability.

bytes: Optional[List[int]]

The bytes that were used to generate the log probability.

logprob: Optional[float]

The log probability of the token.

segment_id: Optional[str]

Identifier of the diarized segment that this delta belongs to. Only present when using gpt-4o-transcribe-diarize.

class TranscriptionTextDoneEvent: …

Emitted when the transcription is complete. Contains the complete transcription text. Only emitted when you create a transcription with the Stream parameter set to true.

text: str

The text that was transcribed.

type: Literal["transcript.text.done"]

The type of the event. Always transcript.text.done.

logprobs: Optional[List[Logprob]]

The log probabilities of the individual tokens in the transcription. Only included if you create a transcription with the include[] parameter set to logprobs.

token: Optional[str]

The token that was used to generate the log probability.

bytes: Optional[List[int]]

The bytes that were used to generate the log probability.

logprob: Optional[float]

The log probability of the token.

usage: Optional[Usage]

Usage statistics for models billed by token usage.

input_tokens: int

Number of input tokens billed for this request.

output_tokens: int

Number of output tokens generated.

total_tokens: int

Total number of tokens used (input + output).

type: Literal["tokens"]

The type of the usage object. Always tokens for this variant.

input_token_details: Optional[UsageInputTokenDetails]

Details about the input tokens billed for this request.

audio_tokens: Optional[int]

Number of audio tokens billed for this request.

text_tokens: Optional[int]

Number of text tokens billed for this request.

class TranscriptionTextSegmentEvent: …

Emitted when a diarized transcription returns a completed segment with speaker information. Only emitted when you create a transcription with stream set to true and response_format set to diarized_json.

id: str

Unique identifier for the segment.

end: float

End timestamp of the segment in seconds.

formatfloat

speaker: str

Speaker label for this segment.

start: float

Start timestamp of the segment in seconds.

formatfloat

text: str

Transcript text for this segment.

type: Literal["transcript.text.segment"]

The type of the event. Always transcript.text.segment.

class TranscriptionVerbose: …

Represents a verbose json transcription response returned by model, based on the provided input.

duration: float

The duration of the input audio.

language: str

The language of the input audio.

text: str

The transcribed text.

segments: Optional[List[TranscriptionSegment]]

Segments of the transcribed text and their corresponding details.

id: int

Unique identifier of the segment.

avg_logprob: float

Average logprob of the segment. If the value is lower than -1, consider the logprobs failed.

formatfloat

compression_ratio: float

Compression ratio of the segment. If the value is greater than 2.4, consider the compression failed.

formatfloat

end: float

End time of the segment in seconds.

formatfloat

no_speech_prob: float

Probability of no speech in the segment. If the value is higher than 1.0 and the avg_logprob is below -1, consider this segment silent.

formatfloat

seek: int

Seek offset of the segment.

start: float

Start time of the segment in seconds.

formatfloat

temperature: float

Temperature parameter used for generating the segment.

formatfloat

text: str

Text content of the segment.

tokens: List[int]

Array of token IDs for the text content.

usage: Optional[Usage]

Usage statistics for models billed by audio input duration.

seconds: float

Duration of the input audio in seconds.

type: Literal["duration"]

The type of the usage object. Always duration for this variant.

words: Optional[List[TranscriptionWord]]

Extracted words and their corresponding timestamps.

end: float

End time of the word in seconds.

formatfloat

start: float

Start time of the word in seconds.

formatfloat

word: str

The text content of the word.

class TranscriptionWord: …

end: float

End time of the word in seconds.

formatfloat

start: float

Start time of the word in seconds.

formatfloat

word: str

The text content of the word.