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API Reference
Audio
Transcriptions

Create transcription

audio.transcriptions.create(**kwargs) -> TranscriptionCreateResponse
POST/audio/transcriptions

Transcribes audio into the input language.

ParametersExpand Collapse
file: FileInput

The audio file object (not file name) to transcribe, in one of these formats: flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm.

model: String | AudioModel

ID of the model to use. The options are gpt-4o-transcribe, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, whisper-1 (which is powered by our open source Whisper V2 model), and gpt-4o-transcribe-diarize.

Accepts one of the following:
String
AudioModel = :"whisper-1" | :"gpt-4o-transcribe" | :"gpt-4o-mini-transcribe" | 2 more
Accepts one of the following:
:"whisper-1"
:"gpt-4o-transcribe"
:"gpt-4o-mini-transcribe"
:"gpt-4o-mini-transcribe-2025-12-15"
:"gpt-4o-transcribe-diarize"
chunking_strategy: :auto | { type, prefix_padding_ms, silence_duration_ms, threshold}

Controls how the audio is cut into chunks. When set to "auto", the server first normalizes loudness and then uses voice activity detection (VAD) to choose boundaries. server_vad object can be provided to tweak VAD detection parameters manually. If unset, the audio is transcribed as a single block. Required when using gpt-4o-transcribe-diarize for inputs longer than 30 seconds.

Accepts one of the following:
ChunkingStrategy = :auto

Automatically set chunking parameters based on the audio. Must be set to "auto".

class VadConfig { type, prefix_padding_ms, silence_duration_ms, threshold }
type: :server_vad

Must be set to server_vad to enable manual chunking using server side VAD.

prefix_padding_ms: Integer

Amount of audio to include before the VAD detected speech (in milliseconds).

silence_duration_ms: Integer

Duration of silence to detect speech stop (in milliseconds). With shorter values the model will respond more quickly, but may jump in on short pauses from the user.

threshold: Float

Sensitivity threshold (0.0 to 1.0) for voice activity detection. A higher threshold will require louder audio to activate the model, and thus might perform better in noisy environments.

include: Array[TranscriptionInclude]

Additional information to include in the transcription response. logprobs will return the log probabilities of the tokens in the response to understand the model's confidence in the transcription. logprobs only works with response_format set to json and only with the models gpt-4o-transcribe, gpt-4o-mini-transcribe, and gpt-4o-mini-transcribe-2025-12-15. This field is not supported when using gpt-4o-transcribe-diarize.

known_speaker_names: Array[String]

Optional list of speaker names that correspond to the audio samples provided in known_speaker_references[]. Each entry should be a short identifier (for example customer or agent). Up to 4 speakers are supported.

known_speaker_references: Array[String]

Optional list of audio samples (as data URLs) that contain known speaker references matching known_speaker_names[]. Each sample must be between 2 and 10 seconds, and can use any of the same input audio formats supported by file.

language: String

The language of the input audio. Supplying the input language in ISO-639-1 (e.g. en) format will improve accuracy and latency.

prompt: String

An optional text to guide the model's style or continue a previous audio segment. The prompt should match the audio language. This field is not supported when using gpt-4o-transcribe-diarize.

response_format: AudioResponseFormat

The format of the output, in one of these options: json, text, srt, verbose_json, vtt, or diarized_json. For gpt-4o-transcribe and gpt-4o-mini-transcribe, the only supported format is json. For gpt-4o-transcribe-diarize, the supported formats are json, text, and diarized_json, with diarized_json required to receive speaker annotations.

Accepts one of the following:
:json
:text
:srt
:verbose_json
:vtt
:diarized_json
stream: false

If set to true, the model response data will be streamed to the client as it is generated using server-sent events. See the Streaming section of the Speech-to-Text guide for more information.

Note: Streaming is not supported for the whisper-1 model and will be ignored.

temperature: Float

The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. If set to 0, the model will use log probability to automatically increase the temperature until certain thresholds are hit.

timestamp_granularities: Array[:word | :segment]

The timestamp granularities to populate for this transcription. response_format must be set verbose_json to use timestamp granularities. Either or both of these options are supported: word, or segment. Note: There is no additional latency for segment timestamps, but generating word timestamps incurs additional latency. This option is not available for gpt-4o-transcribe-diarize.

Accepts one of the following:
:word
:segment
ReturnsExpand Collapse
TranscriptionCreateResponse = Transcription { text, logprobs, usage } | TranscriptionDiarized { duration, segments, task, 2 more } | TranscriptionVerbose { duration, language, text, 3 more }

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

Accepts one of the following:
class Transcription { text, logprobs, usage }

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

text: String

The transcribed text.

logprobs: Array[{ token, bytes, 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: String

The token in the transcription.

bytes: Array[Float]

The bytes of the token.

logprob: Float

The log probability of the token.

usage: { input_tokens, output_tokens, total_tokens, 2 more} | { seconds, type}

Token usage statistics for the request.

Accepts one of the following:
class Tokens { input_tokens, output_tokens, total_tokens, 2 more }

Usage statistics for models billed by token usage.

input_tokens: Integer

Number of input tokens billed for this request.

output_tokens: Integer

Number of output tokens generated.

total_tokens: Integer

Total number of tokens used (input + output).

type: :tokens

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

input_token_details: { audio_tokens, text_tokens}

Details about the input tokens billed for this request.

audio_tokens: Integer

Number of audio tokens billed for this request.

text_tokens: Integer

Number of text tokens billed for this request.

class Duration { seconds, type }

Usage statistics for models billed by audio input duration.

seconds: Float

Duration of the input audio in seconds.

type: :duration

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

class TranscriptionDiarized { duration, segments, task, 2 more }

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: Array[TranscriptionDiarizedSegment { id, end_, speaker, 3 more } ]

Segments of the transcript annotated with timestamps and speaker labels.

id: String

Unique identifier for the segment.

end_: Float

End timestamp of the segment in seconds.

formatfloat
speaker: String

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: String

Transcript text for this segment.

type: :"transcript.text.segment"

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

task: :transcribe

The type of task that was run. Always transcribe.

text: String

The concatenated transcript text for the entire audio input.

usage: { input_tokens, output_tokens, total_tokens, 2 more} | { seconds, type}

Token or duration usage statistics for the request.

Accepts one of the following:
class Tokens { input_tokens, output_tokens, total_tokens, 2 more }

Usage statistics for models billed by token usage.

input_tokens: Integer

Number of input tokens billed for this request.

output_tokens: Integer

Number of output tokens generated.

total_tokens: Integer

Total number of tokens used (input + output).

type: :tokens

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

input_token_details: { audio_tokens, text_tokens}

Details about the input tokens billed for this request.

audio_tokens: Integer

Number of audio tokens billed for this request.

text_tokens: Integer

Number of text tokens billed for this request.

class Duration { seconds, type }

Usage statistics for models billed by audio input duration.

seconds: Float

Duration of the input audio in seconds.

type: :duration

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

class TranscriptionVerbose { duration, language, text, 3 more }

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

duration: Float

The duration of the input audio.

language: String

The language of the input audio.

text: String

The transcribed text.

segments: Array[TranscriptionSegment { id, avg_logprob, compression_ratio, 7 more } ]

Segments of the transcribed text and their corresponding details.

id: Integer

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: Integer

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: String

Text content of the segment.

tokens: Array[Integer]

Array of token IDs for the text content.

usage: { seconds, type}

Usage statistics for models billed by audio input duration.

seconds: Float

Duration of the input audio in seconds.

type: :duration

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

words: Array[TranscriptionWord { end_, start, word } ]

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: String

The text content of the word.

TranscriptionStreamEvent = TranscriptionTextSegmentEvent { id, end_, speaker, 3 more } | TranscriptionTextDeltaEvent { delta, type, logprobs, segment_id } | TranscriptionTextDoneEvent { text, type, logprobs, usage }

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.

Accepts one of the following:
class TranscriptionTextSegmentEvent { id, end_, speaker, 3 more }

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: String

Unique identifier for the segment.

end_: Float

End timestamp of the segment in seconds.

formatfloat
speaker: String

Speaker label for this segment.

start: Float

Start timestamp of the segment in seconds.

formatfloat
text: String

Transcript text for this segment.

type: :"transcript.text.segment"

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

class TranscriptionTextDeltaEvent { delta, type, logprobs, segment_id }

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: String

The text delta that was additionally transcribed.

type: :"transcript.text.delta"

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

logprobs: Array[{ token, bytes, logprob}]

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

token: String

The token that was used to generate the log probability.

bytes: Array[Integer]

The bytes that were used to generate the log probability.

logprob: Float

The log probability of the token.

segment_id: String

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

class TranscriptionTextDoneEvent { text, type, logprobs, usage }

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: String

The text that was transcribed.

type: :"transcript.text.done"

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

logprobs: Array[{ token, bytes, 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: String

The token that was used to generate the log probability.

bytes: Array[Integer]

The bytes that were used to generate the log probability.

logprob: Float

The log probability of the token.

usage: { input_tokens, output_tokens, total_tokens, 2 more}

Usage statistics for models billed by token usage.

input_tokens: Integer

Number of input tokens billed for this request.

output_tokens: Integer

Number of output tokens generated.

total_tokens: Integer

Total number of tokens used (input + output).

type: :tokens

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

input_token_details: { audio_tokens, text_tokens}

Details about the input tokens billed for this request.

audio_tokens: Integer

Number of audio tokens billed for this request.

text_tokens: Integer

Number of text tokens billed for this request.

Create transcription

require "openai"

openai = OpenAI::Client.new(api_key: "My API Key")

transcription = openai.audio.transcriptions.create(file: Pathname(__FILE__), model: :"gpt-4o-transcribe")

puts(transcription)
{
  "text": "text",
  "logprobs": [
    {
      "token": "token",
      "bytes": [
        0
      ],
      "logprob": 0
    }
  ],
  "usage": {
    "input_tokens": 0,
    "output_tokens": 0,
    "total_tokens": 0,
    "type": "tokens",
    "input_token_details": {
      "audio_tokens": 0,
      "text_tokens": 0
    }
  }
}
Returns Examples
{
  "text": "text",
  "logprobs": [
    {
      "token": "token",
      "bytes": [
        0
      ],
      "logprob": 0
    }
  ],
  "usage": {
    "input_tokens": 0,
    "output_tokens": 0,
    "total_tokens": 0,
    "type": "tokens",
    "input_token_details": {
      "audio_tokens": 0,
      "text_tokens": 0
    }
  }
}