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API Reference
Audio
Transcriptions

Create transcription

POST/audio/transcriptions

Transcribes audio into the input language.

Body ParametersForm DataExpand Collapse
file: file

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 or 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:
UnionMember0 = string
AudioModel = "whisper-1" or "gpt-4o-transcribe" or "gpt-4o-mini-transcribe" or 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: optional "auto" or object { 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:
UnionMember0 = "auto"

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

VadConfig = object { 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: optional number

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

silence_duration_ms: optional number

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: optional number

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: optional array of 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: optional array of 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: optional array of 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: optional 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: optional 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: optional 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: optional boolean

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: optional number

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: optional array of "word" or "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
Transcription = object { text, logprobs, usage }

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

text: string

The transcribed text.

logprobs: optional array of object { 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: optional string

The token in the transcription.

bytes: optional array of number

The bytes of the token.

logprob: optional number

The log probability of the token.

usage: optional object { input_tokens, output_tokens, total_tokens, 2 more } or object { seconds, type }

Token usage statistics for the request.

Accepts one of the following:
TokenUsage = object { input_tokens, output_tokens, total_tokens, 2 more }

Usage statistics for models billed by token usage.

input_tokens: number

Number of input tokens billed for this request.

output_tokens: number

Number of output tokens generated.

total_tokens: number

Total number of tokens used (input + output).

type: "tokens"

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

input_token_details: optional object { audio_tokens, text_tokens }

Details about the input tokens billed for this request.

audio_tokens: optional number

Number of audio tokens billed for this request.

text_tokens: optional number

Number of text tokens billed for this request.

DurationUsage = object { seconds, type }

Usage statistics for models billed by audio input duration.

seconds: number

Duration of the input audio in seconds.

type: "duration"

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

TranscriptionDiarized = object { duration, segments, task, 2 more }

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

duration: number

Duration of the input audio in seconds.

segments: array of TranscriptionDiarizedSegment { id, end, speaker, 3 more }

Segments of the transcript annotated with timestamps and speaker labels.

id: string

Unique identifier for the segment.

end: number

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: number

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: optional object { input_tokens, output_tokens, total_tokens, 2 more } or object { seconds, type }

Token or duration usage statistics for the request.

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

Usage statistics for models billed by token usage.

input_tokens: number

Number of input tokens billed for this request.

output_tokens: number

Number of output tokens generated.

total_tokens: number

Total number of tokens used (input + output).

type: "tokens"

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

input_token_details: optional object { audio_tokens, text_tokens }

Details about the input tokens billed for this request.

audio_tokens: optional number

Number of audio tokens billed for this request.

text_tokens: optional number

Number of text tokens billed for this request.

Duration = object { seconds, type }

Usage statistics for models billed by audio input duration.

seconds: number

Duration of the input audio in seconds.

type: "duration"

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

TranscriptionVerbose = object { duration, language, text, 3 more }

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

duration: number

The duration of the input audio.

language: string

The language of the input audio.

text: string

The transcribed text.

segments: optional array of TranscriptionSegment { id, avg_logprob, compression_ratio, 7 more }

Segments of the transcribed text and their corresponding details.

id: number

Unique identifier of the segment.

avg_logprob: number

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

formatfloat
compression_ratio: number

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

formatfloat
end: number

End time of the segment in seconds.

formatfloat
no_speech_prob: number

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: number

Seek offset of the segment.

start: number

Start time of the segment in seconds.

formatfloat
temperature: number

Temperature parameter used for generating the segment.

formatfloat
text: string

Text content of the segment.

tokens: array of number

Array of token IDs for the text content.

usage: optional object { seconds, type }

Usage statistics for models billed by audio input duration.

seconds: number

Duration of the input audio in seconds.

type: "duration"

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

words: optional array of TranscriptionWord { end, start, word }

Extracted words and their corresponding timestamps.

end: number

End time of the word in seconds.

formatfloat
start: number

Start time of the word in seconds.

formatfloat
word: string

The text content of the word.

Create transcription

curl https://api.openai.com/v1/audio/transcriptions \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F file="@/path/to/file/audio.mp3" \
  -F model="gpt-4o-transcribe"

{
  "text": "Imagine the wildest idea that you've ever had, and you're curious about how it might scale to something that's a 100, a 1,000 times bigger. This is a place where you can get to do that.",
  "usage": {
    "type": "tokens",
    "input_tokens": 14,
    "input_token_details": {
      "text_tokens": 0,
      "audio_tokens": 14
    },
    "output_tokens": 45,
    "total_tokens": 59
  }
}

Create transcription

curl https://api.openai.com/v1/audio/transcriptions \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F file="@/path/to/file/meeting.wav" \
  -F model="gpt-4o-transcribe-diarize" \
  -F response_format="diarized_json" \
  -F chunking_strategy=auto \
  -F 'known_speaker_names[]=agent' \
  -F 'known_speaker_references[]=data:audio/wav;base64,AAA...'

{
  "task": "transcribe",
  "duration": 27.4,
  "text": "Agent: Thanks for calling OpenAI support.\nA: Hi, I'm trying to enable diarization.\nAgent: Happy to walk you through the steps.",
  "segments": [
    {
      "type": "transcript.text.segment",
      "id": "seg_001",
      "start": 0.0,
      "end": 4.7,
      "text": "Thanks for calling OpenAI support.",
      "speaker": "agent"
    },
    {
      "type": "transcript.text.segment",
      "id": "seg_002",
      "start": 4.7,
      "end": 11.8,
      "text": "Hi, I'm trying to enable diarization.",
      "speaker": "A"
    },
    {
      "type": "transcript.text.segment",
      "id": "seg_003",
      "start": 12.1,
      "end": 18.5,
      "text": "Happy to walk you through the steps.",
      "speaker": "agent"
    }
  ],
  "usage": {
    "type": "duration",
    "seconds": 27
  }
}

Create transcription

curl https://api.openai.com/v1/audio/transcriptions \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F file="@/path/to/file/audio.mp3" \
  -F model="gpt-4o-mini-transcribe" \
  -F stream=true

data: {"type":"transcript.text.delta","delta":"I","logprobs":[{"token":"I","logprob":-0.00007588794,"bytes":[73]}]}

data: {"type":"transcript.text.delta","delta":" see","logprobs":[{"token":" see","logprob":-3.1281633e-7,"bytes":[32,115,101,101]}]}

data: {"type":"transcript.text.delta","delta":" skies","logprobs":[{"token":" skies","logprob":-2.3392786e-6,"bytes":[32,115,107,105,101,115]}]}

data: {"type":"transcript.text.delta","delta":" of","logprobs":[{"token":" of","logprob":-3.1281633e-7,"bytes":[32,111,102]}]}

data: {"type":"transcript.text.delta","delta":" blue","logprobs":[{"token":" blue","logprob":-1.0280384e-6,"bytes":[32,98,108,117,101]}]}

data: {"type":"transcript.text.delta","delta":" and","logprobs":[{"token":" and","logprob":-0.0005108566,"bytes":[32,97,110,100]}]}

data: {"type":"transcript.text.delta","delta":" clouds","logprobs":[{"token":" clouds","logprob":-1.9361265e-7,"bytes":[32,99,108,111,117,100,115]}]}

data: {"type":"transcript.text.delta","delta":" of","logprobs":[{"token":" of","logprob":-1.9361265e-7,"bytes":[32,111,102]}]}

data: {"type":"transcript.text.delta","delta":" white","logprobs":[{"token":" white","logprob":-7.89631e-7,"bytes":[32,119,104,105,116,101]}]}

data: {"type":"transcript.text.delta","delta":",","logprobs":[{"token":",","logprob":-0.0014890312,"bytes":[44]}]}

data: {"type":"transcript.text.delta","delta":" the","logprobs":[{"token":" the","logprob":-0.0110956915,"bytes":[32,116,104,101]}]}

data: {"type":"transcript.text.delta","delta":" bright","logprobs":[{"token":" bright","logprob":0.0,"bytes":[32,98,114,105,103,104,116]}]}

data: {"type":"transcript.text.delta","delta":" blessed","logprobs":[{"token":" blessed","logprob":-0.000045848617,"bytes":[32,98,108,101,115,115,101,100]}]}

data: {"type":"transcript.text.delta","delta":" days","logprobs":[{"token":" days","logprob":-0.000010802739,"bytes":[32,100,97,121,115]}]}

data: {"type":"transcript.text.delta","delta":",","logprobs":[{"token":",","logprob":-0.00001700133,"bytes":[44]}]}

data: {"type":"transcript.text.delta","delta":" the","logprobs":[{"token":" the","logprob":-0.0000118755715,"bytes":[32,116,104,101]}]}

data: {"type":"transcript.text.delta","delta":" dark","logprobs":[{"token":" dark","logprob":-5.5122365e-7,"bytes":[32,100,97,114,107]}]}

data: {"type":"transcript.text.delta","delta":" sacred","logprobs":[{"token":" sacred","logprob":-5.4385737e-6,"bytes":[32,115,97,99,114,101,100]}]}

data: {"type":"transcript.text.delta","delta":" nights","logprobs":[{"token":" nights","logprob":-4.00813e-6,"bytes":[32,110,105,103,104,116,115]}]}

data: {"type":"transcript.text.delta","delta":",","logprobs":[{"token":",","logprob":-0.0036910512,"bytes":[44]}]}

data: {"type":"transcript.text.delta","delta":" and","logprobs":[{"token":" and","logprob":-0.0031903093,"bytes":[32,97,110,100]}]}

data: {"type":"transcript.text.delta","delta":" I","logprobs":[{"token":" I","logprob":-1.504853e-6,"bytes":[32,73]}]}

data: {"type":"transcript.text.delta","delta":" think","logprobs":[{"token":" think","logprob":-4.3202e-7,"bytes":[32,116,104,105,110,107]}]}

data: {"type":"transcript.text.delta","delta":" to","logprobs":[{"token":" to","logprob":-1.9361265e-7,"bytes":[32,116,111]}]}

data: {"type":"transcript.text.delta","delta":" myself","logprobs":[{"token":" myself","logprob":-1.7432603e-6,"bytes":[32,109,121,115,101,108,102]}]}

data: {"type":"transcript.text.delta","delta":",","logprobs":[{"token":",","logprob":-0.29254505,"bytes":[44]}]}

data: {"type":"transcript.text.delta","delta":" what","logprobs":[{"token":" what","logprob":-0.016815351,"bytes":[32,119,104,97,116]}]}

data: {"type":"transcript.text.delta","delta":" a","logprobs":[{"token":" a","logprob":-3.1281633e-7,"bytes":[32,97]}]}

data: {"type":"transcript.text.delta","delta":" wonderful","logprobs":[{"token":" wonderful","logprob":-2.1008714e-6,"bytes":[32,119,111,110,100,101,114,102,117,108]}]}

data: {"type":"transcript.text.delta","delta":" world","logprobs":[{"token":" world","logprob":-8.180258e-6,"bytes":[32,119,111,114,108,100]}]}

data: {"type":"transcript.text.delta","delta":".","logprobs":[{"token":".","logprob":-0.014231676,"bytes":[46]}]}

data: {"type":"transcript.text.done","text":"I see skies of blue and clouds of white, the bright blessed days, the dark sacred nights, and I think to myself, what a wonderful world.","logprobs":[{"token":"I","logprob":-0.00007588794,"bytes":[73]},{"token":" see","logprob":-3.1281633e-7,"bytes":[32,115,101,101]},{"token":" skies","logprob":-2.3392786e-6,"bytes":[32,115,107,105,101,115]},{"token":" of","logprob":-3.1281633e-7,"bytes":[32,111,102]},{"token":" blue","logprob":-1.0280384e-6,"bytes":[32,98,108,117,101]},{"token":" and","logprob":-0.0005108566,"bytes":[32,97,110,100]},{"token":" clouds","logprob":-1.9361265e-7,"bytes":[32,99,108,111,117,100,115]},{"token":" of","logprob":-1.9361265e-7,"bytes":[32,111,102]},{"token":" white","logprob":-7.89631e-7,"bytes":[32,119,104,105,116,101]},{"token":",","logprob":-0.0014890312,"bytes":[44]},{"token":" the","logprob":-0.0110956915,"bytes":[32,116,104,101]},{"token":" bright","logprob":0.0,"bytes":[32,98,114,105,103,104,116]},{"token":" blessed","logprob":-0.000045848617,"bytes":[32,98,108,101,115,115,101,100]},{"token":" days","logprob":-0.000010802739,"bytes":[32,100,97,121,115]},{"token":",","logprob":-0.00001700133,"bytes":[44]},{"token":" the","logprob":-0.0000118755715,"bytes":[32,116,104,101]},{"token":" dark","logprob":-5.5122365e-7,"bytes":[32,100,97,114,107]},{"token":" sacred","logprob":-5.4385737e-6,"bytes":[32,115,97,99,114,101,100]},{"token":" nights","logprob":-4.00813e-6,"bytes":[32,110,105,103,104,116,115]},{"token":",","logprob":-0.0036910512,"bytes":[44]},{"token":" and","logprob":-0.0031903093,"bytes":[32,97,110,100]},{"token":" I","logprob":-1.504853e-6,"bytes":[32,73]},{"token":" think","logprob":-4.3202e-7,"bytes":[32,116,104,105,110,107]},{"token":" to","logprob":-1.9361265e-7,"bytes":[32,116,111]},{"token":" myself","logprob":-1.7432603e-6,"bytes":[32,109,121,115,101,108,102]},{"token":",","logprob":-0.29254505,"bytes":[44]},{"token":" what","logprob":-0.016815351,"bytes":[32,119,104,97,116]},{"token":" a","logprob":-3.1281633e-7,"bytes":[32,97]},{"token":" wonderful","logprob":-2.1008714e-6,"bytes":[32,119,111,110,100,101,114,102,117,108]},{"token":" world","logprob":-8.180258e-6,"bytes":[32,119,111,114,108,100]},{"token":".","logprob":-0.014231676,"bytes":[46]}],"usage":{"input_tokens":14,"input_token_details":{"text_tokens":0,"audio_tokens":14},"output_tokens":45,"total_tokens":59}}

Create transcription

curl https://api.openai.com/v1/audio/transcriptions \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F file="@/path/to/file/audio.mp3" \
  -F "include[]=logprobs" \
  -F model="gpt-4o-transcribe" \
  -F response_format="json"

{
  "text": "Hey, my knee is hurting and I want to see the doctor tomorrow ideally.",
  "logprobs": [
    { "token": "Hey", "logprob": -1.0415299, "bytes": [72, 101, 121] },
    { "token": ",", "logprob": -9.805982e-5, "bytes": [44] },
    { "token": " my", "logprob": -0.00229799, "bytes": [32, 109, 121] },
    {
      "token": " knee",
      "logprob": -4.7159858e-5,
      "bytes": [32, 107, 110, 101, 101]
    },
    { "token": " is", "logprob": -0.043909557, "bytes": [32, 105, 115] },
    {
      "token": " hurting",
      "logprob": -1.1041146e-5,
      "bytes": [32, 104, 117, 114, 116, 105, 110, 103]
    },
    { "token": " and", "logprob": -0.011076359, "bytes": [32, 97, 110, 100] },
    { "token": " I", "logprob": -5.3193703e-6, "bytes": [32, 73] },
    {
      "token": " want",
      "logprob": -0.0017156356,
      "bytes": [32, 119, 97, 110, 116]
    },
    { "token": " to", "logprob": -7.89631e-7, "bytes": [32, 116, 111] },
    { "token": " see", "logprob": -5.5122365e-7, "bytes": [32, 115, 101, 101] },
    { "token": " the", "logprob": -0.0040786397, "bytes": [32, 116, 104, 101] },
    {
      "token": " doctor",
      "logprob": -2.3392786e-6,
      "bytes": [32, 100, 111, 99, 116, 111, 114]
    },
    {
      "token": " tomorrow",
      "logprob": -7.89631e-7,
      "bytes": [32, 116, 111, 109, 111, 114, 114, 111, 119]
    },
    {
      "token": " ideally",
      "logprob": -0.5800861,
      "bytes": [32, 105, 100, 101, 97, 108, 108, 121]
    },
    { "token": ".", "logprob": -0.00011093382, "bytes": [46] }
  ],
  "usage": {
    "type": "tokens",
    "input_tokens": 14,
    "input_token_details": {
      "text_tokens": 0,
      "audio_tokens": 14
    },
    "output_tokens": 45,
    "total_tokens": 59
  }
}

Create transcription

curl https://api.openai.com/v1/audio/transcriptions \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F file="@/path/to/file/audio.mp3" \
  -F "timestamp_granularities[]=word" \
  -F model="whisper-1" \
  -F response_format="verbose_json"

{
  "task": "transcribe",
  "language": "english",
  "duration": 8.470000267028809,
  "text": "The beach was a popular spot on a hot summer day. People were swimming in the ocean, building sandcastles, and playing beach volleyball.",
  "words": [
    {
      "word": "The",
      "start": 0.0,
      "end": 0.23999999463558197
    },
    ...
    {
      "word": "volleyball",
      "start": 7.400000095367432,
      "end": 7.900000095367432
    }
  ],
  "usage": {
    "type": "duration",
    "seconds": 9
  }
}

Create transcription

curl https://api.openai.com/v1/audio/transcriptions \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F file="@/path/to/file/audio.mp3" \
  -F "timestamp_granularities[]=segment" \
  -F model="whisper-1" \
  -F response_format="verbose_json"

{
  "task": "transcribe",
  "language": "english",
  "duration": 8.470000267028809,
  "text": "The beach was a popular spot on a hot summer day. People were swimming in the ocean, building sandcastles, and playing beach volleyball.",
  "segments": [
    {
      "id": 0,
      "seek": 0,
      "start": 0.0,
      "end": 3.319999933242798,
      "text": " The beach was a popular spot on a hot summer day.",
      "tokens": [
        50364, 440, 7534, 390, 257, 3743, 4008, 322, 257, 2368, 4266, 786, 13, 50530
      ],
      "temperature": 0.0,
      "avg_logprob": -0.2860786020755768,
      "compression_ratio": 1.2363636493682861,
      "no_speech_prob": 0.00985979475080967
    },
    ...
  ],
  "usage": {
    "type": "duration",
    "seconds": 9
  }
}
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
    }
  }
}