OpenAI 创建音频转录接口

创建音频转录

post /audio/transcriptions

将音频转录为其原始语言的文本。

返回 json、diarized_json 或 verbose_json 格式的转录对象， 或 text、srt、vtt 格式的纯文本，也可返回 转录事件流。支持的格式取决于模型。

请求体参数

内容类型为 multipart/form-data。

• file: string

待转录的音频文件对象，须提供文件内容。支持 flac、mp3、mp4、mpeg、mpga、m4a、ogg、wav 或 webm。 请求须提供足够的格式元数据以识别文件，建议使用带扩展名的文件名和合适的内容类型。

• model: string or AudioModel

要使用的模型 ID，可选 gpt-transcribe、gpt-4o-transcribe、gpt-4o-mini-transcribe、gpt-4o-mini-transcribe-2025-12-15、基于开源 Whisper V2 的 whisper-1，以及 gpt-4o-transcribe-diarize。

• string

• AudioModel = "whisper-1" or "gpt-transcribe" or "gpt-4o-transcribe" or 3 more

• "whisper-1"

• "gpt-transcribe"

• "gpt-4o-transcribe"

• "gpt-4o-mini-transcribe"

• "gpt-4o-mini-transcribe-2025-12-15"

• "gpt-4o-transcribe-diarize"

• chunking_strategy: optional "auto" or VadConfig { type, prefix_padding_ms, silence_duration_ms, threshold } or null

控制音频分块方式。设为 "auto" 时，服务器先归一化响度，再用语音活动检测（VAD）确定边界。可提供 server_vad 对象手动调整 VAD 参数。未设置时，将整段音频作为单个块转录。使用 gpt-4o-transcribe-diarize 处理超过 30 秒的输入时，此项为必填。

• "auto"

根据音频自动设置分块参数，值必须为 "auto"。

• "auto"

• VadConfig object { type, prefix_padding_ms, silence_duration_ms, threshold }

• type: "server_vad"

须设为 server_vad 才能通过服务端 VAD 手动配置分块。

• "server_vad"

• prefix_padding_ms: optional integer (default: 300)

在 VAD 检测到语音之前一并保留的音频时长，单位为 毫秒。

• silence_duration_ms: optional integer (default: 200)

用于判断语音结束的静音时长，单位为毫秒。 较短的时长能使模型更快响应， 但可能在用户短暂停顿时提前插入响应。

• threshold: optional number (default: 0.5)

语音活动检测的敏感度阈值，范围为 0.0–1.0。 较高阈值要求更大的音量才能激活模型， 因此可能更适合嘈杂环境。

• include: optional array of TranscriptionInclude

转录响应中需要包含的额外信息。 logprobs 会返回响应中各词元的对数概率， 以便了解模型对转录结果的置信程度。 logprobs 仅在 response_format 设为 json 时有效，且仅支持 gpt-4o-transcribe、gpt-4o-mini-transcribe 和 gpt-4o-mini-transcribe-2025-12-15。使用 gpt-4o-transcribe-diarize 时不支持此字段。

• "logprobs"

• keywords: optional array of string

用于引导输入音频转录的词语或短语，gpt-transcribe 支持。

• known_speaker_names: optional array of string

可选的说话人名称列表，与 known_speaker_references[] 中的音频样本对应。每项应为简短标识，例如 customer 或 agent。最多支持 4 位说话人。

maxItems: 4

• known_speaker_references: optional array of string

可选的已知说话人音频样本列表，采用 data URL 形式，与 known_speaker_names[] 对应。每个样本须为 2–10 秒，可使用 file 支持的任一输入音频格式。

maxItems: 4

• language: optional string

输入音频的语言。以 ISO-639-1 格式（例如 en）提供语言，有助于改善准确率和延迟。

• languages: optional array of string

输入音频可能使用的语言，采用 ISO-639-1 格式。gpt-transcribe 支持。

minItems: 1

• prompt: optional string

可选文本，用于引导模型的表达风格或续接此前音频片段。提示词应与音频语言一致。使用 gpt-4o-transcribe-diarize 时不支持此字段。

• response_format: optional AudioResponseFormat (default: "json")

输出格式，可选 json、text、srt、verbose_json、vtt 或 diarized_json。gpt-4o-transcribe 和 gpt-4o-mini-transcribe 仅支持 json。gpt-4o-transcribe-diarize 支持 json、text 和 diarized_json；如需说话人标注，须使用 diarized_json。

• "json"

• "text"

• "srt"

• "verbose_json"

• "vtt"

• "diarized_json"

• stream: optional boolean or null (default: false)

设为 true 时，模型响应数据会在生成过程中通过 服务器发送事件（SSE）流式发送给客户端。 更多信息请参阅语音转文字指南的流式传输章节 中的说明。

注意：whisper-1 不支持流式传输，将忽略此参数。

• temperature: optional number (default: 0)

采样温度，范围为 0–1。较高值（如 0.8）使输出更随机，较低值（如 0.2）使输出更集中、更确定。设为 0 时，模型根据对数概率自动提高温度，直到满足相应阈值。

• timestamp_granularities: optional array of "word" or "segment" (default: ["segment"])

此次转录所需的时间戳粒度。使用时须将 response_format 设为 verbose_json。可选择 word、segment，也可同时选择。分段时间戳不会增加额外延迟，逐词时间戳会增加延迟。 gpt-4o-transcribe-diarize 不支持此选项。

• "word"

• "segment"

返回值

• Transcription object { text, languages, logprobs, usage }

表示模型根据输入返回的转录响应。

• text: string

转录文本。

• languages: optional array of TranscriptionLanguage

音频中检测到的语言，由 gpt-transcribe 返回。空数组表示未能可靠识别语言。

• code: string

音频中检测到的语言代码。

• logprobs: optional array of object { token, bytes, logprob }

转录词元的对数概率。仅在将 logprobs 加入 include 数组并使用 gpt-4o-transcribe 或 gpt-4o-mini-transcribe 时返回。

• token: optional string

转录中的词元。

• bytes: optional array of number

词元的字节。

• logprob: optional number

词元的对数概率。

• usage: optional Tokens { input_tokens, output_tokens, total_tokens, 2 more } or Duration { seconds, type }

请求的词元用量统计。

• Tokens object { input_tokens, output_tokens, total_tokens, 2 more }

按词元计费的模型用量统计。

• input_tokens: integer

此请求计费的输入词元数量。

• output_tokens: integer

生成的输出词元数量。

• total_tokens: integer

使用的词元总数，为输入与输出之和。

• type: "tokens"

用量对象的类型，此变体固定为 tokens。

• "tokens"

• input_token_details: optional object { audio_tokens, text_tokens }

此请求计费输入词元的明细。

• audio_tokens: optional integer

此请求计费的音频词元数量。

• text_tokens: optional integer

此请求计费的文本词元数量。

• Duration object { seconds, type }

按音频输入时长计费的模型用量统计。

• seconds: number

输入音频时长，单位为秒。

format: double

• type: "duration"

用量对象的类型，此变体固定为 duration。

• "duration"

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

表示模型返回的说话人分离转录响应，包含合并文本及带有说话人标注的分段。

• duration: number

输入音频时长，单位为秒。

format: double

• segments: array of TranscriptionDiarizedSegment

带时间戳和说话人标签的转录分段。

• id: string

分段的唯一标识。

• end: number

分段结束时间戳，单位为秒。

format: double

• speaker: string

分段的说话人标签。提供已知说话人时，标签与 known_speaker_names[] 对应；否则按顺序使用大写字母标记，例如 A、B 等。

• start: number

分段开始时间戳，单位为秒。

format: double

• text: string

此分段的转录文本。

• type: "transcript.text.segment"

分段类型，固定为 transcript.text.segment。

• "transcript.text.segment"

• task: "transcribe"

执行的任务类型，固定为 transcribe。

• "transcribe"

• text: string

整段输入音频的拼接转录文本。

• usage: optional Tokens { input_tokens, output_tokens, total_tokens, 2 more } or Duration { seconds, type }

请求的词元或时长用量统计。

• Tokens object { input_tokens, output_tokens, total_tokens, 2 more }

按词元计费的模型用量统计。

• input_tokens: integer

此请求计费的输入词元数量。

• output_tokens: integer

生成的输出词元数量。

• total_tokens: integer

使用的词元总数，为输入与输出之和。

• type: "tokens"

用量对象的类型，此变体固定为 tokens。

• "tokens"

• input_token_details: optional object { audio_tokens, text_tokens }

此请求计费输入词元的明细。

• audio_tokens: optional integer

此请求计费的音频词元数量。

• text_tokens: optional integer

此请求计费的文本词元数量。

• Duration object { seconds, type }

按音频输入时长计费的模型用量统计。

• seconds: number

输入音频时长，单位为秒。

format: double

• type: "duration"

用量对象的类型，此变体固定为 duration。

• "duration"

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

表示模型根据输入返回的详细 JSON 转录响应。

• duration: number

输入音频的时长。

format: double

• language: string

输入音频的语言。

• text: string

转录文本。

• segments: optional array of TranscriptionSegment

转录文本的分段及其详情。

• id: integer

分段的唯一标识。

• avg_logprob: number

分段的平均对数概率。小于 -1 时，可判定未通过对数概率检查。

format: float

• compression_ratio: number

分段压缩率，大于 2.4 时可判定未通过压缩率检查。

format: float

• end: number

分段结束时间，单位为秒。

format: double

• no_speech_prob: number

分段不含语音的概率。官方原文给出的判定条件为此值大于 1.0 且 avg_logprob 小于 -1 时，将分段视为静音。

format: float

• seek: integer

分段的查找偏移量。

• start: number

分段开始时间，单位为秒。

format: double

• temperature: number

生成分段所用的温度参数。

format: float

• text: string

分段文本内容。

• tokens: array of number

文本内容的词元 ID 数组。

• usage: optional object { seconds, type }

按音频输入时长计费的模型用量统计。

• seconds: number

输入音频时长，单位为秒。

format: double

• type: "duration"

用量对象的类型，此变体固定为 duration。

• "duration"

• words: optional array of TranscriptionWord

提取出的词语及相应时间戳。

• end: number

词语结束时间，单位为秒。

format: double

• start: number

词语开始时间，单位为秒。

format: double

• word: string

词语的文本内容。

示例

## 设置 OPENAI_API_KEY，并按示例替换音频文件、音色或授权录音 ID。
curl https://api.openai.com/v1/audio/transcriptions \
    -H 'Content-Type: multipart/form-data' \
    -H "Authorization: Bearer $OPENAI_API_KEY" \
    -F 'file=@/path/to/file' \
    -F model=gpt-4o-transcribe

200 响应示例

{
  "text": "text",
  "languages": [
    {
      "code": "code"
    }
  ],
  "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
    }
  }
}

示例

## 设置 OPENAI_API_KEY，并按示例替换音频文件、音色或授权录音 ID。
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"

响应示例

{
  "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
  }
}

说话人分离示例

## 设置 OPENAI_API_KEY，并按示例替换音频文件、音色或授权录音 ID。
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...'

响应示例

{
  "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
  }
}

对数概率示例

## 设置 OPENAI_API_KEY，并按示例替换音频文件、音色或授权录音 ID。
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"

响应示例

{
  "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
  }
}

分段时间戳示例

## 设置 OPENAI_API_KEY，并按示例替换音频文件、音色或授权录音 ID。
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"

响应示例

{
  "task": "transcribe",
  "language": "english",
  "duration": 3.32,
  "text": "The beach was a popular spot on a hot summer day.",
  "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": 4
  }
}

流式传输示例

## 设置 OPENAI_API_KEY，并按示例替换音频文件、音色或授权录音 ID。
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

响应示例

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}}

逐词时间戳示例

## 设置 OPENAI_API_KEY，并按示例替换音频文件、音色或授权录音 ID。
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"

响应示例

{
  "task": "transcribe",
  "language": "english",
  "duration": 0.5,
  "text": "Hello.",
  "words": [
    {
      "word": "Hello",
      "start": 0.0,
      "end": 0.5
    }
  ],
  "usage": {
    "type": "duration",
    "seconds": 1
  }
}