Transcribe audio

The input audio file to transcribe. Max audio file size is 1 GB, there is not limit for audio duration. Common file formats such as mp3, flac, and wav are supported. Note that the audio will be resampled to 16kHz, downmixed to mono, and reformatted to 16-bit signed little-endian format before transcription. Pre-converting the file before sending it to the API can improve runtime performance.

String name of the ASR model to use. Can be one of whisper-v3 or whisper-v3-turbo. Please use the following serverless endpoints for evaluation:

• https://audio-prod.us-virginia-1.direct.fireworks.ai (for whisper-v3);
• https://audio-turbo.us-virginia-1.direct.fireworks.ai (for whisper-v3-turbo);

String name of the voice activity detection (VAD) model to use. Can be one of silero, or whisperx-pyannet.

String name of the alignment model to use. Currently supported:

• mms_fa optimal accuracy for multilingual speech.
• tdnn_ffn optimal accuracy for English-only speech.
• gentle best accuracy for English-only speech (requires a dedicated endpoint, contact us at inquiries@fireworks.ai).

The target language for transcription. The set of supported target languages can be found here.

The input prompt with which to prime transcription. This can be used, for example, to continue a prior transcription given new audio data.

Sampling temperature to use when decoding text tokens during transcription.

The format in which to return the response. Can be one of json, text, srt, verbose_json, or vtt.

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. Can be one of word, or segment. If not present, defaults to segment.

Audio preprocessing mode. Currently supported:

• none to skip audio preprocessing.
• dynamic for arbitrary audio content with variable loudness.
• soft_dynamic for speech intense recording such as podcasts and voice-overs.
• bass_dynamic for boosting lower frequencies;