Generating transcriptions¶

Marvin can generate text from speech.

What it does

The transcribe function generates text from audio.

Example

Suppose you have the following audio saved as fancy_computer.mp3:

To generate a transcription, provide the path to the file:

import marvin

transcription = marvin.transcribe("fancy_computer.mp3")

Result

assert transcription == "I sure like being inside this fancy computer."

How it works

Marvin passes your file to the OpenAI transcription API, which returns an transcript.

Supported audio formats¶

You can provide audio data to transcribe in a variety of ways. Marvin supports the following encodings: flac, m4a, mp3, mp4, mpeg, mpga, oga, ogg, wav, and webm.

Marvin `Audio` object¶

Marvin provides an Audio object that makes it easier to work with audio. Typically it is imported from the marvin.audio module, which requires the audio extra to be installed. If it isn't installed, you can still import the Audio object from marvin.types, though some additional functionality will not be available.

from marvin.audio import Audio
# or, if the audio extra is not installed:
# from marvin.types import Audio

audio = Audio.from_path("fancy_computer.mp3")
transcription = marvin.transcribe(audio)

Path to a local file¶

Provide a string or Path representing the path to a local audio file:

marvin.transcribe("fancy_computer.mp3")

File reference¶

Provide the audio data as an in-memory file object:

with open("/path/to/audio.mp3", "rb") as f:
    marvin.transcribe(f)

Raw bytes¶

Provide the audio data as raw bytes:

marvin.transcribe(audio_bytes)

Note that the OpenAI transcription API requires a filename, so Marvin will supply audio.mp3 if you pass raw bytes. In practice, this doesn't appear to make a difference even if your audio is not an mp3 file (e.g. a wav file).

Async support¶

If you are using Marvin in an async environment, you can use transcribe_async:

result = await marvin.transcribe_async('fancy_computer.mp3')
assert result == "I sure like being inside this fancy computer."

Model parameters¶

You can pass parameters to the underlying API via the model_kwargs argument. These parameters are passed directly to the respective APIs, so you can use any supported parameter.