ASR

An ASR Model converts speech audio into text. AgentScope exposes one interface for complete-file transcription and realtime audio input. The 2.0.11 development version includes the following built-in implementation:

Provider	Model Class	Mode	Built-in Models
OpenAI	`OpenAIASRModel`	Batch	`gpt-4o-mini-transcribe`, `gpt-4o-transcribe`

Create an ASR Model

Create an OpenAIASRModel with an OpenAICredential. The default model is gpt-4o-mini-transcribe:

import os

from agentscope.asr import OpenAIASRModel
from agentscope.credential import OpenAICredential

asr = OpenAIASRModel(
    credential=OpenAICredential(
        api_key=os.environ["OPENAI_API_KEY"],
    ),
    model="gpt-4o-mini-transcribe",
    parameters=OpenAIASRModel.Parameters(
        language="en",
        prompt="AgentScope product meeting",
    ),
)

The OpenAI implementation supports two optional parameters:

Parameter	Type	Description
`language`	`str \| None`	ISO-639-1 language code for the audio
`prompt`	`str \| None`	Text that guides transcription vocabulary and style

Transcribe Audio

transcribe() accepts the complete audio as bytes. Pass the original filename so the provider can identify the in-memory audio format:

import asyncio
import os
from pathlib import Path

from agentscope.asr import OpenAIASRModel
from agentscope.credential import OpenAICredential


async def main() -> None:
    audio_path = Path("meeting.wav")
    asr = OpenAIASRModel(
        credential=OpenAICredential(
            api_key=os.environ["OPENAI_API_KEY"],
        ),
    )
    response = await asr.transcribe(
        audio=audio_path.read_bytes(),
        filename=audio_path.name,
    )
    if response.content is not None:
        print(response.content.text)


asyncio.run(main())

The bundled OpenAI model cards advertise FLAC, MP3/MPEG, MP4, OGG, WAV, and WebM input. An empty audio value or filename raises ValueError. Keyword arguments passed directly to transcribe() override values from the model’s Parameters object for that call.

Read the Response

transcribe() returns an ASRResponse:

Field	Type	Description
`content`	`TextBlock \| None`	Transcribed text, or no content when a realtime model has no new text
`id`	`str`	Unique response identifier
`created_at`	`str`	Response creation timestamp
`type`	`"asr"`	Response type discriminator
`metadata`	`dict \| None`	Optional provider-specific metadata
`is_last`	`bool`	Whether this is the final transcription response

Batch models return a final response with is_last=True. Realtime models may return intermediate responses with is_last=False from push().

Discover Available Models

ASR model cards describe supported audio types, output types, realtime capability, and the provider parameter schema. Discover them from either the credential or the model class:

from agentscope.credential import OpenAICredential

cards = OpenAICredential.list_asr_models()
for card in cards:
    print(f"{card.name}: {card.input_types}")

from agentscope.asr import OpenAIASRModel

cards = OpenAIASRModel.list_models()
for card in cards:
    print(f"{card.name}: realtime={card.realtime}")

Implement a Custom ASR Model

Subclass ASRModelBase and implement transcribe() for a batch provider:

async def transcribe(
    self,
    audio: bytes | None = None,
    filename: str = "audio.wav",
    **kwargs: Any,
) -> ASRResponse:
    ...

For realtime input, set realtime=True, implement connect(), push(), and close(), and use the model as an asynchronous context manager. Send audio chunks through push() and call transcribe() without audio to finalize the current transcript.

AgentScope 2.0.11dev currently ships only the batch OpenAI ASR implementation. The realtime lifecycle is available for custom and future providers.

Get Started

Building Blocks

Agent as Service

Others

Create an ASR Model

Transcribe Audio

Read the Response

Discover Available Models

Implement a Custom ASR Model

​Create an ASR Model

​Transcribe Audio

​Read the Response

​Discover Available Models

​Implement a Custom ASR Model

Create an ASR Model

Transcribe Audio

Read the Response

Discover Available Models

Implement a Custom ASR Model