openai_transcription_client.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
This script demonstrates how to use the vLLM API server to perform audio
transcription with the `openai/whisper-large-v3` model.

Before running this script, you must start the vLLM server with the following command:

    vllm serve openai/whisper-large-v3

Requirements:
- vLLM with audio support
- openai Python SDK
- httpx for streaming support

The script performs:
1. Synchronous transcription using OpenAI-compatible API.
2. Streaming transcription using raw HTTP request to the vLLM server.
"""

import asyncio

from openai import AsyncOpenAI, OpenAI

from vllm.assets.audio import AudioAsset


def sync_openai(audio_path: str, client: OpenAI):
    """
    Perform synchronous transcription using OpenAI-compatible API.
    """
    with open(audio_path, "rb") as f:
        transcription = client.audio.transcriptions.create(
            file=f,
            model="openai/whisper-large-v3",
            language="en",
            response_format="json",
            temperature=0.0,
            # Additional sampling params not provided by OpenAI API.
            extra_body=dict(
                seed=4419,
                repetition_penalty=1.3,
            ),
        )
        print("transcription result:", transcription.text)


async def stream_openai_response(audio_path: str, client: AsyncOpenAI):
    """
    Perform asynchronous transcription using OpenAI-compatible API.
    """
    print("\ntranscription result:", end=" ")
    with open(audio_path, "rb") as f:
        transcription = await client.audio.transcriptions.create(
            file=f,
            model="openai/whisper-large-v3",
            language="en",
            response_format="json",
            temperature=0.0,
            # Additional sampling params not provided by OpenAI API.
            extra_body=dict(
                seed=420,
                top_p=0.6,
            ),
            stream=True,
        )
        async for chunk in transcription:
            if chunk.choices:
                content = chunk.choices[0].get("delta", {}).get("content")
                print(content, end="", flush=True)

    print()  # Final newline after stream ends


def main():
    mary_had_lamb = str(AudioAsset("mary_had_lamb").get_local_path())
    winning_call = str(AudioAsset("winning_call").get_local_path())

    # Modify OpenAI's API key and API base to use vLLM's API server.
    openai_api_key = "EMPTY"
    openai_api_base = "http://localhost:8000/v1"
    client = OpenAI(
        api_key=openai_api_key,
        base_url=openai_api_base,
    )

    sync_openai(mary_had_lamb, client)
    # Run the asynchronous function
    client = AsyncOpenAI(
        api_key=openai_api_key,
        base_url=openai_api_base,
    )
    asyncio.run(stream_openai_response(winning_call, client))


if __name__ == "__main__":
    main()