Sync from v0.13

examples/online_serving/openai_transcription_client.py

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+# 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()