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239 lines
7.7 KiB
Python
239 lines
7.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# imports for guided decoding tests
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import io
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import json
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import librosa
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import numpy as np
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import openai
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import pytest
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import pytest_asyncio
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import soundfile as sf
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from vllm.assets.audio import AudioAsset
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from ...utils import RemoteOpenAIServer
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MODEL_NAME = "openai/whisper-large-v3-turbo"
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SERVER_ARGS = ["--enforce-eager"]
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MISTRAL_FORMAT_ARGS = [
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"--tokenizer_mode", "mistral", "--config_format", "mistral",
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"--load_format", "mistral"
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]
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@pytest.fixture
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def mary_had_lamb():
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path = AudioAsset('mary_had_lamb').get_local_path()
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with open(str(path), "rb") as f:
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yield f
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@pytest.fixture
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def winning_call():
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path = AudioAsset('winning_call').get_local_path()
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with open(str(path), "rb") as f:
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yield f
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@pytest.fixture(scope="module")
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def server():
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with RemoteOpenAIServer(MODEL_NAME, SERVER_ARGS) as remote_server:
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yield remote_server
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@pytest_asyncio.fixture
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async def client(server):
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async with server.get_async_client() as async_client:
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yield async_client
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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"model_name",
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["openai/whisper-large-v3-turbo", "mistralai/Voxtral-Mini-3B-2507"])
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async def test_basic_audio(mary_had_lamb, model_name):
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server_args = ["--enforce-eager"]
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if model_name.startswith("mistralai"):
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server_args += MISTRAL_FORMAT_ARGS
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# Based on https://github.com/openai/openai-cookbook/blob/main/examples/Whisper_prompting_guide.ipynb.
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with RemoteOpenAIServer(model_name, server_args) as remote_server:
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client = remote_server.get_async_client()
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transcription = await client.audio.transcriptions.create(
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model=model_name,
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file=mary_had_lamb,
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language="en",
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response_format="text",
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temperature=0.0)
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out = json.loads(transcription)['text']
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assert "Mary had a little lamb," in out
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@pytest.mark.asyncio
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async def test_non_asr_model(winning_call):
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# text to text model
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model_name = "JackFram/llama-68m"
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with RemoteOpenAIServer(model_name, SERVER_ARGS) as remote_server:
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client = remote_server.get_async_client()
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res = await client.audio.transcriptions.create(model=model_name,
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file=winning_call,
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language="en",
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temperature=0.0)
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err = res.error
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assert err["code"] == 400 and not res.text
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assert err[
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"message"] == "The model does not support Transcriptions API"
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@pytest.mark.asyncio
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async def test_bad_requests(mary_had_lamb, client):
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# invalid language
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with pytest.raises(openai.BadRequestError):
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await client.audio.transcriptions.create(model=MODEL_NAME,
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file=mary_had_lamb,
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language="hh",
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temperature=0.0)
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@pytest.mark.asyncio
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async def test_long_audio_request(mary_had_lamb, client):
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mary_had_lamb.seek(0)
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audio, sr = librosa.load(mary_had_lamb)
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# Add small silence after each audio for repeatability in the split process
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audio = np.pad(audio, (0, 1600))
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repeated_audio = np.tile(audio, 10)
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# Repeated audio to buffer
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buffer = io.BytesIO()
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sf.write(buffer, repeated_audio, sr, format='WAV')
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buffer.seek(0)
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transcription = await client.audio.transcriptions.create(
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model=MODEL_NAME,
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file=buffer,
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language="en",
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response_format="text",
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temperature=0.0)
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out = json.loads(transcription)['text']
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counts = out.count("Mary had a little lamb")
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assert counts == 10, counts
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@pytest.mark.asyncio
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async def test_completion_endpoints(client):
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# text to text model
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res = await client.chat.completions.create(
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model=MODEL_NAME,
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messages=[{
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"role": "system",
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"content": "You are a helpful assistant."
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}])
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err = res.error
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assert err["code"] == 400
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assert err["message"] == "The model does not support Chat Completions API"
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res = await client.completions.create(model=MODEL_NAME, prompt="Hello")
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err = res.error
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assert err["code"] == 400
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assert err["message"] == "The model does not support Completions API"
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@pytest.mark.asyncio
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async def test_streaming_response(winning_call, client):
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transcription = ""
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res_no_stream = await client.audio.transcriptions.create(
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model=MODEL_NAME,
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file=winning_call,
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response_format="json",
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language="en",
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temperature=0.0)
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res = await client.audio.transcriptions.create(model=MODEL_NAME,
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file=winning_call,
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language="en",
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temperature=0.0,
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stream=True,
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timeout=30)
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# Reconstruct from chunks and validate
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async for chunk in res:
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text = chunk.choices[0]['delta']['content']
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transcription += text
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assert transcription == res_no_stream.text
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@pytest.mark.asyncio
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async def test_stream_options(winning_call, client):
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res = await client.audio.transcriptions.create(
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model=MODEL_NAME,
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file=winning_call,
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language="en",
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temperature=0.0,
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stream=True,
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extra_body=dict(stream_include_usage=True,
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stream_continuous_usage_stats=True),
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timeout=30)
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final = False
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continuous = True
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async for chunk in res:
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if not len(chunk.choices):
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# final usage sent
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final = True
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else:
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continuous = continuous and hasattr(chunk, 'usage')
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assert final and continuous
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@pytest.mark.asyncio
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async def test_sampling_params(mary_had_lamb, client):
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"""
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Compare sampling with params and greedy sampling to assert results
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are different when extreme sampling parameters values are picked.
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"""
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transcription = await client.audio.transcriptions.create(
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model=MODEL_NAME,
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file=mary_had_lamb,
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language="en",
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temperature=0.8,
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extra_body=dict(seed=42,
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repetition_penalty=1.9,
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top_k=12,
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top_p=0.4,
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min_p=0.5,
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frequency_penalty=1.8,
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presence_penalty=2.0))
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greedy_transcription = await client.audio.transcriptions.create(
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model=MODEL_NAME,
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file=mary_had_lamb,
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language="en",
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temperature=0.0,
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extra_body=dict(seed=42))
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assert greedy_transcription.text != transcription.text
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@pytest.mark.asyncio
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async def test_audio_prompt(mary_had_lamb, client):
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prompt = "This is a speech, recorded in a phonograph."
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#Prompts should not omit the part of original prompt while transcribing.
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prefix = "The first words I spoke in the original phonograph"
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transcription = await client.audio.transcriptions.create(
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model=MODEL_NAME,
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file=mary_had_lamb,
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language="en",
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response_format="text",
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temperature=0.0)
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out = json.loads(transcription)['text']
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assert prefix in out
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transcription_wprompt = await client.audio.transcriptions.create(
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model=MODEL_NAME,
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file=mary_had_lamb,
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language="en",
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response_format="text",
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prompt=prompt,
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temperature=0.0)
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out_prompt = json.loads(transcription_wprompt)['text']
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assert prefix in out_prompt
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