3f3f89529d
Signed-off-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
98 lines
3.2 KiB
Python
98 lines
3.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from dataclasses import asdict
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import pytest
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from mistral_common.audio import Audio
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from mistral_common.protocol.instruct.chunk import RawAudio
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from mistral_common.protocol.transcription.request import (
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StreamingMode,
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TranscriptionRequest,
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)
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from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
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from vllm import LLM, EngineArgs, SamplingParams
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from vllm.assets.audio import AudioAsset
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def _get_engine(path: str) -> LLM:
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engine_args = EngineArgs(
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model=path,
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max_model_len=8192,
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max_num_seqs=1,
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limit_mm_per_prompt={"audio": 1},
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config_format="mistral",
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load_format="mistral",
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tokenizer_mode="mistral",
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enforce_eager=True,
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gpu_memory_utilization=0.4,
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)
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return LLM(**asdict(engine_args))
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@pytest.mark.skip(reason="Voxtral streaming is not yet public")
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def test_voxtral_streaming_forward():
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audio_assets = [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")]
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model_name = "mistralai/Voxtral-Mini-3B-Realtime-2602"
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tokenizer = MistralTokenizer.from_hf_hub(model_name)
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audio_config = tokenizer.instruct_tokenizer.tokenizer.audio
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def from_file(file_path: str):
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audio = Audio.from_file(file_path, strict=False)
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req = TranscriptionRequest(
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audio=RawAudio.from_audio(audio),
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streaming=StreamingMode.OFFLINE,
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language=None,
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)
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tokenized = tokenizer.instruct_tokenizer.encode_transcription(req)
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return (tokenized.tokens, tokenized.audios[0].audio_array)
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tokenized_list = [
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from_file(audio_asset.get_local_path()) for audio_asset in audio_assets
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]
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inputs = []
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sampling_params = []
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for tokens, audio_array in tokenized_list:
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num_samples = audio_array.shape[0]
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max_tokens = (
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audio_config.num_audio_tokens(num_samples)
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- audio_config.num_delay_tokens
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- 1
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)
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sampling_params.append(SamplingParams(temperature=0.0, max_tokens=max_tokens))
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input_dict = {
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"multi_modal_data": {"audio": [(audio_array, None)]},
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"prompt_token_ids": tokens,
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}
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inputs.append(input_dict)
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llm = _get_engine(model_name)
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outputs = llm.generate(
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inputs,
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sampling_params=sampling_params,
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)
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texts = [out.outputs[0].text for out in outputs]
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expected = [
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(
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" First words I spoke in the original phonograph. "
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"A little piece of practical poetry. Mary had a little lamb,"
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" it sleeps with quite a snow, and everywhere that Mary went, "
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"the lamb was sure to go."
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),
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(
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" And the 0-1 pitch on the way to Edgar Martinez. Swung on"
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" the line. Down the left field line for OBS. Here comes Joy. "
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"Here is Junior to third base. They're going to wave him in. "
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"The throw to the plate will be late. The Mariners are going"
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" to play. For the American League Championship, "
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"I don't believe it. It just continues. My oh, my."
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),
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]
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assert texts == expected
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