ultravox/data/configs/audiobench.py (55 lines of code) (raw):

from ultravox.data import types INSTRUCTION_MQA_USER_TEMPLATE = ( f"{types.AUDIO_PLACEHOLDER} \n\n{{{{instruction}}}} \n\n{{{{choices}}}}" ) INSTRUCTION_USER_TEMPLATE = f"{types.AUDIO_PLACEHOLDER} \n\n{{{{instruction}}}}" # English only AB_CN_COLLEGE_LISTEN_MCQ_CONFIG = types.DatasetConfig( name="audiobench-cn-college-listen-mcq", path="fixie-ai/cn_college_listen_mcq_test", splits=[ types.DatasetSplitConfig(name="test", num_samples=2_270), ], eval_config=types.EvalConfig(metric="audiobench_binary"), user_template=INSTRUCTION_MQA_USER_TEMPLATE, transcript_template="{{instruction}}", assistant_template="{{answer}}", ) AB_DREAM_TTS_MCQ_CONFIG = types.DatasetConfig( name="audiobench-dream-tts-mcq", path="fixie-ai/dream_tts_mcq_test", splits=[ types.DatasetSplitConfig(name="test", num_samples=1_910), ], eval_config=types.EvalConfig(metric="audiobench_binary"), user_template=INSTRUCTION_MQA_USER_TEMPLATE, transcript_template="{{instruction}}", assistant_template="{{answer}}", ) AB_SLUE_P2_SQA5_CONFIG = types.DatasetConfig( name="audiobench-slue-p2-sqa5", path="fixie-ai/slue_p2_sqa5_test", splits=[ types.DatasetSplitConfig(name="test", num_samples=408), ], eval_config=types.EvalConfig(metric="audiobench_scalar"), user_template=INSTRUCTION_USER_TEMPLATE, transcript_template="{{instruction}}", assistant_template="{{answer}}", ) AB_PUBLIC_SG_SPEECH_QA_CONFIG = types.DatasetConfig( name="audiobench-public-sg-speech-qa", path="fixie-ai/public_sg_speech_qa_test", splits=[ types.DatasetSplitConfig(name="test", num_samples=688), ], eval_config=types.EvalConfig(metric="audiobench_scalar"), user_template=INSTRUCTION_USER_TEMPLATE, transcript_template="{{instruction}}", assistant_template="{{answer}}", ) configs = [ AB_CN_COLLEGE_LISTEN_MCQ_CONFIG, AB_SLUE_P2_SQA5_CONFIG, AB_DREAM_TTS_MCQ_CONFIG, AB_PUBLIC_SG_SPEECH_QA_CONFIG, ]