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"bleu": string_based.bleu, +} + def evaluate_answer(sample: eval_types.Sample, metric: str) -> eval_types.Result: - if metric == "asr": - return wer.evaluate_answer_asr(sample) - elif metric == "boolq": - return gpt_eval_boolq.evaluate_answer_boolq(sample) - elif metric == "instruct": - return gpt_eval_instruct.evaluate_answer_instruct(sample) - elif metric == "conversation": - return gpt_eval_conv.evaluate_conversation_response(sample) - elif metric == "exact_match_last_word": - return string_based.match_last_word(sample) + if metric in METRIC_REGISTRY: + return METRIC_REGISTRY[metric](sample) else: raise ValueError(f"Unknown metric: {metric}") diff --git a/ultravox/evaluation/eval_types.py b/ultravox/evaluation/eval_types.py index 44c118e3..d2bddc11 100644 --- a/ultravox/evaluation/eval_types.py +++ b/ultravox/evaluation/eval_types.py @@ -35,4 +35,14 @@ class ExactMatchResult: reason: str +@dataclasses.dataclass +class BleuResult: + """ + Score is the BLEU score for the generated answer. + Note: BLEU is supposed to be computed on a corpus level, not on a single sample. + """ + + score: float + + Result = Union[InstructResult, WerResult, ExactMatchResult] diff --git a/ultravox/evaluation/string_based.py b/ultravox/evaluation/string_based.py index c2d1ab03..d835ead0 100644 --- a/ultravox/evaluation/string_based.py +++ b/ultravox/evaluation/string_based.py @@ -1,5 +1,7 @@ import re +import sacrebleu + from ultravox.evaluation import eval_types @@ -21,3 +23,17 @@ def match_last_word(sample: eval_types.Sample) -> eval_types.ExactMatchResult: return eval_types.ExactMatchResult( score=last_word == expected_tf, reason="exact_match check" ) + + +def bleu(sample: eval_types.Sample) -> eval_types.BleuResult: + """ + Compute BLEU score for a single sample. + + Note: BLEU is supposed to be computed on a corpus level, not on a single sample. + As such, reported values here might not be easily comparable to other metrics. + """ + score = sacrebleu.sentence_bleu( + hypothesis=sample.generated_answer, + references=[sample.expected_answer], + ).score + return eval_types.BleuResult(score=score) diff --git a/ultravox/training/evaluation.py b/ultravox/training/evaluation.py index 7368b2af..06d69c2c 100644 --- a/ultravox/training/evaluation.py +++ b/ultravox/training/evaluation.py @@ -58,13 +58,29 @@ class EvalScenario: EVAL_SCENARIOS = [ + # automatic speech recognition scenarios + EvalScenario("boolq__wer", "boolq_in", "asr"), + # automatic speech translation scenarios + EvalScenario("covost2_en_de__bleu", "covost2:en_de", "bleu"), + EvalScenario("covost2_en_zh-CN__bleu", "covost2:en_zh-CN", "bleu"), + EvalScenario("covost2_es_en__bleu", "covost2:es_en", "bleu"), + EvalScenario( + "covost2_en_de__bleu__text_only", "covost2:en_de", "bleu", include_audio=False + ), + EvalScenario( + "covost2_en_zh-CN__bleu__text_only", + "covost2:en_zh-CN", + "bleu", + include_audio=False, + ), + EvalScenario( + "covost2_es_en__bleu__text_only", "covost2:es_en", "bleu", include_audio=False + ), + # SQA scenarios EvalScenario("anyinstruct__instruct_follow", "anyinstruct", "instruct"), EvalScenario( "boolq__binary", "boolq_extended", "exact_match_last_word", new_tokens=128 ), - EvalScenario("boolq__wer", "boolq_in", "asr"), - EvalScenario("soda__sensible_generation", "soda", "conversation", new_tokens=64), - # Text-only scenarios: tests for catastrophic forgetting. EvalScenario( "anyinstruct__instruct_follow__text_only", "anyinstruct", @@ -78,6 +94,8 @@ class EvalScenario: new_tokens=128, include_audio=False, ), + # Conversation dialogue scenarios + EvalScenario("soda__sensible_generation", "soda", "conversation", new_tokens=64), EvalScenario( "soda__sensible_generation__text_only", "soda",