Marco_Longspeech

Marco-LongSpeech Dataset Marco-LongSpeech is a multi-task long speech understanding dataset containing 8 different speech understanding tasks designed to benchmark Large Language Models on lengthy audio inputs. 📊 Dataset Statistics Task Statistics | Task | Train | Val | Test | Total | Unique Audios | |------|-------|-----|------|-------|---------------| | ASR | 71,275 | 15,273 | 15,274 | 101,822 | 101,822 | | Temporal_Relative_QA | 5,886 | 1,261 | 1,262 | 8,409 | 8,409 | | summary | 4,366 | 935 | 937 | 6,238 | 6,238 | | content_separation | 5,887 | 1,261 | 1,263 | 8,411 | 8,411 | | emotionQA | 5,887 | 1,261 | 1,263 | 8,411 | 8,411 | | speaker_count | 5,887 | 1,261 | 1,263 | 8,411 | 8,411 | | translation | 29,435 | 6,307 | 6,309 | 42,051 | 8,411 | | language_detection | 14,789 | 3,169 | 3,170 | 21,128 | 21,128 | | **Total** | **143,412** | **30,728** | **30,741** | **204,881** | - | Audio Subset Statistics | Subset | WAV Files | all_audios.jsonl | metadata.json | |--------|-----------|------------------|----------------| | LongSpeech_p1 | 29,539 | ✓ | ✓ | | LongSpeech_p2 | 22,107 | ✓ | ✓ | | LongSpeech_p3 | 50,176 | ✓ | ✓ | | **Total** | **101,822** | - | - | 📁 Dataset Structure 🎯 Task Descriptions The dataset covers a comprehensive range of capabilities required for long speech understanding: **ASR & S2T Translation**: Core transcription and translation of full-length audio. **Summarization**: Generating concise summaries from lengthy recordings. **Speaker Count & Language Detection**: Identifying speaker and language attributes. **Content Separation**: Detecting unrelated concatenated content to test coherence. **QA & Temporal Localization**: Evaluating comprehension, reasoning, and temporal tracking. **Emotion Analysis**: Determining the overall emotional tone of the speech. 📝 Data Format Each task's `jsonl` file follows the format below: Field Explanations `language`: Speech language code (e.g., en, zh). `task`: The type of task (e.g., ASR, summary). `messages`: