{
  "meta": {
    "pretty_name": "FLEURS",
    "dataset_id": "lmms-lab/fleurs",
    "paper_url": null,
    "tags": [
      "Audio",
      "MultiLingual",
      "SpeechRecognition"
    ],
    "metrics": [
      "wer"
    ],
    "few_shot_num": 0,
    "eval_split": "test",
    "train_split": "",
    "subset_list": [
      "cmn_hans_cn",
      "en_us",
      "yue_hant_hk"
    ],
    "description": "\n## Overview\n\nFLEURS (Few-shot Learning Evaluation of Universal Representations of Speech) is a massively multilingual benchmark covering 102 languages for evaluating automatic speech recognition (ASR), spoken language understanding, and speech translation.\n\n## Task Description\n\n- **Task Type**: Automatic Speech Recognition (ASR)\n- **Input**: Audio recordings with speech in various languages\n- **Output**: Transcribed text in the corresponding language\n- **Languages**: 102 languages including Mandarin Chinese, Cantonese, English, and many more\n\n## Key Features\n\n- Massive multilingual coverage (102 languages)\n- Derived from FLoRes-101 machine translation benchmark\n- Includes diverse language families and scripts\n- High-quality human recordings and transcriptions\n- Metadata includes gender, language group, and speaker information\n\n## Evaluation Notes\n\n- Default configuration uses **test** split\n- Primary metric: **Word Error Rate (WER)**\n- Default subsets: `cmn_hans_cn` (Mandarin), `en_us` (English), `yue_hant_hk` (Cantonese)\n- Language-specific text normalization applied during evaluation\n- Prompt: \"Please recognize the speech and only output the recognized content\"\n",
    "prompt_template": "Please recognize the speech and only output the recognized content:",
    "system_prompt": "",
    "few_shot_prompt_template": "",
    "aggregation": "mean",
    "extra_params": {},
    "sandbox_config": {},
    "category": "vlm"
  },
  "statistics": {
    "total_samples": 2411,
    "subset_stats": [
      {
        "name": "cmn_hans_cn",
        "sample_count": 945,
        "prompt_length_mean": 67,
        "prompt_length_min": 67,
        "prompt_length_max": 67,
        "prompt_length_std": null,
        "target_length_mean": 71.58,
        "multimodal": {
          "has_images": false,
          "has_audio": true,
          "has_video": false,
          "audio": {
            "count_total": 945,
            "count_per_sample": {
              "min": 1,
              "max": 1,
              "mean": 1
            },
            "duration": null,
            "sample_rates": [],
            "formats": [
              "wav"
            ]
          }
        }
      },
      {
        "name": "en_us",
        "sample_count": 647,
        "prompt_length_mean": 67,
        "prompt_length_min": 67,
        "prompt_length_max": 67,
        "prompt_length_std": null,
        "target_length_mean": 129.76,
        "multimodal": {
          "has_images": false,
          "has_audio": true,
          "has_video": false,
          "audio": {
            "count_total": 647,
            "count_per_sample": {
              "min": 1,
              "max": 1,
              "mean": 1
            },
            "duration": null,
            "sample_rates": [],
            "formats": [
              "wav"
            ]
          }
        }
      },
      {
        "name": "yue_hant_hk",
        "sample_count": 819,
        "prompt_length_mean": 67,
        "prompt_length_min": 67,
        "prompt_length_max": 67,
        "prompt_length_std": null,
        "target_length_mean": 69.37,
        "multimodal": {
          "has_images": false,
          "has_audio": true,
          "has_video": false,
          "audio": {
            "count_total": 819,
            "count_per_sample": {
              "min": 1,
              "max": 1,
              "mean": 1
            },
            "duration": null,
            "sample_rates": [],
            "formats": [
              "wav"
            ]
          }
        }
      }
    ],
    "prompt_length": {
      "mean": 67,
      "min": 67,
      "max": 67,
      "std": null
    },
    "target_length_mean": 86.44,
    "computed_at": "2026-01-28T11:13:16.410245",
    "multimodal": {
      "has_images": false,
      "has_audio": true,
      "has_video": false,
      "audio": {
        "count_total": 2411,
        "count_per_sample": {
          "min": 1,
          "max": 1,
          "mean": 1
        },
        "duration": null,
        "sample_rates": [],
        "formats": [
          "wav"
        ]
      }
    }
  },
  "sample_example": {
    "data": {
      "input": [
        {
          "id": "daf508c3",
          "content": [
            {
              "text": "Please recognize the speech and only output the recognized content:"
            },
            {
              "audio": "[BASE64_AUDIO: wav, ~648.8KB]",
              "format": "wav"
            }
          ]
        }
      ],
      "target": "这 并 不 是 告 别 这 是 一 个 篇 章 的 结 束 也 是 新 篇 章 的 开 始",
      "id": 0,
      "group_id": 0,
      "metadata": {
        "id": 1906,
        "num_samples": 166080,
        "raw_transcription": "“这并不是告别。这是一个篇章的结束，也是新篇章的开始。”",
        "language": "Mandarin Chinese",
        "gender": 0,
        "lang_id": "cmn_hans",
        "lang_group_id": 6
      }
    },
    "subset": "cmn_hans_cn",
    "truncated": false
  },
  "readme": {
    "en": "# FLEURS\n\n\n## Overview\n\nFLEURS (Few-shot Learning Evaluation of Universal Representations of Speech) is a massively multilingual benchmark covering 102 languages for evaluating automatic speech recognition (ASR), spoken language understanding, and speech translation.\n\n## Task Description\n\n- **Task Type**: Automatic Speech Recognition (ASR)\n- **Input**: Audio recordings with speech in various languages\n- **Output**: Transcribed text in the corresponding language\n- **Languages**: 102 languages including Mandarin Chinese, Cantonese, English, and many more\n\n## Key Features\n\n- Massive multilingual coverage (102 languages)\n- Derived from FLoRes-101 machine translation benchmark\n- Includes diverse language families and scripts\n- High-quality human recordings and transcriptions\n- Metadata includes gender, language group, and speaker information\n\n## Evaluation Notes\n\n- Default configuration uses **test** split\n- Primary metric: **Word Error Rate (WER)**\n- Default subsets: `cmn_hans_cn` (Mandarin), `en_us` (English), `yue_hant_hk` (Cantonese)\n- Language-specific text normalization applied during evaluation\n- Prompt: \"Please recognize the speech and only output the recognized content\"\n\n\n## Properties\n\n| Property | Value |\n|----------|-------|\n| **Benchmark Name** | `fleurs` |\n| **Dataset ID** | [lmms-lab/fleurs](https://modelscope.cn/datasets/lmms-lab/fleurs/summary) |\n| **Paper** | N/A |\n| **Tags** | `Audio`, `MultiLingual`, `SpeechRecognition` |\n| **Metrics** | `wer` |\n| **Default Shots** | 0-shot |\n| **Evaluation Split** | `test` |\n\n\n## Data Statistics\n\n| Metric | Value |\n|--------|-------|\n| Total Samples | 2,411 |\n| Prompt Length (Mean) | 67 chars |\n| Prompt Length (Min/Max) | 67 / 67 chars |\n\n**Per-Subset Statistics:**\n\n| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |\n|--------|---------|-------------|------------|------------|\n| `cmn_hans_cn` | 945 | 67 | 67 | 67 |\n| `en_us` | 647 | 67 | 67 | 67 |\n| `yue_hant_hk` | 819 | 67 | 67 | 67 |\n\n**Audio Statistics:**\n\n| Metric | Value |\n|--------|-------|\n| Total Audio Files | 2,411 |\n| Audio per Sample | min: 1, max: 1, mean: 1 |\n| Formats | wav |\n\n\n## Sample Example\n\n**Subset**: `cmn_hans_cn`\n\n```json\n{\n  \"input\": [\n    {\n      \"id\": \"daf508c3\",\n      \"content\": [\n        {\n          \"text\": \"Please recognize the speech and only output the recognized content:\"\n        },\n        {\n          \"audio\": \"[BASE64_AUDIO: wav, ~648.8KB]\",\n          \"format\": \"wav\"\n        }\n      ]\n    }\n  ],\n  \"target\": \"这 并 不 是 告 别 这 是 一 个 篇 章 的 结 束 也 是 新 篇 章 的 开 始\",\n  \"id\": 0,\n  \"group_id\": 0,\n  \"metadata\": {\n    \"id\": 1906,\n    \"num_samples\": 166080,\n    \"raw_transcription\": \"“这并不是告别。这是一个篇章的结束，也是新篇章的开始。”\",\n    \"language\": \"Mandarin Chinese\",\n    \"gender\": 0,\n    \"lang_id\": \"cmn_hans\",\n    \"lang_group_id\": 6\n  }\n}\n```\n\n## Prompt Template\n\n**Prompt Template:**\n```text\nPlease recognize the speech and only output the recognized content:\n```\n\n## Usage\n\n### Using CLI\n\n```bash\nevalscope eval \\\n    --model YOUR_MODEL \\\n    --api-url OPENAI_API_COMPAT_URL \\\n    --api-key EMPTY_TOKEN \\\n    --datasets fleurs \\\n    --limit 10  # Remove this line for formal evaluation\n```\n\n### Using Python\n\n```python\nfrom evalscope import run_task\nfrom evalscope.config import TaskConfig\n\ntask_cfg = TaskConfig(\n    model='YOUR_MODEL',\n    api_url='OPENAI_API_COMPAT_URL',\n    api_key='EMPTY_TOKEN',\n    datasets=['fleurs'],\n    dataset_args={\n        'fleurs': {\n            # subset_list: ['cmn_hans_cn', 'en_us', 'yue_hant_hk']  # optional, evaluate specific subsets\n        }\n    },\n    limit=10,  # Remove this line for formal evaluation\n)\n\nrun_task(task_cfg=task_cfg)\n```\n\n\n",
    "zh": "# FLEURS\n\n\n## 概述\n\nFLEURS（Few-shot Learning Evaluation of Universal Representations of Speech）是一个覆盖102种语言的大规模多语言基准测试，用于评估自动语音识别（ASR）、口语理解以及语音翻译任务。\n\n## 任务描述\n\n- **任务类型**：自动语音识别（ASR）\n- **输入**：包含多种语言语音的音频录音\n- **输出**：对应语言的转录文本\n- **语言**：涵盖102种语言，包括简体中文、粤语、英语等\n\n## 主要特点\n\n- 覆盖102种语言的大规模多语言数据\n- 源自FLoRes-101机器翻译基准测试\n- 包含多样化的语系和文字系统\n- 高质量的人工录音与转录\n- 元数据包含性别、语系分组和说话人信息\n\n## 评估说明\n\n- 默认配置使用 **test** 数据划分\n- 主要指标：**词错误率（Word Error Rate, WER）**\n- 默认子集：`cmn_hans_cn`（普通话）、`en_us`（英语）、`yue_hant_hk`（粤语）\n- 评估过程中应用语言特定的文本归一化\n- 提示词：**\"Please recognize the speech and only output the recognized content\"**\n\n## 属性\n\n| 属性 | 值 |\n|----------|-------|\n| **基准测试名称** | `fleurs` |\n| **数据集ID** | [lmms-lab/fleurs](https://modelscope.cn/datasets/lmms-lab/fleurs/summary) |\n| **论文** | N/A |\n| **标签** | `Audio`, `MultiLingual`, `SpeechRecognition` |\n| **指标** | `wer` |\n| **默认样本数（Shots）** | 0-shot |\n| **评估划分** | `test` |\n\n\n## 数据统计\n\n| 指标 | 值 |\n|--------|-------|\n| 总样本数 | 2,411 |\n| 提示词长度（平均） | 67 字符 |\n| 提示词长度（最小/最大） | 67 / 67 字符 |\n\n**各子集统计数据：**\n\n| 子集 | 样本数 | 提示词平均长度 | 提示词最小长度 | 提示词最大长度 |\n|--------|---------|-------------|------------|------------|\n| `cmn_hans_cn` | 945 | 67 | 67 | 67 |\n| `en_us` | 647 | 67 | 67 | 67 |\n| `yue_hant_hk` | 819 | 67 | 67 | 67 |\n\n**音频统计数据：**\n\n| 指标 | 值 |\n|--------|-------|\n| 音频文件总数 | 2,411 |\n| 每样本音频数量 | 最小: 1, 最大: 1, 平均: 1 |\n| 格式 | wav |\n\n\n## 样例示例\n\n**子集**: `cmn_hans_cn`\n\n```json\n{\n  \"input\": [\n    {\n      \"id\": \"daf508c3\",\n      \"content\": [\n        {\n          \"text\": \"Please recognize the speech and only output the recognized content:\"\n        },\n        {\n          \"audio\": \"[BASE64_AUDIO: wav, ~648.8KB]\",\n          \"format\": \"wav\"\n        }\n      ]\n    }\n  ],\n  \"target\": \"这 并 不 是 告 别 这 是 一 个 篇 章 的 结 束 也 是 新 篇 章 的 开 始\",\n  \"id\": 0,\n  \"group_id\": 0,\n  \"metadata\": {\n    \"id\": 1906,\n    \"num_samples\": 166080,\n    \"raw_transcription\": \"“这并不是告别。这是一个篇章的结束，也是新篇章的开始。”\",\n    \"language\": \"Mandarin Chinese\",\n    \"gender\": 0,\n    \"lang_id\": \"cmn_hans\",\n    \"lang_group_id\": 6\n  }\n}\n```\n\n## 提示模板\n\n**提示模板：**\n```text\nPlease recognize the speech and only output the recognized content:\n```\n\n## 使用方法\n\n### 使用命令行（CLI）\n\n```bash\nevalscope eval \\\n    --model YOUR_MODEL \\\n    --api-url OPENAI_API_COMPAT_URL \\\n    --api-key EMPTY_TOKEN \\\n    --datasets fleurs \\\n    --limit 10  # 正式评估时请删除此行\n```\n\n### 使用 Python\n\n```python\nfrom evalscope import run_task\nfrom evalscope.config import TaskConfig\n\ntask_cfg = TaskConfig(\n    model='YOUR_MODEL',\n    api_url='OPENAI_API_COMPAT_URL',\n    api_key='EMPTY_TOKEN',\n    datasets=['fleurs'],\n    dataset_args={\n        'fleurs': {\n            # subset_list: ['cmn_hans_cn', 'en_us', 'yue_hant_hk']  # 可选，用于指定评估子集\n        }\n    },\n    limit=10,  # 正式评估时请删除此行\n)\n\nrun_task(task_cfg=task_cfg)\n```",
    "content_hash": "1cdd1db26ae471653aa40f315185a432",
    "needs_translation": false
  },
  "updated_at": "2026-01-28T17:31:32.230215",
  "translation_updated_at": "2026-01-28T16:09:53Z"
}