{
  "meta": {
    "pretty_name": "WenetSpeech",
    "dataset_id": "lmms-lab/WenetSpeech",
    "paper_url": null,
    "tags": [
      "Audio",
      "SpeechRecognition"
    ],
    "metrics": [
      "mer"
    ],
    "few_shot_num": 0,
    "eval_split": "test_meeting",
    "train_split": "",
    "subset_list": [
      "dev",
      "test_meeting"
    ],
    "description": "\n## Overview\n\nWenetSpeech is a large-scale Mandarin Chinese speech corpus with over 10,000 hours of multi-domain transcribed audio data, designed for speech recognition research.\n\n## Task Description\n\n- **Task Type**: Automatic Speech Recognition (ASR)\n- **Input**: Audio recordings with Mandarin Chinese speech\n- **Output**: Transcribed text in Chinese\n- **Domain**: Multi-domain (internet, meeting)\n\n## Key Features\n\n- Large-scale Mandarin Chinese speech corpus (10,000+ hours)\n- Multi-domain coverage: internet content, meetings\n- High-quality transcriptions\n- Suitable for evaluating Chinese ASR systems\n- Supports mixed Chinese-English text evaluation\n\n## Evaluation Notes\n\n- Default configuration uses **test_meeting** split\n- Subsets by domain: **dev** (development), **test_meeting** (meeting domain)\n- Primary metric: **MER** (Mixed Error Rate)\n- MER tokenizes Chinese characters individually and English words as whole tokens\n- Prompt: \"Please listen to the audio and transcribe what you hear\"\n",
    "prompt_template": "Please listen to the audio and transcribe what you hear. Please only provide the transcription without any additional commentary. Do not include any punctuation.",
    "system_prompt": "",
    "few_shot_prompt_template": "",
    "aggregation": "mean",
    "extra_params": {},
    "sandbox_config": {},
    "category": "vlm"
  },
  "statistics": {
    "total_samples": 22195,
    "subset_stats": [
      {
        "name": "dev",
        "sample_count": 13825,
        "prompt_length_mean": 161,
        "prompt_length_min": 161,
        "prompt_length_max": 161,
        "prompt_length_std": null,
        "target_length_mean": 23.91,
        "multimodal": {
          "has_images": false,
          "has_audio": true,
          "has_video": false,
          "audio": {
            "count_total": 13825,
            "count_per_sample": {
              "min": 1,
              "max": 1,
              "mean": 1
            },
            "duration": null,
            "sample_rates": [],
            "formats": [
              "wav"
            ]
          }
        }
      },
      {
        "name": "test_meeting",
        "sample_count": 8370,
        "prompt_length_mean": 161,
        "prompt_length_min": 161,
        "prompt_length_max": 161,
        "prompt_length_std": null,
        "target_length_mean": 26.33,
        "multimodal": {
          "has_images": false,
          "has_audio": true,
          "has_video": false,
          "audio": {
            "count_total": 8370,
            "count_per_sample": {
              "min": 1,
              "max": 1,
              "mean": 1
            },
            "duration": null,
            "sample_rates": [],
            "formats": [
              "wav"
            ]
          }
        }
      }
    ],
    "prompt_length": {
      "mean": 161,
      "min": 161,
      "max": 161,
      "std": null
    },
    "target_length_mean": 24.82,
    "computed_at": "2026-06-23T18:59:23.855843+08:00",
    "multimodal": {
      "has_images": false,
      "has_audio": true,
      "has_video": false,
      "audio": {
        "count_total": 22195,
        "count_per_sample": {
          "min": 1,
          "max": 1,
          "mean": 1
        },
        "duration": null,
        "sample_rates": [],
        "formats": [
          "wav"
        ]
      }
    }
  },
  "sample_example": {
    "data": {
      "input": [
        {
          "id": "c30c80b4",
          "content": [
            {
              "text": "Please listen to the audio and transcribe what you hear. Please only provide the transcription without any additional commentary. Do not include any punctuation."
            },
            {
              "audio": "[BASE64_AUDIO: wav, ~175.3KB]",
              "format": "wav"
            }
          ]
        }
      ],
      "target": "对我做了介绍啊那么我想说的是呢大家如果对我的研究感兴趣呢嗯",
      "id": 0,
      "group_id": 0,
      "metadata": {
        "text": "对我做了介绍啊那么我想说的是呢大家如果对我的研究感兴趣呢嗯"
      }
    },
    "subset": "dev",
    "truncated": false
  },
  "readme": {
    "en": "# WenetSpeech\n\n\n## Overview\n\nWenetSpeech is a large-scale Mandarin Chinese speech corpus with over 10,000 hours of multi-domain transcribed audio data, designed for speech recognition research.\n\n## Task Description\n\n- **Task Type**: Automatic Speech Recognition (ASR)\n- **Input**: Audio recordings with Mandarin Chinese speech\n- **Output**: Transcribed text in Chinese\n- **Domain**: Multi-domain (internet, meeting)\n\n## Key Features\n\n- Large-scale Mandarin Chinese speech corpus (10,000+ hours)\n- Multi-domain coverage: internet content, meetings\n- High-quality transcriptions\n- Suitable for evaluating Chinese ASR systems\n- Supports mixed Chinese-English text evaluation\n\n## Evaluation Notes\n\n- Default configuration uses **test_meeting** split\n- Subsets by domain: **dev** (development), **test_meeting** (meeting domain)\n- Primary metric: **MER** (Mixed Error Rate)\n- MER tokenizes Chinese characters individually and English words as whole tokens\n- Prompt: \"Please listen to the audio and transcribe what you hear\"\n\n\n## Properties\n\n| Property | Value |\n|----------|-------|\n| **Benchmark Name** | `wenet_speech` |\n| **Dataset ID** | [lmms-lab/WenetSpeech](https://modelscope.cn/datasets/lmms-lab/WenetSpeech/summary) |\n| **Paper** | N/A |\n| **Tags** | `Audio`, `SpeechRecognition` |\n| **Metrics** | `mer` |\n| **Default Shots** | 0-shot |\n| **Evaluation Split** | `test_meeting` |\n\n\n## Data Statistics\n\n| Metric | Value |\n|--------|-------|\n| Total Samples | 22,195 |\n| Prompt Length (Mean) | 161 chars |\n| Prompt Length (Min/Max) | 161 / 161 chars |\n\n**Per-Subset Statistics:**\n\n| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |\n|--------|---------|-------------|------------|------------|\n| `dev` | 13,825 | 161 | 161 | 161 |\n| `test_meeting` | 8,370 | 161 | 161 | 161 |\n\n**Audio Statistics:**\n\n| Metric | Value |\n|--------|-------|\n| Total Audio Files | 22,195 |\n| Audio per Sample | min: 1, max: 1, mean: 1 |\n| Formats | wav |\n\n\n## Sample Example\n\n**Subset**: `dev`\n\n```json\n{\n  \"input\": [\n    {\n      \"id\": \"c30c80b4\",\n      \"content\": [\n        {\n          \"text\": \"Please listen to the audio and transcribe what you hear. Please only provide the transcription without any additional commentary. Do not include any punctuation.\"\n        },\n        {\n          \"audio\": \"[BASE64_AUDIO: wav, ~175.3KB]\",\n          \"format\": \"wav\"\n        }\n      ]\n    }\n  ],\n  \"target\": \"对我做了介绍啊那么我想说的是呢大家如果对我的研究感兴趣呢嗯\",\n  \"id\": 0,\n  \"group_id\": 0,\n  \"metadata\": {\n    \"text\": \"对我做了介绍啊那么我想说的是呢大家如果对我的研究感兴趣呢嗯\"\n  }\n}\n```\n\n## Prompt Template\n\n**Prompt Template:**\n```text\nPlease listen to the audio and transcribe what you hear. Please only provide the transcription without any additional commentary. Do not include any punctuation.\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 wenet_speech \\\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=['wenet_speech'],\n    dataset_args={\n        'wenet_speech': {\n            # subset_list: ['dev', 'test_meeting']  # 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": "# WenetSpeech\n\n\n## 概述\n\nWenetSpeech 是一个大规模中文语音语料库，包含超过 10,000 小时的多领域转录音频数据，专为语音识别研究而设计。\n\n## 任务描述\n\n- **任务类型**：自动语音识别（ASR）\n- **输入**：包含中文语音的音频录音\n- **输出**：中文转录文本\n- **领域**：多领域（互联网、会议）\n\n## 主要特点\n\n- 大规模中文语音语料库（10,000+ 小时）\n- 覆盖多领域：互联网内容、会议\n- 高质量转录文本\n- 适用于评估中文 ASR 系统\n- 支持中英混合文本评估\n\n## 评估说明\n\n- 默认配置使用 **test_meeting** 子集\n- 按领域划分的子集：**dev**（开发集）、**test_meeting**（会议领域）\n- 主要指标：**MER**（Mixed Error Rate，混合错误率）\n- MER 将中文字符逐字切分，英文单词作为整体切分\n- 提示词：\"Please listen to the audio and transcribe what you hear\"\n\n## 属性\n\n| 属性 | 值 |\n|----------|-------|\n| **基准测试名称** | `wenet_speech` |\n| **数据集ID** | [lmms-lab/WenetSpeech](https://modelscope.cn/datasets/lmms-lab/WenetSpeech/summary) |\n| **论文** | N/A |\n| **标签** | `Audio`, `SpeechRecognition` |\n| **指标** | `mer` |\n| **默认样本数** | 0-shot |\n| **评估子集** | `test_meeting` |\n\n\n## 数据统计\n\n| 指标 | 值 |\n|--------|-------|\n| 总样本数 | 22,195 |\n| 提示词长度（平均） | 161 字符 |\n| 提示词长度（最小/最大） | 161 / 161 字符 |\n\n**各子集统计信息：**\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| `dev` | 13,825 | 161 | 161 | 161 |\n| `test_meeting` | 8,370 | 161 | 161 | 161 |\n\n**音频统计信息：**\n\n| 指标 | 值 |\n|--------|-------|\n| 音频文件总数 | 22,195 |\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**: `dev`\n\n```json\n{\n  \"input\": [\n    {\n      \"id\": \"c30c80b4\",\n      \"content\": [\n        {\n          \"text\": \"Please listen to the audio and transcribe what you hear. Please only provide the transcription without any additional commentary. Do not include any punctuation.\"\n        },\n        {\n          \"audio\": \"[BASE64_AUDIO: wav, ~175.3KB]\",\n          \"format\": \"wav\"\n        }\n      ]\n    }\n  ],\n  \"target\": \"对我做了介绍啊那么我想说的是呢大家如果对我的研究感兴趣呢嗯\",\n  \"id\": 0,\n  \"group_id\": 0,\n  \"metadata\": {\n    \"text\": \"对我做了介绍啊那么我想说的是呢大家如果对我的研究感兴趣呢嗯\"\n  }\n}\n```\n\n## 提示模板\n\n**提示模板：**\n```text\nPlease listen to the audio and transcribe what you hear. Please only provide the transcription without any additional commentary. Do not include any punctuation.\n```\n\n## 使用方法\n\n### 使用 CLI\n\n```bash\nevalscope eval \\\n    --model YOUR_MODEL \\\n    --api-url OPENAI_API_COMPAT_URL \\\n    --api-key EMPTY_TOKEN \\\n    --datasets wenet_speech \\\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=['wenet_speech'],\n    dataset_args={\n        'wenet_speech': {\n            # subset_list: ['dev', 'test_meeting']  # 可选，用于评估特定子集\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": "aa6114dac37a6710279e4cc9f7d8be1c",
    "needs_translation": false
  },
  "updated_at": "2026-06-23T19:06:06.244822+08:00",
  "translation_updated_at": "2026-06-23T19:06:21+08:00"
}