# Zhipu Asr

> Automatic Speech Recognition (ASR) using Zhipu AI (BigModel) GLM-ASR model. Use when you need to transcribe audio files to text. Supports Chinese audio transcription with context prompts, custom hotwords, and multiple audio formats.

- Skill: `modbender/zhipu-asr` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds add modbender/zhipu-asr`
- Raw SKILL.md: https://api.skillmd.com/api/skills/modbender/zhipu-asr/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: modbender (https://skillmd.com/u/modbender)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/modbender/zhipu-asr

---


# Zhipu AI Automatic Speech Recognition (ASR)

Transcribe Chinese audio files to text using Zhipu AI's GLM-ASR model.

## Setup

**1. Get your API Key:**
Get a key from [Zhipu AI Console](https://bigmodel.cn/usercenter/proj-mgmt/apikeys)

**2. Set it in your environment:**
```bash
export ZHIPU_API_KEY="your-key-here"
```

## Supported Audio Formats

- **WAV** - Recommended, best quality
- **MP3** - Widely supported
- **OGG** - Auto-converted to MP3
- **M4A** - Auto-converted to MP3
- **AAC** - Auto-converted to MP3
- **FLAC** - Auto-converted to MP3
- **WMA** - Auto-converted to MP3

> **Note:** The script automatically converts unsupported formats to MP3 using ffmpeg. Only WAV and MP3 are accepted by the API, but you can use any format that ffmpeg supports.

## File Constraints

- **Maximum file size:** 25 MB
- **Maximum duration:** 30 seconds
- **Recommended sample rate:** 16000 Hz or higher
- **Audio channels:** Mono or stereo

## Usage

### Basic Transcription

Transcribe an audio file with default settings:

```bash
bash scripts/speech_to_text.sh recording.wav
```

### Transcription with Context

Provide previous transcription or context for better accuracy:

```bash
bash scripts/speech_to_text.sh recording.wav "这是之前的转录内容，有助于提高准确性"
```

### Transcription with Hotwords

Use custom vocabulary to improve recognition of specific terms:

```bash
bash scripts/speech_to_text.sh recording.mp3 "" "人名,地名,专业术语,公司名称"
```

### Full Options

Combine context and hotwords:

```bash
bash scripts/speech_to_text.sh recording.wav "会议记录片段" "张三,李四,项目名称"
```

**Parameters:**
- `audio_file` (required): Path to audio file (.wav or .mp3)
- `prompt` (optional): Previous transcription or context text (max 8000 chars)
- `hotwords` (optional): Comma-separated list of specific terms (max 100 words)

## Features

### Context Prompts

**Why use context prompts:**
- Improves accuracy in long conversations
- Helps with domain-specific terminology
- Maintains consistency across multiple segments

**When to use:**
- Multi-part conversations or meetings
- Technical or specialized content
- Continuing from previous transcriptions

**Example:**
```bash
bash scripts/speech_to_text.sh part2.wav "第一部分的转录内容：讨论了项目进展和下一步计划"
```

### Hotwords

**What are hotwords:**
Custom vocabulary list that boosts recognition accuracy for specific terms.

**Best use cases:**
- Proper names (people, places)
- Domain-specific terminology
- Company names and products
- Technical jargon
- Industry-specific terms

**Examples:**
```bash
# Medical transcription
bash scripts/speech_to_text.sh medical.wav "" "患者,症状,诊断,治疗方案"

# Business meeting
bash scripts/speech_to_text.sh meeting.wav "" "张经理,李总,项目代号,预算"

# Tech discussion
bash scripts/speech_to_text.sh tech.wav "" "API,数据库,算法,框架"
```

## Workflow Examples

### Transcribe a Meeting

```bash
# Part 1
bash scripts/speech_to_text.sh meeting_part1.wav

# Part 2 with context
bash scripts/speech_to_text.sh meeting_part2.wav "第一部分讨论了项目进度" "张总,李经理,项目名称"

# Part 3 with context
bash scripts/speech_to_text.sh meeting_part3.wav "前两部分讨论了项目进度和预算" "张总,李经理,项目名称"
```

### Transcribe a Lecture

```bash
bash scripts/speech_to_text.sh lecture.wav "" "教授,课程名称,专业术语1,专业术语2"
```

### Process Multiple Files

```bash
for file in recording_*.wav; do
    bash scripts/speech_to_text.sh "$file"
done
```

## Audio Quality Tips

**Best practices for accurate transcription:**

1. **Clear audio source**
   - Minimize background noise
   - Use good quality microphone
   - Speak clearly and at moderate pace

2. **Optimal audio settings**
   - Sample rate: 16000 Hz or higher
   - Bit depth: 16-bit or higher
   - Single channel (mono) is sufficient

3. **File preparation**
   - Remove silence from beginning/end
   - Normalize audio levels
   - Ensure consistent volume

## Output Format

The script outputs JSON with:
- `id`: Task ID
- `created`: Request timestamp (Unix timestamp)
- `request_id`: Unique request identifier
- `model`: Model name used
- `text`: Transcribed text

Example output:
```json
{
  "id": "task-12345",
  "created": 1234567890,
  "request_id": "req-abc123",
  "model": "glm-asr-2512",
  "text": "你好，这是转录的文本内容"
}
```

## Troubleshooting

**File Size Issues:**
- Split audio files larger than 25 MB
- Reduce sample rate or bit depth
- Use compression (MP3) for smaller files

**Duration Issues:**
- Split recordings longer than 30 seconds
- Process segments separately
- Use context prompts to maintain continuity

**Poor Accuracy:**
- Improve audio quality
- Use hotwords for specific terms
- Provide context prompts
- Ensure clear speech and minimal noise

**Format Issues:**
- Ensure file is .wav or .mp3
- Check file is not corrupted
- Verify audio can be played by standard players

## Limitations

- Maximum audio duration: 30 seconds per request
- File size limit: 25 MB
- Maximum hotwords: 100 terms
- Context prompt limit: 8000 characters
- Best performance with Chinese language audio

## Performance Notes

- Typical transcription time: 1-3 seconds
- Real-time or faster for most audio
- Processing time scales with audio quality and length