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 scripts/speech_to_text.sh recording.wav

Transcription with Context

Provide previous transcription or context for better accuracy:

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 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 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 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:

# 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

# 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 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

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

Audio Quality Tips

Best practices for accurate transcription:

Clear audio source
- Minimize background noise
- Use good quality microphone
- Speak clearly and at moderate pace
Optimal audio settings
- Sample rate: 16000 Hz or higher
- Bit depth: 16-bit or higher
- Single channel (mono) is sufficient
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:

{
  "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

zhipu-asr

Zhipu AI Automatic Speech Recognition (ASR)

Setup

Supported Audio Formats