Audio & Transcription

The AlphaEdge Audio & Transcription API lets you transcribe audio files to text. This feature is optimized for high performance and accuracy.

This page guides you through using the Audio & Transcription API, from the basics to advanced use cases.

Here is a minimal example to get started with the Audio & Transcription API:

Basic example

import requests

url = "https://api-endpoints.alphaedge-ai.com/models/alpha-audio-v1/transcript"
headers = {"X-API-Key": "TA_CLE"}

with open("/chemin/audio.wav", "rb") as f:
    files = {"audio": ("audio.wav", f, "audio/wav")}
    data = {
        "enable_diarization": "true",
        "enable_postcorrect": "true",
    }
    r = requests.post(url, headers=headers, files=files, data=data, timeout=300)

print(r.status_code)
print(r.json())

curl https://api-endpoints.alphaedge-ai.com/models/alpha-audio-v1/transcript \
  -H "X-API-Key: TA_CLE" \
  -F "audio=@/chemin/audio.wav" \
  -F "enable_diarization=true" \
  -F "enable_postcorrect=true"

import fs from "node:fs";

const form = new FormData();
form.append("audio", new Blob([fs.readFileSync("/chemin/audio.wav")]), "audio.wav");
form.append("enable_diarization", "true");
form.append("enable_postcorrect", "true");

const res = await fetch("https://api-endpoints.alphaedge-ai.com/models/alpha-audio-v1/transcript", {
  method: "POST",
  headers: { "X-API-Key": "TA_CLE" },
  body: form
});

console.log(res.status, await res.json());

Here are the available parameters for the Audio & Transcription API:

The AlphaEdge Audio & Transcription API supports a wide variety of audio formats for transcription. Here is the full list of supported formats:

Compressed audio formats

• MP3 (.mp3) - Most common format, lossy compression
• AAC (.aac, .m4a) - Apple format, good quality at low bitrate
• OGG Vorbis (.ogg) - Open source format, efficient compression
• OPUS (.opus) - Voice-optimized format, excellent for calls
• WMA (.wma) - Windows Media Audio

Uncompressed audio formats

• WAV (.wav) - Uncompressed PCM format, maximum quality
• FLAC (.flac) - Lossless compression, high quality
• AIFF (.aiff, .aif) - Uncompressed Apple format

Streaming audio formats

• WebM Audio (.webm) - Modern web format
• M4A (.m4a) - Apple container format

Technical specifications

• Sampling rate: 8 kHz to 48 kHz (recommended: 16 kHz or 44.1 kHz)
• Bit depth: 16 bit or 24 bit
• Channels: Mono, stereo, or multi-channel (auto-converted to mono)
• Maximum duration: 25 minutes per file
• Maximum size: 25 MB per file

Video formats (audio extraction)

The API can also extract and transcribe audio from video files:

• MP4 (.mp4) - Video with audio track
• AVI (.avi) - Video container format
• MOV (.mov) - QuickTime format
• MKV (.mkv) - Open source container format
• WebM (.webm) - Web video format

Recommendations

• For voice: MP3 at 128 kbps or WAV 16 kHz mono offer a good quality/size trade-off
• For music with vocals: WAV or FLAC to preserve quality
• For phone calls: OPUS or MP3 at 64 kbps mono
• Avoid very low quality audio files (< 16 kHz) for best results
• For long files (> 25 min), split them into segments

The Audio & Transcription API returns a response in JSON format. Here is an example response structure:

{
  "model_slug": "alpha-audio-v1",
  "text": "Le texte transcrit depuis l'audio...",
  "inference_seconds": 1.2,
  "enable_diarization": false,
  "audio_duration_seconds": 45.3,
  "audio_filename": "audio.wav"
}

Transcription with timestamps

Get a transcription with timestamps for each segment:

import requests

url = "https://api-endpoints.alphaedge-ai.com/models/alpha-audio-v1/transcript"
headers = {"X-API-Key": "TA_CLE"}

with open("/chemin/audio.wav", "rb") as f:
    files = {"audio": ("audio.wav", f, "audio/wav")}
    data = {
        "enable_diarization": "true",
        "enable_postcorrect": "true",
    }
    r = requests.post(url, headers=headers, files=files, data=data, timeout=300)

print(r.status_code)
print(r.json())

Error handling

Here is how to handle errors properly:

import requests

url = "https://api-endpoints.alphaedge-ai.com/models/alpha-audio-v1/transcript"
headers = {"X-API-Key": "TA_CLE"}

with open("/chemin/audio.wav", "rb") as f:
    files = {"audio": ("audio.wav", f, "audio/wav")}
    data = {
        "enable_diarization": "true",
        "enable_postcorrect": "true",
    }
    r = requests.post(url, headers=headers, files=files, data=data, timeout=300)

print(r.status_code)
print(r.json())

import fs from "node:fs";

const form = new FormData();
form.append("audio", new Blob([fs.readFileSync("/chemin/audio.wav")]), "audio.wav");
form.append("enable_diarization", "true");
form.append("enable_postcorrect", "true");

const res = await fetch("https://api-endpoints.alphaedge-ai.com/models/alpha-audio-v1/transcript", {
  method: "POST",
  headers: { "X-API-Key": "TA_CLE" },
  body: form
});

console.log(res.status, await res.json());

Here are some common use cases for the Audio & Transcription API:

1. Meeting transcription

Automatically transcribe meetings for archiving and search.

2. Video subtitling

Generate automatic subtitles for your video content.

3. Podcast transcription

Create transcriptions to improve accessibility and SEO.

Limitations

• File size : Files must not exceed 25 MB
• Supported formats : MP3, WAV, M4A, FLAC, AAC, OGG, OPUS, WMA, AIFF, WebM, and video formats (MP4, AVI, MOV, MKV, WebM)
• Maximum duration : 25 minutes per file
• Rate limiting : 60 requests per minute by default (can be increased based on your plan)
• Tokens : 4096 token limit for combined prompts and responses

Best practices

• Use good quality audio files (minimum 16 kHz) for best results
• For long files, split them into segments of 25 minutes maximum
• Handle errors properly with try/except blocks
• Implement a retry mechanism to handle temporary errors
• Cache results when possible to reduce costs
• Monitor your usage to avoid exceeding your limits

To view all available audio & transcription models with their detailed specifications, visit the Our models and filter by type.