Noise Removal & Audio Cleanup API

Clean up speech in audio and video files with one HTTP request. Audo provides two endpoints for speech clean-up and noise reduction:

One-call free API (no API key required)

Send a local file with multipart/form-data or a public HTTPS link as JSON. Without an Authorization header, Audo processes the first minute of each file for free (3 times a day per tool):

# Non-generative noise removal (keeps the speaker's exact voice):
curl -F file=@interview.wav https://audo.ai/api/try/remove-noise -D response.headers -o response.body

# Generative voice enhancement & echo removal (fixes muffled mics and room reverb):
curl -F file=@voice-note.m4a https://audo.ai/api/try/enhance-voice -D response.headers -o response.body

# Or pass a public HTTPS URL as JSON:
curl -X POST https://audo.ai/api/try/remove-noise \
  -H "Content-Type: application/json" \
  -d '{"url": "https://example.com/interview.wav", "noise_reduction": 100, "auto_volume": true}' \
  -D response.headers -o response.body

Inspect response.headers for the HTTP status and Content-Type before naming response.body: a 200 media response is the cleaned audio or video file itself, whereas a 202 JSON response gives a statusUrl to poll for longer jobs (see Responses).

Options

• noise_reduction: integer from 10 to 100 (default 100). On remove-noise, controls how far background noise is turned down (100 removes all it can; lower values leave a natural bed of room tone). On enhance-voice, sets the blend of the regenerated studio voice (100 full studio voice; lower values mix back more of the original recording).
• auto_volume: boolean (default true). Normalizes the output to -16 LUFS with a -1 dBTP true-peak ceiling.
• output_format: same (default), wav, mp3, m4a, or flac.

Whole files with an API key

Add an API key from the API page with free=false and an approved positive max_credits cap (1 credit = 1 US cent, so $0.03/min is 3 credits per minute). You are charged only when the job succeeds:

curl -H "Authorization: Bearer $AUDO_API_KEY" \
  -H "Idempotency-Key: $(uuidgen)" \
  -F file=@interview.wav \
  -F noise_reduction=100 \
  -F free=false \
  -F max_credits="$APPROVED_MAX_CREDITS" \
  https://audo.ai/api/try/remove-noise -D response.headers -o response.body

Video pass-through (picture copied untouched)

Both /api/try/remove-noise and /api/try/enhance-voice accept video files directly (MP4, MOV, MKV, WebM, and AVI, up to 10 GB and 10 hours). When output_format is same (the default), Audo cleans the audio track and copies the original video stream bit-for-bit without re-encoding the picture—so there is zero visual quality loss and no need to demux and remux audio in your own pipeline:

curl -F file=@lecture.mp4 -F noise_reduction=100 https://audo.ai/api/try/remove-noise -D response.headers -o response.body

Authenticated REST API (/api/v1/jobs)

For server backends and files up to 10 GB (uploaded in parts over 16 MB), use the REST API:

1. POST https://audo.ai/api/v1/files with {"filename": "episode.wav", "sizeBytes": 52428800} and upload the bytes to upload.url (or multipart).
2. GET https://audo.ai/api/v1/files/<file_id>?wait=30 until status is ready.
3. POST https://audo.ai/api/v1/jobs to start remove_noise or enhance_voice:

curl -X POST https://audo.ai/api/v1/jobs \
  -H "Authorization: Bearer $AUDO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "tool": "remove_noise",
    "file_id": "file_8Kc2QmP4",
    "noise_reduction": 100,
    "auto_volume": true,
    "output_format": "same",
    "free": false,
    "max_credits": 300,
    "idempotency_key": "550e8400-e29b-41d4-a716-446655440000"
  }'

1. Poll GET https://audo.ai/api/v1/jobs/<job_id> until status is succeeded, then fetch a signed download URL from GET https://audo.ai/api/v1/files/<output_file_id>/download.

Batch-clean a folder of files

To clean a folder of recordings safely, download and inspect the Python client (free-api.py), which checks HTTP statuses, follows 202 job links on audo.ai, verifies downloaded media with ffprobe, and writes each result into a separate directory:

python3 free-api.py remove-noise interview.wav ./cleaned/interview '{"noise_reduction":100,"auto_volume":true}'

for file in ./raw-recordings/*.{wav,mp3,m4a,mp4}; do
  [ -e "$file" ] || continue
  name="$(basename "${file%.*}")"
  python3 free-api.py remove-noise "$file" "./cleaned/$name" '{"noise_reduction":100,"auto_volume":true}'
done

Without an API key, one address can stream 2 uploads at once (an account with an API key, 4 at once, and up to 3 running jobs in parallel).

Command-line afftdn and arnndn filters vs. Audo for speech

Developers building audio pipelines often start with local command-line filters such as afftdn (FFT spectral denoiser), arnndn (RNNoise recurrent network filter), or anlmdn (non-local means denoiser). Here is how they compare with Audo's speech endpoints:

• When `afftdn` or `arnndn` is enough: You have a clean close-mic recording with only light, steady preamp hiss and want zero external network calls.
• When to call `remove_noise`: You have variable background noise (air conditioning, street traffic, café clatter, or background music) and want the speaker's natural vocal timbre preserved without tuning filter thresholds per file.
• When to call `enhance_voice`: The speaker was recorded in an echoey conference room, on a laptop or phone mic, or across a table and needs both reverb removal and vocal restoration.

Related docs

• Free API and REST API reference
• Free LUFS Loudness & Media Info API
• Audio Cleanup & Transcription in n8n
• Audio MCP Server