Voice Isolator: removing noise from audio with ElevenLabs
Voice Isolator from ElevenLabs removes background noise from recordings. We tested it with cafe chatter, street noise and fan hum to measure how much cleaner it gets.

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Voice Isolator from ElevenLabs removes background noise from recordings: it cuts out ambient sound, reverb and music, leaving only the voice. We tested it in English on a recording we deliberately corrupted: a clean monologue mixed in ffmpeg with fan noise, street rain and cafe chatter. We measured how much noise remains, whether the voice sounds like the original and whether the cleaned file transcribes better. The last result surprised us.
Summary
Voice Isolator lowered the noise between words by 50-56 dB and brought the voice closer to the clean original (similarity up to 0.991). For listening and publishing that is a big improvement, and a 28-second recording was ready in 3 seconds for 472 credits. The model can however change phonemes in heavily masked sections: after cleaning the transcription had more errors than the raw noisy file, and with chatter 5 dB louder than speech errors jumped from 2 to 14. Good for: podcasts, phone interviews, audio for video. Not for: recordings you only need to transcribe, and evidence where every word counts.
01ElevenLabs Scribe: English transcription test with hard namesElevenLabs Scribe v2 transcribed a 59-second English interview with tricky names and 12 numbers in 3.6 s with 2 automatic differences, correctly attributed 6 of 6 turns.Read →
02ElevenLabs Voice Changer: AI Voice Swap Test & ResultsVoice Changer keeps your timing and melody while swapping the voice. We tested it on clean and noisy recordings - here's what survived and what didn't.Read →
03ElevenLabs Voice Cloning: clones from 10 s, 30 s and 2 minWe cloned a synthetic voice from 10 s, 30 s and 2 minutes of audio. The 10-second clone reached 0.992 similarity, the 2-minute clone 0.996.Read →
04ElevenLabs Dubbing v1 vs v2: Test on a 24-Second ClipElevenLabs Dubbing v1 vs v2 tested on a 24-second clip: time, credits, voice similarity, and one failure that still cost credits.Read →
05ElevenLabs Sound Effects: Prompt Influence 0.3 vs 1.0 TestWe tested ElevenLabs Sound Effects with five English prompts at prompt influence 0.3 and 1.0. Both settings hit the target 5 of 5 times.Read →What Voice Isolator does
The tool accepts an audio file (AAC, AIFF, OGG, MP3, OPUS, WAV, FLAC, M4A) or video (MP4, MOV, MKV, WEBM among others) up to 500 MB and 1 hour. It returns speech only. The documentation notes that the tool is not optimized for extracting vocals from music, though it sometimes works. The same model also powers the “remove background noise” switch in voice cloning and Voice Changer.
Our test: clean recording, four types of noise
We started with a 28.3-second monologue about apartment hunting (87 words), generated with a synthetic voice in the Eleven v4 model. This gave us a reference: we know exactly how the recording sounded without noise.
We added noise in ffmpeg. “Fan” is pink noise with 50 and 150 Hz hum, generated in ffmpeg itself. “Street” (rain and tram) and “cafe” (chatter, cups, espresso machine) are backgrounds from the sound effects generator. We set the level so the noise was as loud as the speech (0 dB SNR), and in the hardest version the cafe was 5 dB louder than the voice. Example for the fan, simplified:
ffmpeg -f lavfi -i "anoisesrc=color=pink:amplitude=0.5:duration=28.3"
-f lavfi -i "sine=frequency=50:duration=28.3"
-f lavfi -i "sine=frequency=150:duration=28.3"
-filter_complex "[1:a]volume=0.6[h1];[2:a]volume=0.25[h2];[0:a][h1][h2]amix=inputs=3:normalize=0"
-ac 1 noise.wav
ffmpeg -i clean.wav -i noise.wav
-filter_complex "[1:a]volume=0.675[n];[0:a][n]amix=inputs=2:normalize=0:duration=first"
noisy.wav
We calculated the noise gain (here 0.675) separately for each background, from the RMS level of speech and noise. We sent each noisy file on September 29, 2026 to Voice Isolator via the API.
Measurement results
We did not judge quality by ear. We measured three things against the clean reference: the ratio of speech level to what you hear in pauses between words (more dB means quieter pauses), the distance of the spectrum from the reference (less is closer to the original), and voice similarity using the WavLM speaker verification model.
| Condition | Speech-to-pause: before / after | Spectrum distance: before / after | Voice similarity: before / after |
|---|---|---|---|
| Fan, 0 dB | 2.9 / 52.9 dB | 22.52 / 6.18 | 0.959 / 0.991 |
| Street, 0 dB | 7.5 / 57.4 dB | 17.85 / 5.37 | 0.983 / 0.99 |
| Cafe, 0 dB | 4.5 / 60.4 dB | 20.22 / 6.15 | 0.974 / 0.984 |
| Cafe, -5 dB | 2.4 / 56.6 dB | 24.2 / 7.9 | 0.931 / 0.973 |
In terms of sound this is a very good result: noise in pauses dropped by more than 50 dB, and the spectrum distance from the reference fell roughly threefold. In the clean reference this ratio is about 62.6 dB, so it does not quite reach studio silence. The output file has the same length as the input, so you can sync it to video without shifting.
Surprise: clean does not mean faithful
We transcribed each recording before and after cleaning with ElevenLabs Scribe and counted wrong words out of 88.
| Condition | Errors: noisy | Errors: after Voice Isolator | Example |
|---|---|---|---|
| Fan, 0 dB | 0 | 2 | “quickly” → “quickle”, “something” → “some” |
| Street, 0 dB | 0 | 0 | - |
| Cafe, 0 dB | 0 | 6 | “quickly” → “quick”, “three thousand two hundred” deleted, “will” inserted |
| Cafe, -5 dB | 2 | 14 | “recording” → “reporting”, “because” → “as”, “have a” → “am”, “three months” → “a month” |
Sample-by-sample comparison with the reference came out very poorly, even though the spectrum is close to the original. It looks like the model does not just cut noise but reconstructs speech, and where the voice was masked it guesses phonemes. The ear gets clean sound, and transcription sometimes gets different words. Practical conclusion: for transcription send the raw file straight to the transcription service, and use Voice Isolator for what someone will listen to.
How to use Voice Isolator
- In the app: Voice Isolator, upload an audio or video file and download the result.
- Via API (tested September 28, 2026):
curl -X POST https://api.elevenlabs.io/v1/audio-isolation
-H "xi-api-key: $ELEVENLABS_API_KEY"
-F audio=@recording.wav -o clean.mp3
Listen to the result in places where the noise was loudest, and compare with the original before you publish a quote. If the recording will be used for voice changing, you do not need to clean it separately: Voice Changer has the same filter as an option, and in our test it made a huge difference there.
How much does Voice Isolator cost
The documentation states 1000 credits per minute of recording, which matches our measurement: 472 credits for 28.31 s. API pricing from September 28, 2026: $0.12 per minute. An hour-long podcast is therefore $7.20. In the Creator plan at $22 (121,000 credits) you can fit about 2 hours of cleaning per month if you use credits for nothing else. We compare the other plans in the article how much ElevenLabs costs, and all tools in the ElevenLabs guide.
Frequently asked questions
How do you remove noise from a recording with AI?
Upload the file to Voice Isolator in ElevenLabs, in the app or via API. In our test the noise in pauses between words dropped by 50-56 dB, and a 28-second recording was ready in about 3 seconds.
How much does Voice Isolator cost?
1000 credits per minute of recording, via API $0.12 per minute before VAT. Our 28.31 s cost 472 credits.
Does Voice Isolator improve transcription?
In our test no, it made it worse. With chatter louder than speech the number of wrong words rose from 2 to 14 out of 88, because the model reconstructs different phonemes in masked sections.
Does Voice Isolator work with video files?
Yes. It accepts MP4, MOV, MKV and WEBM among others up to 500 MB and an hour long, and returns the cleaned audio track.
Sources and date checked
- documentation: Voice isolator
- API: Audio isolation
- API pricing
- plan pricing
- model microsoft/wavlm-base-plus-sv. Own test September 29
- 2026: 4 recordings of 28.31 s each (1888 credits) plus 6 s curl command test. Checked September 29
- 2026
ElevenLabs Voice Changer: AI Voice Swap Test & ResultsAI Tools
ElevenLabs Voice Cloning: clones from 10 s, 30 s and 2 minAI Tools
ElevenLabs Dubbing v1 vs v2: Test on a 24-Second ClipBenchmarksThe link to ElevenLabs is a partner link: if you buy through it, QUANTSLANT earns a commission and you pay the same price. It has no influence on the content, including where the coverage is critical.
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