Meta Muse Voice Transcribe for batch WAV audio with turn timestamps, speaker diarization and language detection.
25 languages · benchmarked
Overall Score
61.8
Word Error Rate
14.22%
Character Error Rate
10.91%
Match Error Rate
13.19%
Word Info Lost
18.65%
Avg Latency
8.4s
Benchmarks Run
34
overall WER · 37 models
field · 37 models
lower-left is better
8 categories
WER
lower is better
Medical
2.6%
Legal
4.9%
Technical
7.4%
Finance
8.6%
Conversational
10.5%
General
15.0%
Code-Switching
19.8%
Noisy Environment
26.3%
| Category | WER | CER | MER | WIL | Latency | Benchmarks |
|---|---|---|---|---|---|---|
Code-Switching | 19.78% | 15.03% | 18.94% | 22.65% | 15.5s | 2 |
Conversational | 10.49% | 8.04% | 9.89% | 13.09% | 11.5s | 2 |
Finance | 8.56% | 4.89% | 8.26% | 11.75% | 11.6s | 2 |
General | 15.05% | 11.47% | 14.38% | 21.29% | 7.3s | 18 |
Legal | 4.92% | 4.13% | 4.75% | 5.75% | 10.0s | 2 |
Medical | 2.59% | 2.14% | 2.55% | 2.88% | 9.1s | 2 |
Noisy Environment | 26.25% | 20.63% | 21.67% | 30.43% | 4.0s | 4 |
Technical | 7.42% | 6.84% | 7.09% | 8.44% | 11.5s | 2 |
5 accents
| Accent | WER | Benchmarks |
|---|---|---|
African | 26.15% | 1 |
Australian | 18.46% | 1 |
Indian | 28.57% | 1 |
British | 17.91% | 1 |
American | 24.53% | 1 |
Rate
$0.003
/min
Per-second billing. Bring your own provider key and pay your provider directly — a 5% routing fee applies (first 100 min/mo free).
Set up BYOKCost estimator
1 hour
$0.18
10 hours
$1.80
100 hours
$18
1,000 hours
$180
Billed per second of audio processed.
Model ID
meta/muse-voice-transcribe
Authenticate every request with your secret API key as a Bearer token. Issue a key from your dashboard.
Use it with your agent
Paste this into Claude Code, Cursor, Codex or any agent with a shell. It installs the CLI and the OpenTranscription skill, then transcribes with this model.
Quickstart
Upload your audio, create a job with this model, then poll the job or set a webhook_url to be notified when it completes.
Key parameters
file_path
string
required
Storage path returned by the upload step.
model
string
required
The model to run this job on.
models
string[]
Ordered fallback chain: primary first, then backups tried in order if a provider fails.
language
string
ISO 639-1 language code. Omit to auto-detect.
diarization
boolean
Label speakers (A, B, …) in the output.
word_timestamps
boolean
Per-word start, end and confidence. Defaults to true; send false to leave them out.
custom_words
string[]
Names, jargon and product terms to bias the model toward. Up to 1000 entries.
vocabulary_list_id
uuid
A vocabulary list saved in your workspace. Merged with custom_words when both are sent.
webhook_url
string
HTTPS URL notified with the result when the job completes. Delivered events are signed (X-OT-Signature).
title
string
Display name for this job in the dashboard. Falls back to the file name.
Response
A completed job returns the transcript in the OpenTranscription Unified Schema (OTUS).
Webhooks — skip polling
We POST a signed event to your webhook_url when a job completes or fails; verify the X-OT-Signature (HMAC-SHA256, reject if older than 300 s) and dedupe on the event id, then fetch the full transcript via GET /api/v1/transcriptions/{id}.
Rate-limited per tier — see the X-RateLimit-* response headers.
Full API referenceSupported Languages
Supported Formats
Features
Limits
Max file size
32 MB
Max duration
10 min
Muse Voice Transcribe is a speech-to-text model from Meta. It supports 25 languages and runs at $0.003/min — cheaper than most alternatives.
Its nearest benchmarked alternative is MAI-Transcribe-2 (preview). Best suited for multi-speaker conversations and high-volume, cost-sensitive pipelines.