Meta-Omnilingual-ASR-7B by Subformer is a speech-to-text model hosted on Replicate. Upload an MP3, WAV, or FLAC file, leave language set to auto or enter a language identifier such as eng_Latn, run the prediction, and read the returned transcription. The listed deployment accepts audio of up to 40 seconds per prediction.
Meta’s broader Omnilingual ASR project reports support for more than 1,600 languages, but that is the published scope of the model family, not a promise of identical accuracy for every language, dialect, speaker, or recording. Treat the transcript as a draft when names, figures, legal wording, or low-resource languages matter.
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What this model is—and what it is not
Automatic speech recognition (ASR) converts spoken audio into written text. The Subformer deployment named subformer/meta-omnilingual-asr-7b is an online ASR endpoint: it does not translate the speech, summarize it, identify speakers, or answer questions about the recording. Its documented output is a text string.
The “7B” refers to the seven-billion-parameter scale of this LLM-based model variant. It belongs to Meta/Facebook Research’s open-source Omnilingual ASR family, which also contains CTC (connectionist temporal classification) models. The hosted page is a convenient way to run one member of that family without installing the entire research stack.
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| Item | What the Replicate listing establishes |
|---|---|
| Model slug | subformer/meta-omnilingual-asr-7b |
| Task | Speech recognition: audio to a transcription string |
| Input | An uploaded audio file plus a language choice |
| Accepted examples | MP3, WAV, and FLAC |
| Maximum duration | 40 seconds for the listed endpoint |
| Language default | auto, for automatic language detection |
| Displayed version | c1b60d82 on the referenced page; hosted versions and schemas can change |
| Output schema | A string containing the transcription |
Meta’s project description says: “Omnilingual ASR is an open-source speech recognition system supporting over 1,600 languages — including hundreds never previously covered by any ASR technology.” That statement describes the research family. It should not be read as a guarantee that this particular hosted version will recognize every language equally well in every acoustic condition.
What you need before the first transcription
- A browser and a Replicate account with access to the Subformer model page.
- An audio file in a listed format. The model accepts an existing recording; a microphone is optional and is not required.
- A clip no longer than the endpoint’s documented duration limit.
- For manual language selection, the appropriate identifier in the model’s language-and-script format.
You do not need to understand Meta’s training code, install Python, or download model weights to use the hosted interface.
How to transcribe audio in the Replicate interface
- Open the exact model page. Find Subformer’s
meta-omnilingual-asr-7blisting on Replicate. Check the live page before relying on an old screenshot or version-specific instruction, because hosted schemas can be updated. - Choose the audio input. Use the page’s file control to select an MP3, WAV, or FLAC recording. If the file is longer than the allowed duration, create shorter segments first rather than assuming the endpoint will process the whole file.
- Set the language. Keep the field at
autowhen you do not know the language or when the recording may vary. If you know both the language and writing script, enter the documented identifier, for exampleeng_Latnfor English in Latin script orcmn_Hansfor Mandarin Chinese in Simplified script. - Run the prediction. Submit the form and wait for the hosted inference to finish. Processing time depends on the service and the clip; the available material does not establish a fixed latency.
- Read the returned text. The documented result is a string. Copy it into your editor or notes, retaining the original audio so you can check uncertain words.
- Verify important passages. Replay names, numbers, technical terms, code words, and accents. A plausible-looking sentence can still contain a wrong digit or proper noun.
If the page rejects the file before running, check its extension, duration, and current input schema. If it runs but the words are poor, try a cleaner clip, a manually selected language, or a different segmentation.
How to choose the language setting
Use auto when the language is unknown
auto asks the deployment to detect the language while transcribing. It is the simplest starting point for a beginner and is useful when you are unsure which identifier to enter. Very short clips, background music, code-switching, heavy accents, or several speakers can make automatic detection less reliable, so inspect the result rather than treating the detected language as proven.
Select a language and script when you know them
The examples on the model page use a two-part notation: an ISO 639-3 language code, an underscore, and an ISO 15924 script code. The documented examples are:
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| Identifier | Meaning | When it helps |
|---|---|---|
eng_Latn |
English written in the Latin script | Use when the recording is known to be English |
cmn_Hans |
Mandarin Chinese written in Simplified script | Use when both the language and script are known |
auto |
Automatic language detection | Use when the language is uncertain or mixed |
Choosing a language does not improve a noisy microphone, remove overlapping speech, or turn speech recognition into translation. It simply supplies the model with a more specific transcription condition. If a manually selected code produces an obviously wrong result, return to auto and compare the outputs.
Preparing audio that the model can handle
Keep each upload within the duration limit
The Subformer listing caps one uploaded clip at 40 seconds. For a lecture, interview, meeting, or podcast, split the recording into consecutive pieces and transcribe each piece separately. Keep a small amount of overlap—only if your workflow can tolerate repeated words—so a sentence that crosses a cut is easier to reconstruct. Label every result with its segment order or timestamp.
Split a file from the command line
If you already have FFmpeg installed, this command creates 40-second segments without re-encoding the audio:
ffmpeg -i recording.mp3 -f segment -segment_time 40 -c copy segment-%03d.mp3Some codecs do not cut cleanly on exact boundaries when streams are copied. If a segment starts abruptly or fails to decode, re-encode to a common WAV or MP3 format and repeat the split, or use an audio editor with a visible timeline.
Prefer intelligible speech over elaborate conversion
- Use a recording with speech louder than the room or music.
- Trim long silent sections so the short input is mostly useful speech.
- Avoid repeatedly converting between lossy formats; every conversion can add artifacts.
- Keep the original file. A converted upload is only a working copy for transcription.
These are preparation practices, not claims that the hosted model requires a particular sample rate or channel layout; the supplied model information does not specify those parameters.
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What accuracy claims you can reasonably take from the project
Meta’s project materials report that its 7B LLM-ASR system achieved character error rates below 10 for 78% of supported languages in the reported evaluation. That is an upstream research result, not a per-language score for the Subformer Replicate endpoint. The sources do not establish accuracy or latency for every language, dialect, speaker, microphone, or background environment.
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Use the model confidently for a first draft, search index, rough notes, or a quick check of an unfamiliar recording. Add human review when the text will be published, used as evidence, translated, used in a medical or legal decision, or relied on for names and amounts. Compare the text with the audio at normal speed and again at a slower speed for uncertain passages.
Working with recordings longer than one segment
- Make a segment plan. Number files in chronological order, such as
meeting-001.wav,meeting-002.wav, and so on. - Keep timing information. Record each segment’s start time. This lets you return to the original audio when a sentence or name is unclear.
- Use one language strategy. If the entire recording is in one known language, use the same identifier for each segment. If languages change, use
autoor assign a code per segment. - Transcribe independently. Submit each file as a separate prediction and save the returned string with its filename.
- Reassemble carefully. Join results in order, inspect the boundary between every pair of segments, and remove only obvious duplicated words created by overlap.
Splitting solves the endpoint’s per-upload limit; it does not guarantee that a sentence cut between two files will be perfectly reconstructed.
Using the Replicate API instead of the browser
The model page documents Node.js, Python, and HTTP access. API use is appropriate when you need to transcribe many prepared clips, connect speech recognition to an application, or save results automatically. You need a Replicate API token and the client or HTTP setup described on the current page.
Minimal Python pattern
After installing the documented Python client and making your token available to that client, the workflow has this shape:
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import replicate
with open('clip.wav', 'rb') as audio_file:
transcription = replicate.run(
'subformer/meta-omnilingual-asr-7b',
input={
'audio': audio_file,
'language': 'auto',
},
)
print(transcription)The page’s live API example is the authority for the current client syntax, file-upload handling, and version notation. For reproducible automation, pin the immutable version hash shown in the run example rather than assuming that the displayed short version label will remain unchanged. Keep the token in an environment variable or secret manager; do not paste it into source code or commit it to a repository.
What to automate
- Reject files that exceed the duration limit before sending them.
- Store the original filename, segment start time, selected language, model version, and returned text together.
- Retry transient service failures without silently duplicating a completed segment.
- Send low-confidence or business-critical passages to a human review queue.
The endpoint returns text, not a guaranteed timestamped subtitle file. If you need subtitles, diarization, confidence scores, or translation, those are additional processing steps that must be documented and implemented separately.
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Replicate’s Cog route
The model page also documents a local Cog route. Cog is useful when you want to run the packaged deployment in your own environment, but it requires installing and operating the local runtime. It is not necessary for a first test through the web interface, and local hardware, storage, and configuration requirements are not established by the hosted listing.
Meta’s upstream Python package
For research or broader experimentation, Meta/Facebook Research publishes an omnilingual-asr package that can be installed with pip or uv. The repository describes an ASRInferencePipeline for local inference and notes that audio support requires libsndfile. A typical package setup begins with:
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uv add omnilingual-asrThose commands prepare the upstream project, not the Replicate endpoint. The repository’s inference guide supplies the model-selection and ASRInferencePipeline details. Expect a more involved Python environment, model downloads, and local resource management than the single-file hosted workflow.
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Which access path fits a beginner?
| Path | Setup effort | Control | Best fit | Main trade-off |
|---|---|---|---|---|
| Replicate web page | Lowest | Basic input and language selection | Trying one or a few clips | Manual uploads and hosted-service limits |
| Replicate API | Moderate | Batching, logging, and application integration | Developers building a workflow | Token management and code maintenance |
| Local Cog deployment | Higher | More control over where inference runs | Operators familiar with local containers and model serving | Runtime and hardware setup |
Upstream omnilingual-asr |
Highest | Research pipeline and model-family experimentation | Researchers and engineers working locally | Python environment, dependencies, and model management |
Start with the hosted page. Move to the API only when repeated manual uploads become the problem you are solving; choose a local route when environment control or research customization justifies the installation work.
Troubleshooting common failures
| Symptom | Likely cause | Action |
|---|---|---|
| The page will not accept the file | Unsupported format, damaged file, or a clip beyond the allowed duration | Export a short MP3, WAV, or FLAC copy, verify that it plays locally, and check the live page’s limit |
| Transcript is in the wrong language | Automatic detection selected incorrectly, or the chosen identifier does not match the speech | Try the correct language-and-script code, then compare with an auto run |
| Words are missing or nonsensical | Low volume, noise, music, overlapping speakers, clipping, or an abrupt segment boundary | Use a cleaner source, trim silence, rebalance speech and noise, and make shorter segments |
| Names and numbers are wrong | ASR uncertainty that is easy to overlook in fluent text | Replay the exact passage, slow the audio, and verify against the original or a second human listener |
| API authentication fails | Missing, expired, or incorrectly scoped token | Set the token according to the current Replicate client instructions and keep it out of source files |
| A previously working script changes behavior | The hosted model version or input schema changed | Review the live model page and pin the immutable version hash for reproducible runs |
A practical verification checklist
Before you reuse a transcription, check the following:
- Does the selected language match what is actually spoken?
- Did every segment run, or is one missing from the sequence?
- Are names, dates, decimal points, quantities, and abbreviations confirmed against the audio?
- Did a speaker change, laugh, cough, or background voice alter the meaning?
- Have you preserved the original recording and the model/version details used to create the text?
For public-facing copy, treat the first transcript as an editable draft. The broad language coverage is valuable for discovering what is said, but the available project materials do not justify a blanket accuracy promise.
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Use Subformer’s Meta-Omnilingual-ASR-7B listing when you want a quick, browser-based transcription of a short audio clip and the language may be outside the usual high-resource ASR set. Begin with auto, switch to a language-and-script identifier when known, and keep each upload within the documented duration. Choose the API for repeatable automation and the upstream package or Cog route only when you are prepared to manage a local technical environment.
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