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Meta-Omnilingual-Asr-7b is a hosted speech-to-text model from Subformer that runs on Replicate. Upload an MP3, WAV, or FLAC file, leave the language setting at auto for automatic detection, or enter a language identifier such as eng_Latn. The listed deployment returns a text transcription and accepts audio up to 40 seconds per prediction.
This guide explains the hosted workflow first, then shows when the Replicate API, local Cog, or Meta’s upstream Python package makes more sense. Meta describes the broader Omnilingual ASR family as covering more than 1,600 languages, but that figure describes the research family rather than a promise of identical accuracy for every language, dialect, speaker, or recording on this particular endpoint.
What Meta-Omnilingual-Asr-7b does
The model converts spoken audio into written text. It is automatic speech recognition (ASR), not a translation system. If a recording is in Chinese, for example, the expected result is a transcription in Chinese script; translating that text into another language would require a separate, documented translation step.
The model page is a hosted deployment named subformer/meta-omnilingual-asr-7b. The displayed deployment version is identified as c1b60d82. Hosted model pages can change their version, input schema, or operational limits, so check the live page immediately before relying on version-specific instructions.
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Meta/Facebook Research describes Omnilingual ASR as an open-source family containing both connectionist temporal classification (CTC) models and large-language-model (LLM) ASR variants. The 7B model in this guide is one hosted LLM-based member; it is not a description of every model in the family.
At a glance
| Question | Answer for the listed Subformer deployment |
|---|---|
| What goes in? | An audio file |
| Supported formats named on the page | MP3, WAV, and FLAC |
| Maximum duration | 40 seconds per uploaded audio input |
| Language setting | auto by default, or a supplied language identifier |
| Example identifiers | eng_Latn (English, Latin script) and cmn_Hans (Simplified Chinese) |
| What comes out? | A string containing the transcription |
| First-time access | The Replicate hosted interface is the least technical route |
| Code access | Replicate API examples are provided for Node.js, Python, and HTTP |
What you need before starting
- An audio file in MP3, WAV, or FLAC format.
- A clip no longer than 40 seconds for one prediction on the listed endpoint.
- A web browser and access to the Subformer model page on Replicate for the no-code method.
- An API token only if you choose programmatic access rather than the hosted form.
You do not need a microphone to use the model. A microphone is relevant only if you still need to record the speech; the endpoint accepts an existing audio file.
How to transcribe audio in the Replicate interface
- Open the hosted model page. Navigate to the Replicate page for
subformer/meta-omnilingual-asr-7b. Confirm that the currently displayed input fields still include an audio upload and a language selector. - Choose a short audio file. Upload an MP3, WAV, or FLAC file whose duration is 40 seconds or less. If the upload is longer, the prediction may be rejected because the listed endpoint specifies a 40-second maximum.
- Set the language. Keep the selector at
autowhen you do not know the language or want automatic language detection. If you know the language and script, enter the documented identifier instead. - Run the prediction. Submit the form and wait for the hosted prediction to finish. Processing time is not established here as a fixed number; it can vary with the service and request.
- Read the returned text. The documented output schema is a string. Copy that text into your notes or application, then compare important portions with the recording.
For a first experiment, use a clip with one speaker and clear speech. That does not change the service limit, but it makes it easier to tell whether an unexpected result comes from the model or from difficult audio.
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Choosing the language value
Use auto when you are unsure
The default auto setting asks the deployment to detect the language automatically. It is the simplest choice for a mixed collection of clips or when the speaker’s language is not known in advance.
Use a specific identifier when you know the language and script
The examples on the model page combine an ISO 639-3 language code with an ISO 15924 script code. eng_Latn means English written in the Latin script. cmn_Hans means Mandarin Chinese written in Simplified Chinese script. Follow the page’s current list and spelling for other languages; do not substitute an English language name if the input expects the identifier format.
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Why script matters
A language can be represented in more than one writing system. Specifying both language and script gives the model more precise information about the expected transcription form. If you are uncertain which script applies, leave the setting at auto or verify the identifier shown by the current interface.
Working within the 40-second limit
The Subformer page lists a maximum audio duration of 40 seconds. Meta’s upstream documentation also describes an under-40-second inference constraint for its documented model suites. Treat the hosted limit as an endpoint rule, not as evidence that the model can process an entire lecture, meeting, or podcast in one request.
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Divide the recording into segments that stay below the limit, then submit each segment separately and place the returned strings in chronological order. Leave a small amount of context at a split only if your own editing workflow can remove duplicated words afterward. The model page does not promise timestamped output, so you should not assume that returned text includes reliable word-level timing.
Check segment boundaries
A cut in the middle of a word or sentence can make the first or last words of a segment harder to recognize. Prefer boundaries at pauses when your audio editor makes that possible. Keep a record of the segment order so that the final transcript does not silently change the original sequence.
How reliable is the transcription?
Meta’s project materials state that the Omnilingual ASR family supports more than 1,600 languages, including hundreds that previously lacked ASR coverage. This is a published scope for the upstream family. It should not be read as a guarantee that every language has the same training coverage or that this hosted 7B endpoint will perform equally well on every recording.
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The project repository reports character error rates below 10 for 78% of supported languages for its 7B LLM-ASR system. That is an upstream evaluation statement, not a test result for your file and not a per-language guarantee for the Subformer deployment. The reviewed materials do not establish accuracy or latency for every language, dialect, microphone, noise condition, or speaker on this hosted page.
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Listen back to the audio whenever the transcript contains names, numbers, addresses, technical terms, legal wording, medical information, or instructions. Pay particular attention to low-resource languages, strong accents, overlapping speakers, background noise, and code-switching. A plausible-looking sentence can still contain a wrong name or digit.
Use the result as a transcription, not a translation
If you need an English version of a non-English recording, first preserve the original transcription, then use a separate translation workflow. Keeping those stages separate makes it easier to identify whether an error came from speech recognition or translation.
Replicate access paths compared
| Access path | Best for | Setup | Control | Important considerations |
|---|---|---|---|---|
| Hosted Replicate interface | A first trial or occasional short clip | Lowest; upload a file in the browser | Basic input fields | Subject to the page’s current schema and 40-second limit |
| Replicate API | An application or repeatable workflow | Install a client or send HTTP requests and configure an API token | Programmatic submission and result handling | Use the current Node.js, Python, or HTTP example shown on the model page |
| Local Cog route | Users who already work with Cog-based model packages | Local installation and environment configuration | More control over execution than a browser form | Separate from the simple hosted workflow; local requirements apply |
| Upstream Python package | Research or a local Meta Omnilingual ASR workflow | Install omnilingual-asr with pip or uv; configure Python and audio dependencies |
Access to the upstream ASRInferencePipeline |
The repository notes that audio support requires libsndfile |
Using the Replicate API
The model page provides examples for Node.js, Python, and raw HTTP requests. Those examples are the safest reference because they show the current parameter names, file handling, version reference, and output handling expected by the deployment.
- Create or use a Replicate API token according to Replicate’s current account instructions.
- Choose the client language or HTTP method that matches your application.
- Copy the model page’s current example rather than relying on an older snippet. Hosted versions and schemas can change.
- Pass an audio file and either
autoor a supported language identifier such aseng_Latn. - Submit a clip no longer than 40 seconds and read the returned string.
- Handle failed predictions and empty or questionable text in your application instead of assuming every request is a valid transcript.
An API integration is useful when you need to process many short clips, connect transcription to another system, or save results automatically. It does not remove the model’s duration limit or the need to review important output.
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Local and research-oriented options
Cog
The hosted page documents a local Cog route. Cog is a separate deployment workflow from uploading a file on Replicate, so expect local installation and configuration steps. Use the current instructions attached to the model page if you specifically need to run that route.
Meta’s upstream Python package
The official project repository offers a broader local/research path. Install omnilingual-asr with pip or uv, prepare the required Python environment, and call the ASRInferencePipeline. The repository notes that audio support requires libsndfile.
This package route is not required for a beginner who only wants to try the hosted Subformer model. It becomes relevant when you need to investigate the upstream model family, run experiments locally, or work with the inference pipeline rather than a hosted form.
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| Symptom | Likely cause | What to do |
|---|---|---|
| The upload is rejected | The file format is not one of the formats named on the page, or the clip exceeds 40 seconds | Convert or export the audio as MP3, WAV, or FLAC and submit a segment under the limit |
| The transcript is in the wrong language or script | Automatic detection was uncertain, or the selected identifier does not match the speech | Try the correct language-and-script identifier, such as eng_Latn or cmn_Hans, and compare the result with the audio |
| Names or numbers are wrong | Recognition errors in difficult or specialized speech | Replay that portion and verify it manually; do not treat a fluent-looking output as proof of accuracy |
| An API request fails | Outdated example, missing token, incorrect parameter, or an endpoint change | Refresh the live model page and use its current Node.js, Python, or HTTP example |
| The local package cannot read audio | The required audio library is missing | Install and configure libsndfile as noted in the upstream project instructions |
| A long recording cannot be submitted | The specific endpoint is limited to 40 seconds | Split the recording into ordered, shorter segments or use a different current deployment whose documented limits fit the job |
Which route should a beginner choose?
Choose the hosted interface for a quick answer
Use the browser form when you have one or a few short files and want to learn what the model returns. It avoids Python setup, package installation, and token management.
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Choose the API for repeatable application work
Use the API when transcription is part of a script, service, or content workflow. You gain programmatic control, but you also become responsible for authentication, file handling, retries, output storage, and keeping your integration aligned with the current deployment schema.
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Choose local inference for research control
Use Cog or the upstream Python package when local execution, experimentation, or inspection of the broader Omnilingual ASR family matters more than the shortest setup. Plan for environment configuration and audio dependencies before starting.
How this model fits the Omnilingual ASR family
The family’s headline figure is more than 1,600 languages. Meta’s project description says it includes hundreds of languages that had not previously been covered by ASR technology. Those statements explain why the family is notable, but they do not replace testing a particular language and recording.
The upstream materials describe CTC and LLM-based variants. CTC models and LLM-ASR models are different architectural choices, so results and resource requirements should not be assumed to transfer from one variant to another. The title’s Subformer deployment is the hosted 7B LLM-oriented option discussed here.
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Use that statement as context for the project’s intended scope, not as a promise that every input language, accent, or recording will produce a perfect transcript.
A practical first test
- Choose a clear clip shorter than 40 seconds.
- Upload it through the hosted Subformer page.
- Run it once with
auto. - If the language and script are known, run a second prediction with the corresponding identifier.
- Compare both strings with the recording, checking names, numerals, and specialized vocabulary.
- Decide whether the result is good enough for your purpose before building an API or local workflow around it.
This small test distinguishes three separate questions: whether the endpoint accepts your file, whether language detection works for your clip, and whether the transcription quality is suitable for your use case.
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