Direct answer
An offline audio-file translator needs more than an on-device model. The app must be able to let the user choose a file, retain access to its URI, decode the actual media format, bound memory use, segment the audio in a way the model can accept, and separate transcription from translation. It must then show the user what the system heard and translated, preserve incomplete or failed segments, and export only after an explicit action. A filename ending in “.mp3” or a file picker success does not prove the contents can be decoded or translated offline. Nor does offline inference establish that every import provider, speech voice, or export target is local. This page is an architecture and evaluation checklist, not a claim that Cove Voice currently imports and translates arbitrary audio files. Test one exact file format and task with a versioned fixture, verify the full path with network access disabled after setup, and keep raw and derived results distinguishable.
Scenario: translating a lecture recording before a flight
A student has an audio recording supplied by a classmate through a document provider. Before boarding, they want an offline transcript and a translation they can review. The file may be long, its URI may come from local storage or a cloud-backed provider, and a decoding or model limit could stop one segment without invalidating every earlier result.
Decision
Import through a user-selected URI, record the provider and format, process bounded segments, and preserve the source transcript beside translated output. Define what happens when decoding, recognition, translation, or export fails, and do not silently replace a missing segment with a guessed summary.
Expected outcome
The student can see which segments completed, which did not, and whether the task required network access. The app can retry a failed segment or export a clearly marked partial result without implying that the entire recording was translated.
Evidence to collect
The claim becomes reviewable only when the following evidence is attached to the same artifact and test run:
- User-selected URI and provider behavior
- Versioned decoder and audio fixture
- Stage-level results, resource limits, and interrupted-file recovery
Task quality needs a versioned corpus and acceptance rules. Fluent sample output is not an accuracy result.
Implementation workflow
- Use a user-controlled file selection boundary. Prefer the Android system document picker for a user-selected file when broad storage access is unnecessary. Treat the returned content URI as an opaque provider reference, not as a filesystem path. Check read access, whether permission must persist for later work, whether the provider is local or cloud-backed, and how the app should respond if the provider disconnects or revokes access.
- Inspect format and decode incrementally. Read container and codec metadata through a supported media API before inference. Do not trust file extension, advertised duration, or a MIME hint as proof that the stream is decodable. Decode a bounded window, track sample rate and channel conversion, and preserve a record when the provider returns an unsupported format, a truncated stream, or an access error.
- Define segmentation and resource policy. Choose boundaries based on the model input contract and the user task, not only on a convenient fixed byte count. Document overlap or context carryover, timestamp mapping, silence behavior, maximum buffered data, cancellation, and temporary-file cleanup. Observe memory on the exact workload; audio file size does not equal the application’s peak working set.
- Keep recognition and translation results separate. For every segment, retain the source transcript or model interpretation separately from the target-language translation. That makes it possible to identify whether an error came from decoding, recognition, language detection, or translation. Preserve confidence or uncertainty signals only if the implementation actually supplies them; otherwise tell the user where manual review is important.
- Test offline after all required setup. Install or download required model assets first, then disable network access and run the actual import-to-export workflow. Test local-file and provider-backed inputs separately because a cloud document provider can require network access even when inference is local. Capture every stage result, cancellation, retry, partial completion, and recovery behavior.
- Export a transparent result. Show the source file name or a privacy-safe local label, source and target languages, segment completion status, and whether the output is partial. Let the user preview the transcript and translation before creating a new document or sharing it. Keep the original audio unchanged and do not overwrite it with translated content without explicit confirmation.
Keep each transition observable. A failure should identify the stage, artifact, runtime, and recovery action without logging private user content.
Acceptance criteria
- The app reads a user-selected document URI without assuming it maps to a local path.
- Decoder, segmentation, recognition, translation, and export have separate test outcomes.
- The full post-setup task is tested with network disabled, including provider limitations and partial failures.
- The exported file states whether processing was partial and preserves source audio and transcript provenance.
Decision table
| Dimension | Choice or evidence | How to interpret it |
|---|---|---|
| File access | System picker and content URI | User controls the selected document; the provider may still be network-backed. |
| Live microphone capture | AudioRecord or recorder stream | Captures new audio, but does not automatically support importing an existing recording. |
| File decoding | Versioned media decoder with bounded buffers | A valid file name does not establish codec support or safe memory use. |
| Offline completion | Disable networking after model setup and complete import-to-export | Local inference alone does not prove file retrieval or export also work offline. |
Failure patterns to prevent
- Assuming every URI is a local file path
- Loading unbounded audio into memory
- Discarding the source transcript or failures
Also prevent silent fallback, unpinned artifacts, missing cancellation, and conclusions that combine unlike configurations. Store unsuccessful runs alongside successful ones.
Minimum reproducibility record
| Layer | Record |
|---|---|
| Device | Manufacturer, model, chipset, RAM class, operating-system build |
| Software | Application version and git commit |
| Model | Family, variant, revision, format, file length, hash, quantization |
| Runtime | Name, revision, requested backend, observed backend evidence |
| Workload | Fixture revision, input hash, prompt hash, output policy |
| Outcome | Completed, failed, cancelled, fallback, and privacy-safe diagnostics |
Release checklist
- ☐ The primary query is answered without an unsupported number.
- ☐ Every artifact and runtime is pinned.
- ☐ The representative task and failure policy are explicit.
- ☐ Lifecycle, cancellation, cleanup, and fallback are tested.
- ☐ User-content and network boundaries are documented.
- ☐ Result wording applies only to the recorded configuration.
- ☐ The page links to raw method or evidence when results are added.
Sources and related evidence
- Cove Travel voice translation
- Android Storage Access Framework document and file access
- Android MediaExtractor API reference
- Google AI Edge Gemma mobile deployment overview
Chinese deployment and troubleshooting content is organized in the .奇连 AI 端侧专题.
Frequently asked questions
Can an on-device speech model translate any audio file offline?
No. The app must also access the chosen URI, decode its container and codec, convert the audio to the model’s supported input representation, and fit the workload within measured resource limits. Each stage can fail independently, so test and disclose exact formats and conditions.
Does choosing a file from Android’s picker guarantee it is on the device?
No. Android’s document picker can expose providers that represent local storage, removable volumes, or cloud services. An app can receive a content URI while opening or reading the content still depends on the provider. Test the provider with network disabled rather than inferring locality from the picker.
Should an offline translator overwrite the original audio?
No. Keep source audio immutable and create a separate transcript or translated document after user review. Make partial processing visible and avoid overwriting, uploading, or sharing files without an explicit user action.
How should long files be handled?
Decode and process bounded segments, map each segment back to its position in the original recording, and retain completed and failed outcomes. The segmentation policy must be tested against the model input contract; a single successful short clip does not establish behavior on a long file.