Chinese support — model strategy¶

Status: Proposed decision framework, current as of 2026-09-20
P1 decision: Keep the existing faster-whisper runtime and use multilingual small as the supported baseline candidate. Benchmark alternatives behind SttEngine; do not add a second mandatory runtime before it wins on measured product criteria.

0. Model-license gate¶

For this programme, "free/open-source model" is not enough by itself. A model that becomes a recommended YazSes profile must have an auditable model-weight license that is acceptable for redistribution and normal downstream use.

Default policy for production profiles: prefer permissive weight licenses such as MIT, Apache-2.0 or BSD. Record the exact model repository, immutable revision/commit and license source in benchmark metadata. A toolkit's code license does not prove that its model weights carry the same license.

Current verified examples:

• OpenAI Whisper states that its code and model weights are MIT-licensed: https://github.com/openai/whisper#license
• Qwen3-ASR-0.6B's official Hugging Face model card declares Apache-2.0: https://huggingface.co/Qwen/Qwen3-ASR-0.6B
• The current funasr/paraformer-zh model card declares Apache-2.0; the streaming repository also publishes an immutable apache-2.0-20260804 tag that explicitly covers its weights and accompanying files: https://huggingface.co/funasr/paraformer-zh https://huggingface.co/funasr/paraformer-zh-streaming/tree/apache-2.0-20260804
• SenseVoiceSmall currently declares a custom FunASR Model Open Source License, not MIT/Apache/BSD: https://huggingface.co/FunAudioLLM/SenseVoiceSmall https://github.com/modelscope/FunASR/blob/main/MODEL_LICENSE

SenseVoiceSmall may still be benchmarked as research evidence, but under the project's permissive-license preference it must not become a bundled/recommended production model without an explicit maintainer/legal review of that exact weight revision. The current custom agreement contains attribution requirements, conduct-based termination language, and automatic acceptance of future revisions, so treating it as interchangeable with Apache-2.0 would be incorrect.

1. Why model choice is an architecture decision¶

A Chinese recognizer is not just an accuracy number. In YazSes it affects:

• cold-start time;
• hold-to-talk latency;
• CPU saturation;
• RAM and disk footprint;
• streaming behavior;
• word timestamps;
• file and meeting transcription;
• model download/cache behavior;
• packaging on Linux/macOS/Windows;
• air-gapped setup;
• optional dependency size;
• model-weight license;
• whether the existing SttEngine seam can contain it cleanly.

A benchmark improvement that requires making PyTorch/vLLM a mandatory dependency or drops word-timestamp functionality is not automatically a product improvement.

2. P1 baseline: multilingual Whisper small¶

Recommended initial profile:

[stt]
engine = "faster-whisper"
model = "small"
language = "zh"
chinese_script = "simplified" # or traditional

Why this is the first supported baseline candidate¶

1. Zero new recognition runtime. It uses the engine already shipped and tested.
2. All existing decode paths already honor language.
3. The downloader/cache/offline behavior already exists.
4. The Han-script wrapper already covers it.
5. The repository already has Chinese measurements on this model.
6. CPU int8 is aligned with YazSes' default hardware philosophy.
7. Changing back to English does not require changing architecture.

The repository’s current ASCEND probe reported, on 20 clean Mandarin/code-switched utterances, small at 16.9% CER after Simplified normalization versus 35.9% before normalization. This is useful evidence that the model and script layer work, but the sample is too small to be a release-quality benchmark.

Therefore small is a baseline to beat, not an assertion that it is the globally best Chinese model.

3. Whisper candidates to benchmark without a new engine¶

base¶

Potential “fast/low-resource” preset.

Pros:

• same runtime;
• lower resource footprint than small.

Unknowns that must be measured for Chinese:

• CER delta versus small;
• whether lower latency is material in interactive use;
• script inconsistency rate;
• robustness to accents/noise.

Do not publish it as a Chinese preset until measured.

large-v3¶

The existing Chinese page reports 11.3% normalized CER on the same small ASCEND sample versus 16.9% for small, but it is much heavier.

It also interacts with the repository’s existing condition_on_previous_text finding: large-v3 has shown repetition instability in other benchmarks, so Chinese evaluation must include long-form/meeting behavior, not only short utterance CER.

large-v3-turbo / turbo¶

Attractive because it remains inside faster-whisper while reducing the large-v3 parameter/latency burden.

It is a benchmark candidate, not the default, until Chinese CER, latency, RAM and long-form stability are measured on YazSes hardware.

4. Alternative Chinese ASR candidates¶

These candidates are current upstream options worth evaluating. They are not P1 dependencies.

4.1 SenseVoiceSmall¶

Current FunASR documentation describes SenseVoiceSmall as a 234M-parameter checkpoint supporting Chinese, English, Japanese, Korean and Cantonese, with a CPU usage path and GGUF edge checkpoint.

Why it is interesting:

• Chinese-first ecosystem;
• size is plausible for CPU/edge deployment;
• Cantonese support creates a future path distinct from Mandarin;
• a GGUF route may permit a lightweight optional backend.

Integration concerns:

• new runtime and model cache conventions;
• timestamp semantics need mapping to transcribe_words;
• streaming behavior must be verified for the exact backend chosen;
• model-weight license must be reviewed separately from FunASR toolkit licensing;
• packaging impact must be measured.

Priority: research-only benchmark candidate under the current custom model-weight license. Do not bundle or recommend it under the permissive-license policy without explicit approval of the exact revision.

4.2 Paraformer-zh / Paraformer-zh-streaming¶

Current FunASR model zoo lists:

• Paraformer-zh: 220M, zh/en, timestamps;
• Paraformer-zh-streaming: 220M, streaming variant.

Why it is interesting:

• explicit Chinese/English focus;
• separate streaming checkpoint;
• comparatively compact parameter count.

Integration concerns:

• offline and streaming checkpoints may not be interchangeable;
• a single YazSes SttEngine instance must satisfy batch and decode_window behavior coherently;
• new FunASR/PyTorch dependency surface;
• pin the exact Hugging Face revision whose weights are explicitly Apache-2.0; do not infer a checkpoint license from the FunASR toolkit license.

Priority: permissively licensed Chinese-optimized streaming candidate, after exact-revision pinning and product measurements.

4.3 Qwen3-ASR-0.6B¶

The official Qwen3-ASR repository states that the 0.6B and 1.7B models support language identification/ASR across 52 languages and dialects, including Mandarin, Cantonese and many Chinese regional varieties, with offline and streaming inference.

Why it is interesting:

• broad Chinese dialect coverage;
• current upstream development;
• one model for multilingual/dialect expansion;
• explicit streaming/offline unification.

Integration concerns:

• official examples center on qwen-asr with Transformers/vLLM and bfloat16/GPU-oriented acceleration paths;
• even 0.6B may impose a materially larger runtime/RAM/package footprint than current CTranslate2 int8;
• timestamp support may involve the separate forced-aligner model;
• dependency isolation may be advisable upstream itself;
• CPU interactive latency must be measured, not inferred from high-concurrency throughput claims.

Priority: permissively licensed quality/dialect candidate (official model card: Apache-2.0); do not make it a base dependency without a CPU/product benchmark.

4.4 Qwen3-ASR-1.7B¶

Same coverage advantages, larger capacity.

For YazSes' CPU-first interactive path, treat this as an experimental accuracy ceiling unless tests prove acceptable latency/resource use. It is more plausible for optional GPU or file-transcription workloads than as the default hold-to-talk engine.

4.5 Fun-ASR-Nano¶

Current FunASR documentation lists an ~800M base checkpoint covering Chinese, English, Japanese and Chinese dialects/accents, with a native Transformers CPU path.

It is worth benchmarking if it offers better Chinese/dialect quality than compact candidates, but it carries a much larger dependency/model footprint than simply staying on faster-whisper.

5. Candidate matrix¶

This table records integration facts and questions, not an accuracy ranking.

6. Model promotion protocol¶

No alternative engine becomes a recommended Chinese preset because an upstream leaderboard says it is better.

It must pass the same YazSes harness against the current supported baseline.

Required benchmark dimensions¶

Recognition quality¶

• Mandarin character error rate (CER);
• punctuation-aware and punctuation-stripped CER;
• digit/number accuracy;
• named entity / technical-term subset;
• long-utterance deletion/insertion rate;
• hallucination/repetition incidence;
• Simplified and Traditional output after normalization;
• Mandarin-English mixed-token stress set (reported separately, not called code-switch support).

Interactive performance¶

On the project’s declared reference CPU:

• model cold-load wall time;
• first-decode warm-up;
• p50/p95 RTF;
• p50/p95 release-to-final-text latency for 2 s / 5 s / 10 s speech;
• peak RSS;
• CPU core-seconds per burst.

Product capability¶

• transcribe;
• transcribe_words;
• decode_window;
• file transcription;
• meeting post-pass;
• streaming finalization;
• script normalization;
• deterministic/offline startup from cache;
• air-gapped model provisioning.

Operational¶

• artifact size;
• runtime dependency size;
• supported Python/OS architectures;
• package install conflicts;
• model license;
• toolkit license;
• redistribution terms;
• source/download availability in regions relevant to contributors.

7. Proposed quantitative gates¶

These are engineering gates for deciding whether a candidate is worth integrating; they are not marketing claims.

P1 supported baseline gate¶

For the selected baseline model:

• no catastrophic decode/hallucination on the validation corpus;
• normalized Mandarin CER target agreed before final release run and reported with corpus details;
• p95 RTF <= 0.60 on the reference CPU;
• p95 release-to-final-text <= 3.0 s for a 5 s utterance on the reference CPU;
• no new mandatory dependency beyond the existing base path except the small OpenCC script extra;
• model can be predownloaded and run with network disabled;
• all SttEngine methods exercised successfully.

If corpus results make these thresholds unrealistic, change the gate before looking at final test results and record the rationale.

Alternative model promotion gate¶

A new engine should provide at least one material advantage:

• =20% relative CER reduction on the primary Mandarin corpus, or

• materially better validated dialect coverage, or

• =30% latency/core-cost reduction at non-inferior quality, or

• a capability the baseline cannot supply (for example validated Cantonese).

And it must not create an unacceptable regression in packaging, privacy or interactive performance.

A model that improves 3% relative CER while adding gigabytes of mandatory runtime is not a default candidate.

8. Corpus strategy¶

Use more than one source because a single Mandarin corpus can hide domain bias.

Recommended evaluation buckets:

1. Read Mandarin, clean microphone — establishes basic recognition ceiling.
2. Spontaneous/conversational Mandarin — realistic dictation disfluencies.
3. Regional-accent Mandarin — at least several accent groups if licensing permits.
4. Noisy/far-field speech — robustness.
5. YazSes command set — short imperative utterances.
6. Numbers/dates/addresses/code terms — dictation pain points.
7. Long form — file/meeting stability.
8. Native-speaker private acceptance clips — product realism, not redistributed by default.

The existing ASCEND 20-utterance sample remains a regression microset, not the sole benchmark.

Every public corpus must have:

• exact version/split;
• license;
• download procedure;
• normalization rules;
• checksum/manifest;
• scoring code;
• no committed third-party audio if redistribution is not permitted.

9. Script-normalization benchmark rule¶

Recognition CER and script-rendering errors must be reported separately.

For a Simplified reference:

1. score raw model output;
2. score the same output after the configured OpenCC conversion;
3. report the delta.

That prevents a correct Traditional transcription from being counted as an ASR failure while still showing what a mainland user would actually see without normalization.

Do the symmetric test for Traditional references.

10. Licensing gate¶

Before any non-Whisper model becomes a documented option, record:

• code/toolkit license;
• exact model-weight license;
• whether commercial use is permitted;
• whether redistribution is permitted;
• whether converting/quantizing weights changes obligations;
• required attribution;
• download host and checksum.

Do not infer model license from repository/toolkit license. FunASR explicitly warns that model licenses are checkpoint-specific.

11. Dependency policy¶

P1 must not add Qwen/FunASR/PyTorch to the base installation.

Alternative engines, if implemented, use optional extras and lazy imports, following the existing Parakeet/Moonshine pattern:

base install
  -> faster-whisper only

optional Chinese-specialized engine
  -> explicit feature/extra
  -> lazy import
  -> own model preflight
  -> safe fallback or clear refusal

If an engine cannot be packaged cleanly on all desktop platforms, advertise its actual matrix rather than degrading the core installation.

12. Source notes for future implementers¶

Model facts in this document were checked against current upstream repositories on 2026-09-20:

• QwenLM/Qwen3-ASR — official repository and model description.
• modelscope/FunASR — official toolkit/model-zoo documentation.
• YazSes src/yazses/stt/download.py — currently supported faster-whisper short names.

Re-check upstream model cards, versions and licenses at implementation time. Model ecosystems change too quickly for this design record to serve as a permanent version pin.

13. Decision summary¶

• P1: multilingual Whisper small through current faster-whisper engine.
• Same-runtime benchmark: large-v3-turbo and large-v3.
• First compact alternative prototype: SenseVoiceSmall.
• Chinese streaming candidate: Paraformer-zh-streaming.
• Dialect/broad-coverage research candidate: Qwen3-ASR-0.6B.
• No second runtime becomes mandatory unless it beats the baseline on a predeclared product metric and passes licensing/packaging gates.