--- license: cc-by-4.0 task_categories: - automatic-speech-recognition - translation language: - ps - ur - ar - fa - hi - bn - ml - ta - so - ka tags: - script-fidelity - multilingual-asr - fleurs - evaluation - neurips-eandd pretty_name: Script fidelity benchmark size_categories: - 1K - Hugging Face Evaluate metric: - Load with Evaluate: `evaluate.load("themechanism/script_fidelity_rate", module_type="metric")` ## Scope The paper benchmark contains 100 evaluated model-language pairs on FLEURS test splits: | Language | Script | FLEURS code | Role | |---|---|---|---| | Pashto | Perso-Arabic | `ps_af` | collapse target | | Urdu | Perso-Arabic | `ur_pk` | control | | Arabic | Perso-Arabic | `ar_eg` | control | | Persian | Perso-Arabic | `fa_ir` | control | | Hindi | Devanagari | `hi_in` | collapse target | | Bengali | Bengali | `bn_in` | collapse target | | Malayalam | Malayalam | `ml_in` | collapse target | | Tamil | Tamil | `ta_in` | extension | | Somali | Latin | `so_so` | collapse target | | Georgian | Georgian | `ka_ge` | collapse target | Evaluated models: | Family | Model identifiers | |---|---| | Whisper | `openai/whisper-tiny`, `openai/whisper-base`, `openai/whisper-small`, `openai/whisper-medium`, `openai/whisper-large-v2`, `openai/whisper-large-v3`, `openai/whisper-large-v3-turbo` | | MMS-1B | `facebook/mms-1b-all` | | SeamlessM4T-v2 | `facebook/seamless-m4t-v2-large` | | Gemma 4 | `unsloth/gemma-4-E2B-it` | All ten languages, including Pashto, are evaluated directly on FLEURS. ## Repository layout ```text scripts/ eval_multilang.py Main evaluation driver script_fidelity.py SFR metric implementation merge_gemma4.py Merges Gemma 4 results and regenerates figures eval_downstream_mt.py Downstream MT validation for Gemma 4 outputs eval_sfr_lid_hybrid.py SFR+LID audit for saved Gemma 4 outputs run_gemma4.sh Convenience wrapper for the Gemma 4 baseline run analysis/ sf_results.csv Main result table plus six supplemental Pashto rows gemma4_downstream_mt_summary.csv MT validation summary gemma4_downstream_mt_correlations.csv MT validation correlations sfr_lid_hybrid_summary.csv SFR+LID audit summary results_gemma4/ sf_results.csv Gemma 4 result table results_gemma4_prompt_mitigation/ sf_results.csv Gemma 4 script-aware prompt result table results_gemma4_downstream_mt/ translations/ NLLB translations for gold, baseline, and script-hint text figures/ sfr_heatmap.pdf SFR matrix figure wer_vs_sfr_scatter.pdf WER-vs-SFR figure croissant_metadata.json Artifact metadata with Responsible AI fields LICENSE CC-BY-4.0 license notice requirements.txt Python package requirements ``` The general SFR library is published separately as `script-fidelity` on PyPI: . The import name is `script_fidelity`. This artifact keeps a bundled `scripts/script_fidelity.py` for exact reproduction of the submitted benchmark. The same metric is available as a Hugging Face Evaluate community metric: . ## Setup Python 3.10 or newer is recommended. Use `uv` for all environment and package management. ```bash uv venv uv pip install torch --index-url https://download.pytorch.org/whl/cu121 uv pip install -r requirements.txt ``` `datasets==2.21.0` is pinned because `google/fleurs` still uses a dataset script. Install CUDA-compatible `torch` from the cu121 index on common cloud GPU images. The scripts route Hugging Face, evaluate, and temporary files to a writable cache root. Set `SFR_CACHE_ROOT=/path/to/cache` to override the default. ## Running the evaluation Full ASR inference is expensive and was already completed for the submitted results. The commands below reproduce the run configuration. ```bash uv run python scripts/eval_multilang.py \ --hf-token "$HF_TOKEN" \ --results-dir ./analysis \ --hub-repo "$ANONYMIZED_OUTPUT_REPO" \ --languages pashto hindi bengali malayalam somali georgian urdu arabic persian tamil \ --whisper-sizes tiny base small medium large-v2 large-v3 turbo \ --run-mms --run-seamless ``` To refresh an existing model-language row in `sf_results.csv`, add `--force`. Gemma 4 was run separately with instruction-following transcription: ```bash uv run python scripts/eval_multilang.py \ --results-dir ./results_gemma4 \ --languages pashto urdu arabic persian hindi bengali malayalam tamil somali georgian \ --run-gemma4 \ --whisper-sizes ``` Gemma 4 uses the prompt: ```text Transcribe the following speech segment in its original language. Follow these specific instructions for formatting the answer: * Only output the transcription, with no newlines. * When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three. ``` Whisper uses forced language tokens and greedy decoding (`num_beams=1`). Whisper, MMS-1B, and SeamlessM4T use `float16` on CUDA. Gemma 4 uses `bfloat16` on Apple MPS. ## Gemma 4 script-aware prompting The mitigation experiment compares the baseline Gemma 4 prompt above against a script-aware prompt on the same ten FLEURS test splits. The script-aware arm uses: ```text Transcribe the following speech segment in {language_name}. Use {script_name} script only. Do not translate, romanize, or add explanations. Only output the transcription, with no newlines. When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three. ``` Run the experiment after `results_gemma4/sf_results.csv` contains all ten Gemma baseline rows: ```bash uv run python scripts/eval_gemma4_prompt_mitigation.py ``` The script writes: ```text results_gemma4_prompt_mitigation/sf_results.csv results_gemma4_prompt_mitigation/predictions/gemma4_script_hint_{language}_predictions.json analysis/gemma4_prompt_mitigation_summary.csv ``` ## Downstream MT validation The downstream check asks whether script errors damage a later text pipeline. It translates gold FLEURS transcripts, baseline Gemma 4 ASR outputs, and script-aware Gemma 4 ASR outputs into English, then scores chrF and BLEU against the aligned English FLEURS reference. The default MT model is `facebook/nllb-200-distilled-600M`, which supports FLORES-style language codes for the ten paper languages. Run the deadline-friendly diagnostic subset: ```bash uv run python scripts/eval_downstream_mt.py \ --max-examples-per-language 100 \ --sample-mode stratified_sfr ``` Run the full aligned set when time permits: ```bash uv run python scripts/eval_downstream_mt.py \ --max-examples-per-language 0 \ --sample-mode random ``` The script writes: ```text results_gemma4_downstream_mt/translations/{mt_model}_{language}_{variant}_translations.json analysis/gemma4_downstream_mt_summary.csv analysis/gemma4_downstream_mt_utterances.csv analysis/gemma4_downstream_mt_correlations.csv ``` Use NLLB as the primary MT model. Gemma can be used as a secondary sensitivity check, but it should not be the main downstream evaluator because Gemma also produces the ASR outputs in this experiment. ## SFR+LID hybrid audit The hybrid audit runs language identification over saved Gemma 4 outputs only. It does not rerun ASR. ```bash uv run python scripts/eval_sfr_lid_hybrid.py ``` The script writes: ```text analysis/sfr_lid_hybrid_summary.csv analysis/sfr_lid_hybrid_utterances.csv ``` ## Regenerating figures ```bash uv run python scripts/merge_gemma4.py ``` This command merges Gemma 4 rows into `analysis/sf_results.csv` and regenerates the heatmap and scatter figures in `figures/`. The plotting code filters the main figures to the 100 model-language pairs reported in the paper. ## Result fields `analysis/sf_results.csv` has one row per evaluated model-language pair. The paper's 100-pair matrix is the subset with `family` in `Whisper`, `MMS`, `SeamlessM4T`, or `Gemma4`. The six rows with `family=unknown` are supplemental Pashto-only comparisons and are not used in the paper denominator, family summaries, heatmap, or collapse counts. | Column | Meaning | |---|---| | `model` | Hugging Face model identifier | | `family` | Model family used for paper grouping | | `language` | Target language | | `wer_pct` | Word error rate after language-specific normalisation | | `cer_pct` | Character error rate after language-specific normalisation | | `sfr_mean` | Mean utterance-level Script Fidelity Rate, in percent | | `sfr_full_pct` | Percent of utterances with SFR = 100% | | `sfr_zero_pct` | Percent of utterances with SFR = 0% | | `dom_*` | Dominant-script utterance counts | ## SFR library For standalone use outside this artifact, install the published package: ```bash uv add script-fidelity ``` Then import it as `script_fidelity`: ```python from script_fidelity import compute_sfr compute_sfr("کابل کې ښه هوا ده", language="ps_af") compute_sfr("kabul ke sha hawa da", language="pashto") compute_sfr("नमस्ते", language="hindi") ``` One-off CLI use: ```bash uvx --from script-fidelity sfr score --language ps_af --text "کابل کې ښه هوا ده" ``` Hugging Face Evaluate use: ```python import evaluate sfr = evaluate.load("themechanism/script_fidelity_rate", module_type="metric") sfr.compute( predictions=["کابل کې ښه هوا ده", "romanized output"], language="ps_af", ) ``` The validation script has already been run for the submitted artifact. It checks known positive and negative examples for Pashto, Hindi, and Somali. ## Licenses and responsible release FLEURS is CC BY 4.0. Whisper is MIT licensed. MMS-1B and SeamlessM4T-v2 are released under Meta's research licenses. Gemma 4 E2B is Apache-2.0 through the evaluated checkpoint. This supplement releases code, metadata, and evaluation outputs only; it releases no model weights and no new speech recordings. See `croissant_metadata.json` for artifact metadata, intended use, limitations, PII status, and Responsible AI fields. ## Anonymous citation ```bibtex @misc{Anonymous2026ScriptFidelity, author = {Anonymous}, title = {Script collapse in multilingual ASR: A reference-free metric and 100-pair benchmark}, year = {2026}, note = {Anonymous submission} } ```