| --- |
| annotations_creators: |
| - expert-generated |
| language_creators: |
| - expert-generated |
| language: |
| - en |
| license: |
| - mit |
| multilinguality: |
| - monolingual |
| pretty_name: VoiceCodeBench |
| size_categories: |
| - n<1K |
| source_datasets: [] |
| task_categories: |
| - automatic-speech-recognition |
| tags: |
| - audio |
| - speech |
| - speech-recognition |
| - speech-to-text |
| - automatic-speech-recognition |
| - asr |
| - asr-benchmark |
| - benchmark |
| - evaluation |
| - structured-token-recovery |
| - entity-recovery |
| - workplace-speech |
| - english |
| dataset_info: |
| - config_name: default |
| features: |
| - name: file_name |
| dtype: string |
| - name: audio_id |
| dtype: string |
| - name: language |
| dtype: string |
| - name: duration |
| dtype: float64 |
| - name: domain |
| dtype: string |
| - name: scenario |
| dtype: string |
| - name: difficulty |
| dtype: string |
| - name: speaker |
| struct: |
| - name: id |
| dtype: string |
| - name: sex |
| dtype: string |
| - name: accent |
| dtype: string |
| - name: age_bucket |
| dtype: string |
| - name: audio_quality |
| struct: |
| - name: snr_db |
| dtype: float64 |
| - name: noise_rms_dbfs |
| dtype: float64 |
| - name: speech_rms_dbfs |
| dtype: float64 |
| - name: loudness_lufs |
| dtype: float64 |
| - name: click_pop_count_per_min |
| dtype: float64 |
| - name: transcripts |
| struct: |
| - name: template |
| dtype: string |
| - name: acoustic |
| dtype: string |
| - name: canonical |
| dtype: string |
| - name: entities |
| list: |
| - name: id |
| dtype: string |
| - name: type |
| dtype: string |
| - name: role |
| dtype: string |
| - name: acoustic |
| dtype: string |
| - name: canonical |
| dtype: string |
| - name: entity_types |
| list: string |
| - name: entity_count |
| dtype: int64 |
| splits: |
| - name: test |
| num_bytes: 911121 |
| num_examples: 300 |
| download_size: 1068651 |
| dataset_size: 911121 |
| configs: |
| - config_name: default |
| default: true |
| data_files: |
| - split: test |
| path: data/metadata.jsonl |
| --- |
| |
| # VoiceCodeBench |
|
|
| VoiceCodeBench is a test-only benchmark for evaluating whether automatic |
| speech recognition (ASR) systems preserve exact structured values in English |
| workplace speech. |
|
|
| Paper: [VoiceCodeBench: Evaluating Exact Structured-Token Recovery in Automatic Speech Recognition](paper/voice-code-bench.pdf) |
|
|
| The benchmark targets cases where a transcript is software input: callback |
| numbers, email addresses, command-line flags, file paths, URLs, account |
| identifiers, dates, measurements, and similar values that downstream systems may |
| parse, route, store, compare, or execute. |
|
|
| ## Contents |
|
|
| - 300 human-recorded English WAV segments, totaling 5.587 hours. |
| - 85 anonymized speaker IDs. |
| - 1,482 audited target entities across 26 structured entity types. |
| - 8 workplace workflow domains. |
| - 15 tracked baseline ASR system outputs. |
|
|
| Released files: |
|
|
| - `data/audio/*.wav`: benchmark audio files. |
| - `data/metadata.jsonl`: transcripts, entity annotations, speaker metadata, and |
| audio-quality metadata. |
| - `baselines/predictions/*.json`: baseline transcripts plus entity-match |
| decisions. |
| - `baselines/results.csv`: aggregate baseline table. |
| - `scripts/`: transcription, entity verification, scoring, and figure commands. |
| - `paper/`: paper source and PDF. |
| - `DATASET_CARD.md`: datasheet-style documentation for motivation, composition, |
| collection, consent, intended use, limits, maintenance, and licensing. |
|
|
| ## Task |
|
|
| Each item contains an audio recording, three transcript layers, and target |
| entities: |
|
|
| - `template`: script text with entity placeholders. |
| - `acoustic`: what the speaker is expected to say aloud. |
| - `canonical`: the written value a downstream application needs. |
|
|
| For example, "double dash dry dash run" maps to `--dry-run`, and "all caps |
| database underscore URL" maps to `DATABASE_URL`. |
|
|
| ASR systems are evaluated under a raw-audio-only protocol. The system receives |
| only the audio file; benchmark-specific prompts, target entity lists, domain |
| labels, custom vocabulary, grammar constraints, candidate values, and post-ASR |
| correction are excluded from the main setting. |
|
|
| ## Metrics |
|
|
| VoiceCodeBench reports WER as a broad transcript-quality diagnostic, but its |
| main entity-sensitive metrics are: |
|
|
| ```text |
| CTEM = correct target entities / target entities |
| TSR = recordings with all target entities correct / recordings |
| ``` |
|
|
| Canonical Token/Entity Match (CTEM) measures value-level recovery. Task Success |
| Rate (TSR) measures whether every target entity in a recording was recovered. |
|
|
| ## Baselines |
|
|
| The tracked baseline suite contains 15 ASR systems across batch and streaming |
| modes. Current aggregate ranges: |
|
|
| - WER: 8.6% to 26.1%. |
| - CTEM: 72.5% to 91.6%. |
| - TSR: 25.7% to 68.7%. |
|
|
| The strongest baseline by TSR is `deepgram_nova3` at 68.7%. The strongest |
| baseline by CTEM is `elevenlabs_scribe_v2` at 91.6%. |
| `modal_inkling` scores 24.3% WER, 84.3% CTEM, and 49.7% TSR in batch mode. |
| `modal_nvidia_parakeet_tdt_0_6b_v3` scores 23.7% WER, 78.6% CTEM, and 37.3% TSR in batch mode. |
| `modal_meta_omniasr_llm_unlimited_7b_v2` scores 26.1% WER, 72.5% CTEM, and 25.7% TSR in batch mode. |
|
|
| ## Install |
|
|
| For scoring released baseline artifacts: |
|
|
| ```bash |
| python -m pip install -e . |
| ``` |
|
|
| Optional extras are available for heavier workflows: |
|
|
| ```bash |
| python -m pip install -e ".[providers]" # run new ASR baselines |
| python -m pip install -e ".[figures]" # regenerate paper figures |
| python -m pip install -e ".[dev]" # provider and figure dependencies |
| ``` |
|
|
| External tools are only needed for optional workflows: |
|
|
| - `ffmpeg` for live provider audio conversion, including streaming PCM and |
| Inkling's 16 kHz WAV input. |
| - `gcloud` for Google Cloud transcription when application-default credentials |
| are not already configured. |
| - `latexmk` for rebuilding the paper PDF. |
|
|
| ## Tests |
|
|
| ```bash |
| python -m pytest |
| ``` |
|
|
| ## Reproduce |
|
|
| ```bash |
| ./scripts/reproduce_release.sh |
| ``` |
|
|
| This creates `.venv` if needed, installs the package in editable mode with the |
| `figures` extra, validates metadata, scores the released baseline |
| transcripts/entity matches, rewrites `baselines/results.csv`, and regenerates |
| `paper/figures/wer_entity_scatter.pdf` from the frozen 12-model paper baseline |
| set. Post-publication baselines such as Inkling and Parakeet are excluded from that figure. |
|
|
| To generate the current benchmark figure with every row in |
| `baselines/results.csv`, run: |
|
|
| ```bash |
| vcb-make-figures --model-set all |
| ``` |
|
|
| This writes `baselines/figures/wer_entity_scatter.pdf` by default. Use |
| `--model-set paper` to regenerate only the frozen paper figure, or `--output` to |
| choose another path. |
|
|
| To rebuild the paper PDF: |
|
|
| ```bash |
| cd paper |
| latexmk -pdf -interaction=nonstopmode -halt-on-error voice-code-bench.tex |
| ``` |
|
|
| ## Experimental New Baselines |
|
|
| The released scoring and reproduction commands are the stable script surface. |
| Live provider runs are included to make the tracked baselines auditable, but |
| provider APIs and websocket protocols change over time. |
|
|
| Running new ASR baselines requires provider credentials. Copy |
| `scripts/.secret.example` to a private secret file or set equivalent environment |
| variables, including the Modal endpoint settings described below, then run: |
|
|
| ```bash |
| python -m venv .venv |
| . .venv/bin/activate |
| python -m pip install -e ".[providers]" |
| |
| vcb-run \ |
| --stt-mode all \ |
| --output-dir runs/full-local |
| ``` |
|
|
| ### Modal Inkling |
|
|
| Thinking Machines Lab Inkling is available as the batch model ID |
| `modal_inkling`. Modal exposes `thinkingmachines/Inkling-NVFP4` through a shared, |
| OpenAI-compatible managed endpoint with token-based pricing; no dedicated model |
| deployment is required. Open the [Inkling endpoint page](https://modal.com/endpoints?model=thinkingmachines%2FInkling-NVFP4&type=managed), |
| create a Modal account if needed, create the managed endpoint, and copy its URL. |
| Create a proxy token and configure the private `scripts/.secret` file with the |
| endpoint URL, token ID, and token secret: |
|
|
| ```dotenv |
| MODAL_INKLING_ENDPOINT=https://<your-endpoint-host> |
| MODAL_PROXY_TOKEN_ID=wk-... |
| MODAL_PROXY_TOKEN_SECRET=ws-... |
| ``` |
|
|
| The adapter accepts the base endpoint URL, `/v1` URL, or full |
| `/v1/chat/completions` URL shown by Modal. Check the endpoint page for current |
| pricing and rate limits before a full benchmark run. Never commit |
| `scripts/.secret`; it is ignored by Git. |
|
|
| To smoke-test one recording after configuring real credentials: |
|
|
| ```bash |
| vcb-transcribe \ |
| --stt-model-ids modal_inkling \ |
| --limit 1 \ |
| --output-dir runs/inkling-smoke |
| ``` |
|
|
| To run the full transcription, entity-verification, and scoring pipeline, also |
| configure `OPENAI_API_KEY` for the entity verifier and run: |
|
|
| ```bash |
| vcb-run \ |
| --stt-model-ids modal_inkling \ |
| --output-dir runs/inkling-full |
| ``` |
|
|
| The adapter converts source recordings to the model's documented 16 kHz mono |
| WAV input, sends the audio through Modal's chat-completions `audio_url` format, |
| uses Thinking Machines' documented transcription prompt and text-before-audio |
| message order, and sets reasoning effort to `max` (`0.99`). The transcript |
| response is stored verbatim without benchmark-specific hints or post-ASR |
| correction. |
|
|
| Thinking Machines' limited-time free Inkling Playground is useful for manually |
| spot-checking audio, but it is a chat interface rather than the reproducible |
| batch API used by this benchmark. |
|
|
| ### Modal NVIDIA Parakeet TDT 0.6B v3 |
|
|
| NVIDIA Parakeet TDT 0.6B v3 is available as the batch model ID |
| `modal_nvidia_parakeet_tdt_0_6b_v3` through the checked-in Modal app |
| `modal/parakeet_tdt_0_6b_v3.py`. It self-hosts |
| `nvidia/parakeet-tdt-0.6b-v3` on an L40S using official Hugging Face weights |
| and the shared Modal-ASR JSON transport. Configure these private secrets: |
|
|
| ```dotenv |
| MODAL_PARAKEET_ENDPOINT=https://<your-parakeet-web-function>.modal.run |
| MODAL_PROXY_TOKEN_ID=wk-... |
| MODAL_PROXY_TOKEN_SECRET=ws-... |
| ``` |
|
|
| Warm and deploy the app as described in `scripts/MODAL.md`, then smoke-test the |
| longest recording with a one-row metadata file rather than `--limit 1`. The full |
| run command is: |
|
|
| ```bash |
| vcb-run \ |
| --stt-model-ids modal_nvidia_parakeet_tdt_0_6b_v3 \ |
| --resume \ |
| --output-dir runs/parakeet-full |
| ``` |
|
|
| The adapter converts each source recording to one complete 16 kHz mono WAV and |
| sends only raw audio bytes. No chunking, prompts, custom vocabulary, target-value |
| hints, canonicalization, or post-ASR correction are used; the benchmark stores |
| only the returned transcript verbatim. |
|
|
| ### Modal Meta OmniASR LLM Unlimited 7B v2 |
|
|
| Meta OmniASR LLM Unlimited 7B v2 is registered as the batch model ID |
| `modal_meta_omniasr_llm_unlimited_7b_v2` through the checked-in Modal app |
| `modal/omniasr_llm_unlimited_7b_v2.py`. It self-hosts |
| `omniASR_LLM_Unlimited_7B_v2` on an L40S with `omnilingual-asr==0.2.0`, a |
| persistent fairseq2 asset cache, and the shared Modal-ASR JSON transport. |
| Configure these private secrets: |
|
|
| ```dotenv |
| MODAL_OMNIASR_ENDPOINT=https://<your-omniasr-web-function>.modal.run |
| MODAL_PROXY_TOKEN_ID=wk-... |
| MODAL_PROXY_TOKEN_SECRET=ws-... |
| ``` |
|
|
| Warm and deploy the app as described in `scripts/MODAL.md`, then smoke-test the |
| longest recording, `education_workplace_006` (`data/audio/261.wav`, 122.875 |
| seconds), with a one-row metadata file rather than `--limit 1`. The full run |
| command is: |
|
|
| ```bash |
| vcb-run \ |
| --stt-model-ids modal_meta_omniasr_llm_unlimited_7b_v2 \ |
| --resume \ |
| --output-dir runs/omniasr-full |
| ``` |
|
|
| The adapter converts each source recording to one complete 16 kHz mono WAV, |
| sends `language="eng_Latn"`, and stores only the returned transcript verbatim. |
| No chunking, prompts, custom vocabulary, target-value hints, canonicalization, |
| or post-ASR correction are used. A publishable OmniASR baseline additionally |
| requires a real deployed endpoint, proxy token, entity-verifier access or cache, |
| a complete 300-recording artifact, recomputed scores, and the current all-model |
| figure; mock output must not be copied into `baselines/predictions/`. |
|
|
| For publishable results, report provider, model name, API endpoint or endpoint |
| family, evaluation date, mode, inference settings, and any prompting, custom |
| vocabulary, post-processing, fine-tuning, or canonicalization. |
|
|
| ## Use And Limits |
|
|
| VoiceCodeBench is intended for diagnostic ASR evaluation, provider comparison, |
| regression tracking, and per-entity risk analysis. It is not intended as a |
| training corpus, hidden leaderboard, universal ASR-quality measure, speaker |
| identification resource, biometric dataset, or demographic profiling dataset. |
|
|
| See `DATASET_CARD.md` for the full dataset statement. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{voicecodebench2026, |
| title = {VoiceCodeBench: Evaluating Exact Structured-Token Recovery in Automatic Speech Recognition}, |
| author = {Baumgartner, Tyler and Tai, Brandon and Kaelin-Martin, Lisa and Fan, Candice and Debaupte, Luc and Wang, Bill and Zhong, Yi}, |
| year = {2026}, |
| note = {Benchmark dataset and paper} |
| } |
| ``` |
|
|
| ## License |
|
|
| VoiceCodeBench is released under the MIT License. See `LICENSE`. |
|
|
