--- 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:// 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://.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://.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`.