| --- |
| license: |
| - cc-by-4.0 |
| - cc-by-sa-4.0 |
| - cc-by-3.0 |
| language: |
| - hi |
| - bn |
| - ta |
| - te |
| - mr |
| - gu |
| - kn |
| - ml |
| - pa |
| - or |
| - as |
| - ur |
| - en |
| task_categories: |
| - automatic-speech-recognition |
| tags: |
| - indic |
| - hinglish |
| - code-switching |
| - benchmark |
| - speech-to-text |
| - evaluation |
| - logatra |
| pretty_name: Logatra Benchmark |
| size_categories: |
| - 1K<n<10K |
| thumbnail: https://huggingface.co/datasets/CooledNik/logatra-benchmark/resolve/main/assets/logatra-logo.png |
| configs: |
| - config_name: heldout |
| data_files: manifests/heldout_yt.silver.jsonl |
| - config_name: fleurs |
| data_files: manifests/fleurs_*.jsonl |
| - config_name: mucs2021 |
| data_files: manifests/mucs2021_hi_en.jsonl |
| --- |
| |
|  |
|
|
| # Logatra Benchmark |
|
|
| An independent speech-to-text benchmark for Indian languages and Hinglish, built by |
| [Nemukai](https://nemukai.com), the team behind [Logatra](https://logatra.com). |
| Seven commercial STT engines are scored on a public split and on a fresh held-out |
| split of 2026 Creative Commons audio that no model could have trained on. |
|
|
| This repository holds the audio, manifests and every raw provider response, so the |
| whole leaderboard can be re-scored offline without an API key. |
|
|
| | | | |
| |---|---| |
| | Leaderboard and per-case explorer | [nemukai.com/labs/logatra-benchmark](https://nemukai.com/labs/logatra-benchmark) | |
| | Same report as a Space | [CooledNik/logatra-benchmark](https://huggingface.co/spaces/CooledNik/logatra-benchmark) | |
| | Code, harness and methodology (MIT) | [github.com/Nemukai/logatra-benchmark](https://github.com/Nemukai/logatra-benchmark) | |
| | Logatra, the transcription product | [logatra.com](https://logatra.com) | |
|
|
| ## Results |
|
|
| Every engine on the same clips: 324 from the public split and all 132 held-out |
| chunks. Lower is better. |
|
|
|  |
|
|
| | Engine | Public WER | Held-out WER | Held-out median | US$ per audio hour | |
| |---|---|---|---|---| |
| | Sarvam saaras:v3 | 32.3% | 22.8% | 10.6% | ~0.54 | |
| | Sarvam saaras:v4 | 26.1% | 25.8% | 13.4% | ~0.54 | |
| | Gemini 3.5 Flash | 26.5% | 22.2% \* | 11.7% \* | ~0.43 | |
| | Sarvam saarika:v2.5 | 37.3% | 19.7% \* | 0.0% \* | ~0.54 | |
| | OpenAI gpt-audio | 53.5% | 28.5% | 19.8% | ~1.39 | |
| | OpenAI whisper-1 | 78.3% | 47.6% \* | 39.0% \* | 0.36 | |
| | Mistral Voxtral Small 24B | 293.8% | 72.0% | 26.6% | ~0.36 | |
|
|
| \* Contributed to the silver held-out references, so its held-out score is an upper |
| bound (see below). |
|
|
| What stands out: |
|
|
| - **Saaras v4 is the strongest engine on the public split and the fastest of the |
| seven** (0.6 s median per clip), but on fresh held-out audio saaras v3 still |
| edges it by about three points. Neither wrote the held-out references, so that |
| gap is a clean measurement. |
| - **Voxtral falls into repetition loops** on Indic audio: 46 of 896 outputs are |
| runaway repetitions, which is why its mean WER is above 100% while its median is |
| far lower. |
| - **Whisper collapses on conversational Hindi**: usable on read speech, close to |
| unusable on real interview audio. |
|
|
|  |
|
|
|  |
|
|
| ## What is here |
|
|
| | Path | Contents | |
| |---|---| |
| | `manifests/*.jsonl` | one JSON object per sample: `id`, relative `audio_path`, `reference_text`, `language`, `code_mixed`, `source`, `license`, `split` | |
| | `audio/fleurs/` | 1,200 FLEURS test clips, 12 Indic languages, 16 kHz mono WAV | |
| | `audio/mucs2021/` | 3,136 MUCS 2021 Hindi-English code-switched clips, 16 kHz mono WAV | |
| | `audio/heldout_yt/` | 132 chunks (about 48 minutes) cut from 11 CC-BY YouTube videos published January to June 2026 | |
| | `results/<system>/<sample_id>.json` | the raw cached response of every provider call: text, latency and the provider's own payload | |
| | `report/data.json`, `report/rows.json` | aggregated and per-sample metrics (WER, CER, script-agnostic WER, latency, cost) | |
| | `manifests/heldout_yt.adjudications.json` | every adjudication decision for the 36 held-out rows that failed the automatic consensus gate | |
| | `assets/` | the figures on this page and the Logatra logo | |
|
|
| The dataset viewer shows three configs: `heldout` (silver references), `fleurs` |
| and `mucs2021`. Audio paths in the manifests are relative to the `manifests/` |
| folder. |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| path = snapshot_download("CooledNik/logatra-benchmark", repo_type="dataset") |
| # then score offline with the harness from github.com/Nemukai/logatra-benchmark |
| ``` |
|
|
| ## How the held-out split was made |
|
|
| Eleven Hindi and Hinglish YouTube videos published between January and June 2026, |
| each released by its uploader under the Creative Commons Attribution license, were |
| cut into 132 chunks of about 22 seconds. They are long-form interviews, podcasts and |
| talks: conversational speech with code-mixing, the kind of audio |
| [Logatra](https://logatra.com) customers upload. Only the chunks are shipped, never |
| the full videos. |
|
|
| ### References are silver, not gold |
|
|
| There is no human gold transcription of the held-out split yet. Each reference is |
| the medoid of three systems' hypotheses under a script-agnostic consensus distance, |
| and the 36 rows that failed the automatic gate were adjudicated case by case. The |
| systems that contributed hypotheses (saarika, Gemini, Whisper) score against |
| references they partly wrote, so their held-out scores are upper bounds; |
| `self_ref_rate` in `report/data.json` gives the exposure per system (saarika 58%, |
| Gemini 38%, Whisper 5%). Read the methodology in the |
| [GitHub README](https://github.com/Nemukai/logatra-benchmark) before citing held-out |
| numbers. |
|
|
| ## What is deliberately not here |
|
|
| The Gramvaani Hindi telephony subset (OpenSLR 118, 2,917 clips) is licensed for |
| academic use only, with commercial use by permission and no redistribution grant. |
| Its audio, transcripts and the provider outputs derived from it are excluded. The |
| manifests keep the Gramvaani rows with an empty `reference_text` so sample ids stay |
| stable; the GitHub repository documents how to download OpenSLR 118 yourself and |
| merge the references back in. |
|
|
| Nothing in this dataset comes from Logatra customers or private recordings. |
|
|
| ## Licensing |
|
|
| Each subset keeps the license of its source, and every manifest row carries its own |
| `license` field. |
|
|
| | Subset | Source | License | |
| |---|---|---| |
| | `fleurs` | [google/fleurs](https://huggingface.co/datasets/google/fleurs), test split (Conneau et al., 2022) | CC-BY-4.0 | |
| | `mucs2021` | [OpenSLR 104](https://www.openslr.org/104/), MUCS 2021 subtask 2 (Diwan et al., 2021) | CC-BY-SA-4.0 | |
| | `heldout_yt` | 11 YouTube videos whose uploaders chose the Creative Commons Attribution license; per-video credit in `ATTRIBUTION.md` | CC-BY-3.0 | |
| | `results/`, `report/` | Nemukai's own API calls and measurements, derived from the audio above | same license as the underlying subset; the MUCS-derived outputs are CC-BY-SA-4.0 | |
|
|
| If you hold the rights to any of the held-out videos and want your material removed, |
| open an issue on the [GitHub repository](https://github.com/Nemukai/logatra-benchmark) |
| and it will be pulled promptly. |
|
|
| ## Disclosure |
|
|
| [Logatra](https://logatra.com) is Nemukai's transcription product, so we have a |
| stake in these numbers. The benchmark exists because we needed to choose a provider |
| and did not trust vendor-reported results. Logatra runs Sarvam saaras:v4 in |
| production as of October 2026, even though saaras v3 leads on the held-out split |
| here. Every raw provider response is shipped so the scoring can be checked. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{logatra-benchmark-2026, |
| title = {Logatra Benchmark: speech-to-text on Indian languages and Hinglish}, |
| author = {Singh, Priyansh Kumar}, |
| year = {2026}, |
| publisher = {Nemukai}, |
| url = {https://huggingface.co/datasets/CooledNik/logatra-benchmark} |
| } |
| ``` |
|
|
| Please also cite the source datasets: FLEURS (Conneau et al., 2022) and MUCS 2021 |
| (Diwan et al., 2021). |
|
|
