OmniASR LLM-1B — Mongolian Speech-to-Text Benchmark
Real results from the OmniASR LLM-1B Batch API (enhanced operating point, language mn) evaluated across 4 Mongolian datasets and 265 audio samples. Every number below is measured — not marketing. WER, speed, and pricing are shown as-is, brutally honest.
265/265 samples transcribed · 100% success rate
Lower is better · across 265 samples
Pricing
OmniASR LLM-1B list pricing for batch transcription. No discounts, no negotiated rates applied — the raw per-minute rate.
Pricing source: OmniASR LLM-1B public pricing. Duudlaga Flow is shown for context only — this page isolates OmniASR LLM-1B so the number is not padded by our own product.
WER & CER by dataset
Word and character error rates per dataset. Lower is better — and these are the real OmniASR LLM-1B numbers, which are weak on Common Voice 24.
Dataset summary
Aggregate accuracy, speed, and timing for each dataset.
| Dataset | Source | Samples | Success | WER | CER | Accuracy | Speed | Audio (s) | Proc (s) |
|---|---|---|---|---|---|---|---|---|---|
| Common Voice 24 (MN) | Hugging Face → | 59 | 59/59 | 36.6% | 15.5% | 63.4% | 0.3× | 330.6s | 1088.7s |
| Shunya Labs Mongolian Speech | Hugging Face → | 60 | 60/60 | 22.9% | 6.9% | 77.1% | 0.25× | 645.1s | 2583.1s |
| Common Voice 20 (MN) | Hugging Face → | 54 | 54/54 | 37.2% | 18% | 62.8% | 0.29× | 269.8s | 931.7s |
| Modern Voice | — | 92 | 92/92 | 36.9% | 15.8% | 63.1% | 0.26× | 603.8s | 2304.1s |
WER vs speed — per sample
Each dot is one audio sample. The sweet spot is the bottom-left (low error, fast). OmniASR LLM-1B sits high on error for many Common Voice 24 samples.
Per-sample results
Ground truth shown verbatim in the Expected column. The result column highlights only the words OmniASR LLM-1B got wrong, in red — no strikethrough/swap gymnastics, just the mistakes.
| # | Audio | Sample | Dataset | Expected (ground truth) | OmniASR LLM-1B result | WER | CER | I/D/S |
|---|---|---|---|---|---|---|---|---|
| 1 | btsee_0001 | Common Voice 24 (MN) | Гэхдээ амьсгал хураахаасаа өмнө танд мэдэж байгаагаа хэлье. | гэхдээ амьсгал хураахаасаа өмнө танд мэдэж айгаагаа хэлье | 12.5% | 1.7% | 0/0/1 | |
| 2 | btsee_0002 | Common Voice 24 (MN) | Надад заяасан аз жаргал гэдэг ердөө гуравхан сарын хугацаатай байсан гэж үү? | და́დ წე́ი სა́ნათ ჭირალო̆ გე́ტდეგერ და ყო́რუხ უ̂ნ სარენ ხო́ხ წათდა́ ბჵაჲსნი̆ გეჩუ́ჲნი̆ | 108.3% | 104.0% | 1/0/12 | |
| 3 | btsee_0003 | Common Voice 24 (MN) | Одоо бид өөрсдөө өвчин эмгэгээсээ салахыг хичээцгээе. | одоо бид өөрсдөө өвчин эмгэгээсээ салахыг ч хэвцгээв | 28.6% | 9.6% | 1/0/1 | |
| 4 | btsee_0004 | Common Voice 24 (MN) | Би бол голдуу хээрээр гэр, хэцээр дэр хийж явдаг хүн. | би бол голдуу хээрээр гэр хэцээр дэр хийж явдаг хүн | 0.0% | 0.0% | 0/0/0 | |
| 5 | btsee_0005 | Common Voice 24 (MN) | Хан хурмаст уурлаж, Болдоггүй Бор өвгөнийг хор луугаараа ниргүүлэхээр явуулжээ. | хан хурамч дуурлаж болдохгүй бор өгнө хоёр луугаар нэргүүлэхээр явуулжээ | 70.0% | 18.2% | 0/0/7 | |
| 6 | btsee_0006 | Common Voice 24 (MN) | Алив наашаа ороод ир гээд гэртээ оров. | алив нашаа ороодор гээд гэртээ оров | 42.9% | 8.1% | 0/1/2 | |
| 7 | btsee_0007 | Common Voice 24 (MN) | Өө өндөр дээдэс таны тухайд би баталж чадахгүй. | өө өндөр дээд таны тухайд би ботолж чадахгүй | 25.0% | 8.7% | 0/0/2 | |
| 8 | btsee_0008 | Common Voice 24 (MN) | Харин гурав дахь удаагаас эхлэн хүмүүсийг сонирхож эхлэв. | харин гурав дахь удаагаас эхлэн хүмүүсийг сонирхож эхлэв | 0.0% | 0.0% | 0/0/0 | |
| 9 | btsee_0009 | Common Voice 24 (MN) | Та нар очингуутаа шөл л өгч үз. | та нар очингуутаа шүлэл өгч үз | 28.6% | 6.7% | 0/1/1 | |
| 10 | btsee_0010 | Common Voice 24 (MN) | Ерөөсөө литр үйлдвэрээсээ салаагүй явсан юм чинь. | эрөөсөө л үйлдвэрээсээ салуугүй явсан юм чинь | 42.9% | 12.5% | 0/0/3 |
Methodology
How these numbers were produced.
Provider: OmniASR LLM-1B (Meta Omnilingual ASR (omniASR_LLM_1B_v2) run locally — 1,600+ languages, decoding Halh Mongolian (khk_Cyrl).).
Endpoint local (omnilingual-asr). Language mn. Operating point omniASR_LLM_1B_v2 (lang=khk_Cyrl). Diarization none.
Datasets: Common Voice 24 (MN), Shunya Labs Mongolian Speech, Common Voice 20 (MN), Modern Voice — 265 samples, 1849.3s of audio total.
- Common Voice 24 (MN)https://huggingface.co/datasets/btsee/common-voices-24-mn
- Shunya Labs Mongolian Speechhttps://huggingface.co/datasets/shunyalabs/mongolian-speech-dataset
- Common Voice 20 (MN)https://huggingface.co/datasets/warmestman/common-voice-20-mn-normalized
- Modern Voice—
Metrics: WER and CER are computed with a standard word/character Levenshtein alignment, normalized for case and punctuation. Accuracy = 100 − WER. Speed factor = audio duration ÷ processing time (× realtime). All requests are real OmniASR LLM-1B Batch API calls, not cached or simulated.
Diff highlighting: The result column aligns to the ground truth and colors every substitution and insertion red. Deletions (words missing from the result) are not shown in the result column — the Expected column already holds the full ground truth as-is.