SeamlessM4T v2 — Mongolian Speech-to-Text Benchmark
Real results from the SeamlessM4T v2 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
SeamlessM4T v2 list pricing for batch transcription. No discounts, no negotiated rates applied — the raw per-minute rate.
Pricing source: SeamlessM4T v2 public pricing. Duudlaga Flow is shown for context only — this page isolates SeamlessM4T v2 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 SeamlessM4T v2 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 | 17.6% | 6.3% | 82.4% | 3.59× | 330.6s | 92.1s |
| Shunya Labs Mongolian Speech | Hugging Face → | 60 | 60/60 | 22.2% | 10.2% | 77.8% | 0.25× | 645.1s | 2569.2s |
| Common Voice 20 (MN) | Hugging Face → | 54 | 54/54 | 21.7% | 7.9% | 78.3% | 0.14× | 269.8s | 1897s |
| Modern Voice | — | 92 | 92/92 | 30.9% | 16.7% | 69.1% | 4.26× | 603.8s | 141.6s |
WER vs speed — per sample
Each dot is one audio sample. The sweet spot is the bottom-left (low error, fast). SeamlessM4T v2 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 SeamlessM4T v2 got wrong, in red — no strikethrough/swap gymnastics, just the mistakes.
| # | Audio | Sample | Dataset | Expected (ground truth) | SeamlessM4T v2 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 байгаагаа хэлье. | 0.0% | 0.0% | 0/0/0 | |
| 2 | btsee_0002 | Common Voice 24 (MN) | Надад заяасан аз жаргал гэдэг ердөө гуравхан сарын хугацаатай байсан гэж үү? | Надад аясан аз жаргал гэдэг ердөө гуравхан сарын хугацаатай байсан гэж үү? | 8.3% | 2.7% | 0/0/1 | |
| 3 | btsee_0003 | Common Voice 24 (MN) | Одоо бид өөрсдөө өвчин эмгэгээсээ салахыг хичээцгээе. | Одоо бид өөрсдөө өвчнөөр эмгэгээсээ салахыг хичээцгээе. | 14.3% | 7.7% | 0/0/1 | |
| 4 | btsee_0004 | Common Voice 24 (MN) | Би бол голдуу хээрээр гэр, хэцээр дэр хийж явдаг хүн. | Би бол голдуу хээрээр гэр, хетээр дэр хийж явдаг хүн. | 10.0% | 3.9% | 0/0/1 | |
| 5 | btsee_0005 | Common Voice 24 (MN) | Хан хурмаст уурлаж, Болдоггүй Бор өвгөнийг хор луугаараа ниргүүлэхээр явуулжээ. | Хан хурамс уурлаж, болдоггүй бор уг нь хоёр луугаар нэхүүлэхээр явуулжээ. | 60.0% | 19.5% | 1/0/5 | |
| 6 | btsee_0006 | Common Voice 24 (MN) | Алив наашаа ороод ир гээд гэртээ оров. | Алив наашаа ороод ир гээд гэртээ оров. | 0.0% | 0.0% | 0/0/0 | |
| 7 | btsee_0007 | Common Voice 24 (MN) | Өө өндөр дээдэс таны тухайд би баталж чадахгүй. | Өө, өндөр дээдэс таны тухайд би батлаж чадахгүй. | 12.5% | 4.3% | 0/0/1 | |
| 8 | btsee_0008 | Common Voice 24 (MN) | Харин гурав дахь удаагаас эхлэн хүмүүсийг сонирхож эхлэв. | харин гурав дахь удаагаа эхлээд хүмүүсийг сонирхож эхлэв. | 25.0% | 5.4% | 0/0/2 | |
| 9 | btsee_0009 | Common Voice 24 (MN) | Та нар очингуутаа шөл л өгч үз. | Та нар очингуутаа шөл л өгч үз. | 0.0% | 0.0% | 0/0/0 | |
| 10 | btsee_0010 | Common Voice 24 (MN) | Ерөөсөө литр үйлдвэрээсээ салаагүй явсан юм чинь. | Ерөөсөө л үйлдвэрээсээ салаагүй явсан юм чинь. | 14.3% | 6.2% | 0/0/1 |
Methodology
How these numbers were produced.
Provider: SeamlessM4T v2 (Meta SeamlessM4T v2 Large speech-to-text run locally, decoding to Halh Mongolian (khk) — 2.3B params covering ~100 source languages.).
Endpoint local (transformers). Language mn. Operating point facebook/seamless-m4t-v2-large (tgt_lang=khk) on mps. 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 SeamlessM4T v2 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.