Batuka0901/wenet-mn

WeNet Conformer — Mongolian (Монгол хэл)

WeNet U2++ Conformer model trained on google/fleurs-mn for Mongolian (Cyrillic) automatic speech recognition.

Model architecture

• Encoder: Conformer, 12 blocks × 256 dim, 4 heads
• Decoder: Bi-transformer (U2++), 3 L→R + 3 R→L blocks
• Tokenizer: char-level (38 Cyrillic tokens)
• Loss: CTC + Attention hybrid (ctc_weight=0.3, reverse_weight=0.3)

Training data

• Dataset: google/fleurs-mn
• Train: 3,074 utterances · ~11.5 h
• Test: 949 utterances · ~2.85 h
• Audio: 16 kHz mono

Training results

• Epochs run: 100
• Final train loss: N/A
• Final epoch: 99 — cv_loss N/A, acc N/A
• Best epoch: 21 — cv_loss N/A, acc N/A
• TensorBoard: this repo has a TensorBoard tab (see runs/).

Files

File	Description
avg_10.pt	Best model (averaged top-10 checkpoints by default)
train.yaml	Training config
lang_char.txt	Character vocabulary (38 tokens)
global_cmvn	Feature normalization stats
train.log	Full training log
runs/	TensorBoard events

Download model files from this repo, then:

python wenet/bin/recognize.py
--config train.yaml
--checkpoint avg_10.pt
--dict lang_char.txt
--test_data your_data.list
--mode attention_rescoring
--beam_size 10
--result_file result.txt

## Limitations

- Trained on ~11.5 h of FLEURS Mongolian — small-scale; WER/CER will be relatively high on out-of-domain speech.
- Only Cyrillic script supported; Latin characters and digits are stripped.
- No language model rescoring applied.