| --- |
| language: |
| - mn |
| license: apache-2.0 |
| tags: |
| - moonshine |
| - speech |
| - asr |
| - mongolian |
| - automatic-speech-recognition |
| base_model: UsefulSensors/moonshine-base |
| metrics: |
| - wer |
| --- |
| |
| # Moonshine-MN: Mongolian ASR |
|
|
| Fine-tuned [UsefulSensors/moonshine-base](https://huggingface.co/UsefulSensors/moonshine-base) on Mongolian (Cyrillic) speech from Mozilla Common Voice. |
|
|
| ## Performance |
|
|
| | Checkpoint | WER | |
| |---|---| |
| | final (step 15000) | **11.88%** | |
|
|
| ## Quickstart |
|
|
| ```python |
| import torch, librosa |
| from transformers import MoonshineForConditionalGeneration, AutoFeatureExtractor |
| from mn_tokenizer import MnBPETokenizer |
| from huggingface_hub import hf_hub_download |
| |
| model = MoonshineForConditionalGeneration.from_pretrained("orgilj/moonshine-mn").eval() |
| fe = AutoFeatureExtractor.from_pretrained("orgilj/moonshine-mn") |
| tok = MnBPETokenizer(vocab_file=hf_hub_download("orgilj/moonshine-mn", "mn_bpe.model")) |
| |
| def transcribe(path, num_beams=5): |
| audio, _ = librosa.load(path, sr=16000) |
| inp = fe(audio, sampling_rate=16000, return_tensors="pt") |
| with torch.no_grad(): |
| ids = model.generate( |
| inp.input_values, |
| num_beams=num_beams, |
| max_new_tokens=180, # under the model's max_length=194 |
| ) |
| return tok.decode_ids(ids[0].tolist()) |
| |
| if __name__ == "__main__": |
| print(transcribe("/workspace/data/cv-corpus-24.0-2025-12-05/mn/clips/common_voice_mn_44590402.mp3")) |
| ``` |
|
|
| ## Live streaming |
|
|
| ```bash |
| # From finetune-moonshine-asr repo: |
| python scripts/stream_mn.py --model orgilj/moonshine-mn --live |
| ``` |
|
|
| ## Training details |
|
|
| - Base: moonshine-base (hidden_size=416, 8-layer enc+dec) |
| - Tokenizer: custom Mongolian SentencePiece BPE (vocab 2003) |
| - Data: Mozilla Common Voice Mongolian |
| - LR: 1e-4 cosine, warmup 500 steps, 15000 total steps, fp16, batch 64 |
| |
