whisper-small-mn

Fine-tune of openai/whisper-small for Mongolian (mn) automatic speech recognition.

Results

Evaluated on a held-out subset of the validation split:

Metric	Value
WER	22.1%
CER	10.3%

Training data

Trained on Blgn94/mongolian-stt-dataset (~60 h, 57,320 clips), a combination of:

• MBSpeech (read Mongolian Bible) — MIT
• Mozilla Common Voice 20 (mn, normalized) — CC0-1.0
• Google FLEURS (mn) — CC-BY-4.0

All audio is 16 kHz mono.

Training procedure

• 3 epochs, effective batch size 8 (batch 2 × grad-accum 4), fp16
• learning rate 1e-5, 500 warmup steps
• AdamW, gradient checkpointing

Usage

from transformers import pipeline

pipe = pipeline("automatic-speech-recognition", model="Blgn94/whisper-small-mn")
text = pipe("audio.wav")["text"]
print(text)

Or with the model/processor directly:

import torch, librosa
from transformers import WhisperProcessor, WhisperForConditionalGeneration

processor = WhisperProcessor.from_pretrained("Blgn94/whisper-small-mn")
model = WhisperForConditionalGeneration.from_pretrained("Blgn94/whisper-small-mn")

audio, _ = librosa.load("audio.wav", sr=16000)
inputs = processor(audio, sampling_rate=16000, return_tensors="pt")
ids = model.generate(inputs.input_features, language="mn", task="transcribe")
print(processor.batch_decode(ids, skip_special_tokens=True)[0])

License

CC-BY-4.0, reflecting the most restrictive license among the combined training sources.