Whisper Small Mn

--- license: cc-by-4.0 language: - mn base_model: openai/whisper-small tags: - whisper - automatic-speech-recognition - mongolian - asr datasets: - Blgn94/mongolian-stt-dataset metrics: - wer - cer pipeline_tag: automatic-speech-recognition model-index: - name: whisper-small-mn results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Blgn94/mongolian-stt-dataset type: Blgn94/mongolian-stt-dataset metrics: - type: wer value: 22.12 name: WER - type: cer value: 10.29 name: CER --- # whisper-small-mn Fine-tune of [`openai/whisper-small`](https://huggingface.co/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`](https://huggingface.co/datasets/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 ```python 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: ```python 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.

Source context: 41 downloads · 0 likes · Pipeline automatic-speech-recognition · Repo Blgn94/whisper-small-mn

Source context: 178 downloads · 0 likes · Pipeline automatic-speech-recognition · Repo Blgn94/whisper-small-mn