Update README.md

README.md

@@ -1,3 +1,66 @@
 ---
 license: apache-2.0
+datasets:
+  - doof-ferb/vlsp2020_vinai_100h
+  - doof-ferb/fpt_fosd
+  - doof-ferb/infore1_25hours
+  - doof-ferb/infore2_audiobooks
+  - quocanh34/viet_vlsp
+  - linhtran92/final_dataset_500hrs_wer0
+  - linhtran92/viet_youtube_asr_corpus_v2
+  - google/fleurs
+  - mozilla-foundation/common_voice_16_1
+  - vivos
+language: ["vi"]
+metrics: ["wer"]
+library_name: transformers
+base_model: openai/whisper-tiny
+pipeline_tag: automatic-speech-recognition
+model-index:
+- name: doof-ferb/whisper-tiny-vi
+  results:
+  - task:
+      type: automatic-speech-recognition
+    dataset:
+      type: mozilla-foundation/common_voice_16_1
+      name: Mozilla CommonVoice (Vietnamese) v16.1
+      config: vi
+      split: test
+    metrics:
+      - type: wer
+        value: 26.6
+        verified: false
+  - task:
+      type: automatic-speech-recognition
+    dataset:
+      type: google/fleurs
+      name: Google FLEURS (Vietnamese)
+      config: vi_vn
+      split: test
+    metrics:
+      - type: wer
+        value: 37.1
+        verified: false
+  - task:
+      type: automatic-speech-recognition
+    dataset:
+      type: vivos
+      name: ĐHQG TPHCM VIVOS
+      split: test
+    metrics:
+      - type: wer
+        value: 18.7
+        verified: false
 ---
+
+whisper tiny fine-tuned on a very big collection of vietnamese speech datasets
+
+TODO:
+- [x] training then publish checkpoint (*no ETA*)
+- [x] evaluate WER on Common Voice & FLEURS
+- [ ] convert to `openai-whisper`, `whisper.cpp`, `faster-whisper`
+- [ ] convert to ONNX: to try `k2-fsa/sherpa-onnx` & `zhuzilin/whisper-openvino`
+
+21k steps, warm-up 5%, batch size 16×2 (kaggle free T4×2)
+
+all training + evaluation scripts are on my repo: https://github.com/phineas-pta/fine-tune-whisper-vi