Update README.md

README.md

@@ -5,29 +5,98 @@ tags:
 - automatic-speech-recognition
 - fi
 - finnish
+model-index:
+  - name: wav2vec2-base-fi-voxpopuli-v2-100h
+    results:
+      - task:
+          name: Automatic Speech Recognition
+          type: automatic-speech-recognition
+        dataset:
+          name: Lahjoita puhetta (Donate Speech)
+          type: lahjoita-puhetta
+          args: fi
+        metrics:
+          - name: Dev WER
+            type: wer
+            value: 29.35
+          - name: Dev CER
+            type: cer
+            value: 7.94
+          - name: Test WER
+            type: wer
+            value: 31.90
+          - name: Test CER
+            type: cer
+            value: 9.05
 ---
-# Colloquial Finnish Wav2vec2-Base
+# Colloquial Finnish Wav2vec2-Base ASR
 
-The base model pre-trained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. 
+[facebook/wav2vec2-base-fi-voxpopuli-v2](https://huggingface.co/facebook/wav2vec2-base-fi-voxpopuli-v2) fine-tuned on 100 hours of [Lahjoita puhetta (Donate Speech)](https://link.springer.com/article/10.1007/s10579-022-09606-3) on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
 
-**Note**: This model does not have a tokenizer as it was pre-trained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for more in-detail explanation of how to fine-tune the model.
 
 ## Model description
 
 The Finnish Wav2Vec2 Base has the same architecture and uses the same training objective as the English and multilingual one described in [Paper](https://arxiv.org/abs/2006.11477). It is pre-trained on 2600 hours of unlabeled colloquial Finnish speech from [Lahjoita puhetta (Donate Speech)](https://link.springer.com/article/10.1007/s10579-022-09606-3).
 
-You can read more about the pre-trained model from [this paper](TODO).
+You can read more about the pre-trained model from [this paper](TODO). The training scripts are available on [GitHub](https://github.com/aalto-speech/colloquial-Finnish-wav2vec2)
 
 ## Intended uses & limitations
 
-You can use this model for Finnish ASR (speech-to-text) and SER (Spoken Emotion Recognition) tasks. 
+You can use this model for Finnish ASR (speech-to-text). 
 
 ### How to use
 
-See [this notebook](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLS_R_on_Common_Voice.ipynb) for more information on how to fine-tune the model.
+To transcribe audio files the model can be used as a standalone acoustic model as follows:
+
+```
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+from datasets import load_dataset
+import torch
+
+# load model and processor
+processor = Wav2Vec2Processor.from_pretrained("wav2vec2-base-fi-voxpopuli-v2-100h")
+model = Wav2Vec2ForCTC.from_pretrained("wav2vec2-base-fi-voxpopuli-v2-100h")
+
+# load dummy dataset and read soundfiles
+ds = load_dataset("mozilla-foundation/common_voice_16_1", "fi", split='test')
+
+# tokenize
+input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values  # Batch size 1
+
+# retrieve logits
+logits = model(input_values).logits
+
+# take argmax and decode
+predicted_ids = torch.argmax(logits, dim=-1)
+transcription = processor.batch_decode(predicted_ids)
+```
 
 ### Limitations and bias
 
-This model was pre-trained with audio samples whose maximum length was 60 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in [this blog post](https://huggingface.co/blog/asr-chunking).
+This model was fine-tuned with audio samples whose maximum length was 50 seconds so this model most likely works the best for short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in [this blog post](https://huggingface.co/blog/asr-chunking).
+
+The model was fine-tuned on the data from the [Lahjoita puhetta (Donate Speech) corpus](https://link.springer.com/article/10.1007/s10579-022-09606-3) so this model might have biases towards colloquial Finnish.
+
+## Citation
+
+If you use our models or scripts, please cite our article as:
+
+```bibtex
+@inproceedings{getman24a_interspeech,
+  author={Yaroslav Getman and Tamas Grosz and Mikko Kurimo},
+  title={{What happens in continued pre-training? Analysis of self-supervised speech
+models with continued pre-training for colloquial Finnish ASR}},
+  year=2024,
+  booktitle={Proc. INTERSPEECH 2024},
+  pages={XX--XX},
+  doi={XXXX},
+  issn={XXXX-XXXX}
+}
+```
+
+## Team Members
+
+- Yaroslav Getman, [Hugging Face profile](https://huggingface.co/GetmanY1), [LinkedIn profile](https://www.linkedin.com/in/yaroslav-getman/)
+- Tamas Grosz, [Hugging Face profile](https://huggingface.co/Grosy), [LinkedIn profile](https://www.linkedin.com/in/tam%C3%A1s-gr%C3%B3sz-950a049a/)
 
-A vast majority of the data used for pre-training was from the [Lahjoita puhetta (Donate Speech) corpus](https://link.springer.com/article/10.1007/s10579-022-09606-3) so this model might have biases towards colloquial Finnish.
+Feel free to contact us for more details 🤗