Update README.md

README.md

@@ -1,3 +1,125 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- fr
+metrics:
+- wer
+base_model:
+- LeBenchmark/wav2vec2-FR-7K-large
+pipeline_tag: automatic-speech-recognition
+library_name: speechbrain
+tags:
+- Transformer
+- wav2vec2
+- CTC
+- inference
+---
+
+# asr-wav2vec2-orfeo-fr : LeBenchmark/wav2vec2-FR-7K-large fine-tuned on Orféo dataset
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+*asr-wav2vec2-orfeo-fr* is an Automatic Speech Recognition model fine-tuned on Orféo with *LeBenchmark/wav2vec2-FR-7K-large* as the pretrained wav2vec2 model.
+
+The fine-tuned model achieves the following performance :
+| Release | Valid WER | Test WER | GPUs | Epochs
+|:-------------:|:--------------:|:--------------:| :--------:|:--------:|
+| 2023-09-08 | 23.24 | 23.29  | 4xV100 32GB | 30 |
+
+## 📝 Model Details
+
+The ASR system is composed of:
+- the **Tokenizer** (char) that transforms the input text into a sequence of characters ("cat" into ["c", "a", "t"]) and trained with the train transcriptions (train.tsv).
+- the **Acoustic model** (wav2vec2.0 + DNN + CTC greedy decode). The pretrained wav2vec 2.0 model [LeBenchmark/wav2vec2-FR-7K-large](https://huggingface.co/LeBenchmark/wav2vec2-FR-7K-large) is combined with two DNN layers and fine-tuned on Orféo.
+The final acoustic representation is given to the CTC greedy decode.
+
+We used recordings sampled at 16kHz (single channel).
+
+## 💻 How to transcribe a file with the model
+
+### Install and import speechbrain
+
+```bash
+pip install speechbrain
+```
+
+```python
+from speechbrain.inference.ASR import EncoderASR
+```
+
+### Pipeline
+
+```python
+def transcribe(audio, model):
+    return model.transcribe_file(audio).lower()
+
+
+def save_transcript(transcript, audio, output_file):
+    with open(output_file, 'w', encoding='utf-8') as file:
+        file.write(f"{audio}\t{transcript}\n")
+
+
+def main():
+    model = EncoderASR.from_hparams("Propicto/asr-wav2vec2-orfeo-fr", savedir="tmp/")
+    transcript = transcribe(audio, model)
+    save_transcript(transcript, audio, "out.txt")
+```
+
+## ⚙️ Training Details
+
+### Training Data
+
+We use train/validation/test splits with an 80/10/10 distribution, corresponding to:
+| | Train | Valid | Test |
+|:-------------:|:-------------:|:--------------:|:--------------:|
+| # utterances | 231,374 | 28,796 | 29,009 |
+| # hours | 147.26 | 18.43 | 13.95 |
+
+### Training Procedure
+
+We follow the training procedure provided in the [ASR-CTC speechbrain recipe](https://github.com/speechbrain/speechbrain/tree/develop/recipes/CommonVoice/ASR/CTC).
+
+#### Training Hyperparameters
+
+Refer to the hyperparams.yaml file to get the hyperparameters' information.
+
+#### Training time
+
+With 4xV100 32GB, the training took ~ 22 hours.
+
+#### Libraries
+
+[Speechbrain](https://speechbrain.github.io/):
+```bibtex
+@misc{SB2021,
+    author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
+    title = {SpeechBrain},
+    year = {2021},
+    publisher = {GitHub},
+    journal = {GitHub repository},
+    howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
+  }
+```
+
+## 💡 Information
+
+- **Developed by:** Cécile Macaire
+- **Funded by [optional]:** GENCI-IDRIS (Grant 2023-AD011013625R1)
+PROPICTO ANR-20-CE93-0005
+- **Language(s) (NLP):** French
+- **License:** Apache-2.0
+- **Finetuned from model:** LeBenchmark/wav2vec2-FR-7K-large
+
+## 📌 Citation
+
+```bibtex
+@inproceedings{macaire24_interspeech,
+  title     = {Towards Speech-to-Pictograms Translation},
+  author    = {Cécile Macaire and Chloé Dion and Didier Schwab and Benjamin Lecouteux and Emmanuelle Esperança-Rodier},
+  year      = {2024},
+  booktitle = {Interspeech 2024},
+  pages     = {857--861},
+  doi       = {10.21437/Interspeech.2024-490},
+  issn      = {2958-1796},
+}
+```