Update README.md

README.md

@@ -1,30 +1,27 @@
 ---
-datasets:
-- Cnam-LMSSC/vibravox
 language: fr
-library_name: transformers
 license: mit
+library_name: transformers
 tags:
-- audio
-- audio-to-audio
-- speech
+  - audio
+  - audio-to-audio
+  - speech
+datasets:
+  - Cnam-LMSSC/vibravox
 model-index:
-- name: EBEN(M=?,P=?,Q=?)
-  results:
-  - task:
-      type: speech-enhancement
-      name: Bandwidth Extension
-    dataset:
-      name: Vibravox["YOUR_MIC"]
-      type: Cnam-LMSSC/vibravox
-      args: fr
-    metrics:
-    - type: stoi
-      value: ???
-      name: Test STOI, in-domain training
-    - type: n-mos
-      value: ???
-      name: Test Noresqa-MOS, in-domain training
+  - name: EBEN(M=4,P=4,Q=4)
+    results:
+      - task:
+          name: Bandwidth Extension
+          type: speech-enhancement
+        dataset:
+          name: Vibravox["headset_microphone"] to Vibravox["forehead_accelerometer"]
+          type: Cnam-LMSSC/vibravox
+          args: fr
+        metrics:
+          - name: Test STOI, in-domain training
+            type: stoi
+            value: 0.7488
 ---
 
 <p align="center">
@@ -34,7 +31,7 @@ model-index:
 # Model Card 
 
 - **Developed by:** [Cnam-LMSSC](https://huggingface.co/Cnam-LMSSC)
-- **Model:** [EBEN(M=?,P=?,Q=?)](https://github.com/jhauret/vibravox/blob/main/vibravox/torch_modules/dnn/eben_generator.py) (see [publication in IEEE TASLP](https://ieeexplore.ieee.org/document/10244161) - [arXiv link](https://arxiv.org/abs/2303.10008))
+- **Model:** [EBEN(M=4,P=4,Q=4)](https://github.com/jhauret/vibravox/blob/main/vibravox/torch_modules/dnn/eben_generator.py) (see [publication in IEEE TASLP](https://ieeexplore.ieee.org/document/10244161) - [arXiv link](https://arxiv.org/abs/2303.10008))
 - **Language:** French
 - **License:** MIT
 - **Training dataset:** `speech_clean` subset of [Cnam-LMSSC/vibravox](https://huggingface.co/datasets/Cnam-LMSSC/vibravox)
@@ -42,18 +39,7 @@ model-index:
 
 ## Overview
 
-This bandwidth extension model, trained on [Vibravox](https://huggingface.co/datasets/Cnam-LMSSC/vibravox) body conduction sensor data, enhances body-conducted speech audio by denoising and regenerating mid and high frequencies from low-frequency content.
-
-## Disclaimer
-This model, trained for **a specific non-conventional speech sensor**, is intended to be used with **in-domain data**. Using it with other sensor data may lead to suboptimal performance.
-
-## Link to BWE models trained on other body conducted sensors : 
-
-The entry point to all EBEN models for Bandwidth Extension (BWE) is available at [https://huggingface.co/Cnam-LMSSC/vibravox_EBEN_models](https://huggingface.co/Cnam-LMSSC/vibravox_EBEN_models).  
-
-## Training procedure
-
-Detailed instructions for reproducing the experiments are available on the [jhauret/vibravox](https://github.com/jhauret/vibravox) Github repository.
+This model, trained on [Vibravox](https://huggingface.co/datasets/Cnam-LMSSC/vibravox) body conduction sensor data, maps clean speech to body-conducted speech.
 
 ## Inference script : 
 
@@ -62,12 +48,12 @@ import torch, torchaudio
 from vibravox.torch_modules.dnn.eben_generator import EBENGenerator
 from datasets import load_dataset
 
-model = EBENGenerator.from_pretrained("Cnam-LMSSC/EBEN_YOUR_MIC")
+model = EBENGenerator.from_pretrained("Cnam-LMSSC/EBEN_reverse_forehead_accelerometer")
 test_dataset = load_dataset("Cnam-LMSSC/vibravox", "speech_clean", split="test", streaming=True)
 
-audio_48kHz = torch.Tensor(next(iter(test_dataset))["audio.YOUR_MIC"]["array"])
+audio_48kHz = torch.Tensor(next(iter(test_dataset))["audio.headset_microphone"]["array"])
 audio_16kHz = torchaudio.functional.resample(audio_48kHz, orig_freq=48_000, new_freq=16_000)
 
 cut_audio_16kHz = model.cut_to_valid_length(audio_16kHz[None, None, :])
-enhanced_audio_16kHz = model(cut_audio_16kHz)
+degraded_audio_16kHz, _ = model(cut_audio_16kHz)
 ```