Upload EBENGenerator after 0 epochs

README.md

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+---
+language: fr
+license: mit
+library_name: transformers
+tags:
+- 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
+---
+
+<p align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/65302a613ecbe51d6a6ddcec/zhB1fh-c0pjlj-Tr4Vpmr.png" style="object-fit:contain; width:280px; height:280px;" >
+</p>
+
+# 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))
+- **Language:** French
+- **License:** MIT
+- **Training dataset:** `speech_clean` subset of [Cnam-LMSSC/vibravox](https://huggingface.co/datasets/Cnam-LMSSC/vibravox)
+- **Samplerate for usage:** 16kHz
+
+## 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.
+
+## Inference script : 
+
+```python
+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")
+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_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, enhanced_audio_decomposed_4kHz = model(cut_audio_16kHz)
+```

config.json

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+{
+  "m": 4,
+  "n": 32,
+  "p": 2
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dc2ee0e2519d02f2d0975fa266c93ee8184b755c23601017ff5a24f1c4a6a3a0
+size 7797832