Update README.md

README.md

@@ -295,20 +295,20 @@ If you use the Vibravox dataset for research, **cite this paper** :
 ### Automatic-speech-recognition  
 
 - The model is presented with an audio file and asked to transcribe the audio file to written text (either normalized text of phonemized text). The most common evaluation metrics are the word error rate (WER), character error rate (CER), or phoneme error rate (PER).
-- **Training code:** An example of implementation for the speech-to-phoneme task using [wav2vec2.0](https://arxiv.org/abs/2006.11477) is available on the [Vibravox Github repository](https://github.com/jhauret/vibravox).
+- **Training code:** An example of implementation for the speech-to-phoneme task using [wav2vec2.0](https://arxiv.org/abs/2006.11477) is available on the [Vibravox Github repository](https://github.com/jhauret/vibravox) and with the [pip-installable Python package](https://pypi.org/project/vibravox/).
 - **Trained models:** We also provide trained models for the speech-to-phoneme task for each of the 6 speech sensors of the Vibravox dataset on Huggingface at [Cnam-LMSSC/vibravox_phonemizers](https://huggingface.co/Cnam-LMSSC/vibravox_phonemizers)
 
 ### Bandwidth-extension
 
 - Also known as audio super-resolution, which is required to enhance the audio quality of body-conducted captured speech. The model is presented with a pair of audio clips (from a body-conducted captured speech, and from the corresponding clean, full bandwidth  airborne-captured speech), and asked to enhance the audio by denoising and regenerating mid and high frequencies from low frequency content only.
-- **Training code:** An example of implementation of this task using [Configurable EBEN](https://ieeexplore.ieee.org/document/10244161) ([arXiv link](https://arxiv.org/abs/2303.10008)) is available on the [Vibravox Github repository](https://github.com/jhauret/vibravox).
+- **Training code:** An example of implementation of this task using [Configurable EBEN](https://ieeexplore.ieee.org/document/10244161) ([arXiv link](https://arxiv.org/abs/2303.10008)) is available on the [Vibravox Github repository](https://github.com/jhauret/vibravox) and with the [pip-installable Python package](https://pypi.org/project/vibravox/).
 - **Trained models:** We also provide trained models for the BWE task for each of the 6 speech sensors of the Vibravox dataset on Huggingface at [Cnam-LMSSC/vibravox_EBEN_bwe_models](https://huggingface.co/Cnam-LMSSC/vibravox_EBEN_bwe_models).
 - **BWE-Enhanced dataset:** An EBEN-enhanced version of the `test`splits of the Vibravox dataset, generated using these 6 bwe models, is also available on Huggingface at [Cnam-LMSSC/vibravox_enhanced_by_EBEN](https://huggingface.co/datasets/Cnam-LMSSC/vibravox_enhanced_by_EBEN).
 
 ### Speaker-verification
 
   - Given an input audio clip and a reference audio clip of a known speaker, the model's objective is to compare the two clips and verify if they are from the same individual.  This often involves extracting embeddings from a deep neural network trained on a large dataset of voices. The model then measures the similarity between these feature sets using techniques like cosine similarity or a learned distance metric. This task is crucial in applications requiring secure access control, such as biometric authentication systems, where a person's voice acts as a unique identifier.
-  - **Testing code:** An example of implementation of this task using a pretrained [ECAPA2 model](https://arxiv.org/abs/2401.08342) is available on the [Vibravox Github repository](https://github.com/jhauret/vibravox).
+  - **Testing code:** An example of implementation of this task using a pretrained [ECAPA2 model](https://arxiv.org/abs/2401.08342) is available on the [Vibravox Github repository](https://github.com/jhauret/vibravox) and with the [pip-installable Python package](https://pypi.org/project/vibravox/).
 
 
 ### Adding your models for supported tasks or contributing for new tasks