CONCREE
/

Adia_TTS

@@ -8,204 +8,92 @@ base_model:
 pipeline_tag: text-to-speech
 ---
-# Model Card for Model ID
-<img src="https://huggingface.co/datasets/Adia-tts/images/resolve/main/thumbnail.png" alt="Parler Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
-# Adia-TTS
-<a target="_blank" href="https://huggingface.co/spaces/parler-tts/parler_tts">
-  <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/>
-</a>
-## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 pipeline_tag: text-to-speech
 ---
+# Documentation de Adia_TTS
+## Introduction
+Adia_TTS est une extension monolocuteur wolof du modèle `parler-tts-mini-multilingual-v1.1`. Il a été entraîné sur un ensemble de données de 40 heures en wolof et affiné pendant 100 epochs, soit environ 168 heures d'entraînement.
+Le modèle génère une voix plus naturelle et fluide, comparable à celle d'un humain.
+## Installation
+L'utilisation d'Adia_TTS est simple. Tout d'abord, installez la bibliothèque `Parler-TTS` en exécutant la commande suivante :
+```sh
+pip install git+https://github.com/huggingface/parler-tts.git
+```
+## Utilisation
+Adia_TTS suit les mêmes interfaces que les versions précédentes de `Parler-TTS`. La qualité de la voix peut être ajustée en modifiant la description fournie au modèle, en précisant des critères comme : voix claire, monotone, sans bruit de fond, etc.
+### Exemple d'utilisation
+Le code suivant montre comment utiliser Adia_TTS pour générer un fichier audio :
+```py
+import torch
+from parler_tts import ParlerTTSForConditionalGeneration
+from transformers import AutoTokenizer
+from IPython.display import Audio
+import soundfile as sf
+# Détection de l'appareil disponible
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+# Chargement du modèle et du tokenizer
+model = ParlerTTSForConditionalGeneration.from_pretrained("Moustapha91/parler-tts-wolof").to(device)
+tokenizer = AutoTokenizer.from_pretrained("Moustapha91/parler-tts-wolof")
+# Définition du texte d'entrée (exemple en wolof)
+prompt = """Am na yoon yu bari yoo mëna amee xaalis ngir sa projet: Liggéeyandoo ak: Sàkku jàppale ci yokkute mbay ak transformation produit yi. Defar bu baax sa projet te jokkoo ak ñoom. Banqi yi: Demal ci banq yi ngir ñu may la crédit ngir tambali sa projet"""
+# Description influençant la qualité de la synthèse vocale
+description = "A crystal clear and distinct voice, with a moderate reading rate that facilitates understanding. The tone is monotonous, without variations or inflections, which provides a uniform listening experience. The voice is free of background noise and allows for continuous reading, without inappropriate pauses, thus ensuring a constant and pleasant flow."
+# Tokenisation des entrées
+input_ids = tokenizer(description, return_tensors="pt").input_ids.to(device)
+prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
+# Génération de l'audio
+generation = model.generate(
+    input_ids=input_ids,
+    prompt_input_ids=prompt_input_ids,
+)
+audio_arr = generation.cpu().numpy().squeeze()
+sf.write("parler_tts_out.wav", audio_arr, model.config.sampling_rate)
+Audio(audio_arr, rate=model.config.sampling_rate)
+```
+## Ajustement de la Qualité Audio
+La qualité de l'audio peut être modifiée en ajustant la description fournie au modèle. Voici quelques exemples :
+```py
+description = "Aida speaks slowly with a very clear recording but a monotone voice."
+description = "Adia's speech is very quiet and monotone, delivered with a very small amount of discernible expression."
+description = "Adia's voice comes across as very monotone, speaking slowly with very clear sounds and no background noise."
+```
+En variant la description, vous pouvez obtenir une sortie vocale différente en termes de clarté, d'expression et de débit.
+## Références
+```
+@misc{CONCREE-2024-Adia_TTS,
+  author = {CONCREE},
+  title = {Adia_TTS},
+  year = {2024},
+  publisher = {Hugging Face},
+  journal = {Hugging Face repository},
+  howpublished = {\url{https://huggingface.co/CONCREE/Adia_TTS}}
+}
+@misc{lyth2024natural,
+  title={Natural language guidance of high-fidelity text-to-speech with synthetic annotations},
+  author={Dan Lyth and Simon King},
+  year={2024},
+  eprint={2402.01912},
+  archivePrefix={arXiv},
+  primaryClass={cs.SD}
+}
+```
+## Licence
+Ce modèle est publié sous la licence permissive Apache 2.0, permettant son utilisation libre et sa modification sous certaines conditions.