Update README.md
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README.md
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@@ -67,7 +67,7 @@ pip install git+https://github.com/huggingface/parler-tts.git
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## Utilisation
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## Exemple de base
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-
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import torch
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from parler_tts import ParlerTTSForConditionalGeneration
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from transformers import AutoTokenizer
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@@ -96,7 +96,7 @@ audio = model.generate(
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# Sauvegarde
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sf.write("output.wav", audio.cpu().numpy().squeeze(), model.config.sampling_rate)
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-
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<audio controls>
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<source src="https://huggingface.co/spaces/CONCREE/Adia_TTS/resolve/main/snt.wav" type="audio/wav">
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</audio>
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@@ -104,7 +104,7 @@ sf.write("output.wav", audio.cpu().numpy().squeeze(), model.config.sampling_rate
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## Configuration avancée
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## Paramètres de génération
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-
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generation_config = {
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"temperature": 0.8, # Contrôle la variabilité de la sortie
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"max_new_tokens": 1000, # Longueur maximale de la séquence générée
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prompt_input_ids=prompt_ids,
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**generation_config
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)
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<audio controls>
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<source src="https://huggingface.co/spaces/CONCREE/Adia_TTS/resolve/main/avct.wav" type="audio/wav">
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2. ### Voix professionnelle
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description = "A professional, clear and composed voice, perfect for formal presentations"
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<audio controls>
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<source src="https://huggingface.co/spaces/CONCREE/Adia_TTS/resolve/main/styl2.wav" type="audio/wav">
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</audio>
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3. ### Voix pour l'éducation
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description = "A clear and educational voice, with a flow adapted to learning"
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<audio controls>
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<source src="https://huggingface.co/spaces/CONCREE/Adia_TTS/resolve/main/styl3.wav" type="audio/wav">
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</audio>
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@@ -161,7 +161,7 @@ description = "A clear and educational voice, with a flow adapted to learning"
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* Temps de chargement initial du modèle relativement long
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## Références
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@misc{CONCREE-2024-Adia_TTS,
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author = {CONCREE},
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title = {Adia_TTS},
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journal = {Hugging Face repository},
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howpublished = {\url{https://huggingface.co/CONCREE/Adia_TTS}}
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}
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@misc{lyth2024natural,
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title={Natural language guidance of high-fidelity text-to-speech with synthetic annotations},
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author={Dan Lyth and Simon King},
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## Utilisation
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## Exemple de base
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```py
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import torch
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from parler_tts import ParlerTTSForConditionalGeneration
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from transformers import AutoTokenizer
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# Sauvegarde
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sf.write("output.wav", audio.cpu().numpy().squeeze(), model.config.sampling_rate)
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```
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<audio controls>
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<source src="https://huggingface.co/spaces/CONCREE/Adia_TTS/resolve/main/snt.wav" type="audio/wav">
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</audio>
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## Configuration avancée
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## Paramètres de génération
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```py
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generation_config = {
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"temperature": 0.8, # Contrôle la variabilité de la sortie
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"max_new_tokens": 1000, # Longueur maximale de la séquence générée
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prompt_input_ids=prompt_ids,
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**generation_config
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)
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```
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<audio controls>
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<source src="https://huggingface.co/spaces/CONCREE/Adia_TTS/resolve/main/avct.wav" type="audio/wav">
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2. ### Voix professionnelle
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```py
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description = "A professional, clear and composed voice, perfect for formal presentations"
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```
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<audio controls>
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<source src="https://huggingface.co/spaces/CONCREE/Adia_TTS/resolve/main/styl2.wav" type="audio/wav">
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</audio>
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3. ### Voix pour l'éducation
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```py
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description = "A clear and educational voice, with a flow adapted to learning"
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```
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<audio controls>
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<source src="https://huggingface.co/spaces/CONCREE/Adia_TTS/resolve/main/styl3.wav" type="audio/wav">
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</audio>
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* Temps de chargement initial du modèle relativement long
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## Références
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```
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@misc{CONCREE-2024-Adia_TTS,
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author = {CONCREE},
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title = {Adia_TTS},
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journal = {Hugging Face repository},
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howpublished = {\url{https://huggingface.co/CONCREE/Adia_TTS}}
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}
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```
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```
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@misc{lyth2024natural,
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title={Natural language guidance of high-fidelity text-to-speech with synthetic annotations},
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author={Dan Lyth and Simon King},
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