hi-paris/ssml-text2breaks-fr-lora

🗣️ French Text-to-Breaks LoRA Model

hi-paris/ssml-text2breaks-fr-lora is a LoRA adapter fine-tuned on Qwen2.5-7B to predict natural pause locations in French text by adding symbolic <break/> markers.

This is the first stage of a two-step SSML cascade pipeline for improving French text-to-speech prosody control.

📄 Paper: "Improving Synthetic Speech Quality via SSML Prosody Control"
Authors: Nassima Ould-Ouali, Awais Sani, Ruben Bueno, Jonah Dauvet, Tim Luka Horstmann, Eric Moulines
Conference: ICNLSP 2025
🔗 Demo & Audio Samples: https://horstmann.tech/ssml-prosody-control/

🧩 Pipeline Overview

Stage	Model	Purpose
1️⃣	hi-paris/ssml-text2breaks-fr-lora	Predicts natural pause locations
2️⃣	hi-paris/ssml-breaks2ssml-fr-lora	Converts breaks to full SSML with prosody

✨ Example

Input:

Bonjour comment allez-vous aujourd'hui ?

Output:

Bonjour comment allez-vous aujourd'hui ?<break/>

🚀 Quick Start

Installation

pip install torch transformers peft accelerate

Basic Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

# Load base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-7B",
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "hi-paris/ssml-text2breaks-fr-lora")

# Prepare input
text = "Bonjour comment allez-vous aujourd'hui ?"
formatted_input = f"### Task:\nConvert text to SSML with pauses:\n\n### Text:\n{text}\n\n### SSML:\n"

# Generate
inputs = tokenizer(formatted_input, return_tensors="pt").to(model.device)
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
result = response.split("### SSML:\n")[-1].strip()
print(result)  # "Bonjour comment allez-vous aujourd'hui ?<break/>"

Production Usage (Recommended)

For production use with memory optimization and full cascade, see our inference repository:

from text2breaks_inference import Text2BreaksInference

# Memory-efficient shared model approach
model = Text2BreaksInference()
result = model.predict("Bonjour comment allez-vous aujourd'hui ?")

🔧 Full Cascade Example

from breaks2ssml_inference import CascadedInference

# Initialize full pipeline (memory efficient)
cascade = CascadedInference()

# Convert plain text directly to full SSML
text = "Bonjour comment allez-vous aujourd'hui ?"
ssml_output = cascade.predict(text)
print(ssml_output)  
# Output: '<prosody pitch="+2.5%" rate="-1.2%" volume="-5.0%">Bonjour comment allez-vous aujourd'hui ?</prosody><break time="300ms"/>'

🧠 Model Details

• Base Model: Qwen/Qwen2.5-7B
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• LoRA Rank: 8, Alpha: 16
• Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Training: 5 epochs, batch size 1 with gradient accumulation
• Language: French
• Model Size: 7B parameters (LoRA adapter: ~81MB)
• License: Apache 2.0

📊 Performance

The model achieves high accuracy in predicting natural pause locations in French text, contributing to improved prosody in text-to-speech synthesis when combined with the second-stage model.

🔗 Resources

• Full Pipeline Code: https://github.com/TimLukaHorstmann/cascading_model
• Interactive Demo: Colab Notebook
• Stage 2 Model: hi-paris/ssml-breaks2ssml-fr-lora

📖 Citation

@inproceedings{ould-ouali2025_improving,
  title     = {Improving Synthetic Speech Quality via SSML Prosody Control},
  author    = {Ould-Ouali, Nassima and Sani, Awais and Bueno, Ruben and Dauvet, Jonah and Horstmann, Tim Luka and Moulines, Eric},
  booktitle = {Proceedings of the 8th International Conference on Natural Language and Speech Processing (ICNLSP)},
  year      = {2025},
  url       = {https://huggingface.co/hi-paris}
}

📜 License

Apache 2.0 License (same as the base Qwen2.5-7B model)